'
- image_path = single_turn['images'][image_idx]
- if image_path == '':
- message += text_list[image_idx] + 'main title
" - """ - if self.asGroupList: - raise TypeError("cannot use sub() with Regex(asGroupList=True)") - - if self.asMatch and callable(repl): - raise TypeError("cannot use sub() with a callable with Regex(asMatch=True)") - - if self.asMatch: - - def pa(tokens): - return tokens[0].expand(repl) - - else: - - def pa(tokens): - return self.re.sub(repl, tokens[0]) - - return self.add_parse_action(pa) - - -class QuotedString(Token): - r""" - Token for matching strings that are delimited by quoting characters. - - Defined with the following parameters: - - - ``quote_char`` - string of one or more characters defining the - quote delimiting string - - ``esc_char`` - character to re_escape quotes, typically backslash - (default= ``None``) - - ``esc_quote`` - special quote sequence to re_escape an embedded quote - string (such as SQL's ``""`` to re_escape an embedded ``"``) - (default= ``None``) - - ``multiline`` - boolean indicating whether quotes can span - multiple lines (default= ``False``) - - ``unquote_results`` - boolean indicating whether the matched text - should be unquoted (default= ``True``) - - ``end_quote_char`` - string of one or more characters defining the - end of the quote delimited string (default= ``None`` => same as - quote_char) - - ``convert_whitespace_escapes`` - convert escaped whitespace - (``'\t'``, ``'\n'``, etc.) to actual whitespace - (default= ``True``) - - Example:: - - qs = QuotedString('"') - print(qs.search_string('lsjdf "This is the quote" sldjf')) - complex_qs = QuotedString('{{', end_quote_char='}}') - print(complex_qs.search_string('lsjdf {{This is the "quote"}} sldjf')) - sql_qs = QuotedString('"', esc_quote='""') - print(sql_qs.search_string('lsjdf "This is the quote with ""embedded"" quotes" sldjf')) - - prints:: - - [['This is the quote']] - [['This is the "quote"']] - [['This is the quote with "embedded" quotes']] - """ - ws_map = ((r"\t", "\t"), (r"\n", "\n"), (r"\f", "\f"), (r"\r", "\r")) - - def __init__( - self, - quote_char: str = "", - esc_char: typing.Optional[str] = None, - esc_quote: typing.Optional[str] = None, - multiline: bool = False, - unquote_results: bool = True, - end_quote_char: typing.Optional[str] = None, - convert_whitespace_escapes: bool = True, - *, - quoteChar: str = "", - escChar: typing.Optional[str] = None, - escQuote: typing.Optional[str] = None, - unquoteResults: bool = True, - endQuoteChar: typing.Optional[str] = None, - convertWhitespaceEscapes: bool = True, - ): - super().__init__() - escChar = escChar or esc_char - escQuote = escQuote or esc_quote - unquoteResults = unquoteResults and unquote_results - endQuoteChar = endQuoteChar or end_quote_char - convertWhitespaceEscapes = ( - convertWhitespaceEscapes and convert_whitespace_escapes - ) - quote_char = quoteChar or quote_char - - # remove white space from quote chars - wont work anyway - quote_char = quote_char.strip() - if not quote_char: - raise ValueError("quote_char cannot be the empty string") - - if endQuoteChar is None: - endQuoteChar = quote_char - else: - endQuoteChar = endQuoteChar.strip() - if not endQuoteChar: - raise ValueError("endQuoteChar cannot be the empty string") - - self.quoteChar = quote_char - self.quoteCharLen = len(quote_char) - self.firstQuoteChar = quote_char[0] - self.endQuoteChar = endQuoteChar - self.endQuoteCharLen = len(endQuoteChar) - self.escChar = escChar - self.escQuote = escQuote - self.unquoteResults = unquoteResults - self.convertWhitespaceEscapes = convertWhitespaceEscapes - - sep = "" - inner_pattern = "" - - if escQuote: - inner_pattern += r"{}(?:{})".format(sep, re.escape(escQuote)) - sep = "|" - - if escChar: - inner_pattern += r"{}(?:{}.)".format(sep, re.escape(escChar)) - sep = "|" - self.escCharReplacePattern = re.escape(self.escChar) + "(.)" - - if len(self.endQuoteChar) > 1: - inner_pattern += ( - "{}(?:".format(sep) - + "|".join( - "(?:{}(?!{}))".format( - re.escape(self.endQuoteChar[:i]), - re.escape(self.endQuoteChar[i:]), - ) - for i in range(len(self.endQuoteChar) - 1, 0, -1) - ) - + ")" - ) - sep = "|" - - if multiline: - self.flags = re.MULTILINE | re.DOTALL - inner_pattern += r"{}(?:[^{}{}])".format( - sep, - _escape_regex_range_chars(self.endQuoteChar[0]), - (_escape_regex_range_chars(escChar) if escChar is not None else ""), - ) - else: - self.flags = 0 - inner_pattern += r"{}(?:[^{}\n\r{}])".format( - sep, - _escape_regex_range_chars(self.endQuoteChar[0]), - (_escape_regex_range_chars(escChar) if escChar is not None else ""), - ) - - self.pattern = "".join( - [ - re.escape(self.quoteChar), - "(?:", - inner_pattern, - ")*", - re.escape(self.endQuoteChar), - ] - ) - - try: - self.re = re.compile(self.pattern, self.flags) - self.reString = self.pattern - self.re_match = self.re.match - except re.error: - raise ValueError( - "invalid pattern {!r} passed to Regex".format(self.pattern) - ) - - self.errmsg = "Expected " + self.name - self.mayIndexError = False - self.mayReturnEmpty = True - - def _generateDefaultName(self): - if self.quoteChar == self.endQuoteChar and isinstance(self.quoteChar, str_type): - return "string enclosed in {!r}".format(self.quoteChar) - - return "quoted string, starting with {} ending with {}".format( - self.quoteChar, self.endQuoteChar - ) - - def parseImpl(self, instring, loc, doActions=True): - result = ( - instring[loc] == self.firstQuoteChar - and self.re_match(instring, loc) - or None - ) - if not result: - raise ParseException(instring, loc, self.errmsg, self) - - loc = result.end() - ret = result.group() - - if self.unquoteResults: - - # strip off quotes - ret = ret[self.quoteCharLen : -self.endQuoteCharLen] - - if isinstance(ret, str_type): - # replace escaped whitespace - if "\\" in ret and self.convertWhitespaceEscapes: - for wslit, wschar in self.ws_map: - ret = ret.replace(wslit, wschar) - - # replace escaped characters - if self.escChar: - ret = re.sub(self.escCharReplacePattern, r"\g<1>", ret) - - # replace escaped quotes - if self.escQuote: - ret = ret.replace(self.escQuote, self.endQuoteChar) - - return loc, ret - - -class CharsNotIn(Token): - """Token for matching words composed of characters *not* in a given - set (will include whitespace in matched characters if not listed in - the provided exclusion set - see example). Defined with string - containing all disallowed characters, and an optional minimum, - maximum, and/or exact length. The default value for ``min`` is - 1 (a minimum value < 1 is not valid); the default values for - ``max`` and ``exact`` are 0, meaning no maximum or exact - length restriction. - - Example:: - - # define a comma-separated-value as anything that is not a ',' - csv_value = CharsNotIn(',') - print(delimited_list(csv_value).parse_string("dkls,lsdkjf,s12 34,@!#,213")) - - prints:: - - ['dkls', 'lsdkjf', 's12 34', '@!#', '213'] - """ - - def __init__( - self, - not_chars: str = "", - min: int = 1, - max: int = 0, - exact: int = 0, - *, - notChars: str = "", - ): - super().__init__() - self.skipWhitespace = False - self.notChars = not_chars or notChars - self.notCharsSet = set(self.notChars) - - if min < 1: - raise ValueError( - "cannot specify a minimum length < 1; use " - "Opt(CharsNotIn()) if zero-length char group is permitted" - ) - - self.minLen = min - - if max > 0: - self.maxLen = max - else: - self.maxLen = _MAX_INT - - if exact > 0: - self.maxLen = exact - self.minLen = exact - - self.errmsg = "Expected " + self.name - self.mayReturnEmpty = self.minLen == 0 - self.mayIndexError = False - - def _generateDefaultName(self): - not_chars_str = _collapse_string_to_ranges(self.notChars) - if len(not_chars_str) > 16: - return "!W:({}...)".format(self.notChars[: 16 - 3]) - else: - return "!W:({})".format(self.notChars) - - def parseImpl(self, instring, loc, doActions=True): - notchars = self.notCharsSet - if instring[loc] in notchars: - raise ParseException(instring, loc, self.errmsg, self) - - start = loc - loc += 1 - maxlen = min(start + self.maxLen, len(instring)) - while loc < maxlen and instring[loc] not in notchars: - loc += 1 - - if loc - start < self.minLen: - raise ParseException(instring, loc, self.errmsg, self) - - return loc, instring[start:loc] - - -class White(Token): - """Special matching class for matching whitespace. Normally, - whitespace is ignored by pyparsing grammars. This class is included - when some whitespace structures are significant. Define with - a string containing the whitespace characters to be matched; default - is ``" \\t\\r\\n"``. Also takes optional ``min``, - ``max``, and ``exact`` arguments, as defined for the - :class:`Word` class. - """ - - whiteStrs = { - " ": "
-
- )
-}
\ No newline at end of file
diff --git a/spaces/Bart92/RVC_HF/colab_for_mdx.py b/spaces/Bart92/RVC_HF/colab_for_mdx.py
deleted file mode 100644
index 274846d0b5395865a05fce0da86b96d26ac06999..0000000000000000000000000000000000000000
--- a/spaces/Bart92/RVC_HF/colab_for_mdx.py
+++ /dev/null
@@ -1,71 +0,0 @@
-import json
-import os
-import gc
-import psutil
-import requests
-import subprocess
-import time
-import logging
-import sys
-import shutil
-now_dir = os.getcwd()
-sys.path.append(now_dir)
-first_cell_executed = False
-file_folder = "Colab-for-MDX_B"
-def first_cell_ran():
- global first_cell_executed
- if first_cell_executed:
- #print("The 'first_cell_ran' function has already been executed.")
- return
-
-
-
- first_cell_executed = True
- os.makedirs("tmp_models", exist_ok=True)
-
-
-
- class hide_opt: # hide outputs
- def __enter__(self):
- self._original_stdout = sys.stdout
- sys.stdout = open(os.devnull, "w")
-
- def __exit__(self, exc_type, exc_val, exc_tb):
- sys.stdout.close()
- sys.stdout = self._original_stdout
-
- def get_size(bytes, suffix="B"): # read ram
- global svmem
- factor = 1024
- for unit in ["", "K", "M", "G", "T", "P"]:
- if bytes < factor:
- return f"{bytes:.2f}{unit}{suffix}"
- bytes /= factor
- svmem = psutil.virtual_memory()
-
-
- def use_uvr_without_saving():
- print("Notice: files won't be saved to personal drive.")
- print(f"Downloading {file_folder}...", end=" ")
- with hide_opt():
- #os.chdir(mounting_path)
- items_to_move = ["demucs", "diffq","julius","model","separated","tracks","mdx.py","MDX-Net_Colab.ipynb"]
- subprocess.run(["git", "clone", "https://github.com/NaJeongMo/Colab-for-MDX_B.git"])
- for item_name in items_to_move:
- item_path = os.path.join(file_folder, item_name)
- if os.path.exists(item_path):
- if os.path.isfile(item_path):
- shutil.move(item_path, now_dir)
- elif os.path.isdir(item_path):
- shutil.move(item_path, now_dir)
- try:
- shutil.rmtree(file_folder)
- except PermissionError:
- print(f"No se pudo eliminar la carpeta {file_folder}. Puede estar relacionada con Git.")
-
-
- use_uvr_without_saving()
- print("done!")
- if not os.path.exists("tracks"):
- os.mkdir("tracks")
-first_cell_ran()
\ No newline at end of file
diff --git a/spaces/BartPoint/VoiceChange_Beta/infer_pack/transforms.py b/spaces/BartPoint/VoiceChange_Beta/infer_pack/transforms.py
deleted file mode 100644
index a11f799e023864ff7082c1f49c0cc18351a13b47..0000000000000000000000000000000000000000
--- a/spaces/BartPoint/VoiceChange_Beta/infer_pack/transforms.py
+++ /dev/null
@@ -1,209 +0,0 @@
-import torch
-from torch.nn import functional as F
-
-import numpy as np
-
-
-DEFAULT_MIN_BIN_WIDTH = 1e-3
-DEFAULT_MIN_BIN_HEIGHT = 1e-3
-DEFAULT_MIN_DERIVATIVE = 1e-3
-
-
-def piecewise_rational_quadratic_transform(
- inputs,
- unnormalized_widths,
- unnormalized_heights,
- unnormalized_derivatives,
- inverse=False,
- tails=None,
- tail_bound=1.0,
- min_bin_width=DEFAULT_MIN_BIN_WIDTH,
- min_bin_height=DEFAULT_MIN_BIN_HEIGHT,
- min_derivative=DEFAULT_MIN_DERIVATIVE,
-):
- if tails is None:
- spline_fn = rational_quadratic_spline
- spline_kwargs = {}
- else:
- spline_fn = unconstrained_rational_quadratic_spline
- spline_kwargs = {"tails": tails, "tail_bound": tail_bound}
-
- outputs, logabsdet = spline_fn(
- inputs=inputs,
- unnormalized_widths=unnormalized_widths,
- unnormalized_heights=unnormalized_heights,
- unnormalized_derivatives=unnormalized_derivatives,
- inverse=inverse,
- min_bin_width=min_bin_width,
- min_bin_height=min_bin_height,
- min_derivative=min_derivative,
- **spline_kwargs
- )
- return outputs, logabsdet
-
-
-def searchsorted(bin_locations, inputs, eps=1e-6):
- bin_locations[..., -1] += eps
- return torch.sum(inputs[..., None] >= bin_locations, dim=-1) - 1
-
-
-def unconstrained_rational_quadratic_spline(
- inputs,
- unnormalized_widths,
- unnormalized_heights,
- unnormalized_derivatives,
- inverse=False,
- tails="linear",
- tail_bound=1.0,
- min_bin_width=DEFAULT_MIN_BIN_WIDTH,
- min_bin_height=DEFAULT_MIN_BIN_HEIGHT,
- min_derivative=DEFAULT_MIN_DERIVATIVE,
-):
- inside_interval_mask = (inputs >= -tail_bound) & (inputs <= tail_bound)
- outside_interval_mask = ~inside_interval_mask
-
- outputs = torch.zeros_like(inputs)
- logabsdet = torch.zeros_like(inputs)
-
- if tails == "linear":
- unnormalized_derivatives = F.pad(unnormalized_derivatives, pad=(1, 1))
- constant = np.log(np.exp(1 - min_derivative) - 1)
- unnormalized_derivatives[..., 0] = constant
- unnormalized_derivatives[..., -1] = constant
-
- outputs[outside_interval_mask] = inputs[outside_interval_mask]
- logabsdet[outside_interval_mask] = 0
- else:
- raise RuntimeError("{} tails are not implemented.".format(tails))
-
- (
- outputs[inside_interval_mask],
- logabsdet[inside_interval_mask],
- ) = rational_quadratic_spline(
- inputs=inputs[inside_interval_mask],
- unnormalized_widths=unnormalized_widths[inside_interval_mask, :],
- unnormalized_heights=unnormalized_heights[inside_interval_mask, :],
- unnormalized_derivatives=unnormalized_derivatives[inside_interval_mask, :],
- inverse=inverse,
- left=-tail_bound,
- right=tail_bound,
- bottom=-tail_bound,
- top=tail_bound,
- min_bin_width=min_bin_width,
- min_bin_height=min_bin_height,
- min_derivative=min_derivative,
- )
-
- return outputs, logabsdet
-
-
-def rational_quadratic_spline(
- inputs,
- unnormalized_widths,
- unnormalized_heights,
- unnormalized_derivatives,
- inverse=False,
- left=0.0,
- right=1.0,
- bottom=0.0,
- top=1.0,
- min_bin_width=DEFAULT_MIN_BIN_WIDTH,
- min_bin_height=DEFAULT_MIN_BIN_HEIGHT,
- min_derivative=DEFAULT_MIN_DERIVATIVE,
-):
- if torch.min(inputs) < left or torch.max(inputs) > right:
- raise ValueError("Input to a transform is not within its domain")
-
- num_bins = unnormalized_widths.shape[-1]
-
- if min_bin_width * num_bins > 1.0:
- raise ValueError("Minimal bin width too large for the number of bins")
- if min_bin_height * num_bins > 1.0:
- raise ValueError("Minimal bin height too large for the number of bins")
-
- widths = F.softmax(unnormalized_widths, dim=-1)
- widths = min_bin_width + (1 - min_bin_width * num_bins) * widths
- cumwidths = torch.cumsum(widths, dim=-1)
- cumwidths = F.pad(cumwidths, pad=(1, 0), mode="constant", value=0.0)
- cumwidths = (right - left) * cumwidths + left
- cumwidths[..., 0] = left
- cumwidths[..., -1] = right
- widths = cumwidths[..., 1:] - cumwidths[..., :-1]
-
- derivatives = min_derivative + F.softplus(unnormalized_derivatives)
-
- heights = F.softmax(unnormalized_heights, dim=-1)
- heights = min_bin_height + (1 - min_bin_height * num_bins) * heights
- cumheights = torch.cumsum(heights, dim=-1)
- cumheights = F.pad(cumheights, pad=(1, 0), mode="constant", value=0.0)
- cumheights = (top - bottom) * cumheights + bottom
- cumheights[..., 0] = bottom
- cumheights[..., -1] = top
- heights = cumheights[..., 1:] - cumheights[..., :-1]
-
- if inverse:
- bin_idx = searchsorted(cumheights, inputs)[..., None]
- else:
- bin_idx = searchsorted(cumwidths, inputs)[..., None]
-
- input_cumwidths = cumwidths.gather(-1, bin_idx)[..., 0]
- input_bin_widths = widths.gather(-1, bin_idx)[..., 0]
-
- input_cumheights = cumheights.gather(-1, bin_idx)[..., 0]
- delta = heights / widths
- input_delta = delta.gather(-1, bin_idx)[..., 0]
-
- input_derivatives = derivatives.gather(-1, bin_idx)[..., 0]
- input_derivatives_plus_one = derivatives[..., 1:].gather(-1, bin_idx)[..., 0]
-
- input_heights = heights.gather(-1, bin_idx)[..., 0]
-
- if inverse:
- a = (inputs - input_cumheights) * (
- input_derivatives + input_derivatives_plus_one - 2 * input_delta
- ) + input_heights * (input_delta - input_derivatives)
- b = input_heights * input_derivatives - (inputs - input_cumheights) * (
- input_derivatives + input_derivatives_plus_one - 2 * input_delta
- )
- c = -input_delta * (inputs - input_cumheights)
-
- discriminant = b.pow(2) - 4 * a * c
- assert (discriminant >= 0).all()
-
- root = (2 * c) / (-b - torch.sqrt(discriminant))
- outputs = root * input_bin_widths + input_cumwidths
-
- theta_one_minus_theta = root * (1 - root)
- denominator = input_delta + (
- (input_derivatives + input_derivatives_plus_one - 2 * input_delta)
- * theta_one_minus_theta
- )
- derivative_numerator = input_delta.pow(2) * (
- input_derivatives_plus_one * root.pow(2)
- + 2 * input_delta * theta_one_minus_theta
- + input_derivatives * (1 - root).pow(2)
- )
- logabsdet = torch.log(derivative_numerator) - 2 * torch.log(denominator)
-
- return outputs, -logabsdet
- else:
- theta = (inputs - input_cumwidths) / input_bin_widths
- theta_one_minus_theta = theta * (1 - theta)
-
- numerator = input_heights * (
- input_delta * theta.pow(2) + input_derivatives * theta_one_minus_theta
- )
- denominator = input_delta + (
- (input_derivatives + input_derivatives_plus_one - 2 * input_delta)
- * theta_one_minus_theta
- )
- outputs = input_cumheights + numerator / denominator
-
- derivative_numerator = input_delta.pow(2) * (
- input_derivatives_plus_one * theta.pow(2)
- + 2 * input_delta * theta_one_minus_theta
- + input_derivatives * (1 - theta).pow(2)
- )
- logabsdet = torch.log(derivative_numerator) - 2 * torch.log(denominator)
-
- return outputs, logabsdet
diff --git a/spaces/Benson/text-generation/Examples/Avakin Life Pc.md b/spaces/Benson/text-generation/Examples/Avakin Life Pc.md
deleted file mode 100644
index 02926a30996e18c4a25723d7ec42b59c5bfc562f..0000000000000000000000000000000000000000
--- a/spaces/Benson/text-generation/Examples/Avakin Life Pc.md
+++ /dev/null
@@ -1,50 +0,0 @@
-
-
- Zoom
-
-
- setZoomLevel(value[0] || 10)}
- value={[zoomLevel]}
- className="h-64 md:h-80"
- orientation="vertical"
- />
-
- - H2: Cómo descargar e instalar Avakin Life en PC
- H3: Cómo usar BlueStacks para jugar Avakin Life en PC
- H3: Cómo usar el sitio web oficial de Avakin para jugar Avakin Life en PC
- H2: Beneficios de jugar Avakin Life en PC
- H3: Más control y personalización
- H3: Comunicación y traducción más fáciles
- H2: Conclusión: Comienza tu segunda vida en el PC hoy
- H4: Preguntas frecuentes | Tabla 2: Artículo con formato HTML
Avakin Life PC: Cómo jugar al mundo virtual en 3D en tu ordenador
-Si estás buscando un juego de rol que te permita crear tu propio avatar, explorar un mundo virtual y conocer nuevos amigos, entonces deberías echar un vistazo a Avakin Life. Avakin Life es un juego de mundo virtual en 3D de Lockwood Publishing que está disponible en dispositivos iOS y Android. Puede personalizar su apariencia, estilo y hogar, ir a aventuras, unirse a concursos de moda y socializar con millones de jugadores de todo el mundo.
-avakin life pc
Download Zip 🆗 https://bltlly.com/2v6Jwg
-
Pero ¿sabías que también puedes jugar Avakin Life en tu PC? Sí, lo has oído bien. Puedes disfrutar de este increíble juego en una pantalla más grande, con mejores gráficos, rendimiento y control. En este artículo, le mostraremos cómo descargar e instalar Avakin Life en su computadora usando dos métodos diferentes. También te contaremos los beneficios de jugar a Avakin Life en PC y responderemos algunas preguntas frecuentes. ¡Así que, empecemos!
-Cómo descargar e instalar Avakin Life en PC
-Hay dos formas de jugar Avakin Life en tu PC. Uno es utilizar un software emulador como BlueStacks, que le permite ejecutar aplicaciones y juegos de Android en su ordenador. La otra es utilizar el sitio web oficial de Avakin, que ofrece una versión web del juego a la que puedes acceder a través de tu navegador. Estos son los pasos para cada método:
-Cómo usar BlueStacks para jugar Avakin Life en PC
--
-
-
- Inicie BlueStacks e inicie sesión en su cuenta de Google. Esto le permitirá acceder a la Google Play Store. -
- Busque Avakin Life en Google Play Store y haga clic en el botón de instalación. Alternativamente, puede descargar el archivo APK de una fuente de confianza y arrastrarlo y soltarlo en BlueStacks. -
- Una vez completada la instalación, haga clic en el icono de Avakin Life en la pantalla de inicio de BlueStacks para comenzar a jugar. -
Cómo usar el sitio web oficial de Avakin para jugar Avakin Life en PC
--
-
- Vaya al sitio web oficial de Avakin (https://avakin.com) y haga clic en el botón "Descargar" en la esquina superior derecha. -
- Seleccione su plataforma preferida entre las opciones disponibles. Puede elegir entre Windows, Mac, Linux o Web. -
- Si elige Web, será redirigido a una página donde puede jugar Avakin Life directamente en su navegador. Tendrá que iniciar sesión con su cuenta de Facebook o crear una nueva cuenta con su dirección de correo electrónico. -
- Si elige cualquiera de las otras plataformas, tendrá que descargar e instalar un pequeño archivo lanzador que le permitirá jugar Avakin Life en su computadora. Siga las instrucciones en la pantalla para completar el proceso. -
- Una vez instalado el lanzador, ábralo e inicie sesión con su cuenta de Facebook o dirección de correo electrónico. A continuación, puede comenzar a jugar Avakin Life en su PC. -
Beneficios de jugar Avakin Life en PC
-Ahora que sabe cómo jugar Avakin Life en su PC, es posible que se pregunte por qué debe hacerlo. Bueno, hay muchas ventajas de jugar a este juego en un ordenador en lugar de en un dispositivo móvil. Estas son algunas de ellas:
-Mejores gráficos y rendimiento
- -Más control y personalización
-Otro beneficio de jugar Avakin Life en PC es que puedes tener más opciones de control y personalización. Puede utilizar el teclado y el ratón para navegar por el juego, que puede ser más conveniente y preciso que el uso de una pantalla táctil. También puedes ajustar la configuración del juego según tus preferencias, como la resolución, el sonido y el idioma. Incluso puedes usar trucos y hacks para mejorar tu juego, como conseguir monedas, gemas o objetos ilimitados. Sin embargo, ten cuidado de no abusar de estas características o podrías ser expulsado del juego.
-Comunicación y traducción más fáciles
-Un tercer beneficio de jugar Avakin Life en PC es que puedes comunicarte y traducir más fácilmente con otros jugadores. Puede usar su teclado para escribir más rápido y cómodamente que usando un teclado virtual. También puedes usar chat de voz o video chat para hablar con tus amigos o hacer otros nuevos. También puedes usar herramientas de traducción para entender e interactuar con jugadores de diferentes países y culturas. Puedes aprender nuevos idiomas, intercambiar ideas y divertirte con gente de todo el mundo.
-Conclusión: Comience su segunda vida en el PC hoy
-Avakin Life es un fantástico juego que te permite crear tu propio avatar, explorar un mundo virtual y conocer nuevos amigos. Pero si quieres llevar tu experiencia de juego al siguiente nivel, deberías intentar jugar a Avakin Life en PC. Puede disfrutar de mejores gráficos, rendimiento, control y personalización. También puede comunicarse y traducir más fácilmente con otros jugadores. Jugar a Avakin Life en PC te hará sentir que estás viviendo una segunda vida en un mundo virtual en 3D.
- - -Esperamos que este artículo te haya ayudado a aprender a jugar Avakin Life en PC y por qué deberías hacerlo. Si tiene alguna pregunta o comentario, háganoslo saber en los comentarios a continuación. Nos encantaría saber de usted.
-Preguntas frecuentes
--
-
- Q: ¿Avakin Life es libre de jugar? -
- A: Sí, Avakin Life es gratis para jugar en dispositivos móviles y PC. Sin embargo, hay algunos elementos del juego y características que requieren dinero real para comprar. También puedes ver anuncios u ofertas completas para ganar monedas y gemas gratis. -
- Q: ¿Es la vida de Avakin segura para los niños? -
- A: Avakin Life está clasificado 12+ por la App Store y 13+ por la Google Play Store. Contiene violencia leve, contenido sexual, desnudez, blasfemia, alcohol, tabaco y drogas. También permite a los usuarios chatear con extraños en línea, lo que puede plantear algunos riesgos. Por lo tanto, se recomienda la orientación y supervisión de los padres para los jugadores más jóvenes. -
- Q: ¿Cómo puedo actualizar Avakin Life en PC? -
- A: Si está utilizando BlueStacks para jugar Avakin Life en PC, puede actualizar el juego yendo a la Google Play Store y haciendo clic en el botón de actualización. Si estás usando el sitio web oficial de Avakin para jugar a Avakin Life en PC, no necesitas actualizar el juego manualmente, ya que se actualizará automáticamente. -
- Q: ¿Cómo puedo eliminar mi cuenta de Avakin Life? -
- A: Si desea eliminar su cuenta de Avakin Life, debe ponerse en contacto con el equipo de atención al cliente a través de su sitio web (https://avakin.com/ support/) o correo electrónico (support@avakin.com). Deberá proporcionar su nombre de usuario, dirección de correo electrónico, ID de dispositivo y razón para eliminar su cuenta. Una vez procesada su solicitud, su cuenta será eliminada permanentemente. -
- Q: ¿Cómo me comunico con el soporte de Avakin Life? - -
-
-
\ No newline at end of file diff --git a/spaces/Benson/text-generation/Examples/Descargar Bus De Conduccin De Telolet 3d Mod Apk V1.2. 4b.md b/spaces/Benson/text-generation/Examples/Descargar Bus De Conduccin De Telolet 3d Mod Apk V1.2. 4b.md deleted file mode 100644 index 6e1baf5bb90310755622487cfab50a5fb6f4dd66..0000000000000000000000000000000000000000 --- a/spaces/Benson/text-generation/Examples/Descargar Bus De Conduccin De Telolet 3d Mod Apk V1.2. 4b.md +++ /dev/null @@ -1,60 +0,0 @@ - -
Descargar Telolet autobús de conducción 3D Mod APK v1.2. 4b y disfrutar de la diversión de conducir un autobús realista en Indonesia
-Si eres un fan de los juegos de conducción de autobuses, es posible que hayas oído hablar de Telolet Bus Driving 3D, un juego revolucionario en el género de la conducción árcade sin fin con gráficos y control realistas en 3D. En este juego, usted puede viajar a través de los coches de tráfico de la carretera de Indonesia con un autobús muy fresco y hacer que los niños felices tocando la bocina de su único autobús telolet. Pero lo que si quieres disfrutar del juego sin limitaciones o interrupciones? Bueno, puedes hacerlo descargando Telolet Bus Driving 3D Mod APK v1.2. 4b, que te da dinero ilimitado, todos los autobuses desbloqueados, y sin anuncios. En este artículo, te contaremos más sobre este juego, sus características y cómo descargarlo e instalarlo en tu dispositivo.
-descargar bus de conducción de telolet 3d mod apk v1.2. 4b
DOWNLOAD ►►► https://bltlly.com/2v6JJz
-
¿Qué es Telolet Bus Driving 3D?
-Telolet Bus Driving 3D es un juego desarrollado por LOCOS, un estudio de juegos indonesio que tiene como objetivo crear juegos divertidos y atractivos para todos. El juego se inspiró en el fenómeno viral de "Om Telolet Om", que significa "Señor, toca la bocina, señor" en indonesio. Esta es una frase que los niños gritan a los conductores de autobús para pedirles que toquen sus distintivos cuernos telolet, que producen un sonido musical. El juego fue lanzado en diciembre de 2016 y desde entonces ha ganado más de 10 millones de descargas en Google Play Store.
-Características de Telolet Bus Driving 3D
-Telolet Bus Driving 3D no es solo un juego de conducción simple. Tiene muchas características que lo hacen destacar de otros juegos del mismo género. Estos son algunos de ellos:
-Impresionantes gráficos 3D
-El juego tiene increíbles gráficos en 3D que te hacen sentir como si estuvieras conduciendo un autobús real en Indonesia. Puedes ver los detalles del autobús, el tráfico, el medio ambiente y los niños que te animan cuando tocas la bocina.
-Manejo del coche suave y realista
- -Muchos autobuses para elegir
-El juego tiene muchos autobuses para elegir, cada uno con su propio diseño, color, velocidad y melodía de cuerno telolet. Puedes desbloquear nuevos buses ganando monedas o usando la versión mod APK.
-3 lugares famosos en Indonesia
-El juego tiene 3 lugares famosos en Indonesia que puedes explorar: Pantura, Kampoeng y Cipali. Cada lugar tiene su propio paisaje, tráfico y desafíos.
- -3 modos de juego
-El juego tiene 3 modos de juego: One Way, Rush Hour y Two Way. En el modo One Way, conduce en una carretera de un solo sentido con tráfico moderado. En el modo de hora punta, se enfrenta a un atasco de tráfico pesado y tiene que evitar colisiones. En el modo de dos vías, se conduce en una carretera de dos vías con tráfico entrante y tiene que adelantar a otros vehículos.
-Tipos ricos de tráfico NPC Indonesia
-El juego tiene ricos tipos de tráfico NPC Indonesia que hacen el juego más realista y desafiante. Usted encontrará coches, camiones, motocicletas, autobuses y otros vehículos que tienen diferentes comportamientos y velocidades. También verás peatones, animales y obstáculos en la carretera.
-Actualizaciones de atributos
-El juego tiene actualizaciones de atributos que le permiten mejorar el rendimiento y la apariencia de su autobús. Puedes actualizar tu velocidad, freno, bocina y color usando las monedas que ganes del juego o la versión mod APK.
-Misiones diarias difíciles
-El juego tiene desafiantes misiones diarias que te dan recompensas y objetivos adicionales. Puede completar varias tareas, como conducir cierta distancia, tocar la bocina un cierto número de veces, adelantar un cierto número de vehículos y más.
-Tablas de clasificación en línea y logros
-El juego tiene tablas de clasificación en línea y logros que le permiten competir con otros jugadores y mostrar sus habilidades. Puedes posicionarte en las tablas de clasificación globales y regionales al ganar altas puntuaciones y monedas. También puedes desbloquear logros al completar varios desafíos e hitos.
- -Telolet Bus Driving 3D es un juego divertido y adictivo que te mantendrá entretenido durante horas. Sin embargo, si quieres disfrutar del juego sin limitaciones ni interrupciones, debes descargar Telolet Bus Driving 3D Mod APK v1.2. 4b, que le da los siguientes beneficios:
-Dinero ilimitado
-Con la versión APK mod, usted tendrá dinero ilimitado que se puede utilizar para comprar y actualizar cualquier autobús que desee. No tienes que preocuparte por quedarte sin monedas o gastar dinero real para conseguir más.
-Todos los autobuses desbloqueados
-Con la versión mod APK, tendrás todos los buses desbloqueados desde el principio. No tienes que jugar durante horas o completar misiones para desbloquear nuevos autobuses. Puedes elegir el autobús que quieras y disfrutar de sus características únicas.
-No hay anuncios
-Con la versión mod APK, no tendrás anuncios que interrumpan tu juego o te molesten. No tienes que ver videos o hacer clic en banners para obtener monedas o recompensas adicionales. Puedes jugar el juego sin problemas y sin distracciones.
-Cómo descargar e instalar Telolet Bus Driving 3D Mod APK v1.2. 4b?
-Si está interesado en descargar e instalar Telolet Bus Driving 3D Mod APK v1.2. 4b en su dispositivo, puede seguir estos sencillos pasos:
-Paso 1: Descargar el archivo APK de una fuente de confianza
-El primer paso es descargar el archivo APK de una fuente de confianza que proporciona descargas seguras y libres de virus. Puede utilizar este enlace para descargar el archivo directamente a su dispositivo o transferirlo desde su PC.
-Paso 2: Habilitar fuentes desconocidas en el dispositivo
-El segundo paso es habilitar fuentes desconocidas en su dispositivo para que pueda instalar aplicaciones desde fuentes distintas de Google Play Store. Para hacer esto, vaya a Configuración > Seguridad > Fuentes desconocidas y conéctelo.
-Paso 3: Instalar el archivo APK y disfrutar del juego
- -Esperamos que este artículo le haya ayudado a aprender más sobre Telolet Bus Driving 3D Mod APK v1.2. 4b y cómo descargarlo e instalarlo en su dispositivo. Este es un gran juego para los entusiastas de la conducción de autobuses que quieren experimentar la emoción de conducir un autobús realista en Indonesia con un cuerno musical. ¡Descárgalo ahora y diviértete!
-Conclusión
-Telolet Bus Driving 3D es un juego innovador en el género de la conducción árcade sin fin con gráficos y control 3D realistas. Fue inspirado por el fenómeno viral de "Om Telolet Om", que significa "Señor, toca la bocina, señor" en indonesio. El juego tiene muchas características que lo hacen destacar de otros juegos en el mismo género, tales como impresionantes gráficos en 3D, manejo de automóviles suave y realista, muchos autobuses para elegir, 3 lugares famosos en Indonesia, 3 modos de juego, ricos tipos de tráfico NPC Indonesia, actualizaciones de atributos, misiones diarias desafiantes, y tablas de clasificación en línea y logros. Sin embargo, si quieres disfrutar del juego sin limitaciones ni interrupciones, debes descargar Telolet Bus Driving 3D Mod APK v1.2. 4b, que le da dinero ilimitado, todos los autobuses desbloqueados, y sin anuncios. Para descargar e instalar la versión mod APK, solo tiene que seguir tres sencillos pasos: descargar el archivo APK de una fuente de confianza, habilitar fuentes desconocidas en su dispositivo, e instalar el archivo APK y disfrutar del juego. Este es un gran juego para los entusiastas de la conducción de autobuses que quieren experimentar la emoción de conducir un autobús realista en Indonesia con un cuerno musical. ¡Descárgalo ahora y diviértete!
-Preguntas frecuentes
-Aquí hay algunas preguntas frecuentes sobre Telolet Bus Driving 3D Mod APK v1.2. 4b:
-Es Telolet autobús de conducción 3D Mod APK v1.2. 4b seguro para descargar e instalar?
-Sí, Telolet autobús de conducción 3D Mod APK v1.2. 4b es seguro para descargar e instalar siempre y cuando utilice una fuente de confianza que proporciona descargas libres de virus. Puede utilizar este enlace para descargar el archivo de forma segura.
- -No, no es necesario rootear el dispositivo para usar Telolet Bus Driving 3D Mod APK v1.2. 4b. Solo necesitas habilitar fuentes desconocidas en la configuración de tu dispositivo e instalar el archivo APK como de costumbre.
-Será Telolet autobús de conducción 3D Mod APK v1.2. 4b afectar mi progreso original del juego?
-No, Telolet Bus Driving 3D Mod APK v1.2. 4b no afectará su progreso original del juego. Puedes jugar ambas versiones por separado y cambiar entre ellas cuando quieras.
-¿Puedo jugar Telolet autobús de conducción 3D Mod APK v1.2. 4b en línea con otros jugadores?
-Sí, se puede jugar Telolet autobús de conducción 3D Mod APK v1.2. 4b en línea con otros jugadores y competir en las tablas de clasificación y logros. Sin embargo, es posible que encuentre algunos problemas de compatibilidad con los jugadores que utilizan la versión original del juego.
-¿Cómo puedo contactar al desarrollador de Telolet Bus Driving 3D Mod APK v1.2. 4b si tengo alguna pregunta o comentario?
-Puede ponerse en contacto con el desarrollador de Telolet Bus Driving 3D Mod APK v1.2. 4b enviando un correo electrónico a locosgames@gmail.com o visitando su página de Facebook en https://www.facebook.com/locosgames/ Estarán encantados de saber de usted y responder a sus preguntas o comentarios.
64aa2da5cf-
-
\ No newline at end of file diff --git a/spaces/Big-Web/MMSD/env/Lib/site-packages/pip/_vendor/rich/json.py b/spaces/Big-Web/MMSD/env/Lib/site-packages/pip/_vendor/rich/json.py deleted file mode 100644 index ea94493f21e6f5583469d882d08203381ee31117..0000000000000000000000000000000000000000 --- a/spaces/Big-Web/MMSD/env/Lib/site-packages/pip/_vendor/rich/json.py +++ /dev/null @@ -1,140 +0,0 @@ -from pathlib import Path -from json import loads, dumps -from typing import Any, Callable, Optional, Union - -from .text import Text -from .highlighter import JSONHighlighter, NullHighlighter - - -class JSON: - """A renderable which pretty prints JSON. - - Args: - json (str): JSON encoded data. - indent (Union[None, int, str], optional): Number of characters to indent by. Defaults to 2. - highlight (bool, optional): Enable highlighting. Defaults to True. - skip_keys (bool, optional): Skip keys not of a basic type. Defaults to False. - ensure_ascii (bool, optional): Escape all non-ascii characters. Defaults to False. - check_circular (bool, optional): Check for circular references. Defaults to True. - allow_nan (bool, optional): Allow NaN and Infinity values. Defaults to True. - default (Callable, optional): A callable that converts values that can not be encoded - in to something that can be JSON encoded. Defaults to None. - sort_keys (bool, optional): Sort dictionary keys. Defaults to False. - """ - - def __init__( - self, - json: str, - indent: Union[None, int, str] = 2, - highlight: bool = True, - skip_keys: bool = False, - ensure_ascii: bool = False, - check_circular: bool = True, - allow_nan: bool = True, - default: Optional[Callable[[Any], Any]] = None, - sort_keys: bool = False, - ) -> None: - data = loads(json) - json = dumps( - data, - indent=indent, - skipkeys=skip_keys, - ensure_ascii=ensure_ascii, - check_circular=check_circular, - allow_nan=allow_nan, - default=default, - sort_keys=sort_keys, - ) - highlighter = JSONHighlighter() if highlight else NullHighlighter() - self.text = highlighter(json) - self.text.no_wrap = True - self.text.overflow = None - - @classmethod - def from_data( - cls, - data: Any, - indent: Union[None, int, str] = 2, - highlight: bool = True, - skip_keys: bool = False, - ensure_ascii: bool = False, - check_circular: bool = True, - allow_nan: bool = True, - default: Optional[Callable[[Any], Any]] = None, - sort_keys: bool = False, - ) -> "JSON": - """Encodes a JSON object from arbitrary data. - - Args: - data (Any): An object that may be encoded in to JSON - indent (Union[None, int, str], optional): Number of characters to indent by. Defaults to 2. - highlight (bool, optional): Enable highlighting. Defaults to True. - default (Callable, optional): Optional callable which will be called for objects that cannot be serialized. Defaults to None. - skip_keys (bool, optional): Skip keys not of a basic type. Defaults to False. - ensure_ascii (bool, optional): Escape all non-ascii characters. Defaults to False. - check_circular (bool, optional): Check for circular references. Defaults to True. - allow_nan (bool, optional): Allow NaN and Infinity values. Defaults to True. - default (Callable, optional): A callable that converts values that can not be encoded - in to something that can be JSON encoded. Defaults to None. - sort_keys (bool, optional): Sort dictionary keys. Defaults to False. - - Returns: - JSON: New JSON object from the given data. - """ - json_instance: "JSON" = cls.__new__(cls) - json = dumps( - data, - indent=indent, - skipkeys=skip_keys, - ensure_ascii=ensure_ascii, - check_circular=check_circular, - allow_nan=allow_nan, - default=default, - sort_keys=sort_keys, - ) - highlighter = JSONHighlighter() if highlight else NullHighlighter() - json_instance.text = highlighter(json) - json_instance.text.no_wrap = True - json_instance.text.overflow = None - return json_instance - - def __rich__(self) -> Text: - return self.text - - -if __name__ == "__main__": - - import argparse - import sys - - parser = argparse.ArgumentParser(description="Pretty print json") - parser.add_argument( - "path", - metavar="PATH", - help="path to file, or - for stdin", - ) - parser.add_argument( - "-i", - "--indent", - metavar="SPACES", - type=int, - help="Number of spaces in an indent", - default=2, - ) - args = parser.parse_args() - - from pip._vendor.rich.console import Console - - console = Console() - error_console = Console(stderr=True) - - try: - if args.path == "-": - json_data = sys.stdin.read() - else: - json_data = Path(args.path).read_text() - except Exception as error: - error_console.print(f"Unable to read {args.path!r}; {error}") - sys.exit(-1) - - console.print(JSON(json_data, indent=args.indent), soft_wrap=True) diff --git a/spaces/CAMP-ViL/Xplainer/app.py b/spaces/CAMP-ViL/Xplainer/app.py deleted file mode 100644 index b2b673afd870e6fb48cf1f3d791007c931d20c6b..0000000000000000000000000000000000000000 --- a/spaces/CAMP-ViL/Xplainer/app.py +++ /dev/null @@ -1,137 +0,0 @@ -from pathlib import Path - -import gradio as gr -import numpy as np -from matplotlib import pyplot as plt - -from descriptors import disease_descriptors_chexpert, disease_descriptors_chestxray14 -from model import InferenceModel - - -def plot_bars(model_output): - # sort model_output by overall_probability - model_output = {k: v for k, v in sorted(model_output.items(), key=lambda item: item[1]['overall_probability'], reverse=True)} - - # Create a figure with as many subplots as there are diseases, arranged vertically - fig, axs = plt.subplots(len(model_output), 1, figsize=(10, 5 * len(model_output))) - # axs is not iterable if only one subplot is created, so make it a list - if len(model_output) == 1: - axs = [axs] - - for ax, (disease, data) in zip(axs, model_output.items()): - desc_probs = list(data['descriptor_probabilities'].items()) - # sort descending - desc_probs = sorted(desc_probs, key=lambda item: item[1], reverse=True) - - my_probs = [p[1] for p in desc_probs] - min_prob = min(my_probs) - max_prob = max(my_probs) - my_labels = [p[0] for p in desc_probs] - - # Convert probabilities to differences from 0.5 - diffs = np.abs(np.array(my_probs) - 0.5) - - # Set colors based on sign of difference - colors = ['red' if p < 0.5 else 'forestgreen' for p in my_probs] - - # Plot bars with appropriate colors and left offsets - left = [p if p < 0.5 else 0.5 for p in my_probs] - bars = ax.barh(my_labels, diffs, left=left, color=colors, alpha=0.3) - - for i, bar in enumerate(bars): - ax.text(min_prob - 0.04, bar.get_y() + bar.get_height() / 2, my_labels[i], ha='left', va='center', color='black', fontsize=15) - - ax.set_xlim(min(min_prob - 0.05, 0.49), max(max_prob + 0.05, 0.51)) - - # Invert the y-axis to show bars with values less than 0.5 to the left of the center - ax.invert_yaxis() - - ax.set_yticks([]) - - # Add a title for the disease - if data['overall_probability'] >= 0.5: - ax.set_title(f"{disease} : score of {data['overall_probability']:.2f}") - else: - ax.set_title(f"No {disease} : score of {data['overall_probability']:.2f}") - - # make title larger and bold - ax.title.set_fontsize(15) - ax.title.set_fontweight(600) - - # Save the plot - plt.tight_layout() # Adjust subplot parameters to give specified padding - file_path = 'plot.png' - plt.savefig(file_path) - plt.close(fig) - - return file_path - - -def classify_image(inference_model, image_path, diseases_to_predict): - descriptors_with_indication = [d + " indicating " + disease for disease, descriptors in diseases_to_predict.items() for d in descriptors] - probs, negative_probs = inference_model.get_descriptor_probs(image_path=Path(image_path), descriptors=descriptors_with_indication, - do_negative_prompting=True, demo=True) - - disease_probs, negative_disease_probs = inference_model.get_diseases_probs(diseases_to_predict, pos_probs=probs, negative_probs=negative_probs) - - model_output = {} - for idx, disease in enumerate(diseases_to_predict.keys()): - model_output[disease] = { - 'overall_probability': disease_probs[disease], - 'descriptor_probabilities': {descriptor: probs[f'{descriptor} indicating {disease}'].item() for descriptor in - diseases_to_predict[disease]} - } - - file_path = plot_bars(model_output) - return file_path - - -# Define the function you want to wrap -def process_input(image_path, prompt_names: list, disease_name: str, descriptors: str): - diseases_to_predict = {} - - for prompt in prompt_names: - if prompt == 'Custom': - diseases_to_predict[disease_name] = descriptors.split('\n') - else: - if prompt in disease_descriptors_chexpert: - diseases_to_predict[prompt] = disease_descriptors_chexpert[prompt] - else: # only chestxray14 - diseases_to_predict[prompt] = disease_descriptors_chestxray14[prompt] - - # classify - model = InferenceModel() - output = classify_image(model, image_path, diseases_to_predict) - - return output - -with open("article.md", "r") as f: - article = f.read() -with open("description.md", "r") as f: - description = f.read() - -# Define the Gradio interface -iface = gr.Interface( - fn=process_input, - examples = [['examples/enlarged_cardiomediastinum.jpg', ['Enlarged Cardiomediastinum'], '', ''],['examples/edema.jpg', ['Edema'], '', ''], - ['examples/support_devices.jpg', ['Custom'], 'Pacemaker', 'metalic object\nimplant on the left side of the chest\nimplanted cardiac device']], - inputs=[gr.inputs.Image(type="filepath"), gr.inputs.CheckboxGroup( - choices=['Enlarged Cardiomediastinum', 'Cardiomegaly', 'Lung Opacity', 'Lung Lesion', 'Edema', 'Consolidation', 'Pneumonia', - 'Atelectasis', 'Pneumothorax', 'Pleural Effusion', 'Pleural Other', 'Fracture', 'Support Devices', - 'Infiltration', 'Mass', 'Nodule', 'Emphysema', 'Fibrosis', 'Pleural Thickening', 'Hernia', - 'Custom'], - default=['Enlarged Cardiomediastinum', 'Cardiomegaly', 'Lung Opacity', 'Lung Lesion', 'Edema', 'Consolidation', 'Pneumonia', - 'Atelectasis', 'Pneumothorax', 'Pleural Effusion', 'Pleural Other', 'Fracture', 'Support Devices'], - label='Select to use predefined disease descriptors. Select "Custom" to define your own observations.'), - gr.inputs.Textbox(lines=2, placeholder="Name of pathology for which you want to define custom observations", label='Pathology:'), - gr.inputs.Textbox(lines=2, placeholder="Add your custom (positive) observations separated by a new line" - "\n Note: Each descriptor will automatically be embedded into our prompt format: There is/are (no)
" + "
\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))}
-{message}
-
-
- 
-
-- -We use 20K hours of licensed music to train MusicGen. Specifically, we rely on an internal dataset of 10K high-quality music tracks, and on the ShutterStock and Pond5 music data. - -## Installation -Audiocraft requires Python 3.9, PyTorch 2.0.0, and a GPU with at least 16 GB of memory (for the medium-sized model). To install Audiocraft, you can run the following: - -```shell -# Best to make sure you have torch installed first, in particular before installing xformers. -# Don't run this if you already have PyTorch installed. -pip install 'torch>=2.0' -# Then proceed to one of the following -pip install -U audiocraft # stable release -pip install -U git+https://git@github.com/facebookresearch/audiocraft#egg=audiocraft # bleeding edge -pip install -e . # or if you cloned the repo locally -``` - -## Usage -We offer a number of way to interact with MusicGen: -1. A demo is also available on the [`facebook/MusicGen` HuggingFace Space](https://huggingface.co/spaces/facebook/MusicGen) (huge thanks to all the HF team for their support). -2. You can run the extended demo on a Colab: [colab notebook](https://colab.research.google.com/drive/1fxGqfg96RBUvGxZ1XXN07s3DthrKUl4-?usp=sharing). -3. You can use the gradio demo locally by running `python app.py`. -4. You can play with MusicGen by running the jupyter notebook at [`demo.ipynb`](./demo.ipynb) locally (if you have a GPU). -5. Finally, checkout [@camenduru Colab page](https://github.com/camenduru/MusicGen-colab) which is regularly - updated with contributions from @camenduru and the community. - -## API - -We provide a simple API and 4 pre-trained models. The pre trained models are: -- `small`: 300M model, text to music only - [🤗 Hub](https://huggingface.co/facebook/musicgen-small) -- `medium`: 1.5B model, text to music only - [🤗 Hub](https://huggingface.co/facebook/musicgen-medium) -- `melody`: 1.5B model, text to music and text+melody to music - [🤗 Hub](https://huggingface.co/facebook/musicgen-melody) -- `large`: 3.3B model, text to music only - [🤗 Hub](https://huggingface.co/facebook/musicgen-large) - -We observe the best trade-off between quality and compute with the `medium` or `melody` model. -In order to use MusicGen locally **you must have a GPU**. We recommend 16GB of memory, but smaller -GPUs will be able to generate short sequences, or longer sequences with the `small` model. - -**Note**: Please make sure to have [ffmpeg](https://ffmpeg.org/download.html) installed when using newer version of `torchaudio`. -You can install it with: -``` -apt-get install ffmpeg -``` - -See after a quick example for using the API. - -```python -import torchaudio -from audiocraft.models import MusicGen -from audiocraft.data.audio import audio_write - -model = MusicGen.get_pretrained('melody') -model.set_generation_params(duration=8) # generate 8 seconds. -wav = model.generate_unconditional(4) # generates 4 unconditional audio samples -descriptions = ['happy rock', 'energetic EDM', 'sad jazz'] -wav = model.generate(descriptions) # generates 3 samples. - -melody, sr = torchaudio.load('./assets/bach.mp3') -# generates using the melody from the given audio and the provided descriptions. -wav = model.generate_with_chroma(descriptions, melody[None].expand(3, -1, -1), sr) - -for idx, one_wav in enumerate(wav): - # Will save under {idx}.wav, with loudness normalization at -14 db LUFS. - audio_write(f'{idx}', one_wav.cpu(), model.sample_rate, strategy="loudness", loudness_compressor=True) -``` - - -## Model Card - -See [the model card page](./MODEL_CARD.md). - -## FAQ - -#### Will the training code be released? - -Yes. We will soon release the training code for MusicGen and EnCodec. - - -#### I need help on Windows - -@FurkanGozukara made a complete tutorial for [Audiocraft/MusicGen on Windows](https://youtu.be/v-YpvPkhdO4) - -#### I need help for running the demo on Colab - -Check [@camenduru tutorial on Youtube](https://www.youtube.com/watch?v=EGfxuTy9Eeo). - - -## Citation -``` -@article{copet2023simple, - title={Simple and Controllable Music Generation}, - author={Jade Copet and Felix Kreuk and Itai Gat and Tal Remez and David Kant and Gabriel Synnaeve and Yossi Adi and Alexandre Défossez}, - year={2023}, - journal={arXiv preprint arXiv:2306.05284}, -} -``` - -## License -* The code in this repository is released under the MIT license as found in the [LICENSE file](LICENSE). -* The weights in this repository are released under the CC-BY-NC 4.0 license as found in the [LICENSE_weights file](LICENSE_weights). - -[arxiv]: https://arxiv.org/abs/2306.05284 -[musicgen_samples]: https://ai.honu.io/papers/musicgen/ diff --git a/spaces/HMinions/new-Bing-with_your_cookies/README.md b/spaces/HMinions/new-Bing-with_your_cookies/README.md deleted file mode 100644 index 3763a6531c275a7e6e1d23d916fc76b2b9788d38..0000000000000000000000000000000000000000 --- a/spaces/HMinions/new-Bing-with_your_cookies/README.md +++ /dev/null @@ -1,19 +0,0 @@ ---- -title: New-Bing-with Your Cookies -emoji: 🐨 -colorFrom: green -colorTo: pink -sdk: gradio -sdk_version: 3.23.0 -app_file: app.py -pinned: false -license: other -duplicated_from: hOTZR/new-Bing-with_your_cookies ---- -## Inspired By: -- [EdgeGPT](https://github.com/acheong08/EdgeGPT) -- [DiscordBot-EdgeGPT](https://github.com/FuseFairy/DiscordBot-EdgeGPT) -- [chatdemo](https://github.com/simpx/chatdemo) -- [Chatbot](https://medium.datadriveninvestor.com/build-your-own-chatbot-using-chatgpt-for-inspiration-2a2ae6ebb288) - -Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference diff --git a/spaces/HaloMaster/chinesesummary/fengshen/examples/zen2_finetune/fs_zen2_large_afqmc.sh b/spaces/HaloMaster/chinesesummary/fengshen/examples/zen2_finetune/fs_zen2_large_afqmc.sh deleted file mode 100644 index 1f44844a127b5bb39226c56b70bba85957dd735a..0000000000000000000000000000000000000000 --- a/spaces/HaloMaster/chinesesummary/fengshen/examples/zen2_finetune/fs_zen2_large_afqmc.sh +++ /dev/null @@ -1,93 +0,0 @@ -#!/bin/bash -#SBATCH --job-name=zen2_large_afqmc # create a short name for your job -#SBATCH --nodes=1 # node count -#SBATCH --ntasks=1 # total number of tasks across all nodes -#SBATCH --cpus-per-task=30 # cpu-cores per task (>1 if multi-threaded tasks) -#SBATCH --gres=gpu:1 # number of gpus per node -#SBATCH --mail-type=ALL # send email when job begins, ends or failed etc. -#SBATCH -o %x-%j.log # output and error file name (%x=job name, %j=job id) - - -export CUDA_VISIBLE_DEVICES='1' -export TORCH_EXTENSIONS_DIR=/cognitive_comp/ganruyi/tmp/torch_extendsions - -MODEL_NAME=zen2_large - -TASK=afqmc - -ZERO_STAGE=1 -STRATEGY=deepspeed_stage_${ZERO_STAGE} - -ROOT_DIR=/cognitive_comp/ganruyi/experiments/classification_finetune/${MODEL_NAME}_${TASK} -if [ ! -d ${ROOT_DIR} ];then - mkdir -p ${ROOT_DIR} - echo ${ROOT_DIR} created!!!!!!!!!!!!!! -else - echo ${ROOT_DIR} exist!!!!!!!!!!!!!!! -fi - -DATA_DIR=/cognitive_comp/yangping/data/ChineseCLUE_DATA/${TASK}_public/ -PRETRAINED_MODEL_PATH=/cognitive_comp/ganruyi/hf_models/zen/zh_zen_large_2.0 - -CHECKPOINT_PATH=${ROOT_DIR}/ckpt/ -OUTPUT_PATH=${ROOT_DIR}/predict.json - -DATA_ARGS="\ - --data_dir $DATA_DIR \ - --train_data train.json \ - --valid_data dev.json \ - --test_data test.json \ - --train_batchsize 32 \ - --valid_batchsize 16 \ - --max_seq_length 128 \ - --texta_name sentence \ - --label_name label \ - --id_name id \ - --task_name afqmc \ - " - -MODEL_ARGS="\ - --learning_rate 2e-5 \ - --weight_decay 0.1 \ - --warmup_ratio 0.01 \ - --num_labels 2 \ - " - -MODEL_CHECKPOINT_ARGS="\ - --monitor val_acc \ - --save_top_k 3 \ - --mode max \ - --every_n_train_steps 100 \ - --save_weights_only True \ - --dirpath $CHECKPOINT_PATH \ - --filename model-{epoch:02d}-{val_acc:.4f} \ - " - -TRAINER_ARGS="\ - --max_epochs 10 \ - --gpus 1 \ - --check_val_every_n_epoch 1 \ - --val_check_interval 100 \ - --default_root_dir $ROOT_DIR \ - " - - -options=" \ - --pretrained_model_path $PRETRAINED_MODEL_PATH \ - --vocab_file $PRETRAINED_MODEL_PATH/vocab.txt \ - --do_lower_case \ - --output_save_path $OUTPUT_PATH \ - $DATA_ARGS \ - $MODEL_ARGS \ - $MODEL_CHECKPOINT_ARGS \ - $TRAINER_ARGS \ -" -SCRIPT_PATH=/cognitive_comp/ganruyi/Fengshenbang-LM/fengshen/examples/zen2_finetune/fengshen_sequence_level_ft_task.py -/home/ganruyi/anaconda3/bin/python $SCRIPT_PATH $options - -# SINGULARITY_PATH=/cognitive_comp/ganruyi/pytorch21_06_py3_docker_image_v2.sif -# python3 $SCRIPT_PATH $options -# source activate base -# singularity exec --nv -B /cognitive_comp/:/cognitive_comp/ $SINGULARITY_PATH /home/ganruyi/anaconda3/bin/python $SCRIPT_PATH $options -# /home/ganruyi/anaconda3/bin/python $SCRIPT_PATH $options - diff --git a/spaces/HarryLee/eCommerceImageCaptioning/fairseq/examples/wav2vec/unsupervised/models/__init__.py b/spaces/HarryLee/eCommerceImageCaptioning/fairseq/examples/wav2vec/unsupervised/models/__init__.py deleted file mode 100644 index 3e3039b7081a9e3228c8abefb6391a75b4864439..0000000000000000000000000000000000000000 --- a/spaces/HarryLee/eCommerceImageCaptioning/fairseq/examples/wav2vec/unsupervised/models/__init__.py +++ /dev/null @@ -1,11 +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 .wav2vec_u import Wav2vec_U - - -__all__ = [ - "Wav2vec_U", -] diff --git a/spaces/Harveenchadha/BioGPT/app.py b/spaces/Harveenchadha/BioGPT/app.py deleted file mode 100644 index c7c7cbd343dda5ee536931b0ad0c3728e30866ed..0000000000000000000000000000000000000000 --- a/spaces/Harveenchadha/BioGPT/app.py +++ /dev/null @@ -1,37 +0,0 @@ -import torch -import gradio as gr -from transformers import BioGptTokenizer, BioGptForCausalLM, set_seed - -tokenizer = BioGptTokenizer.from_pretrained("microsoft/biogpt") -model = BioGptForCausalLM.from_pretrained("microsoft/biogpt") - -sentence = "COVID-19 is" - - -set_seed(42) - -def get_beam_output(sentence): - inputs = tokenizer(sentence, return_tensors="pt") - with torch.no_grad(): - beam_output = model.generate(**inputs, - min_length=100, - max_length=1024, - num_beams=5, - early_stopping=True - ) - output=tokenizer.decode(beam_output[0], skip_special_tokens=True) - return output - - -txt1 = gr.Textbox( - label="Input", - lines=3, - ) - -txt2 = gr.Textbox( - label="Output", - lines=10, - ) - -demo = gr.Interface(fn=get_beam_output, inputs=txt1, outputs=txt2) -demo.launch() \ No newline at end of file diff --git a/spaces/Harveenchadha/Vakyansh-Tamil-TTS/ttsv/utils/inference/__init__.py b/spaces/Harveenchadha/Vakyansh-Tamil-TTS/ttsv/utils/inference/__init__.py deleted file mode 100644 index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000 diff --git a/spaces/Has-ai/text-speech/app.py b/spaces/Has-ai/text-speech/app.py deleted file mode 100644 index c652b507995accca288a88cec2ba16f225f964da..0000000000000000000000000000000000000000 --- a/spaces/Has-ai/text-speech/app.py +++ /dev/null @@ -1,92 +0,0 @@ - - -import logging -from typing import cast - -import gradio as gr -from balacoon_tts import TTS -from huggingface_hub import hf_hub_download, list_repo_files - - -# global tts module, initialized from a model selected -tts = None - - -def main(): - logging.basicConfig(level=logging.INFO) - - with gr.Blocks() as demo: - gr.Markdown( - """ -
Clone your voice
- - 1. Write an utterance to generate, - 2. Select the model to synthesize with - 3. Select speaker - 4. Hit "Generate" and listen to the result! - - When you select model for the first time, - it will take a little time to download it. - """ - ) - with gr.Row(variant="panel"): - text = gr.Textbox(label="Text", placeholder="Type something here...") - - with gr.Row(): - with gr.Column(variant="panel"): - repo_files = list_repo_files(repo_id="balacoon/tts") - model_files = [x for x in repo_files if x.endswith("_cpu.addon")] - model_name = gr.Dropdown( - label="Model", - choices=model_files, - ) - with gr.Column(variant="panel"): - speaker = gr.Dropdown(label="Speaker", choices=[]) - - def set_model(model_name_str: str): - """ - gets value from `model_name`, loads model, - re-initializes tts object, gets list of - speakers that model supports and set them to `speaker` - """ - model_path = hf_hub_download( - repo_id="balacoon/tts", filename=model_name_str - ) - global tts - tts = TTS(model_path) - speakers = tts.get_speakers() - value = speakers[-1] - return gr.Dropdown.update( - choices=speakers, value=value, visible=True - ) - - model_name.change(set_model, inputs=model_name, outputs=speaker) - - with gr.Row(variant="panel"): - generate = gr.Button("Generate") - with gr.Row(variant="panel"): - audio = gr.Audio() - - def synthesize_audio(text_str: str, speaker_str: str = ""): - """ - gets utterance to synthesize from `text` Textbox - and speaker name from `speaker` dropdown list. - speaker name might be empty for single-speaker models. - Synthesizes the waveform and updates `audio` with it. - """ - if not text_str: - logging.info("text or speaker are not provided") - return None - global tts - if len(text_str) > 1024: - text_str = text_str[:1024] - samples = cast(TTS, tts).synthesize(text_str, speaker_str) - return gr.Audio.update(value=(cast(TTS, tts).get_sampling_rate(), samples)) - - generate.click(synthesize_audio, inputs=[text, speaker], outputs=audio) - - demo.launch() - - -if __name__ == "__main__": - main() \ No newline at end of file diff --git a/spaces/Hermit591/anime-remove-background/app.py b/spaces/Hermit591/anime-remove-background/app.py deleted file mode 100644 index 230a0d5f8a3da6ab18ecb8db1cd90016a489b96a..0000000000000000000000000000000000000000 --- a/spaces/Hermit591/anime-remove-background/app.py +++ /dev/null @@ -1,52 +0,0 @@ -import gradio as gr -import huggingface_hub -import onnxruntime as rt -import numpy as np -import cv2 - - -def get_mask(img, s=1024): - img = (img / 255).astype(np.float32) - h, w = h0, w0 = img.shape[:-1] - h, w = (s, int(s * w / h)) if h > w else (int(s * h / w), s) - ph, pw = s - h, s - w - img_input = np.zeros([s, s, 3], dtype=np.float32) - img_input[ph // 2:ph // 2 + h, pw // 2:pw // 2 + w] = cv2.resize(img, (w, h)) - img_input = np.transpose(img_input, (2, 0, 1)) - img_input = img_input[np.newaxis, :] - mask = rmbg_model.run(None, {'img': img_input})[0][0] - mask = np.transpose(mask, (1, 2, 0)) - mask = mask[ph // 2:ph // 2 + h, pw // 2:pw // 2 + w] - mask = cv2.resize(mask, (w0, h0))[:, :, np.newaxis] - return mask - - -def rmbg_fn(img): - mask = get_mask(img) - img = (mask * img + 255 * (1 - mask)).astype(np.uint8) - mask = (mask * 255).astype(np.uint8) - img = np.concatenate([img, mask], axis=2, dtype=np.uint8) - mask = mask.repeat(3, axis=2) - return mask, img - - -if __name__ == "__main__": - providers = ['CUDAExecutionProvider', 'CPUExecutionProvider'] - model_path = huggingface_hub.hf_hub_download("skytnt/anime-seg", "isnetis.onnx") - rmbg_model = rt.InferenceSession(model_path, providers=providers) - app = gr.Blocks() - with app: - gr.Markdown("# Anime Remove Background\n\n" - "\n\n" - "demo for [https://github.com/SkyTNT/anime-segmentation/](https://github.com/SkyTNT/anime-segmentation/)") - with gr.Row(): - with gr.Column(): - input_img = gr.Image(label="input image") - examples_data = [[f"examples/{x:02d}.jpg"] for x in range(1, 4)] - examples = gr.Dataset(components=[input_img], samples=examples_data) - run_btn = gr.Button(variant="primary") - output_mask = gr.Image(label="mask") - output_img = gr.Image(label="result", image_mode="RGBA") - examples.click(lambda x: x[0], [examples], [input_img]) - run_btn.click(rmbg_fn, [input_img], [output_mask, output_img]) - app.launch() diff --git a/spaces/HighCWu/starganv2vc-paddle/README.md b/spaces/HighCWu/starganv2vc-paddle/README.md deleted file mode 100644 index ec4483aa8bf52fa9ed7aa6bb7e04f89ab8f6a509..0000000000000000000000000000000000000000 --- a/spaces/HighCWu/starganv2vc-paddle/README.md +++ /dev/null @@ -1,84 +0,0 @@ ---- -title: StarGANv2 Voice Conversion on PaddlePaddle -emoji: 🗣️ -colorFrom: green -colorTo: blue -sdk: gradio -sdk_version: 2.9.4 -app_file: app.py -pinned: false -license: mit ---- - -# StarGANv2-VC-Paddle -[](https://aistudio.baidu.com/aistudio/projectdetail/3955253) -[](https://huggingface.co/spaces/HighCWu/starganv2vc-paddle) - -A paddlepaddle version of [StarGANv2-VC](https://github.com/yl4579/StarGANv2-VC). - -Download pretrained models [here](https://aistudio.baidu.com/aistudio/datasetdetail/145012). - -Getting started with free v100/a100 in [AI Studio](https://aistudio.baidu.com/aistudio/projectdetail/3955253) or fast try with [HugginFace Spaces](https://huggingface.co/spaces/HighCWu/starganv2vc-paddle). - ---- - -Original PyTorch Repo [README](https://github.com/yl4579/StarGANv2-VC) 👇 - ---- - - -# StarGANv2-VC: A Diverse, Unsupervised, Non-parallel Framework for Natural-Sounding Voice Conversion - -### Yinghao Aaron Li, Ali Zare, Nima Mesgarani - -> We present an unsupervised non-parallel many-to-many voice conversion (VC) method using a generative adversarial network (GAN) called StarGAN v2. Using a combination of adversarial source classifier loss and perceptual loss, our model significantly outperforms previous VC models. Although our model is trained only with 20 English speakers, it generalizes to a variety of voice conversion tasks, such as any-to-many, cross-lingual, and singing conversion. Using a style encoder, our framework can also convert plain reading speech into stylistic speech, such as emotional and falsetto speech. Subjective and objective evaluation experiments on a non-parallel many-to-many voice conversion task revealed that our model produces natural sounding voices, close to the sound quality of state-of-the-art text-tospeech (TTS) based voice conversion methods without the need for text labels. Moreover, our model is completely convolutional and with a faster-than-real-time vocoder such as Parallel WaveGAN can perform real-time voice conversion. - -Paper: https://arxiv.org/abs/2107.10394 - -Audio samples: https://starganv2-vc.github.io/ - -## Pre-requisites -1. Python >= 3.7 -2. Clone this repository: -```bash -git https://github.com/yl4579/StarGANv2-VC.git -cd StarGANv2-VC -``` -3. Install python requirements: -```bash -pip install SoundFile torchaudio munch parallel_wavegan torch pydub -``` -4. Download and extract the [VCTK dataset](https://datashare.ed.ac.uk/handle/10283/3443) -and use [VCTK.ipynb](https://github.com/yl4579/StarGANv2-VC/blob/main/Data/VCTK.ipynb) to prepare the data (downsample to 24 kHz etc.). You can also [download the dataset](https://drive.google.com/file/d/1t7QQbu4YC_P1mv9puA_KgSomSFDsSzD6/view?usp=sharing) we have prepared and unzip it to the `Data` folder, use the provided `config.yml` to reproduce our models. - -## Training -```bash -python train.py --config_path ./Configs/config.yml -``` -Please specify the training and validation data in `config.yml` file. Change `num_domains` to the number of speakers in the dataset. The data list format needs to be `filename.wav|speaker_number`, see [train_list.txt](https://github.com/yl4579/StarGANv2-VC/blob/main/Data/train_list.txt) as an example. - -Checkpoints and Tensorboard logs will be saved at `log_dir`. To speed up training, you may want to make `batch_size` as large as your GPU RAM can take. However, please note that `batch_size = 5` will take around 10G GPU RAM. - -## Inference - -Please refer to [inference.ipynb](https://github.com/yl4579/StarGANv2-VC/blob/main/Demo/inference.ipynb) for details. - -The pretrained StarGANv2 and ParallelWaveGAN on VCTK corpus can be downloaded at [StarGANv2 Link](https://drive.google.com/file/d/1nzTyyl-9A1Hmqya2Q_f2bpZkUoRjbZsY/view?usp=sharing) and [ParallelWaveGAN Link](https://drive.google.com/file/d/1q8oSAzwkqi99oOGXDZyLypCiz0Qzn3Ab/view?usp=sharing). Please unzip to `Models` and `Vocoder` respectivey and run each cell in the notebook. - -## ASR & F0 Models - -The pretrained F0 and ASR models are provided under the `Utils` folder. Both the F0 and ASR models are trained with melspectrograms preprocessed using [meldataset.py](https://github.com/yl4579/StarGANv2-VC/blob/main/meldataset.py), and both models are trained on speech data only. - -The ASR model is trained on English corpus, but it appears to work when training StarGANv2 models in other languages such as Japanese. The F0 model also appears to work with singing data. For the best performance, however, training your own ASR and F0 models is encouraged for non-English and non-speech data. - -You can edit the [meldataset.py](https://github.com/yl4579/StarGANv2-VC/blob/main/meldataset.py) with your own melspectrogram preprocessing, but the provided pretrained models will no longer work. You will need to train your own ASR and F0 models with the new preprocessing. You may refer to repo [Diamondfan/CTC_pytorch](https://github.com/Diamondfan/CTC_pytorch) and [keums/melodyExtraction_JDC](https://github.com/keums/melodyExtraction_JDC) to train your own the ASR and F0 models, for example. - -## References -- [clovaai/stargan-v2](https://github.com/clovaai/stargan-v2) -- [kan-bayashi/ParallelWaveGAN](https://github.com/kan-bayashi/ParallelWaveGAN) -- [tosaka-m/japanese_realtime_tts](https://github.com/tosaka-m/japanese_realtime_tts) -- [keums/melodyExtraction_JDC](https://github.com/keums/melodyExtraction_JDC) -- [Diamondfan/CTC_pytorch](https://github.com/Diamondfan/CTC_pytorch) - -## Acknowledgement -The author would like to thank [@tosaka-m](https://github.com/tosaka-m) for his great repository and valuable discussions. diff --git a/spaces/Hugorowan/BardJukebox/README.md b/spaces/Hugorowan/BardJukebox/README.md deleted file mode 100644 index 226df0a3c4a472c47580dd7fd88c90eda9a3b56c..0000000000000000000000000000000000000000 --- a/spaces/Hugorowan/BardJukebox/README.md +++ /dev/null @@ -1,13 +0,0 @@ ---- -title: BardJukebox -emoji: 🌍 -colorFrom: blue -colorTo: green -sdk: gradio -sdk_version: 3.32.0 -app_file: app.py -pinned: true -license: other ---- - -Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference \ No newline at end of file diff --git a/spaces/ICML2022/OFA/fairseq/examples/textless_nlp/gslm/unit2speech/tacotron2/stft.py b/spaces/ICML2022/OFA/fairseq/examples/textless_nlp/gslm/unit2speech/tacotron2/stft.py deleted file mode 100644 index 63fcd431e2d7746b696aaa0d4172bc04ffb88efa..0000000000000000000000000000000000000000 --- a/spaces/ICML2022/OFA/fairseq/examples/textless_nlp/gslm/unit2speech/tacotron2/stft.py +++ /dev/null @@ -1,141 +0,0 @@ -""" -BSD 3-Clause License - -Copyright (c) 2017, Prem Seetharaman -All rights reserved. - -* Redistribution and use in source and binary forms, with or without - modification, are permitted provided that the following conditions are met: - -* Redistributions of source code must retain the above copyright notice, - this list of conditions and the following disclaimer. - -* Redistributions in binary form must reproduce the above copyright notice, this - list of conditions and the following disclaimer in the - documentation and/or other materials provided with the distribution. - -* Neither the name of the copyright holder nor the names of its - contributors may be used to endorse or promote products derived from this - software without specific prior written permission. - -THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND -ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED -WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE -DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR -ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES -(INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; -LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON -ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT -(INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS -SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. -""" - -import torch -import numpy as np -import torch.nn.functional as F -from torch.autograd import Variable -from scipy.signal import get_window -from librosa.util import pad_center, tiny -from .audio_processing import window_sumsquare - - -class STFT(torch.nn.Module): - """adapted from Prem Seetharaman's https://github.com/pseeth/pytorch-stft""" - def __init__(self, filter_length=800, hop_length=200, win_length=800, - window='hann'): - super(STFT, self).__init__() - self.filter_length = filter_length - self.hop_length = hop_length - self.win_length = win_length - self.window = window - self.forward_transform = None - scale = self.filter_length / self.hop_length - fourier_basis = np.fft.fft(np.eye(self.filter_length)) - - cutoff = int((self.filter_length / 2 + 1)) - fourier_basis = np.vstack([np.real(fourier_basis[:cutoff, :]), - np.imag(fourier_basis[:cutoff, :])]) - - forward_basis = torch.FloatTensor(fourier_basis[:, None, :]) - inverse_basis = torch.FloatTensor( - np.linalg.pinv(scale * fourier_basis).T[:, None, :]) - - if window is not None: - assert(filter_length >= win_length) - # get window and zero center pad it to filter_length - fft_window = get_window(window, win_length, fftbins=True) - fft_window = pad_center(fft_window, filter_length) - fft_window = torch.from_numpy(fft_window).float() - - # window the bases - forward_basis *= fft_window - inverse_basis *= fft_window - - self.register_buffer('forward_basis', forward_basis.float()) - self.register_buffer('inverse_basis', inverse_basis.float()) - - def transform(self, input_data): - num_batches = input_data.size(0) - num_samples = input_data.size(1) - - self.num_samples = num_samples - - # similar to librosa, reflect-pad the input - input_data = input_data.view(num_batches, 1, num_samples) - input_data = F.pad( - input_data.unsqueeze(1), - (int(self.filter_length / 2), int(self.filter_length / 2), 0, 0), - mode='reflect') - input_data = input_data.squeeze(1) - - forward_transform = F.conv1d( - input_data, - Variable(self.forward_basis, requires_grad=False), - stride=self.hop_length, - padding=0) - - cutoff = int((self.filter_length / 2) + 1) - real_part = forward_transform[:, :cutoff, :] - imag_part = forward_transform[:, cutoff:, :] - - magnitude = torch.sqrt(real_part**2 + imag_part**2) - phase = torch.autograd.Variable( - torch.atan2(imag_part.data, real_part.data)) - - return magnitude, phase - - def inverse(self, magnitude, phase): - recombine_magnitude_phase = torch.cat( - [magnitude*torch.cos(phase), magnitude*torch.sin(phase)], dim=1) - - inverse_transform = F.conv_transpose1d( - recombine_magnitude_phase, - Variable(self.inverse_basis, requires_grad=False), - stride=self.hop_length, - padding=0) - - if self.window is not None: - window_sum = window_sumsquare( - self.window, magnitude.size(-1), hop_length=self.hop_length, - win_length=self.win_length, n_fft=self.filter_length, - dtype=np.float32) - # remove modulation effects - approx_nonzero_indices = torch.from_numpy( - np.where(window_sum > tiny(window_sum))[0]) - window_sum = torch.autograd.Variable( - torch.from_numpy(window_sum), requires_grad=False) - window_sum = window_sum.cuda() if magnitude.is_cuda else window_sum - inverse_transform[:, :, approx_nonzero_indices] /= window_sum[approx_nonzero_indices] - - # scale by hop ratio - inverse_transform *= float(self.filter_length) / self.hop_length - - inverse_transform = inverse_transform[:, :, int(self.filter_length/2):] - inverse_transform = inverse_transform[:, :, :-int(self.filter_length/2):] - - return inverse_transform - - def forward(self, input_data): - self.magnitude, self.phase = self.transform(input_data) - reconstruction = self.inverse(self.magnitude, self.phase) - return reconstruction diff --git a/spaces/ICML2022/OFA/fairseq/fairseq/binarizer.py b/spaces/ICML2022/OFA/fairseq/fairseq/binarizer.py deleted file mode 100644 index ae4d02a6dbbb523b76eb8684e87e38c74fe7c4a1..0000000000000000000000000000000000000000 --- a/spaces/ICML2022/OFA/fairseq/fairseq/binarizer.py +++ /dev/null @@ -1,80 +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 Counter -from typing import Dict - -import torch - -from fairseq.file_chunker_utils import Chunker -from fairseq.file_io import PathManager -from fairseq.tokenizer import tokenize_line - - -class Binarizer: - @staticmethod - def binarize( - filename, - dict, - consumer, - tokenize=tokenize_line, - append_eos=True, - reverse_order=False, - offset=0, - end=-1, - already_numberized=False, - ) -> Dict[str, int]: - nseq, ntok = 0, 0 - replaced = Counter() - - def replaced_consumer(word, idx): - if idx == dict.unk_index and word != dict.unk_word: - replaced.update([word]) - - with Chunker( - PathManager.get_local_path(filename), offset, end - ) as line_iterator: - for line in line_iterator: - if already_numberized: - id_strings = line.strip().split() - id_list = [int(id_string) for id_string in id_strings] - if reverse_order: - id_list.reverse() - if append_eos: - id_list.append(dict.eos()) - ids = torch.IntTensor(id_list) - else: - ids = dict.encode_line( - line=line, - line_tokenizer=tokenize, - add_if_not_exist=False, - consumer=replaced_consumer, - append_eos=append_eos, - reverse_order=reverse_order, - ) - nseq += 1 - ntok += len(ids) - consumer(ids) - return { - "nseq": nseq, - "nunk": sum(replaced.values()), - "ntok": ntok, - "replaced": replaced, - } - - @staticmethod - def binarize_alignments( - filename, alignment_parser, consumer, offset=0, end=-1 - ) -> Dict[str, int]: - nseq = 0 - - with Chunker( - PathManager.get_local_path(filename), offset, end - ) as line_iterator: - for line in line_iterator: - ids = alignment_parser(line) - nseq += 1 - consumer(ids) - return {"nseq": nseq} diff --git a/spaces/ICML2022/YourTTS/README.md b/spaces/ICML2022/YourTTS/README.md deleted file mode 100644 index 334d7b7a700b3ef950a22b594a0200c3aa62f499..0000000000000000000000000000000000000000 --- a/spaces/ICML2022/YourTTS/README.md +++ /dev/null @@ -1,12 +0,0 @@ ---- -title: YourTTS -emoji: 🔥 -colorFrom: indigo -colorTo: red -sdk: gradio -sdk_version: 3.1.0 -app_file: app.py -pinned: false ---- - -Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference diff --git a/spaces/IDEA-Research/Grounded-SAM/GroundingDINO/groundingdino/models/GroundingDINO/backbone/position_encoding.py b/spaces/IDEA-Research/Grounded-SAM/GroundingDINO/groundingdino/models/GroundingDINO/backbone/position_encoding.py deleted file mode 100644 index eac7e896bbe85a670824bfe8ef487d0535d5bd99..0000000000000000000000000000000000000000 --- a/spaces/IDEA-Research/Grounded-SAM/GroundingDINO/groundingdino/models/GroundingDINO/backbone/position_encoding.py +++ /dev/null @@ -1,186 +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] -# ------------------------------------------------------------------------ -# DINO -# Copyright (c) 2022 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. -# ------------------------------------------------------------------------ - -""" -Various positional encodings for the transformer. -""" -import math - -import torch -from torch import nn - -from groundingdino.util.misc import NestedTensor - - -class PositionEmbeddingSine(nn.Module): - """ - This is a more standard version of the position embedding, very similar to the one - used by the Attention is all you need paper, generalized to work on images. - """ - - def __init__(self, num_pos_feats=64, temperature=10000, normalize=False, scale=None): - super().__init__() - self.num_pos_feats = num_pos_feats - self.temperature = temperature - self.normalize = normalize - if scale is not None and normalize is False: - raise ValueError("normalize should be True if scale is passed") - if scale is None: - scale = 2 * math.pi - self.scale = scale - - def forward(self, tensor_list: NestedTensor): - x = tensor_list.tensors - mask = tensor_list.mask - assert mask is not None - not_mask = ~mask - y_embed = not_mask.cumsum(1, dtype=torch.float32) - x_embed = not_mask.cumsum(2, dtype=torch.float32) - if self.normalize: - eps = 1e-6 - # if os.environ.get("SHILONG_AMP", None) == '1': - # eps = 1e-4 - # else: - # eps = 1e-6 - y_embed = y_embed / (y_embed[:, -1:, :] + eps) * self.scale - x_embed = x_embed / (x_embed[:, :, -1:] + eps) * self.scale - - dim_t = torch.arange(self.num_pos_feats, dtype=torch.float32, device=x.device) - dim_t = self.temperature ** (2 * (dim_t // 2) / self.num_pos_feats) - - pos_x = x_embed[:, :, :, None] / dim_t - pos_y = y_embed[:, :, :, None] / dim_t - pos_x = torch.stack( - (pos_x[:, :, :, 0::2].sin(), pos_x[:, :, :, 1::2].cos()), dim=4 - ).flatten(3) - pos_y = torch.stack( - (pos_y[:, :, :, 0::2].sin(), pos_y[:, :, :, 1::2].cos()), dim=4 - ).flatten(3) - pos = torch.cat((pos_y, pos_x), dim=3).permute(0, 3, 1, 2) - return pos - - -class PositionEmbeddingSineHW(nn.Module): - """ - This is a more standard version of the position embedding, very similar to the one - used by the Attention is all you need paper, generalized to work on images. - """ - - def __init__( - self, num_pos_feats=64, temperatureH=10000, temperatureW=10000, normalize=False, scale=None - ): - super().__init__() - self.num_pos_feats = num_pos_feats - self.temperatureH = temperatureH - self.temperatureW = temperatureW - self.normalize = normalize - if scale is not None and normalize is False: - raise ValueError("normalize should be True if scale is passed") - if scale is None: - scale = 2 * math.pi - self.scale = scale - - def forward(self, tensor_list: NestedTensor): - x = tensor_list.tensors - mask = tensor_list.mask - assert mask is not None - not_mask = ~mask - y_embed = not_mask.cumsum(1, dtype=torch.float32) - x_embed = not_mask.cumsum(2, dtype=torch.float32) - - # import ipdb; ipdb.set_trace() - - if self.normalize: - eps = 1e-6 - y_embed = y_embed / (y_embed[:, -1:, :] + eps) * self.scale - x_embed = x_embed / (x_embed[:, :, -1:] + eps) * self.scale - - dim_tx = torch.arange(self.num_pos_feats, dtype=torch.float32, device=x.device) - dim_tx = self.temperatureW ** (2 * (torch.div(dim_tx, 2, rounding_mode='floor')) / self.num_pos_feats) - pos_x = x_embed[:, :, :, None] / dim_tx - - dim_ty = torch.arange(self.num_pos_feats, dtype=torch.float32, device=x.device) - dim_ty = self.temperatureH ** (2 * (torch.div(dim_ty, 2, rounding_mode='floor')) / self.num_pos_feats) - pos_y = y_embed[:, :, :, None] / dim_ty - - pos_x = torch.stack( - (pos_x[:, :, :, 0::2].sin(), pos_x[:, :, :, 1::2].cos()), dim=4 - ).flatten(3) - pos_y = torch.stack( - (pos_y[:, :, :, 0::2].sin(), pos_y[:, :, :, 1::2].cos()), dim=4 - ).flatten(3) - pos = torch.cat((pos_y, pos_x), dim=3).permute(0, 3, 1, 2) - - # import ipdb; ipdb.set_trace() - - return pos - - -class PositionEmbeddingLearned(nn.Module): - """ - Absolute pos embedding, learned. - """ - - def __init__(self, num_pos_feats=256): - super().__init__() - self.row_embed = nn.Embedding(50, num_pos_feats) - self.col_embed = nn.Embedding(50, num_pos_feats) - self.reset_parameters() - - def reset_parameters(self): - nn.init.uniform_(self.row_embed.weight) - nn.init.uniform_(self.col_embed.weight) - - def forward(self, tensor_list: NestedTensor): - x = tensor_list.tensors - h, w = x.shape[-2:] - i = torch.arange(w, device=x.device) - j = torch.arange(h, device=x.device) - x_emb = self.col_embed(i) - y_emb = self.row_embed(j) - pos = ( - torch.cat( - [ - x_emb.unsqueeze(0).repeat(h, 1, 1), - y_emb.unsqueeze(1).repeat(1, w, 1), - ], - dim=-1, - ) - .permute(2, 0, 1) - .unsqueeze(0) - .repeat(x.shape[0], 1, 1, 1) - ) - return pos - - -def build_position_encoding(args): - N_steps = args.hidden_dim // 2 - if args.position_embedding in ("v2", "sine"): - # TODO find a better way of exposing other arguments - position_embedding = PositionEmbeddingSineHW( - N_steps, - temperatureH=args.pe_temperatureH, - temperatureW=args.pe_temperatureW, - normalize=True, - ) - elif args.position_embedding in ("v3", "learned"): - position_embedding = PositionEmbeddingLearned(N_steps) - else: - raise ValueError(f"not supported {args.position_embedding}") - - return position_embedding diff --git a/spaces/InpaintAI/Inpaint-Anything/third_party/segment-anything/demo/src/components/helpers/scaleHelper.tsx b/spaces/InpaintAI/Inpaint-Anything/third_party/segment-anything/demo/src/components/helpers/scaleHelper.tsx deleted file mode 100644 index 815ceaac472a18915b33e78c70231b88e5dd2eee..0000000000000000000000000000000000000000 --- a/spaces/InpaintAI/Inpaint-Anything/third_party/segment-anything/demo/src/components/helpers/scaleHelper.tsx +++ /dev/null @@ -1,18 +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. - - -// Helper function for handling image scaling needed for SAM -const handleImageScale = (image: HTMLImageElement) => { - // Input images to SAM must be resized so the longest side is 1024 - const LONG_SIDE_LENGTH = 1024; - let w = image.naturalWidth; - let h = image.naturalHeight; - const samScale = LONG_SIDE_LENGTH / Math.max(h, w); - return { height: h, width: w, samScale }; -}; - -export { handleImageScale }; diff --git a/spaces/JDWebProgrammer/space-weather/README.md b/spaces/JDWebProgrammer/space-weather/README.md deleted file mode 100644 index cd3a2385a709844d2f2fcb6bd80596abbad99ea1..0000000000000000000000000000000000000000 --- a/spaces/JDWebProgrammer/space-weather/README.md +++ /dev/null @@ -1,13 +0,0 @@ ---- -title: Space Weather -emoji: 📈 -colorFrom: indigo -colorTo: purple -sdk: gradio -sdk_version: 4.1.2 -app_file: app.py -pinned: false -license: mit ---- - -Machine learning functions applied to GOES XRS data diff --git a/spaces/Jackflack09/diffuse-custom/diffusers/utils/dummy_pt_objects.py b/spaces/Jackflack09/diffuse-custom/diffusers/utils/dummy_pt_objects.py deleted file mode 100644 index 23afb51cf30c0273507d296a47e96da087ea5f2d..0000000000000000000000000000000000000000 --- a/spaces/Jackflack09/diffuse-custom/diffusers/utils/dummy_pt_objects.py +++ /dev/null @@ -1,527 +0,0 @@ -# This file is autogenerated by the command `make fix-copies`, do not edit. -# flake8: noqa - -from ..utils import DummyObject, requires_backends - - -class ModelMixin(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - @classmethod - def from_config(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - - -class AutoencoderKL(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - @classmethod - def from_config(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - - -class Transformer2DModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - @classmethod - def from_config(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - - -class UNet1DModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - @classmethod - def from_config(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - - -class UNet2DConditionModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - @classmethod - def from_config(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - - -class UNet2DModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - @classmethod - def from_config(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - - -class VQModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - @classmethod - def from_config(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - - -def get_constant_schedule(*args, **kwargs): - requires_backends(get_constant_schedule, ["torch"]) - - -def get_constant_schedule_with_warmup(*args, **kwargs): - requires_backends(get_constant_schedule_with_warmup, ["torch"]) - - -def get_cosine_schedule_with_warmup(*args, **kwargs): - requires_backends(get_cosine_schedule_with_warmup, ["torch"]) - - -def get_cosine_with_hard_restarts_schedule_with_warmup(*args, **kwargs): - requires_backends(get_cosine_with_hard_restarts_schedule_with_warmup, ["torch"]) - - -def get_linear_schedule_with_warmup(*args, **kwargs): - requires_backends(get_linear_schedule_with_warmup, ["torch"]) - - -def get_polynomial_decay_schedule_with_warmup(*args, **kwargs): - requires_backends(get_polynomial_decay_schedule_with_warmup, ["torch"]) - - -def get_scheduler(*args, **kwargs): - requires_backends(get_scheduler, ["torch"]) - - -class DiffusionPipeline(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - @classmethod - def from_config(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - - -class DanceDiffusionPipeline(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - @classmethod - def from_config(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - - -class DDIMPipeline(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - @classmethod - def from_config(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - - -class DDPMPipeline(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - @classmethod - def from_config(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - - -class KarrasVePipeline(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - @classmethod - def from_config(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - - -class LDMPipeline(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - @classmethod - def from_config(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - - -class LDMSuperResolutionPipeline(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - @classmethod - def from_config(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - - -class PNDMPipeline(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - @classmethod - def from_config(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - - -class RePaintPipeline(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - @classmethod - def from_config(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - - -class ScoreSdeVePipeline(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - @classmethod - def from_config(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - - -class DDIMScheduler(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - @classmethod - def from_config(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - - -class DDPMScheduler(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - @classmethod - def from_config(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - - -class DPMSolverMultistepScheduler(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - @classmethod - def from_config(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - - -class EulerAncestralDiscreteScheduler(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - @classmethod - def from_config(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - - -class EulerDiscreteScheduler(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - @classmethod - def from_config(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - - -class HeunDiscreteScheduler(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - @classmethod - def from_config(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - - -class IPNDMScheduler(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - @classmethod - def from_config(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - - -class KarrasVeScheduler(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - @classmethod - def from_config(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - - -class KDPM2AncestralDiscreteScheduler(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - @classmethod - def from_config(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - - -class KDPM2DiscreteScheduler(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - @classmethod - def from_config(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - - -class PNDMScheduler(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - @classmethod - def from_config(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - - -class RePaintScheduler(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - @classmethod - def from_config(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - - -class SchedulerMixin(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - @classmethod - def from_config(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - - -class ScoreSdeVeScheduler(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - @classmethod - def from_config(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - - -class VQDiffusionScheduler(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - @classmethod - def from_config(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - - -class EMAModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - @classmethod - def from_config(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) diff --git a/spaces/JamesStratford/Identify-Pest-Predators-Demo/model.py b/spaces/JamesStratford/Identify-Pest-Predators-Demo/model.py deleted file mode 100644 index c687d06b14798be8e94cf6de2e8bd7117a30fbd7..0000000000000000000000000000000000000000 --- a/spaces/JamesStratford/Identify-Pest-Predators-Demo/model.py +++ /dev/null @@ -1,22 +0,0 @@ -import pytorch_lightning as pl -from transformers import AutoModelForImageClassification, ViTConfig, AutoConfig -import torch - -class ImageClassificationModel(pl.LightningModule): - def __init__(self, HF_MODEL_NAME): - super().__init__() - config = AutoConfig.from_pretrained(HF_MODEL_NAME, num_labels=6) - self.model = AutoModelForImageClassification.from_config(config=config) - self.model.config = config - self.model.config.id2label = { - 0: "None", - 1: "Cat", - 2: "Mouse", - 3: "Possum", - 4: "Rat", - 5: "Stoat", - } - self.model.config.label2id = {v: k for k, v in self.model.config.id2label.items()} - - def forward(self, x): - return self.model(x) \ No newline at end of file diff --git a/spaces/Jasonyoyo/CodeFormer/CodeFormer/facelib/utils/__init__.py b/spaces/Jasonyoyo/CodeFormer/CodeFormer/facelib/utils/__init__.py deleted file mode 100644 index f03b1c2bafcd7759cb7e8722a0c6715f201a46dc..0000000000000000000000000000000000000000 --- a/spaces/Jasonyoyo/CodeFormer/CodeFormer/facelib/utils/__init__.py +++ /dev/null @@ -1,7 +0,0 @@ -from .face_utils import align_crop_face_landmarks, compute_increased_bbox, get_valid_bboxes, paste_face_back -from .misc import img2tensor, load_file_from_url, download_pretrained_models, scandir - -__all__ = [ - 'align_crop_face_landmarks', 'compute_increased_bbox', 'get_valid_bboxes', 'load_file_from_url', - 'download_pretrained_models', 'paste_face_back', 'img2tensor', 'scandir' -] diff --git a/spaces/JohanDL/GPT4Readability/app.py b/spaces/JohanDL/GPT4Readability/app.py deleted file mode 100644 index 544021798b44c990e75d4c13510bef3c34d69a92..0000000000000000000000000000000000000000 --- a/spaces/JohanDL/GPT4Readability/app.py +++ /dev/null @@ -1,93 +0,0 @@ -import gradio as gr -import os -import subprocess -import shutil -import openai - -css_style = """ -.gradio-container { - font-family: "IBM Plex Mono"; -} -""" - -def process_repository(url, model): - - - # Split the URL to get the repo name - repo_name = url.split('/')[-1] - if repo_name.endswith('.git'): - repo_name = repo_name[:-4] - - # Change permissions - subprocess.run(['chmod', 'u+w', '.']) - - # Clone the repo - subprocess.run(['git', 'clone', url], check=True) - - try: - # Change directory to the cloned repo - os.chdir(repo_name) - - # Run your package command - subprocess.run(['gpt4readability', '.', '--function', 'readme','--include-md', 'false', '--model', model]) - - # Open the README.md file and return its contents - with open('README.md', 'r') as readme_file: - readme_contents = readme_file.read() - - return readme_contents - finally: - # Change back to the original directory - os.chdir('..') - - # Delete the repo directory - if os.path.exists(repo_name): - shutil.rmtree(repo_name) - -def generate_repo(url, api_key, model): - if api_key: - os.environ['OPENAI_API_KEY'] = api_key.strip() - # if model == 'gpt-4': - # try: - # response = openai.Completion.create( - # model="gpt-4", # or whatever the exact model ID is - # prompt="test", - # max_tokens=5 - # ) - # print("Access to GPT-4 confirmed!") - # except: - # return "The API key either does not have access to GPT-4 or is not valid." - return process_repository(url, model) - else: - return "Please add a valid OpenAI API Key (you can get them [here](https://platform.openai.com/account/api-keys))" - -with gr.Blocks(css=css_style) as demo: - gr.Markdown(f""" - # Hello from GPT4Readability (v0.1.3) - - *Project by Dennis Loevlie ([@DennisLoevlie](https://twitter.com/DennisLoevlie))* - [](https://github.com/loevlie/GPT4Readability/blob/main/LICENSE) - - Welcome to GPT4Readability, a tool designed to help you generate README.md files and suggest improvements for your code repositories. - - - You can find the source code at [GPT4Readability](https://github.com/loevlie/GPT4Readability). - - It's making use of the [langchain](https://github.com/hwchase17/langchain) library. - - ## Here's how to get started: - 1. Please enter your API Key ([Need more information?](https://platform.openai.com/account/api-keys)) - 2. Provide the GitHub Repository URL that you'd like to analyze - 3. Select a model (Please note, the gpt-4 API isn't available to all as of July 2023) - 4. Click to generate a README or suggestions markdown file - """) - - openai_api_key = gr.Textbox( - label="OpenAI API Key", placeholder="sk-...", type="password") - url = gr.Textbox(label="GitHub Repository URL") - model = gr.Dropdown(["gpt-3.5-turbo", "gpt-4"], type="value", label='Model Type') - output = gr.Markdown(label="README.md") - btn = gr.Button("Generate README.md") - btn.click(fn=generate_repo, inputs=[url, openai_api_key, model], outputs=[output], api_name="Generate README.md") - - -demo.queue(concurrency_count=20) -demo.launch(share=False) \ No newline at end of file diff --git a/spaces/Jonni/03-Streamlit-Vido_ASR-NLP/README.md b/spaces/Jonni/03-Streamlit-Vido_ASR-NLP/README.md deleted file mode 100644 index ec01dba379092a7b7504b4abfbc35a65e588967a..0000000000000000000000000000000000000000 --- a/spaces/Jonni/03-Streamlit-Vido_ASR-NLP/README.md +++ /dev/null @@ -1,13 +0,0 @@ ---- -title: 03-Streamlit-Vido ASR-NLP -emoji: 🐢 -colorFrom: yellow -colorTo: indigo -sdk: streamlit -sdk_version: 1.10.0 -app_file: app.py -pinned: false -license: apache-2.0 ---- - -Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference diff --git a/spaces/Joom/Front-end-code-generation-from-images/compiler/Utils.py b/spaces/Joom/Front-end-code-generation-from-images/compiler/Utils.py deleted file mode 100644 index d84bae6559c8e752d4c034663cae22dd7b631952..0000000000000000000000000000000000000000 --- a/spaces/Joom/Front-end-code-generation-from-images/compiler/Utils.py +++ /dev/null @@ -1,51 +0,0 @@ -__author__ = 'Taneem Jan, taneemishere.github.io' - -import string -import random - - -class Utils: - @staticmethod - def get_random_text(length_text=10, space_number=1, with_upper_case=True): - results = [] - while len(results) < length_text: - char = random.choice(string.ascii_letters[:26]) - results.append(char) - if with_upper_case: - results[0] = results[0].upper() - - current_spaces = [] - while len(current_spaces) < space_number: - space_pos = random.randint(2, length_text - 3) - if space_pos in current_spaces: - break - results[space_pos] = " " - if with_upper_case: - results[space_pos + 1] = results[space_pos - 1].upper() - - current_spaces.append(space_pos) - - return ''.join(results) - - @staticmethod - def get_ios_id(length=10): - results = [] - - while len(results) < length: - char = random.choice(string.digits + string.ascii_letters) - results.append(char) - - results[3] = "-" - results[6] = "-" - - return ''.join(results) - - @staticmethod - def get_android_id(length=10): - results = [] - - while len(results) < length: - char = random.choice(string.ascii_letters) - results.append(char) - - return ''.join(results) diff --git a/spaces/KPCGD/bingo/src/components/tailwind-indicator.tsx b/spaces/KPCGD/bingo/src/components/tailwind-indicator.tsx deleted file mode 100644 index f2a1291213dd67055fcebe67fab574c8441338df..0000000000000000000000000000000000000000 --- a/spaces/KPCGD/bingo/src/components/tailwind-indicator.tsx +++ /dev/null @@ -1,14 +0,0 @@ -export function TailwindIndicator() { - if (process.env.NODE_ENV === 'production') return null - - return ( -
-
- )
-}
diff --git a/spaces/Kamtera/persian-tts-mimic3/README.md b/spaces/Kamtera/persian-tts-mimic3/README.md
deleted file mode 100644
index 0f3341bdbfdf5f8b21bf353a27db1068b6c197ad..0000000000000000000000000000000000000000
--- a/spaces/Kamtera/persian-tts-mimic3/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: Persian Tts Mimic3
-emoji: 🌍
-colorFrom: green
-colorTo: yellow
-sdk: gradio
-sdk_version: 3.16.1
-app_file: app.py
-pinned: false
-license: openrail
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/KevlarVK/content_summarizer/README.md b/spaces/KevlarVK/content_summarizer/README.md
deleted file mode 100644
index e0bf4f6d01a9c5e105d3203cc006f1c81266e664..0000000000000000000000000000000000000000
--- a/spaces/KevlarVK/content_summarizer/README.md
+++ /dev/null
@@ -1,30 +0,0 @@
----
-title: Content Summarizer
-emoji: 🔥
-colorFrom: purple
-colorTo: green
-sdk: streamlit
-sdk_version: 1.17.0
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
-
-### Content Summarizer
-
-The Content Summarizer is a project that can generate summaries for various types of content including text, URLs, audio, video, and YouTube. It utilizes the transformers library and leverages the BART-large-CNN, T5-small and Whisper-tiny.en models to provide effective summarization.
-
-It contains two options for summarization:
- - Overall summary
- - Auto-Chapters summary
-
-#### Overall summary
-The overall summary is generated using BART-large-CNN with chunk split algorithm.
-
-#### Auto Chapters summary
-In this type, the text content is split using clustering techniques and chunk split algorithm and uses BART-large-CNN and T5-small for summarization which gives blocks of summary with headings for each.
-
-To run the app, install the packages from requirements.txt and execute the command `streamlit run app.py` from the root of this project.
-
-This repository has also been added as a space in huggingface: https://huggingface.co/spaces/KevlarVK/content_summarizer
diff --git a/spaces/Kirokowa/hakurei-waifu-diffusion/app.py b/spaces/Kirokowa/hakurei-waifu-diffusion/app.py
deleted file mode 100644
index ccef706bf3035fe470bf6a4f5bd701b18bf59133..0000000000000000000000000000000000000000
--- a/spaces/Kirokowa/hakurei-waifu-diffusion/app.py
+++ /dev/null
@@ -1,3 +0,0 @@
-import gradio as gr
-
-gr.Interface.load("models/hakurei/waifu-diffusion").launch()
\ No newline at end of file
diff --git a/spaces/KuraiYuki/openai-reverse-proxy/server.js b/spaces/KuraiYuki/openai-reverse-proxy/server.js
deleted file mode 100644
index 04a48b7a429c4d0ad0b772ba1edf503e349eda21..0000000000000000000000000000000000000000
--- a/spaces/KuraiYuki/openai-reverse-proxy/server.js
+++ /dev/null
@@ -1,32 +0,0 @@
-const express = require('express');
-const proxy = require('express-http-proxy');
-const app = express();
-const targetUrl = 'https://api.openai.com';
-const openaiKey = process.env.OPENAI_KEY
-const port = 7860;
-const baseUrl = getExternalUrl(process.env.SPACE_ID);
-
-app.use('/api', proxy(targetUrl, {
- proxyReqOptDecorator: (proxyReqOpts, srcReq) => {
- // Modify the request headers if necessary
- proxyReqOpts.headers['Authorization'] = 'Bearer '+openaiKey;
- return proxyReqOpts;
- },
-}));
-
-app.get("/", (req, res) => {
- res.send(`This is your OpenAI Reverse Proxy URL: ${baseUrl}`);
-});
-
-function getExternalUrl(spaceId) {
- try {
- const [username, spacename] = spaceId.split("/");
- return `https://${username}-${spacename.replace(/_/g, "-")}.hf.space/api/v1`;
- } catch (e) {
- return "";
- }
-}
-
-app.listen(port, () => {
- console.log(`Reverse proxy server running on ${baseUrl}`);
-});
\ No newline at end of file
diff --git a/spaces/KyanChen/FunSR/models/baselines/OverNet.py b/spaces/KyanChen/FunSR/models/baselines/OverNet.py
deleted file mode 100644
index 92bec65fbc7e1b6ded88c34bacdd35b194e20857..0000000000000000000000000000000000000000
--- a/spaces/KyanChen/FunSR/models/baselines/OverNet.py
+++ /dev/null
@@ -1,239 +0,0 @@
-import torch.nn as nn
-import torch
-import torch.nn.functional as F
-import math
-from models import register
-
-
-class MeanShift(nn.Module):
- def __init__(self, mean_rgb, sub):
- super(MeanShift, self).__init__()
-
- sign = -1 if sub else 1
- r = mean_rgb[0] * sign
- g = mean_rgb[1] * sign
- b = mean_rgb[2] * sign
-
- self.shifter = nn.Conv2d(3, 3, 1, 1, 0)
- self.shifter.weight.data = torch.eye(3).view(3, 3, 1, 1)
- self.shifter.bias.data = torch.Tensor([r, g, b])
-
- # Freeze the mean shift layer
- for params in self.shifter.parameters():
- params.requires_grad = False
-
- def forward(self, x):
- x = self.shifter(x)
- return x
-
-class Scale(nn.Module):
-
- def __init__(self, init_value=1e-3):
- super(Scale, self).__init__()
- self.scale = nn.Parameter(torch.FloatTensor([init_value]))
-
- def forward(self, input):
- return input * self.scale
-
-class SE(nn.Module):
- def __init__(self, channel, reduction=16):
- super(SE, self).__init__()
-
- self.avg_pool = nn.AdaptiveAvgPool2d(1)
-
- self.conv = nn.Sequential(
- nn.Conv2d(channel, channel // reduction, 1, padding=0, bias=True),
- nn.ReLU(inplace=True),
- nn.Conv2d(channel // reduction, channel, 1, padding=0, bias=True),
- nn.Sigmoid()
- )
-
- def forward(self, x):
- y = self.avg_pool(x)
- y = self.conv(y)
- return x * y
-
-
-class ResidualBlock(nn.Module):
- def __init__(self,
- wn, in_channels, out_channels):
- super(ResidualBlock, self).__init__()
- self.res_scale = Scale(1)
- self.x_scale = Scale(1)
- self.SE = SE(64, reduction=16)
- body = []
- expand = 6
- linear = 0.8
- body.append(
- wn(nn.Conv2d(64, 64*expand, 1, padding=1//2)))
- body.append(nn.ReLU(inplace=True))
- body.append(
- wn(nn.Conv2d(64*expand, int(64*linear), 1, padding=1//2)))
- body.append(
- wn(nn.Conv2d(int(64*linear), 64, 3, padding=3//2)))
- self.body = nn.Sequential(*body)
-
-
- def forward(self, x):
-
- out = self.body(x)
- out = self.SE(out)
- out = self.res_scale(out) + self.x_scale(x)
- return out
-
-
-
-class BasicConv2d(nn.Module):
- def __init__(self, wn, in_planes, out_planes, kernel_size, stride, padding=0):
- super(BasicConv2d, self).__init__()
- self.conv = wn(nn.Conv2d(in_planes, out_planes,
- kernel_size=kernel_size, stride=stride,
- padding=padding, bias=True))
-
- self.LR = nn.ReLU(inplace=True)
-
- def forward(self, x):
- x = self.conv(x)
- x = self.LR(x)
- return x
-
-
-
-class UpsampleBlock(nn.Module):
- def __init__(self, n_channels, upscale, wn, group=1):
- super(UpsampleBlock, self).__init__()
-
- self.up = _UpsampleBlock(n_channels, upscale=upscale, wn=wn, group=group)
-
-
- def forward(self, x, upscale):
- return self.up(x)
-
-
-class _UpsampleBlock(nn.Module):
- def __init__(self, n_channels, upscale, wn, group=1):
- super(_UpsampleBlock, self).__init__()
-
- modules = []
-
- if upscale == 2 or upscale == 4 or upscale == 8:
- for _ in range(int(math.log(upscale, 2))):
- modules += [wn(nn.Conv2d(n_channels, 4 * n_channels, 3, 1, 1, groups=group)),
- nn.ReLU(inplace=True)]
- modules += [nn.PixelShuffle(2)]
-
- elif upscale == 3:
- modules += [wn(nn.Conv2d(n_channels, 9 * n_channels, 3, 1, 1, groups=group)),
- nn.ReLU(inplace=True)]
- modules += [nn.PixelShuffle(3)]
-
- elif upscale == 5:
- modules += [wn(nn.Conv2d(n_channels, 25 * n_channels, 3, 1, 1, groups=group)),
- nn.ReLU(inplace=True)]
- modules += [nn.PixelShuffle(5)]
-
- self.body = nn.Sequential(*modules)
-
- def forward(self, x):
- out = self.body(x)
- return out
-
-#Local Dense Groups (LDGs)
-class LDGs(nn.Module):
- def __init__(self,
- in_channels, out_channels, wn,
- group=1):
- super(LDGs, self).__init__()
-
- self.RB1 = ResidualBlock(wn, in_channels, out_channels)
- self.RB2 = ResidualBlock(wn, in_channels, out_channels)
- self.RB3 = ResidualBlock(wn, in_channels, out_channels)
-
- self.reduction1 = BasicConv2d(wn, in_channels*2, out_channels, 1, 1, 0)
- self.reduction2 = BasicConv2d(wn, in_channels*3, out_channels, 1, 1, 0)
- self.reduction3 = BasicConv2d(wn, in_channels*4, out_channels, 1, 1, 0)
-
- def forward(self, x):
- c0 = o0 = x
-
- RB1 = self.RB1(o0)
- concat1 = torch.cat([c0, RB1], dim=1)
- out1 = self.reduction1(concat1)
-
- RB2 = self.RB2(out1)
- concat2 = torch.cat([concat1, RB2], dim=1)
- out2 = self.reduction2(concat2)
-
- RB3 = self.RB3(out2)
- concat3 = torch.cat([concat2, RB3], dim=1)
- out3 = self.reduction3(concat3)
-
- return out3
-
-
-@register('overnet')
-class OverNet(nn.Module):
-
- def __init__(self, upscale=5, group=4, *args, **kwargs):
- super(OverNet, self).__init__()
- wn = lambda x: torch.nn.utils.weight_norm(x)
- self.upscale = upscale
-
- # self.sub_mean = MeanShift((0.4488, 0.4371, 0.4040), sub=True)
- # self.add_mean = MeanShift((0.4488, 0.4371, 0.4040), sub=False)
-
- self.entry_1 = wn(nn.Conv2d(3, 64, 3, 1, 1))
-
- self.GDG1 = LDGs(64, 64, wn=wn)
- self.GDG2 = LDGs(64, 64, wn=wn)
- self.GDG3 = LDGs(64, 64, wn=wn)
-
- self.reduction1 = BasicConv2d(wn, 64*2, 64, 1, 1, 0)
- self.reduction2 = BasicConv2d(wn, 64*3, 64, 1, 1, 0)
- self.reduction3 = BasicConv2d(wn, 64*4, 64, 1, 1, 0)
-
- self.reduction = BasicConv2d(wn, 64*3, 64, 1, 1, 0)
-
- self.Global_skip = nn.Sequential(nn.AdaptiveAvgPool2d(1), nn.Conv2d(64, 64, 1, 1, 0), nn.ReLU(inplace=True))
-
- self.upsample = UpsampleBlock(64, upscale=upscale, wn=wn, group=group)
-
- self.exit1 = wn(nn.Conv2d(64, 3, 3, 1, 1))
-
- self.res_scale = Scale(1)
- self.x_scale = Scale(1)
-
- def forward(self, x, out_size):
- ori_h, ori_w = x.shape[-2:]
- target_h, target_w = out_size
- # x = self.sub_mean(x)
- skip = x
-
- x = self.entry_1(x)
-
- c0 = o0 = x
-
- GDG1 = self.GDG1(o0)
- concat1 = torch.cat([c0, GDG1], dim=1)
- out1 = self.reduction1(concat1)
-
- GDG2 = self.GDG2(out1)
- concat2 = torch.cat([concat1, GDG2], dim=1)
- out2 = self.reduction2(concat2)
-
- GDG3 = self.GDG3(out2)
- concat3 = torch.cat([concat2, GDG3], dim=1)
- out3 = self.reduction3(concat3)
-
- output = self.reduction(torch.cat((out1, out2, out3),1))
- output = self.res_scale(output) + self.x_scale(self.Global_skip(x))
-
- output = self.upsample(output, upscale=self.upscale)
-
- output = F.interpolate(output, out_size, mode='bicubic', align_corners=False)
- skip = F.interpolate(skip, out_size, mode='bicubic', align_corners=False)
-
- output = self.exit1(output) + skip
- # output = self.add_mean(output)
-
- return output
diff --git a/spaces/KyanChen/FunSR/models/models.py b/spaces/KyanChen/FunSR/models/models.py
deleted file mode 100644
index 0625a2d2fd2d8ee147930ec3cda0a643c895d8aa..0000000000000000000000000000000000000000
--- a/spaces/KyanChen/FunSR/models/models.py
+++ /dev/null
@@ -1,23 +0,0 @@
-import copy
-
-
-models = {}
-
-
-def register(name):
- def decorator(cls):
- models[name] = cls
- return cls
- return decorator
-
-
-def make(model_spec, args=None, load_sd=False):
- if args is not None:
- model_args = copy.deepcopy(model_spec['args'])
- model_args.update(args)
- else:
- model_args = model_spec['args']
- model = models[model_spec['name']](**model_args)
- if load_sd:
- model.load_state_dict(model_spec['sd'], strict=True)
- return model
diff --git a/spaces/KyanChen/RSPrompter/mmdet/models/detectors/point_rend.py b/spaces/KyanChen/RSPrompter/mmdet/models/detectors/point_rend.py
deleted file mode 100644
index 5062ac0c945e79bd53e66e1642aec51113475cad..0000000000000000000000000000000000000000
--- a/spaces/KyanChen/RSPrompter/mmdet/models/detectors/point_rend.py
+++ /dev/null
@@ -1,35 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-from mmengine.config import ConfigDict
-
-from mmdet.registry import MODELS
-from mmdet.utils import OptConfigType, OptMultiConfig
-from .two_stage import TwoStageDetector
-
-
-@MODELS.register_module()
-class PointRend(TwoStageDetector):
- """PointRend: Image Segmentation as Rendering
-
- This detector is the implementation of
- `PointRend xs
- sm
- md
- lg
- xl
- 2xl
- Rapid⚡OCR
", - unsafe_allow_html=True, - ) - st.markdown( - """ -
- 
- 
- 
-
-
-Loss: {loss_step:.7f}
-Step: {steps_done}
-Last prompt: {html.escape(batch.cond_text[0])}
-Last saved embedding: {html.escape(last_saved_file)}
-Last saved image: {html.escape(last_saved_image)}
-
Towards Real-World Blind Face Restoration with Generative Facial Prior | Github Repo
Towards Real-World Blind Face Restoration with Generative Facial Prior | Github Repo
A Giant amp; 39;s Field Hd Full Movie Download
DOWNLOAD ✺✺✺ https://urloso.com/2uyOah
-
- aaccfb2cb3
-
-
- diff --git a/spaces/bioriAsaeru/text-to-voice/JetBrains Rider 2019.3.3 Win Mac Linux ? __TOP__.md b/spaces/bioriAsaeru/text-to-voice/JetBrains Rider 2019.3.3 Win Mac Linux ? __TOP__.md deleted file mode 100644 index 1b55f96be5ec54b22e3ec6b833186ee23f9e5759..0000000000000000000000000000000000000000 --- a/spaces/bioriAsaeru/text-to-voice/JetBrains Rider 2019.3.3 Win Mac Linux ? __TOP__.md +++ /dev/null @@ -1,16 +0,0 @@ -
JetBrains Rider 2019.3.3 Win Mac Linux –
Download » https://urloso.com/2uyR9R
-
-In the lower left-hand corner of the window, you should see a button that says free 30-day evaluation or something similar.. Get a free 30-day evaluation of the latest version of Rider for Windows, macOS or Linux.. Make sure you have a working internet connection, and then select Get Installer to download the installer for Rider. - -. Rider Pro/Team Rider Pro/Team Dual licenses include a free 30-day evaluation of all product versions. Rider Pro/Team is a professional and team edition of Rider, the professional engineering development environment. It is designed specifically for professional use, and provides features that are not included in Rider or Rider Dual. Includes advanced language tools for C, C++, C#, F#, VB.NET, SQL, JavaScript, HTML, XML, CSS, ASP.NET, JSON, PHP, Ruby, Python, R, Golang, and XML/XSD. Includes unlimited number of installed users, unlimited connections, unlimited versions, unlimited physical resources and unlimited data files. Also included are more than 20 database types, including MyISAM, MariaDB, and MySQL. - -. Details: The software download includes a 30-day free evaluation of the software. The free evaluation license can be used on one computer or in a network license. - -. Details: This product requires a serial number, activation code, and product key to be used. Installation instructions will be sent via email after the serial number is entered and the product key is confirmed. The product key can be used on up to two computers, each of which is installed by a different user. - -. Details: Requires a serial number, activation code, and product key to be used. Installation instructions will be sent via email after the serial number is entered and the product key is confirmed. The product key can be used on up to two computers, each of which is installed by a different user. - -. Details: Requires a serial number, activation code, and product key to be used. Installation instructions will be sent via email after the serial number is entered and the product key is confirmed. The product key can be used on up to two computers, each of which is 4fefd39f24
-
-
- diff --git a/spaces/bioriAsaeru/text-to-voice/Kabhi Kahin 2 Movie In Hindi 720p Download ((HOT)) Torrent.md b/spaces/bioriAsaeru/text-to-voice/Kabhi Kahin 2 Movie In Hindi 720p Download ((HOT)) Torrent.md deleted file mode 100644 index a21dee2c6d0b1f991271a4e77f135220acaab52a..0000000000000000000000000000000000000000 --- a/spaces/bioriAsaeru/text-to-voice/Kabhi Kahin 2 Movie In Hindi 720p Download ((HOT)) Torrent.md +++ /dev/null @@ -1,12 +0,0 @@ -
Kabhi Kahin 2 Movie In Hindi 720p Download Torrent
Download File - https://urloso.com/2uyQA3
-
-atreya sasthak serial full video, free download atreya sasthak mp3 song, kahin bhi kahin bhi online free hindi mp4 video song, hindi movie songs download full hd 720p atreya sasthak, download hindi movie song. Atreya sasthak serial full video, free download atreya sasthak mp3 song, kahin bhi kahin bhi online free hindi mp4 video song, hindi movie songs download full hd 720p. It's Bollywood Drama film, it's action, love story, romantic movie. that's story of the movie. most of the story of the movie is about man, power and money. The movie is story of different characters. and the story of the movie is about man's future. watch the best indian kollywood movie(it's. Free download atreya sasthak serial full video, free download atreya sasthak mp3 song, kahin bhi kahin bhi online free hindi mp4 video song, hindi movie songs download full hd 720p. Atreya sasthak serial full video, free download atreya sasthak mp3 song, kahin bhi kahin bhi online free hindi mp4 video song, hindi movie songs download full hd 720p. It's Bollywood Drama film, it's action, love story, romantic movie. that's story of the movie. most of the story of the movie is about man, power and money. The movie is story of different characters. and the story of the movie is about man's future. watch the best indian kollywood movie(it's.1. Field of the Invention - -The invention relates generally to the field of well bore hydrocarbon fluid production, and more particularly to systems and methods for recovering methane gas from a well bore hydrocarbon fluid production stream. - -2. Background of the Invention - -In the oil and gas industry, it is desirable to extract oil from oil bearing formations or reservoirs that exist below the earth's surface and bring the extracted oil to the surface for collection, processing, and transport to oil refineries or other locations. The most common method for extracting oil from subterranean reservoirs is by utilizing the natural pressure that exists within the reservoir to force the oil to the surface. In this method, referred to as � 4fefd39f24
-
-
- diff --git a/spaces/brainblow/beat_remixer/beat_manipulator/beatmap.py b/spaces/brainblow/beat_remixer/beat_manipulator/beatmap.py deleted file mode 100644 index 7536a8b66a139d54d7b47abce5f115cabeb8f6fa..0000000000000000000000000000000000000000 --- a/spaces/brainblow/beat_remixer/beat_manipulator/beatmap.py +++ /dev/null @@ -1,195 +0,0 @@ -import numpy as np -from . import utils - - -def scale(beatmap:np.ndarray, scale:float, log = True, integer = True) -> np.ndarray: - if isinstance(scale, str): scale = utils._safer_eval(scale) - assert scale>0, f"scale should be > 0, your scale is {scale}" - if scale == 1: return beatmap - else: - import math - if log is True: print(f'scale={scale}; ') - a = 0 - b = np.array([], dtype=int) - if scale%1==0: - while a < len(beatmap): - b = np.append(b, beatmap[int(a)]) - a += scale - else: - if integer is True: - while a + 1 < len(beatmap): - b = np.append(b, int((1 - (a % 1)) * beatmap[math.floor(a)] + (a % 1) * beatmap[math.ceil(a)])) - a += scale - else: - while a + 1 < len(beatmap): - b = np.append(b, (1 - (a % 1)) * beatmap[math.floor(a)] + (a % 1) * beatmap[math.ceil(a)]) - a += scale - return b - -def shift(beatmap:np.ndarray, shift:float, log = True, mode = 1) -> np.ndarray: - if isinstance(shift, str): shift = utils._safer_eval(shift) - if shift == 0: return beatmap - # positive shift - elif shift > 0: - # full value of beats is removed from the beginning - if shift >= 1: beatmap = beatmap[int(shift//1):] - # shift beatmap by the decimal value - if shift%1 != 0: - shift = shift%1 - for i in range(len(beatmap) - int(shift) - 1): - beatmap[i] = int(beatmap[i] + shift * (beatmap[i + 1] - beatmap[i])) - - # negative shift - else: - shift = -shift - # full values are inserted in between first beats - if shift >= 1: - if mode == 1: - step = int((beatmap[1] - beatmap[0]) / (int(shift//1) + 1)) - beatmap = np.insert(arr = beatmap, obj = 1, values = np.linspace(start = beatmap[0] + step - 1, stop = 1 + beatmap[1] - step, num = int(shift//1))) - elif mode == 2: - for i in range(int(shift//1)): - beatmap = np.insert(arr = beatmap, obj = (i*2)+1, values = int((beatmap[i*2] + beatmap[(i*2)+1])/2)) - # shift beatmap by the decimal value - if shift%1 != 0: - shift = shift%1 - for i in reversed(range(len(beatmap))): - if i==0: continue - beatmap[i] = int(beatmap[i] - shift * (beatmap[i] - beatmap[i-1])) - return beatmap - -def generate(audio: np.ndarray, sr: int, lib='madmom.BeatDetectionProcessor', caching=True, filename: str = None, log = True, load_settings = True, split=None): - """Creates beatmap attribute with a list of positions of beats in samples.""" - if log is True: print(f'Analyzing beats using {lib}; ', end='') - - # load a beatmap if it is cached: - if caching is True and filename is not None: - audio_id=hex(len(audio[0])) - import os - if not os.path.exists('beat_manipulator/beatmaps'): - os.mkdir('beat_manipulator/beatmaps') - cacheDir="beat_manipulator/beatmaps/" + ''.join(filename.replace('\\', '/').split('/')[-1]) + "_"+lib+"_"+audio_id+'.txt' - try: - beatmap=np.loadtxt(cacheDir, dtype=int) - if log is True: print('loaded cached beatmap.') - except OSError: - if log is True:print("beatmap hasn't been generated yet. Generating...") - beatmap = None - - #generate the beatmap - if beatmap is None: - if 'madmom' in lib.lower(): - from collections.abc import MutableMapping, MutableSequence - import madmom - assert len(audio[0])>sr*2, f'Audio file is too short, len={len(audio[0])} samples, or {len(audio[0])/sr} seconds. Minimum length is 2 seconds, audio below that breaks madmom processors.' - if lib=='madmom.BeatTrackingProcessor': - proc = madmom.features.beats.BeatTrackingProcessor(fps=100) - act = madmom.features.beats.RNNBeatProcessor()(madmom.audio.signal.Signal(audio.T, sr)) - beatmap= proc(act)*sr - elif lib=='madmom.BeatTrackingProcessor.constant': - proc = madmom.features.beats.BeatTrackingProcessor(fps=100, look_ahead=None) - act = madmom.features.beats.RNNBeatProcessor()(madmom.audio.signal.Signal(audio.T, sr)) - beatmap= proc(act)*sr - elif lib=='madmom.BeatTrackingProcessor.consistent': - proc = madmom.features.beats.BeatTrackingProcessor(fps=100, look_ahead=None, look_aside=0) - act = madmom.features.beats.RNNBeatProcessor()(madmom.audio.signal.Signal(audio.T, sr)) - beatmap= proc(act)*sr - elif lib=='madmom.BeatDetectionProcessor': - proc = madmom.features.beats.BeatDetectionProcessor(fps=100) - act = madmom.features.beats.RNNBeatProcessor()(madmom.audio.signal.Signal(audio.T, sr)) - beatmap= proc(act)*sr - elif lib=='madmom.BeatDetectionProcessor.consistent': - proc = madmom.features.beats.BeatDetectionProcessor(fps=100, look_aside=0) - act = madmom.features.beats.RNNBeatProcessor()(madmom.audio.signal.Signal(audio.T, sr)) - beatmap= proc(act)*sr - elif lib=='madmom.CRFBeatDetectionProcessor': - proc = madmom.features.beats.CRFBeatDetectionProcessor(fps=100) - act = madmom.features.beats.RNNBeatProcessor()(madmom.audio.signal.Signal(audio.T, sr)) - beatmap= proc(act)*sr - elif lib=='madmom.CRFBeatDetectionProcessor.constant': - proc = madmom.features.beats.CRFBeatDetectionProcessor(fps=100, use_factors=True, factors=[0.5, 1, 2]) - act = madmom.features.beats.RNNBeatProcessor()(madmom.audio.signal.Signal(audio.T, sr)) - beatmap= proc(act)*sr - elif lib=='madmom.DBNBeatTrackingProcessor': - proc = madmom.features.beats.DBNBeatTrackingProcessor(fps=100) - act = madmom.features.beats.RNNBeatProcessor()(madmom.audio.signal.Signal(audio.T, sr)) - beatmap= proc(act)*sr - elif lib=='madmom.DBNBeatTrackingProcessor.1000': - proc = madmom.features.beats.DBNBeatTrackingProcessor(fps=100, transition_lambda=1000) - act = madmom.features.beats.RNNBeatProcessor()(madmom.audio.signal.Signal(audio.T, sr)) - beatmap= proc(act)*sr - elif lib=='madmom.DBNDownBeatTrackingProcessor': - proc = madmom.features.downbeats.DBNDownBeatTrackingProcessor(beats_per_bar=[4], fps=100) - act = madmom.features.downbeats.RNNDownBeatProcessor()(madmom.audio.signal.Signal(audio.T, sr)) - beatmap= proc(act)*sr - beatmap=beatmap[:,0] - elif lib=='madmom.PatternTrackingProcessor': #broken - from madmom.models import PATTERNS_BALLROOM - proc = madmom.features.downbeats.PatternTrackingProcessor(PATTERNS_BALLROOM, fps=50) - from madmom.audio.spectrogram import LogarithmicSpectrogramProcessor, SpectrogramDifferenceProcessor, MultiBandSpectrogramProcessor - from madmom.processors import SequentialProcessor - log = LogarithmicSpectrogramProcessor() - diff = SpectrogramDifferenceProcessor(positive_diffs=True) - mb = MultiBandSpectrogramProcessor(crossover_frequencies=[270]) - pre_proc = SequentialProcessor([log, diff, mb]) - act = pre_proc(madmom.audio.signal.Signal(audio.T, sr)) - beatmap= proc(act)*sr - beatmap=beatmap[:,0] - elif lib=='madmom.DBNBarTrackingProcessor': #broken - beats = generate(audio=audio, sr=sr, filename=filename, lib='madmom.DBNBeatTrackingProcessor', caching = caching) - proc = madmom.features.downbeats.DBNBarTrackingProcessor(beats_per_bar=[4], fps=100) - act = madmom.features.downbeats.RNNBarProcessor()(((madmom.audio.signal.Signal(audio.T, sr)), beats)) - beatmap= proc(act)*sr - elif lib=='librosa': #broken in 3.9, works in 3.8 - import librosa - beat_frames = librosa.beat.beat_track(y=audio[0], sr=sr, hop_length=512) - beatmap = librosa.frames_to_samples(beat_frames[1]) - - # save the beatmap and return - if caching is True: np.savetxt(cacheDir, beatmap.astype(int), fmt='%d') - if not isinstance(beatmap, np.ndarray): beatmap=np.asarray(beatmap, dtype=int) - else: beatmap=beatmap.astype(int) - - if load_settings is True: - settingsDir="beat_manipulator/beatmaps/" + ''.join(filename.split('/')[-1]) + "_"+lib+"_"+audio_id+'_settings.txt' - if os.path.exists(settingsDir): - with open(settingsDir, 'r') as f: - settings = f.read().split(',') - if settings[0] != 'None': beatmap = scale(beatmap, settings[0], log = False) - if settings[1] != 'None': beatmap = shift(beatmap, settings[1], log = False) - if settings[2] != 'None': beatmap = np.sort(np.absolute(beatmap - int(settings[2]))) - - return beatmap - - - -def save_settings(audio: np.ndarray, filename: str = None, lib: str = 'madmom.BeatDetectionProcessor', scale: float = None, shift: float = None, adjust: int = None, normalized: str = None, log = True, overwrite = 'ask'): - if isinstance(overwrite, str): overwrite = overwrite.lower() - audio_id=hex(len(audio[0])) - cacheDir="beat_manipulator/beatmaps/" + ''.join(filename.split('/')[-1]) + "_"+lib+"_"+audio_id+'.txt' - import os - assert os.path.exists(cacheDir), f"Beatmap `{cacheDir}` doesn't exist" - settingsDir="beat_manipulator/beatmaps/" + ''.join(filename.split('/')[-1]) + "_"+lib+"_"+audio_id+'_settings.txt' - - try: - a = utils._safer_eval_strict(scale) - if a == 1: scale = None - except Exception as e: assert scale is None, f'scale = `{scale}` - Not a valid scale, should be either a number, a math expression, or None: {e}' - try: - a = utils._safer_eval_strict(shift) - if a == 0: shift = None - except Exception as e: assert shift is None, f'shift = `{shift}` - Not a valid shift: {e}' - assert isinstance(adjust, int) or adjust is None, f'adjust = `{adjust}` should be int, but it is `{type(adjust)}`' - - if adjust == 0: adjust = None - - if os.path.exists(settingsDir): - if overwrite == 'ask' or overwrite =='a': - what = input(f'`{settingsDir}` already exists. Overwrite (y/n)?: ') - if not (what.lower() == 'y' or what.lower() == 'yes'): return - elif not (overwrite == 'true' or overwrite =='y' or overwrite =='yes' or overwrite is True): return - - with open(settingsDir, 'w') as f: - f.write(f'{scale},{shift},{adjust},{normalized}') - if log is True: print(f"Saved scale = `{scale}`, shift = `{shift}`, adjust = `{adjust}` to `{settingsDir}`") - diff --git a/spaces/brjathu/HMR2.0/vendor/detectron2/detectron2/export/torchscript_patch.py b/spaces/brjathu/HMR2.0/vendor/detectron2/detectron2/export/torchscript_patch.py deleted file mode 100644 index da9b324f1582e31d1a16d2fe462ac2989bea56ea..0000000000000000000000000000000000000000 --- a/spaces/brjathu/HMR2.0/vendor/detectron2/detectron2/export/torchscript_patch.py +++ /dev/null @@ -1,406 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. - -import os -import sys -import tempfile -from contextlib import ExitStack, contextmanager -from copy import deepcopy -from unittest import mock -import torch -from torch import nn - -# need some explicit imports due to https://github.com/pytorch/pytorch/issues/38964 -import detectron2 # noqa F401 -from detectron2.structures import Boxes, Instances -from detectron2.utils.env import _import_file - -_counter = 0 - - -def _clear_jit_cache(): - from torch.jit._recursive import concrete_type_store - from torch.jit._state import _jit_caching_layer - - concrete_type_store.type_store.clear() # for modules - _jit_caching_layer.clear() # for free functions - - -def _add_instances_conversion_methods(newInstances): - """ - Add from_instances methods to the scripted Instances class. - """ - cls_name = newInstances.__name__ - - @torch.jit.unused - def from_instances(instances: Instances): - """ - Create scripted Instances from original Instances - """ - fields = instances.get_fields() - image_size = instances.image_size - ret = newInstances(image_size) - for name, val in fields.items(): - assert hasattr(ret, f"_{name}"), f"No attribute named {name} in {cls_name}" - setattr(ret, name, deepcopy(val)) - return ret - - newInstances.from_instances = from_instances - - -@contextmanager -def patch_instances(fields): - """ - A contextmanager, under which the Instances class in detectron2 is replaced - by a statically-typed scriptable class, defined by `fields`. - See more in `scripting_with_instances`. - """ - - with tempfile.TemporaryDirectory(prefix="detectron2") as dir, tempfile.NamedTemporaryFile( - mode="w", encoding="utf-8", suffix=".py", dir=dir, delete=False - ) as f: - try: - # Objects that use Instances should not reuse previously-compiled - # results in cache, because `Instances` could be a new class each time. - _clear_jit_cache() - - cls_name, s = _gen_instance_module(fields) - f.write(s) - f.flush() - f.close() - - module = _import(f.name) - new_instances = getattr(module, cls_name) - _ = torch.jit.script(new_instances) - # let torchscript think Instances was scripted already - Instances.__torch_script_class__ = True - # let torchscript find new_instances when looking for the jit type of Instances - Instances._jit_override_qualname = torch._jit_internal._qualified_name(new_instances) - - _add_instances_conversion_methods(new_instances) - yield new_instances - finally: - try: - del Instances.__torch_script_class__ - del Instances._jit_override_qualname - except AttributeError: - pass - sys.modules.pop(module.__name__) - - -def _gen_instance_class(fields): - """ - Args: - fields (dict[name: type]) - """ - - class _FieldType: - def __init__(self, name, type_): - assert isinstance(name, str), f"Field name must be str, got {name}" - self.name = name - self.type_ = type_ - self.annotation = f"{type_.__module__}.{type_.__name__}" - - fields = [_FieldType(k, v) for k, v in fields.items()] - - def indent(level, s): - return " " * 4 * level + s - - lines = [] - - global _counter - _counter += 1 - - cls_name = "ScriptedInstances{}".format(_counter) - - field_names = tuple(x.name for x in fields) - extra_args = ", ".join([f"{f.name}: Optional[{f.annotation}] = None" for f in fields]) - lines.append( - f""" -class {cls_name}: - def __init__(self, image_size: Tuple[int, int], {extra_args}): - self.image_size = image_size - self._field_names = {field_names} -""" - ) - - for f in fields: - lines.append( - indent(2, f"self._{f.name} = torch.jit.annotate(Optional[{f.annotation}], {f.name})") - ) - - for f in fields: - lines.append( - f""" - @property - def {f.name}(self) -> {f.annotation}: - # has to use a local for type refinement - # https://pytorch.org/docs/stable/jit_language_reference.html#optional-type-refinement - t = self._{f.name} - assert t is not None, "{f.name} is None and cannot be accessed!" - return t - - @{f.name}.setter - def {f.name}(self, value: {f.annotation}) -> None: - self._{f.name} = value -""" - ) - - # support method `__len__` - lines.append( - """ - def __len__(self) -> int: -""" - ) - for f in fields: - lines.append( - f""" - t = self._{f.name} - if t is not None: - return len(t) -""" - ) - lines.append( - """ - raise NotImplementedError("Empty Instances does not support __len__!") -""" - ) - - # support method `has` - lines.append( - """ - def has(self, name: str) -> bool: -""" - ) - for f in fields: - lines.append( - f""" - if name == "{f.name}": - return self._{f.name} is not None -""" - ) - lines.append( - """ - return False -""" - ) - - # support method `to` - none_args = ", None" * len(fields) - lines.append( - f""" - def to(self, device: torch.device) -> "{cls_name}": - ret = {cls_name}(self.image_size{none_args}) -""" - ) - for f in fields: - if hasattr(f.type_, "to"): - lines.append( - f""" - t = self._{f.name} - if t is not None: - ret._{f.name} = t.to(device) -""" - ) - else: - # For now, ignore fields that cannot be moved to devices. - # Maybe can support other tensor-like classes (e.g. __torch_function__) - pass - lines.append( - """ - return ret -""" - ) - - # support method `getitem` - none_args = ", None" * len(fields) - lines.append( - f""" - def __getitem__(self, item) -> "{cls_name}": - ret = {cls_name}(self.image_size{none_args}) -""" - ) - for f in fields: - lines.append( - f""" - t = self._{f.name} - if t is not None: - ret._{f.name} = t[item] -""" - ) - lines.append( - """ - return ret -""" - ) - - # support method `cat` - # this version does not contain checks that all instances have same size and fields - none_args = ", None" * len(fields) - lines.append( - f""" - def cat(self, instances: List["{cls_name}"]) -> "{cls_name}": - ret = {cls_name}(self.image_size{none_args}) -""" - ) - for f in fields: - lines.append( - f""" - t = self._{f.name} - if t is not None: - values: List[{f.annotation}] = [x.{f.name} for x in instances] - if torch.jit.isinstance(t, torch.Tensor): - ret._{f.name} = torch.cat(values, dim=0) - else: - ret._{f.name} = t.cat(values) -""" - ) - lines.append( - """ - return ret""" - ) - - # support method `get_fields()` - lines.append( - """ - def get_fields(self) -> Dict[str, Tensor]: - ret = {} - """ - ) - for f in fields: - if f.type_ == Boxes: - stmt = "t.tensor" - elif f.type_ == torch.Tensor: - stmt = "t" - else: - stmt = f'assert False, "unsupported type {str(f.type_)}"' - lines.append( - f""" - t = self._{f.name} - if t is not None: - ret["{f.name}"] = {stmt} - """ - ) - lines.append( - """ - return ret""" - ) - return cls_name, os.linesep.join(lines) - - -def _gen_instance_module(fields): - # TODO: find a more automatic way to enable import of other classes - s = """ -from copy import deepcopy -import torch -from torch import Tensor -import typing -from typing import * - -import detectron2 -from detectron2.structures import Boxes, Instances - -""" - - cls_name, cls_def = _gen_instance_class(fields) - s += cls_def - return cls_name, s - - -def _import(path): - return _import_file( - "{}{}".format(sys.modules[__name__].__name__, _counter), path, make_importable=True - ) - - -@contextmanager -def patch_builtin_len(modules=()): - """ - Patch the builtin len() function of a few detectron2 modules - to use __len__ instead, because __len__ does not convert values to - integers and therefore is friendly to tracing. - - Args: - modules (list[stsr]): names of extra modules to patch len(), in - addition to those in detectron2. - """ - - def _new_len(obj): - return obj.__len__() - - with ExitStack() as stack: - MODULES = [ - "detectron2.modeling.roi_heads.fast_rcnn", - "detectron2.modeling.roi_heads.mask_head", - "detectron2.modeling.roi_heads.keypoint_head", - ] + list(modules) - ctxs = [stack.enter_context(mock.patch(mod + ".len")) for mod in MODULES] - for m in ctxs: - m.side_effect = _new_len - yield - - -def patch_nonscriptable_classes(): - """ - Apply patches on a few nonscriptable detectron2 classes. - Should not have side-effects on eager usage. - """ - # __prepare_scriptable__ can also be added to models for easier maintenance. - # But it complicates the clean model code. - - from detectron2.modeling.backbone import ResNet, FPN - - # Due to https://github.com/pytorch/pytorch/issues/36061, - # we change backbone to use ModuleList for scripting. - # (note: this changes param names in state_dict) - - def prepare_resnet(self): - ret = deepcopy(self) - ret.stages = nn.ModuleList(ret.stages) - for k in self.stage_names: - delattr(ret, k) - return ret - - ResNet.__prepare_scriptable__ = prepare_resnet - - def prepare_fpn(self): - ret = deepcopy(self) - ret.lateral_convs = nn.ModuleList(ret.lateral_convs) - ret.output_convs = nn.ModuleList(ret.output_convs) - for name, _ in self.named_children(): - if name.startswith("fpn_"): - delattr(ret, name) - return ret - - FPN.__prepare_scriptable__ = prepare_fpn - - # Annotate some attributes to be constants for the purpose of scripting, - # even though they are not constants in eager mode. - from detectron2.modeling.roi_heads import StandardROIHeads - - if hasattr(StandardROIHeads, "__annotations__"): - # copy first to avoid editing annotations of base class - StandardROIHeads.__annotations__ = deepcopy(StandardROIHeads.__annotations__) - StandardROIHeads.__annotations__["mask_on"] = torch.jit.Final[bool] - StandardROIHeads.__annotations__["keypoint_on"] = torch.jit.Final[bool] - - -# These patches are not supposed to have side-effects. -patch_nonscriptable_classes() - - -@contextmanager -def freeze_training_mode(model): - """ - A context manager that annotates the "training" attribute of every submodule - to constant, so that the training codepath in these modules can be - meta-compiled away. Upon exiting, the annotations are reverted. - """ - classes = {type(x) for x in model.modules()} - # __constants__ is the old way to annotate constants and not compatible - # with __annotations__ . - classes = {x for x in classes if not hasattr(x, "__constants__")} - for cls in classes: - cls.__annotations__["training"] = torch.jit.Final[bool] - yield - for cls in classes: - cls.__annotations__["training"] = bool diff --git a/spaces/bzd4576/sovits-sin/losses.py b/spaces/bzd4576/sovits-sin/losses.py deleted file mode 100644 index fb22a0e834dd87edaa37bb8190eee2c3c7abe0d5..0000000000000000000000000000000000000000 --- a/spaces/bzd4576/sovits-sin/losses.py +++ /dev/null @@ -1,61 +0,0 @@ -import torch -from torch.nn import functional as F - -import commons - - -def feature_loss(fmap_r, fmap_g): - loss = 0 - for dr, dg in zip(fmap_r, fmap_g): - for rl, gl in zip(dr, dg): - rl = rl.float().detach() - gl = gl.float() - loss += torch.mean(torch.abs(rl - gl)) - - return loss * 2 - - -def discriminator_loss(disc_real_outputs, disc_generated_outputs): - loss = 0 - r_losses = [] - g_losses = [] - for dr, dg in zip(disc_real_outputs, disc_generated_outputs): - dr = dr.float() - dg = dg.float() - r_loss = torch.mean((1-dr)**2) - g_loss = torch.mean(dg**2) - loss += (r_loss + g_loss) - r_losses.append(r_loss.item()) - g_losses.append(g_loss.item()) - - return loss, r_losses, g_losses - - -def generator_loss(disc_outputs): - loss = 0 - gen_losses = [] - for dg in disc_outputs: - dg = dg.float() - l = torch.mean((1-dg)**2) - gen_losses.append(l) - loss += l - - return loss, gen_losses - - -def kl_loss(z_p, logs_q, m_p, logs_p, z_mask): - """ - z_p, logs_q: [b, h, t_t] - m_p, logs_p: [b, h, t_t] - """ - z_p = z_p.float() - logs_q = logs_q.float() - m_p = m_p.float() - logs_p = logs_p.float() - z_mask = z_mask.float() - - kl = logs_p - logs_q - 0.5 - kl += 0.5 * ((z_p - m_p)**2) * torch.exp(-2. * logs_p) - kl = torch.sum(kl * z_mask) - l = kl / torch.sum(z_mask) - return l diff --git a/spaces/caffeinum/VToonify/vtoonify/model/encoder/encoders/model_irse.py b/spaces/caffeinum/VToonify/vtoonify/model/encoder/encoders/model_irse.py deleted file mode 100644 index 6698d9705321dd4a27681ea15204e9ffaa51f62a..0000000000000000000000000000000000000000 --- a/spaces/caffeinum/VToonify/vtoonify/model/encoder/encoders/model_irse.py +++ /dev/null @@ -1,84 +0,0 @@ -from torch.nn import Linear, Conv2d, BatchNorm1d, BatchNorm2d, PReLU, Dropout, Sequential, Module -from model.encoder.encoders.helpers import get_blocks, Flatten, bottleneck_IR, bottleneck_IR_SE, l2_norm - -""" -Modified Backbone implementation from [TreB1eN](https://github.com/TreB1eN/InsightFace_Pytorch) -""" - - -class Backbone(Module): - def __init__(self, input_size, num_layers, mode='ir', drop_ratio=0.4, affine=True): - super(Backbone, self).__init__() - assert input_size in [112, 224], "input_size should be 112 or 224" - assert num_layers in [50, 100, 152], "num_layers should be 50, 100 or 152" - assert mode in ['ir', 'ir_se'], "mode should be ir or ir_se" - blocks = get_blocks(num_layers) - if mode == 'ir': - unit_module = bottleneck_IR - elif mode == 'ir_se': - unit_module = bottleneck_IR_SE - self.input_layer = Sequential(Conv2d(3, 64, (3, 3), 1, 1, bias=False), - BatchNorm2d(64), - PReLU(64)) - if input_size == 112: - self.output_layer = Sequential(BatchNorm2d(512), - Dropout(drop_ratio), - Flatten(), - Linear(512 * 7 * 7, 512), - BatchNorm1d(512, affine=affine)) - else: - self.output_layer = Sequential(BatchNorm2d(512), - Dropout(drop_ratio), - Flatten(), - Linear(512 * 14 * 14, 512), - BatchNorm1d(512, affine=affine)) - - modules = [] - for block in blocks: - for bottleneck in block: - modules.append(unit_module(bottleneck.in_channel, - bottleneck.depth, - bottleneck.stride)) - self.body = Sequential(*modules) - - def forward(self, x): - x = self.input_layer(x) - x = self.body(x) - x = self.output_layer(x) - return l2_norm(x) - - -def IR_50(input_size): - """Constructs a ir-50 model.""" - model = Backbone(input_size, num_layers=50, mode='ir', drop_ratio=0.4, affine=False) - return model - - -def IR_101(input_size): - """Constructs a ir-101 model.""" - model = Backbone(input_size, num_layers=100, mode='ir', drop_ratio=0.4, affine=False) - return model - - -def IR_152(input_size): - """Constructs a ir-152 model.""" - model = Backbone(input_size, num_layers=152, mode='ir', drop_ratio=0.4, affine=False) - return model - - -def IR_SE_50(input_size): - """Constructs a ir_se-50 model.""" - model = Backbone(input_size, num_layers=50, mode='ir_se', drop_ratio=0.4, affine=False) - return model - - -def IR_SE_101(input_size): - """Constructs a ir_se-101 model.""" - model = Backbone(input_size, num_layers=100, mode='ir_se', drop_ratio=0.4, affine=False) - return model - - -def IR_SE_152(input_size): - """Constructs a ir_se-152 model.""" - model = Backbone(input_size, num_layers=152, mode='ir_se', drop_ratio=0.4, affine=False) - return model diff --git a/spaces/camenduru-com/audioldm-text-to-audio-generation/share_btn.py b/spaces/camenduru-com/audioldm-text-to-audio-generation/share_btn.py deleted file mode 100644 index b8c2ed17439625f85fd0e910766c727b29131e3d..0000000000000000000000000000000000000000 --- a/spaces/camenduru-com/audioldm-text-to-audio-generation/share_btn.py +++ /dev/null @@ -1,60 +0,0 @@ -community_icon_html = """""" - -loading_icon_html = """""" - -share_js = """async () => { - async function uploadFile(file){ - const UPLOAD_URL = 'https://huggingface.co/uploads'; - const response = await fetch(UPLOAD_URL, { - method: 'POST', - headers: { - 'Content-Type': file.type, - 'X-Requested-With': 'XMLHttpRequest', - }, - body: file, /// <- File inherits from Blob - }); - const url = await response.text(); - return url; - } - const gradioEl = document.querySelector('body > gradio-app'); - const imgEls = gradioEl.querySelectorAll('#gallery img'); - const promptTxt = gradioEl.querySelector('#prompt-text-input input').value; - const shareBtnEl = gradioEl.querySelector('#share-btn'); - const shareIconEl = gradioEl.querySelector('#share-btn-share-icon'); - const loadingIconEl = gradioEl.querySelector('#share-btn-loading-icon'); - if(!imgEls.length){ - return; - }; - shareBtnEl.style.pointerEvents = 'none'; - shareIconEl.style.display = 'none'; - loadingIconEl.style.removeProperty('display'); - const files = await Promise.all( - [...imgEls].map(async (imgEl) => { - const res = await fetch(imgEl.src); - const blob = await res.blob(); - const imgId = Date.now() % 200; - const fileName = `diffuse-the-rest-${{imgId}}.jpg`; - return new File([blob], fileName, { type: 'image/jpeg' }); - }) - ); - const urls = await Promise.all(files.map((f) => uploadFile(f))); - const htmlImgs = urls.map(url => `
-${htmlImgs.join(`\n`)}
-
`;
- const params = new URLSearchParams({
- title: promptTxt,
- description: descriptionMd,
- });
- const paramsStr = params.toString();
- window.open(`https://huggingface.co/spaces/haoheliu/audioldm-text-to-audio-generation/discussions/new?${paramsStr}`, '_blank');
- shareBtnEl.style.removeProperty('pointer-events');
- shareIconEl.style.removeProperty('display');
- loadingIconEl.style.display = 'none';
-}"""
\ No newline at end of file
diff --git a/spaces/camilosegura/traductor-multilenguaje/Lib/site-packages/PIL/CurImagePlugin.py b/spaces/camilosegura/traductor-multilenguaje/Lib/site-packages/PIL/CurImagePlugin.py
deleted file mode 100644
index 94efff3415679a5bf5b7038f9a1da15ebc6d04ca..0000000000000000000000000000000000000000
--- a/spaces/camilosegura/traductor-multilenguaje/Lib/site-packages/PIL/CurImagePlugin.py
+++ /dev/null
@@ -1,75 +0,0 @@
-#
-# The Python Imaging Library.
-# $Id$
-#
-# Windows Cursor support for PIL
-#
-# notes:
-# uses BmpImagePlugin.py to read the bitmap data.
-#
-# history:
-# 96-05-27 fl Created
-#
-# Copyright (c) Secret Labs AB 1997.
-# Copyright (c) Fredrik Lundh 1996.
-#
-# See the README file for information on usage and redistribution.
-#
-from . import BmpImagePlugin, Image
-from ._binary import i16le as i16
-from ._binary import i32le as i32
-
-#
-# --------------------------------------------------------------------
-
-
-def _accept(prefix):
- return prefix[:4] == b"\0\0\2\0"
-
-
-##
-# Image plugin for Windows Cursor files.
-
-
-class CurImageFile(BmpImagePlugin.BmpImageFile):
- format = "CUR"
- format_description = "Windows Cursor"
-
- def _open(self):
- offset = self.fp.tell()
-
- # check magic
- s = self.fp.read(6)
- if not _accept(s):
- msg = "not a CUR file"
- raise SyntaxError(msg)
-
- # pick the largest cursor in the file
- m = b""
- for i in range(i16(s, 4)):
- s = self.fp.read(16)
- if not m:
- m = s
- elif s[0] > m[0] and s[1] > m[1]:
- m = s
- if not m:
- msg = "No cursors were found"
- raise TypeError(msg)
-
- # load as bitmap
- self._bitmap(i32(m, 12) + offset)
-
- # patch up the bitmap height
- self._size = self.size[0], self.size[1] // 2
- d, e, o, a = self.tile[0]
- self.tile[0] = d, (0, 0) + self.size, o, a
-
- return
-
-
-#
-# --------------------------------------------------------------------
-
-Image.register_open(CurImageFile.format, CurImageFile, _accept)
-
-Image.register_extension(CurImageFile.format, ".cur")
diff --git a/spaces/camilosegura/traductor-multilenguaje/Lib/site-packages/PIL/MspImagePlugin.py b/spaces/camilosegura/traductor-multilenguaje/Lib/site-packages/PIL/MspImagePlugin.py
deleted file mode 100644
index c6567b2ae626fd83ef21575a59374c922d5392a9..0000000000000000000000000000000000000000
--- a/spaces/camilosegura/traductor-multilenguaje/Lib/site-packages/PIL/MspImagePlugin.py
+++ /dev/null
@@ -1,194 +0,0 @@
-#
-# The Python Imaging Library.
-#
-# MSP file handling
-#
-# This is the format used by the Paint program in Windows 1 and 2.
-#
-# History:
-# 95-09-05 fl Created
-# 97-01-03 fl Read/write MSP images
-# 17-02-21 es Fixed RLE interpretation
-#
-# Copyright (c) Secret Labs AB 1997.
-# Copyright (c) Fredrik Lundh 1995-97.
-# Copyright (c) Eric Soroos 2017.
-#
-# See the README file for information on usage and redistribution.
-#
-# More info on this format: https://archive.org/details/gg243631
-# Page 313:
-# Figure 205. Windows Paint Version 1: "DanM" Format
-# Figure 206. Windows Paint Version 2: "LinS" Format. Used in Windows V2.03
-#
-# See also: https://www.fileformat.info/format/mspaint/egff.htm
-
-import io
-import struct
-
-from . import Image, ImageFile
-from ._binary import i16le as i16
-from ._binary import o16le as o16
-
-#
-# read MSP files
-
-
-def _accept(prefix):
- return prefix[:4] in [b"DanM", b"LinS"]
-
-
-##
-# Image plugin for Windows MSP images. This plugin supports both
-# uncompressed (Windows 1.0).
-
-
-class MspImageFile(ImageFile.ImageFile):
- format = "MSP"
- format_description = "Windows Paint"
-
- def _open(self):
- # Header
- s = self.fp.read(32)
- if not _accept(s):
- msg = "not an MSP file"
- raise SyntaxError(msg)
-
- # Header checksum
- checksum = 0
- for i in range(0, 32, 2):
- checksum = checksum ^ i16(s, i)
- if checksum != 0:
- msg = "bad MSP checksum"
- raise SyntaxError(msg)
-
- self.mode = "1"
- self._size = i16(s, 4), i16(s, 6)
-
- if s[:4] == b"DanM":
- self.tile = [("raw", (0, 0) + self.size, 32, ("1", 0, 1))]
- else:
- self.tile = [("MSP", (0, 0) + self.size, 32, None)]
-
-
-class MspDecoder(ImageFile.PyDecoder):
- # The algo for the MSP decoder is from
- # https://www.fileformat.info/format/mspaint/egff.htm
- # cc-by-attribution -- That page references is taken from the
- # Encyclopedia of Graphics File Formats and is licensed by
- # O'Reilly under the Creative Common/Attribution license
- #
- # For RLE encoded files, the 32byte header is followed by a scan
- # line map, encoded as one 16bit word of encoded byte length per
- # line.
- #
- # NOTE: the encoded length of the line can be 0. This was not
- # handled in the previous version of this encoder, and there's no
- # mention of how to handle it in the documentation. From the few
- # examples I've seen, I've assumed that it is a fill of the
- # background color, in this case, white.
- #
- #
- # Pseudocode of the decoder:
- # Read a BYTE value as the RunType
- # If the RunType value is zero
- # Read next byte as the RunCount
- # Read the next byte as the RunValue
- # Write the RunValue byte RunCount times
- # If the RunType value is non-zero
- # Use this value as the RunCount
- # Read and write the next RunCount bytes literally
- #
- # e.g.:
- # 0x00 03 ff 05 00 01 02 03 04
- # would yield the bytes:
- # 0xff ff ff 00 01 02 03 04
- #
- # which are then interpreted as a bit packed mode '1' image
-
- _pulls_fd = True
-
- def decode(self, buffer):
- img = io.BytesIO()
- blank_line = bytearray((0xFF,) * ((self.state.xsize + 7) // 8))
- try:
- self.fd.seek(32)
- rowmap = struct.unpack_from(
- f"<{self.state.ysize}H", self.fd.read(self.state.ysize * 2)
- )
- except struct.error as e:
- msg = "Truncated MSP file in row map"
- raise OSError(msg) from e
-
- for x, rowlen in enumerate(rowmap):
- try:
- if rowlen == 0:
- img.write(blank_line)
- continue
- row = self.fd.read(rowlen)
- if len(row) != rowlen:
- msg = f"Truncated MSP file, expected {rowlen} bytes on row {x}"
- raise OSError(msg)
- idx = 0
- while idx < rowlen:
- runtype = row[idx]
- idx += 1
- if runtype == 0:
- (runcount, runval) = struct.unpack_from("Bc", row, idx)
- img.write(runval * runcount)
- idx += 2
- else:
- runcount = runtype
- img.write(row[idx : idx + runcount])
- idx += runcount
-
- except struct.error as e:
- msg = f"Corrupted MSP file in row {x}"
- raise OSError(msg) from e
-
- self.set_as_raw(img.getvalue(), ("1", 0, 1))
-
- return -1, 0
-
-
-Image.register_decoder("MSP", MspDecoder)
-
-
-#
-# write MSP files (uncompressed only)
-
-
-def _save(im, fp, filename):
- if im.mode != "1":
- msg = f"cannot write mode {im.mode} as MSP"
- raise OSError(msg)
-
- # create MSP header
- header = [0] * 16
-
- header[0], header[1] = i16(b"Da"), i16(b"nM") # version 1
- header[2], header[3] = im.size
- header[4], header[5] = 1, 1
- header[6], header[7] = 1, 1
- header[8], header[9] = im.size
-
- checksum = 0
- for h in header:
- checksum = checksum ^ h
- header[12] = checksum # FIXME: is this the right field?
-
- # header
- for h in header:
- fp.write(o16(h))
-
- # image body
- ImageFile._save(im, fp, [("raw", (0, 0) + im.size, 32, ("1", 0, 1))])
-
-
-#
-# registry
-
-Image.register_open(MspImageFile.format, MspImageFile, _accept)
-Image.register_save(MspImageFile.format, _save)
-
-Image.register_extension(MspImageFile.format, ".msp")
diff --git a/spaces/camilosegura/traductor-multilenguaje/Lib/site-packages/PIL/PaletteFile.py b/spaces/camilosegura/traductor-multilenguaje/Lib/site-packages/PIL/PaletteFile.py
deleted file mode 100644
index 4a2c497fc495a271cbab204db0197d776442ac5c..0000000000000000000000000000000000000000
--- a/spaces/camilosegura/traductor-multilenguaje/Lib/site-packages/PIL/PaletteFile.py
+++ /dev/null
@@ -1,51 +0,0 @@
-#
-# Python Imaging Library
-# $Id$
-#
-# stuff to read simple, teragon-style palette files
-#
-# History:
-# 97-08-23 fl Created
-#
-# Copyright (c) Secret Labs AB 1997.
-# Copyright (c) Fredrik Lundh 1997.
-#
-# See the README file for information on usage and redistribution.
-#
-
-from ._binary import o8
-
-
-class PaletteFile:
- """File handler for Teragon-style palette files."""
-
- rawmode = "RGB"
-
- def __init__(self, fp):
- self.palette = [(i, i, i) for i in range(256)]
-
- while True:
- s = fp.readline()
-
- if not s:
- break
- if s[:1] == b"#":
- continue
- if len(s) > 100:
- msg = "bad palette file"
- raise SyntaxError(msg)
-
- v = [int(x) for x in s.split()]
- try:
- [i, r, g, b] = v
- except ValueError:
- [i, r] = v
- g = b = r
-
- if 0 <= i <= 255:
- self.palette[i] = o8(r) + o8(g) + o8(b)
-
- self.palette = b"".join(self.palette)
-
- def getpalette(self):
- return self.palette, self.rawmode
diff --git a/spaces/candlend/vits-hoshimi/sovits/models.py b/spaces/candlend/vits-hoshimi/sovits/models.py
deleted file mode 100644
index f4941c211eed9a025536456c2aa110141ab7e3ff..0000000000000000000000000000000000000000
--- a/spaces/candlend/vits-hoshimi/sovits/models.py
+++ /dev/null
@@ -1,351 +0,0 @@
-import copy
-import math
-import torch
-from torch import nn
-from torch.nn import functional as F
-
-from sovits import attentions
-from sovits import commons
-from sovits import modules
-
-from torch.nn import Conv1d, ConvTranspose1d, AvgPool1d, Conv2d
-from torch.nn.utils import weight_norm, remove_weight_norm, spectral_norm
-from sovits.commons import init_weights, get_padding
-from sovits.vdecoder.hifigan.models import Generator
-from sovits.utils import f0_to_coarse
-
-class ResidualCouplingBlock(nn.Module):
- def __init__(self,
- channels,
- hidden_channels,
- kernel_size,
- dilation_rate,
- n_layers,
- n_flows=4,
- gin_channels=0):
- super().__init__()
- self.channels = channels
- self.hidden_channels = hidden_channels
- self.kernel_size = kernel_size
- self.dilation_rate = dilation_rate
- self.n_layers = n_layers
- self.n_flows = n_flows
- self.gin_channels = gin_channels
-
- self.flows = nn.ModuleList()
- for i in range(n_flows):
- self.flows.append(modules.ResidualCouplingLayer(channels, hidden_channels, kernel_size, dilation_rate, n_layers, gin_channels=gin_channels, mean_only=True))
- self.flows.append(modules.Flip())
-
- def forward(self, x, x_mask, g=None, reverse=False):
- if not reverse:
- for flow in self.flows:
- x, _ = flow(x, x_mask, g=g, reverse=reverse)
- else:
- for flow in reversed(self.flows):
- x = flow(x, x_mask, g=g, reverse=reverse)
- return x
-
-
-class Encoder(nn.Module):
- def __init__(self,
- in_channels,
- out_channels,
- hidden_channels,
- kernel_size,
- dilation_rate,
- n_layers,
- gin_channels=0):
- super().__init__()
- self.in_channels = in_channels
- self.out_channels = out_channels
- self.hidden_channels = hidden_channels
- self.kernel_size = kernel_size
- self.dilation_rate = dilation_rate
- self.n_layers = n_layers
- self.gin_channels = gin_channels
-
- self.pre = nn.Conv1d(in_channels, hidden_channels, 1)
- self.enc = modules.WN(hidden_channels, kernel_size, dilation_rate, n_layers, gin_channels=gin_channels)
- self.proj = nn.Conv1d(hidden_channels, out_channels * 2, 1)
-
- def forward(self, x, x_lengths, g=None):
- # print(x.shape,x_lengths.shape)
- x_mask = torch.unsqueeze(commons.sequence_mask(x_lengths, x.size(2)), 1).to(x.dtype)
- x = self.pre(x) * x_mask
- x = self.enc(x, x_mask, g=g)
- stats = self.proj(x) * x_mask
- m, logs = torch.split(stats, self.out_channels, dim=1)
- z = (m + torch.randn_like(m) * torch.exp(logs)) * x_mask
- return z, m, logs, x_mask
-
-
-class TextEncoder(nn.Module):
- def __init__(self,
- in_channels,
- out_channels,
- hidden_channels,
- kernel_size,
- dilation_rate,
- n_layers,
- gin_channels=0,
- filter_channels=None,
- n_heads=None,
- p_dropout=None):
- super().__init__()
- self.in_channels = in_channels
- self.out_channels = out_channels
- self.hidden_channels = hidden_channels
- self.kernel_size = kernel_size
- self.dilation_rate = dilation_rate
- self.n_layers = n_layers
- self.gin_channels = gin_channels
- self.pre = nn.Conv1d(in_channels, hidden_channels, 1)
- self.proj = nn.Conv1d(hidden_channels, out_channels * 2, 1)
- self.f0_emb = nn.Embedding(256, hidden_channels)
-
- self.enc_ = attentions.Encoder(
- hidden_channels,
- filter_channels,
- n_heads,
- n_layers,
- kernel_size,
- p_dropout)
-
- def forward(self, x, x_lengths, f0=None):
- x_mask = torch.unsqueeze(commons.sequence_mask(x_lengths, x.size(2)), 1).to(x.dtype)
- x = self.pre(x) * x_mask
- x = x + self.f0_emb(f0).transpose(1,2)
- x = self.enc_(x * x_mask, x_mask)
- stats = self.proj(x) * x_mask
- m, logs = torch.split(stats, self.out_channels, dim=1)
- z = (m + torch.randn_like(m) * torch.exp(logs)) * x_mask
-
- return z, m, logs, x_mask
-
-
-
-class DiscriminatorP(torch.nn.Module):
- def __init__(self, period, kernel_size=5, stride=3, use_spectral_norm=False):
- super(DiscriminatorP, self).__init__()
- self.period = period
- self.use_spectral_norm = use_spectral_norm
- norm_f = weight_norm if use_spectral_norm == False else spectral_norm
- self.convs = nn.ModuleList([
- norm_f(Conv2d(1, 32, (kernel_size, 1), (stride, 1), padding=(get_padding(kernel_size, 1), 0))),
- norm_f(Conv2d(32, 128, (kernel_size, 1), (stride, 1), padding=(get_padding(kernel_size, 1), 0))),
- norm_f(Conv2d(128, 512, (kernel_size, 1), (stride, 1), padding=(get_padding(kernel_size, 1), 0))),
- norm_f(Conv2d(512, 1024, (kernel_size, 1), (stride, 1), padding=(get_padding(kernel_size, 1), 0))),
- norm_f(Conv2d(1024, 1024, (kernel_size, 1), 1, padding=(get_padding(kernel_size, 1), 0))),
- ])
- self.conv_post = norm_f(Conv2d(1024, 1, (3, 1), 1, padding=(1, 0)))
-
- def forward(self, x):
- fmap = []
-
- # 1d to 2d
- b, c, t = x.shape
- if t % self.period != 0: # pad first
- n_pad = self.period - (t % self.period)
- x = F.pad(x, (0, n_pad), "reflect")
- t = t + n_pad
- x = x.view(b, c, t // self.period, self.period)
-
- for l in self.convs:
- x = l(x)
- x = F.leaky_relu(x, modules.LRELU_SLOPE)
- fmap.append(x)
- x = self.conv_post(x)
- fmap.append(x)
- x = torch.flatten(x, 1, -1)
-
- return x, fmap
-
-
-class DiscriminatorS(torch.nn.Module):
- def __init__(self, use_spectral_norm=False):
- super(DiscriminatorS, self).__init__()
- norm_f = weight_norm if use_spectral_norm == False else spectral_norm
- self.convs = nn.ModuleList([
- norm_f(Conv1d(1, 16, 15, 1, padding=7)),
- norm_f(Conv1d(16, 64, 41, 4, groups=4, padding=20)),
- norm_f(Conv1d(64, 256, 41, 4, groups=16, padding=20)),
- norm_f(Conv1d(256, 1024, 41, 4, groups=64, padding=20)),
- norm_f(Conv1d(1024, 1024, 41, 4, groups=256, padding=20)),
- norm_f(Conv1d(1024, 1024, 5, 1, padding=2)),
- ])
- self.conv_post = norm_f(Conv1d(1024, 1, 3, 1, padding=1))
-
- def forward(self, x):
- fmap = []
-
- for l in self.convs:
- x = l(x)
- x = F.leaky_relu(x, modules.LRELU_SLOPE)
- fmap.append(x)
- x = self.conv_post(x)
- fmap.append(x)
- x = torch.flatten(x, 1, -1)
-
- return x, fmap
-
-
-class MultiPeriodDiscriminator(torch.nn.Module):
- def __init__(self, use_spectral_norm=False):
- super(MultiPeriodDiscriminator, self).__init__()
- periods = [2,3,5,7,11]
-
- discs = [DiscriminatorS(use_spectral_norm=use_spectral_norm)]
- discs = discs + [DiscriminatorP(i, use_spectral_norm=use_spectral_norm) for i in periods]
- self.discriminators = nn.ModuleList(discs)
-
- def forward(self, y, y_hat):
- y_d_rs = []
- y_d_gs = []
- fmap_rs = []
- fmap_gs = []
- for i, d in enumerate(self.discriminators):
- y_d_r, fmap_r = d(y)
- y_d_g, fmap_g = d(y_hat)
- y_d_rs.append(y_d_r)
- y_d_gs.append(y_d_g)
- fmap_rs.append(fmap_r)
- fmap_gs.append(fmap_g)
-
- return y_d_rs, y_d_gs, fmap_rs, fmap_gs
-
-
-class SpeakerEncoder(torch.nn.Module):
- def __init__(self, mel_n_channels=80, model_num_layers=3, model_hidden_size=256, model_embedding_size=256):
- super(SpeakerEncoder, self).__init__()
- self.lstm = nn.LSTM(mel_n_channels, model_hidden_size, model_num_layers, batch_first=True)
- self.linear = nn.Linear(model_hidden_size, model_embedding_size)
- self.relu = nn.ReLU()
-
- def forward(self, mels):
- self.lstm.flatten_parameters()
- _, (hidden, _) = self.lstm(mels)
- embeds_raw = self.relu(self.linear(hidden[-1]))
- return embeds_raw / torch.norm(embeds_raw, dim=1, keepdim=True)
-
- def compute_partial_slices(self, total_frames, partial_frames, partial_hop):
- mel_slices = []
- for i in range(0, total_frames-partial_frames, partial_hop):
- mel_range = torch.arange(i, i+partial_frames)
- mel_slices.append(mel_range)
-
- return mel_slices
-
- def embed_utterance(self, mel, partial_frames=128, partial_hop=64):
- mel_len = mel.size(1)
- last_mel = mel[:,-partial_frames:]
-
- if mel_len > partial_frames:
- mel_slices = self.compute_partial_slices(mel_len, partial_frames, partial_hop)
- mels = list(mel[:,s] for s in mel_slices)
- mels.append(last_mel)
- mels = torch.stack(tuple(mels), 0).squeeze(1)
-
- with torch.no_grad():
- partial_embeds = self(mels)
- embed = torch.mean(partial_embeds, axis=0).unsqueeze(0)
- #embed = embed / torch.linalg.norm(embed, 2)
- else:
- with torch.no_grad():
- embed = self(last_mel)
-
- return embed
-
-
-class SynthesizerTrn(nn.Module):
- """
- Synthesizer for Training
- """
-
- def __init__(self,
- spec_channels,
- segment_size,
- inter_channels,
- hidden_channels,
- filter_channels,
- n_heads,
- n_layers,
- kernel_size,
- p_dropout,
- resblock,
- resblock_kernel_sizes,
- resblock_dilation_sizes,
- upsample_rates,
- upsample_initial_channel,
- upsample_kernel_sizes,
- gin_channels,
- ssl_dim,
- n_speakers,
- **kwargs):
-
- super().__init__()
- self.spec_channels = spec_channels
- self.inter_channels = inter_channels
- self.hidden_channels = hidden_channels
- self.filter_channels = filter_channels
- self.n_heads = n_heads
- self.n_layers = n_layers
- self.kernel_size = kernel_size
- self.p_dropout = p_dropout
- self.resblock = resblock
- self.resblock_kernel_sizes = resblock_kernel_sizes
- self.resblock_dilation_sizes = resblock_dilation_sizes
- self.upsample_rates = upsample_rates
- self.upsample_initial_channel = upsample_initial_channel
- self.upsample_kernel_sizes = upsample_kernel_sizes
- self.segment_size = segment_size
- self.gin_channels = gin_channels
- self.ssl_dim = ssl_dim
- self.emb_g = nn.Embedding(n_speakers, gin_channels)
-
- self.enc_p_ = TextEncoder(ssl_dim, inter_channels, hidden_channels, 5, 1, 16,0, filter_channels, n_heads, p_dropout)
- hps = {
- "sampling_rate": 32000,
- "inter_channels": 192,
- "resblock": "1",
- "resblock_kernel_sizes": [3, 7, 11],
- "resblock_dilation_sizes": [[1, 3, 5], [1, 3, 5], [1, 3, 5]],
- "upsample_rates": [10, 8, 2, 2],
- "upsample_initial_channel": 512,
- "upsample_kernel_sizes": [16, 16, 4, 4],
- "gin_channels": 256,
- }
- self.dec = Generator(h=hps)
- self.enc_q = Encoder(spec_channels, inter_channels, hidden_channels, 5, 1, 16, gin_channels=gin_channels)
- self.flow = ResidualCouplingBlock(inter_channels, hidden_channels, 5, 1, 4, gin_channels=gin_channels)
-
- def forward(self, c, f0, spec, g=None, mel=None, c_lengths=None, spec_lengths=None):
- if c_lengths == None:
- c_lengths = (torch.ones(c.size(0)) * c.size(-1)).to(c.device)
- if spec_lengths == None:
- spec_lengths = (torch.ones(spec.size(0)) * spec.size(-1)).to(spec.device)
-
- g = self.emb_g(g).transpose(1,2)
-
- z_ptemp, m_p, logs_p, _ = self.enc_p_(c, c_lengths, f0=f0_to_coarse(f0))
- z, m_q, logs_q, spec_mask = self.enc_q(spec, spec_lengths, g=g)
-
- z_p = self.flow(z, spec_mask, g=g)
- z_slice, pitch_slice, ids_slice = commons.rand_slice_segments_with_pitch(z, f0, spec_lengths, self.segment_size)
-
- # o = self.dec(z_slice, g=g)
- o = self.dec(z_slice, g=g, f0=pitch_slice)
-
- return o, ids_slice, spec_mask, (z, z_p, m_p, logs_p, m_q, logs_q)
-
- def infer(self, c, f0, g=None, mel=None, c_lengths=None):
- if c_lengths == None:
- c_lengths = (torch.ones(c.size(0)) * c.size(-1)).to(c.device)
- g = self.emb_g(g).transpose(1,2)
-
- z_p, m_p, logs_p, c_mask = self.enc_p_(c, c_lengths, f0=f0_to_coarse(f0))
- z = self.flow(z_p, c_mask, g=g, reverse=True)
-
- o = self.dec(z * c_mask, g=g, f0=f0)
-
- return o
diff --git a/spaces/carlosalonso/Detection-video/carpeta_deteccion/detectron2/solver/__init__.py b/spaces/carlosalonso/Detection-video/carpeta_deteccion/detectron2/solver/__init__.py
deleted file mode 100644
index 9a2dbd35bb24f0d4a979bc8f304142376d87e7ec..0000000000000000000000000000000000000000
--- a/spaces/carlosalonso/Detection-video/carpeta_deteccion/detectron2/solver/__init__.py
+++ /dev/null
@@ -1,5 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-from .build import build_lr_scheduler, build_optimizer, get_default_optimizer_params
-from .lr_scheduler import WarmupCosineLR, WarmupMultiStepLR, LRMultiplier, WarmupParamScheduler
-
-__all__ = [k for k in globals().keys() if not k.startswith("_")]
diff --git a/spaces/carlosalonso/Detection-video/carpeta_deteccion/projects/MViTv2/configs/cascade_mask_rcnn_mvitv2_l_in21k_lsj_50ep.py b/spaces/carlosalonso/Detection-video/carpeta_deteccion/projects/MViTv2/configs/cascade_mask_rcnn_mvitv2_l_in21k_lsj_50ep.py
deleted file mode 100644
index 38da8958e0174d378555887d72a9956f4b3f8e58..0000000000000000000000000000000000000000
--- a/spaces/carlosalonso/Detection-video/carpeta_deteccion/projects/MViTv2/configs/cascade_mask_rcnn_mvitv2_l_in21k_lsj_50ep.py
+++ /dev/null
@@ -1,31 +0,0 @@
-from fvcore.common.param_scheduler import MultiStepParamScheduler
-
-from detectron2.config import LazyCall as L
-from detectron2.solver import WarmupParamScheduler
-
-from .cascade_mask_rcnn_mvitv2_b_3x import model, optimizer, train
-from .common.coco_loader_lsj import dataloader
-
-
-model.backbone.bottom_up.embed_dim = 144
-model.backbone.bottom_up.depth = 48
-model.backbone.bottom_up.num_heads = 2
-model.backbone.bottom_up.last_block_indexes = (1, 7, 43, 47)
-model.backbone.bottom_up.drop_path_rate = 0.5
-
-train.init_checkpoint = "detectron2://ImageNetPretrained/mvitv2/MViTv2_L_in21k.pyth"
-
-# Schedule
-# 50ep = 184375 // 2 iters * 64 images/iter / 118000 images/ep
-train.max_iter = 184375 // 2
-lr_multiplier = L(WarmupParamScheduler)(
- scheduler=L(MultiStepParamScheduler)(
- values=[1.0, 0.1, 0.01],
- milestones=[163889 // 2, 177546 // 2],
- num_updates=train.max_iter,
- ),
- warmup_length=250 / train.max_iter,
- warmup_factor=0.001,
-)
-
-optimizer.lr = 1e-4
diff --git a/spaces/cfwef/gpt/crazy_functions/test_project/cpp/cppipc/shm.cpp b/spaces/cfwef/gpt/crazy_functions/test_project/cpp/cppipc/shm.cpp
deleted file mode 100644
index 593ce3129dc1574dbc8fc8b088cf595df215de93..0000000000000000000000000000000000000000
--- a/spaces/cfwef/gpt/crazy_functions/test_project/cpp/cppipc/shm.cpp
+++ /dev/null
@@ -1,103 +0,0 @@
-
-#include Aliança Shinobi High Five MOD APK: A Ninja Adventure Game for Android
-If you are a fan of ninja-themed games, you might want to check out Aliança Shinobi High Five, a new RPG game for Android devices. In this game, you can create your own ninja character, join a shinobi alliance, and embark on exciting missions and battles. You can also enjoy stunning graphics, immersive sound effects, and smooth controls.
-But what if you want to unlock all the features and items in the game without spending real money? Well, there is a solution for that. You can download Aliança Shinobi High Five MOD APK, a modified version of the game that gives you unlimited resources, free shopping, and more. In this article, we will tell you more about this game and how to get the mod apk on your device.
-aliança shinobi high five mod apk
Download 🗹 https://urlca.com/2uO8ew
-
What is Aliança Shinobi High Five?
-The story and the gameplay
-Aliança Shinobi High Five is a game inspired by the popular anime and manga series Naruto. The game is set in a world where ninjas have special abilities called chakra. You can choose from different classes of ninjas, such as taijutsu, genjutsu, or ninjutsu. You can also customize your appearance, skills, weapons, and outfits.
-The game has a rich and engaging story mode, where you can follow the adventures of your character and interact with other characters from the Naruto universe. You can also join an alliance with other players and cooperate in various missions and events. You can also challenge other players in PvP battles and rank up in the leaderboard.
-The features and the graphics
-Aliança Shinobi High Five has many features that make it a fun and addictive game. Some of them are:
--
-
- Over 100 characters to collect and upgrade -
- Over 200 skills to learn and master -
- Over 300 items to equip and enhance -
- Over 500 quests to complete and rewards to claim -
- Different modes to play, such as story mode, alliance mode, arena mode, survival mode, etc. -
- Different events to participate in, such as daily tasks, weekly challenges, seasonal festivals, etc. -
The game also has amazing graphics that bring the ninja world to life. The characters are designed with high-quality 3D models and animations. The environments are detailed and colorful. The effects are realistic and dynamic. The game also has a catchy soundtrack and voice-overs that match the mood of the game.
-Why download Aliança Shinobi High Five MOD APK?
-The benefits of the mod version
-While Aliança Shinobi High Five is a free-to-play game, it also has some in-app purchases that can enhance your gaming experience. For example, you can buy gems, coins, energy, VIP membership, etc. However, these items can be quite expensive and not everyone can afford them.
-That's why some people prefer to download Aliança Shinobi High Five MOD APK, a modified version of the game that gives you access to all the premium features for free. With this mod apk, you can enjoy:
-aliança shinobi high five mod apk download
-aliança shinobi high five mod apk unlimited money
-aliança shinobi high five mod apk latest version
-aliança shinobi high five mod apk android
-aliança shinobi high five mod apk free
-aliança shinobi high five mod apk offline
-aliança shinobi high five mod apk 2023
-aliança shinobi high five mod apk hack
-aliança shinobi high five mod apk no root
-aliança shinobi high five mod apk obb
-aliança shinobi high five mod apk revdl
-aliança shinobi high five mod apk rexdl
-aliança shinobi high five mod apk pure
-aliança shinobi high five mod apk happymod
-aliança shinobi high five mod apk an1
-aliança shinobi high five mod apk vip
-aliança shinobi high five mod apk mega
-aliança shinobi high five mod apk mediafıre
-aliança shinobi high five mod apk uptodown
-aliança shinobi high five mod apk 1.8
-aliança shinobi high five mod apk gameplay
-aliança shinobi high five mod apk cheats
-aliança shinobi high five mod apk features
-aliança shinobi high five mod apk review
-aliança shinobi high five mod apk online
-aliança shinobi high five rpg mod apk
-aliança shinobi high five ninja mod apk
-aliança shinobi high five anime mod apk
-aliança shinobi high five naruto mod apk
-aliança shinobi high five boruto mod apk
-aliança shinobi high five adventure mod apk
-aliança shinobi high five action mod apk
-aliança shinobi high five strategy mod apk
-aliança shinobi high five simulation mod apk
-aliança shinobi high five role playing mod apk
-download game aliança shinobi high five mod apk
-download aplikasi aliança shinobi high five mod apk
-descargar aliança shinobi high five mod apk
-baixar aliança shinobi high five mod apk
-telecharger aliança shinobi high five mod apk
-installieren aliança shinobi high five mod apk
-scaricare aliança shinobi high five mod apk
-indir aliança shinobi high five mod apk
-скачать алианса шиноби хай файв мод апк
-下载联盟忍者高五模式apk
-ダウンロードアリアンサシノビハイファイブモッドapk
-다운로드 알리안사 시노비 하이 파이브 모드 APK
-تحميل تحالف شينوبي هاي فايف مود APK
-
-
- Unlimited gems -
- Unlimited coins -
- Unlimited energy -
- Free shopping -
- No ads -
- No root required -
With these benefits, you can play the game without any limitations or interruptions. You can unlock all the characters, skills, items, modes, etc. You can also
How to download and install the mod apk
-If you want to download Aliança Shinobi High Five MOD APK, you need to follow these simple steps:
--
-
- Click on the download button below to get the mod apk file. -
- Allow unknown sources on your device settings. -
- Locate the downloaded file and tap on it to install it. -
- Launch the game and enjoy the mod features. -
Download Aliança Shinobi High Five MOD APK
-Note: Before you install the mod apk, make sure you uninstall the original game if you have it on your device. Also, make sure you have enough storage space and a stable internet connection.
-Conclusion
-Aliança Shinobi High Five is a great game for anyone who loves ninjas and Naruto. It has a captivating story, a diverse gameplay, and a stunning graphics. It also has a lot of features and modes to keep you entertained for hours. However, if you want to enjoy the game without spending money, you can download Aliança Shinobi High Five MOD APK and get unlimited resources, free shopping, and more. This way, you can unlock all the content and have more fun with the game.
-We hope this article was helpful and informative. If you have any questions or feedback, feel free to leave a comment below. Thank you for reading and happy gaming!
-FAQs
-Here are some frequently asked questions about Aliança Shinobi High Five MOD APK:
-Is Aliança Shinobi High Five MOD APK safe to use?
-Yes, Aliança Shinobi High Five MOD APK is safe to use. It does not contain any viruses or malware that can harm your device or data. However, you should always download the mod apk from a trusted source and scan it with an antivirus before installing it.
-Is Aliança Shinobi High Five MOD APK compatible with my device?
-Aliança Shinobi High Five MOD APK is compatible with most Android devices that run on Android 4.4 or higher. However, some devices may not support the game or the mod features due to different specifications or settings. If you encounter any problems with the game or the mod apk, you can try to update your device software, clear your cache, or contact the developer for assistance.
-Can I play Aliança Shinobi High Five MOD APK online with other players?
-Yes, you can play Aliança Shinobi High Five MOD APK online with other players. However, you should be aware that using the mod apk may give you an unfair advantage over other players and may result in your account being banned or suspended by the game developer. Therefore, we advise you to use the mod apk at your own risk and discretion.
-Can I update Aliança Shinobi High Five MOD APK to the latest version?
-Yes, you can update Aliança Shinobi High Five MOD APK to the latest version. However, you should always check if the mod apk is compatible with the new version of the game before updating it. You should also backup your game data before updating it in case something goes wrong.
-Can I request more features for Aliança Shinobi High Five MOD APK?
-Yes, you can request more features for Aliança Shinobi High Five MOD APK. However, we cannot guarantee that your requests will be fulfilled or that the mod apk will work as expected. The mod apk is created by independent developers who may or may not update it regularly or add new features to it. Therefore, we suggest you to be patient and grateful for what you have.
197e85843d-
-
\ No newline at end of file diff --git a/spaces/congsaPfin/Manga-OCR/logs/Plink Balls A New Way to Play with Physics and Mathematics.md b/spaces/congsaPfin/Manga-OCR/logs/Plink Balls A New Way to Play with Physics and Mathematics.md deleted file mode 100644 index 2f3ea28bca801ece4d0ab09720a32ead9521fd3e..0000000000000000000000000000000000000000 --- a/spaces/congsaPfin/Manga-OCR/logs/Plink Balls A New Way to Play with Physics and Mathematics.md +++ /dev/null @@ -1,140 +0,0 @@ -
-
Plink Balls: A Fun and Addictive Game for All Ages
-Do you love games that are simple, yet challenging and rewarding? Do you enjoy watching balls fall and bounce on pegs and slots? Do you want to become a millionaire or lose it all in a matter of seconds? If you answered yes to any of these questions, then you will love Plink Balls, the latest sensation in the gaming world.
-What are Plink Balls?
-Plink Balls are small, colorful balls that you can drop from the top of a triangular grid of pegs. As the balls fall, they hit the pegs and change their direction randomly. Some of the balls will land in containers at the bottom of the grid, while others will fall out of the screen. Each container has a multiplier value that determines how much you win or lose by dropping a ball into it. The goal is to drop as many balls as possible into the highest multipliers and avoid the lowest ones.
-plink balls
DOWNLOAD »»» https://urlca.com/2uO7gV
-
The origin and history of Plink Balls
-Plink Balls are inspired by a classic game show called Plinko, which was first introduced in 1983 on The Price is Right. In Plinko, contestants had to drop large discs from the top of a board with pegs and slots. Depending on where the discs landed, they could win up to $50,000 or nothing at all. Plinko became one of the most popular and exciting games on the show, and has been featured in many variations and spin-offs over the years.
-How to play Plink Balls
-Playing Plink Balls is very easy and fun. All you need is a smartphone or tablet with the app installed. You can download the app for free from Google Play or App Store. Once you open the app, you will see a screen with a grid of pegs and containers. You can choose how many balls you want to drop by tapping on the plus or minus buttons at the bottom. You can also choose how much you want to wager by tapping on the dollar sign button. The minimum wager is $1 and the maximum is $1000 per ball.
-After you have set your preferences, you can start dropping balls by tapping on the screen. You can watch as the balls fall and bounce on the pegs, creating a mesmerizing spectacle. You can also use exciting boosts to increase your chances of winning, such as extra balls, magnets, bombs, and more. You can earn these boosts by playing regularly or by watching ads.
-The benefits of playing Plink Balls
-Plink Balls is not only a fun game, but also a beneficial one. Playing Plink Balls can help you improve your skills and abilities in various ways, such as:
--
-
- Cognitive skills: Playing Plink Balls can enhance your memory, attention, concentration, logic, problem-solving, and decision-making skills. You have to remember where the balls land, pay attention to the multipliers, use logic to predict where the balls will go, solve problems when they get stuck, and make quick decisions when dropping balls. -
- Mental health: Playing Plink Balls can reduce your stress, anxiety, boredom, and depression. You can relax and enjoy watching the balls fall and bounce, creating a soothing sound and visual effect. You can also feel happy and satisfied when you win big or overcome a challenge. -
- Social skills: Playing Plink Balls can improve your social skills by allowing you to interact with other players online. You can share your scores and achievements with other players online. You can also join or create clubs, chat with other members, send and receive gifts, and participate in tournaments and events. -
- Financial skills: Playing Plink Balls can teach you how to manage your money wisely. You have to budget your funds, balance your risks and rewards, and plan your moves carefully. You can also learn how to deal with losses and gains, and how to cope with uncertainty and luck. -
How to master Plink Balls
-Plink Balls may seem like a game of chance, but there is also a lot of skill involved. If you want to become a Plink Balls master, you need to practice and learn some tips and tricks that can help you win more often. Here are some of them:
-Tips and tricks for winning Plink Balls
--
-
- Drop the balls from different angles: Don't always drop the balls from the center of the screen. Try dropping them from the left or right edges, or from different heights. This can create different trajectories and outcomes for the balls, and increase your chances of hitting the high multipliers. -
- Use the boosts wisely: Don't waste your boosts on low stakes or easy levels. Save them for when you really need them, such as when you are playing for high wagers or facing difficult challenges. Also, don't use the same boost all the time. Mix and match different boosts to create different effects and combinations. -
- Watch the ads: Watching ads can be annoying, but it can also be rewarding. By watching ads, you can earn free coins, extra balls, or other boosts that can help you play better. You can also watch ads to double your winnings or to continue playing after losing. -
The best strategies for Plink Balls
--
-
- Set a limit: Before you start playing, decide how much you are willing to spend and how much you want to win. Stick to your limit and don't go over it. This way, you can avoid losing more than you can afford or getting greedy and losing what you have won. -
- Start small: Don't bet too much on your first few drops. Start with small wagers and test the waters. See how the balls behave and where they land. Once you get a feel for the game, you can increase your bets gradually. -
- Aim for the middle: The middle containers usually have the highest multipliers, but they are also the hardest to hit. However, if you aim for the middle, you have a better chance of hitting something than if you aim for the edges. Even if you miss the middle, you might still hit a decent multiplier on either side. -
The most common mistakes to avoid in Plink Balls
--
-
- Dropping too many balls at once: Dropping too many balls at once can be tempting, but it can also be risky. You might end up hitting the same containers repeatedly, or missing them altogether. You might also run out of balls quickly and lose your chance to win more. It is better to drop one ball at a time and see where it lands before dropping another one. -
- Dropping too fast or too slow: Dropping too fast or too slow can affect the outcome of the game. If you drop too fast, you might not have enough time to react or adjust your strategy. If you drop too slow, you might lose your momentum or miss an opportunity. It is better to drop at a moderate pace that suits your style and preference. -
- Getting distracted or impatient: Plink Balls is a game that requires focus and patience. If you get distracted by other things or impatient with the results, you might make mistakes or lose interest. It is better to play when you are relaxed and attentive, and enjoy the game as it unfolds. -
How to enjoy Plink Balls more
-Plink Balls is already a fun and addictive game, but there are ways to make it even more enjoyable. Here are some of them:
-plink balls game
-plink balls app
-plink balls online
-plink balls simulator
-plink balls probability
-plink balls experiment
-plink balls physics
-plink balls statistics
-plink balls histogram
-plink balls download
-plink balls free
-plink balls android
-plink balls ios
-plink balls rollic games
-plink balls phet
-plink balls youtube
-plink balls video
-plink balls review
-plink balls tips
-plink balls tricks
-plink balls hack
-plink balls cheat
-plink balls mod apk
-plink balls unblocked
-plink balls play store
-plink balls amazon
-plink balls walmart
-plink balls target
-plink balls toy
-plink balls machine
-plink balls board game
-plink balls arcade game
-plink balls casino game
-plink balls slot machine
-plink balls bingo game
-plink balls lottery game
-plink balls scratch card game
-plink balls math game
-plink balls educational game
-plink balls science game
-plink balls fun game
-plink balls addictive game
-plink balls relaxing game
-plink balls challenging game
-plink balls strategy game
-plink balls puzzle game
-plink balls logic game
-plink balls skill game
-plink balls luck game
The different modes and levels of Plink Balls
-Plink Balls has different modes and levels that offer different challenges and rewards. You can choose from:
--
-
- Classic mode: This is the basic mode where you drop balls into containers with fixed multipliers. The multipliers range from x0 to x1000. -
- Casino mode: This is the mode where you drop balls into containers with variable multipliers. The multipliers change every time you drop a ball, and can range from x0 to x10000. -
- Adventure mode: This is the mode where you drop balls into containers with special effects. The effects can be positive or negative, such as double, half, freeze, shuffle, or bomb. -
- Challenge mode: This is the mode where you face different tasks and goals. The tasks and goals can be time-based, score-based, or skill-based, such as dropping a certain number of balls, hitting a certain multiplier, or avoiding a certain container. -
You can also unlock new levels by earning stars. Each level has a different theme and design, such as jungle, space, candy, or pirate. The higher the level, the harder the challenge and the bigger the reward.
-The best features and boosts of Plink Balls
-Plink Balls has many features and boosts that can make the game more fun and exciting. Some of the best ones are:
--
-
- Extra balls: These are balls that you can get for free by watching ads, completing tasks, or opening chests. You can use them to drop more balls and increase your chances of winning. -
- Magnets: These are boosts that you can activate by tapping on the magnet icon at the bottom of the screen. They can attract the balls to the nearest container with the highest multiplier. -
- Bombs: These are boosts that you can activate by tapping on the bomb icon at the bottom of the screen. They can explode and clear all the pegs in a certain area, creating a path for the balls to fall into the containers. -
- Leaderboards: These are features that show your rank and score compared to other players around the world. You can see how you are doing and try to beat your own or others' records. -
- Achievements: These are features that reward you for reaching certain milestones or completing certain challenges in the game. You can earn coins, stars, or other prizes for achieving them. -
The best ways to share and compete with your friends in Plink Balls
-Plink Balls is more fun when you play with your friends. You can share and compete with your friends in various ways, such as:
--
-
- Invite your friends: You can invite your friends to join Plink Balls by sending them a link or a code through social media, email, or text message. You can also scan their QR codes to add them as friends. -
- Send and receive gifts: You can send and receive gifts from your friends every day. The gifts can be coins, extra balls, or other boosts that can help you play better. -
- Join or create clubs: You can join or create clubs with your friends or other players who share your interests or goals. You can chat with your club members, exchange tips and tricks, and participate in club events and tournaments. -
- Challenge your friends: You can challenge your friends to a friendly match or a duel in Plink Balls. You can choose the mode, level, wager, and number of balls for each challenge. The winner gets to keep all the winnings and bragging rights. -
Conclusion
-Summary of the main points
-Plink Balls is a fun and addictive game that anyone can enjoy. It is based on a classic game show called Plinko, where contestants had to drop discs from a board with pegs and slots. In Plink Balls, you drop balls from a grid of pegs and containers with different multipliers. The goal is to drop as many balls as possible into the highest multipliers and avoid the lowest ones.
-Plink Balls is not only a fun game, but also a beneficial one. It can improve your cognitive skills, mental health, social skills, and financial skills. It can also teach you how to manage your money wisely, balance your risks and rewards, and plan your moves carefully.
-Plink Balls has different modes and levels that offer different challenges and rewards. It also has many features and boosts that can make the game more fun and exciting. You can also share and compete with your friends in various ways.
-Call to action
-If you are looking for a game that is simple, yet challenging and rewarding; a game that is relaxing, yet stimulating and engaging; a game that is entertaining, yet educational and beneficial; then look no further than Plink Balls. Download Plink Balls today and start dropping balls into containers with multipliers. You will be amazed by how much fun you will have and how much you will learn. You will be amazed by how much fun you will have and how much you will learn.
-So what are you waiting for? Download Plink Balls now and join the millions of players who are already hooked on this game. You won't regret it!
-FAQs
-Here are some of the most frequently asked questions about Plink Balls:
--
-
- Q: Is Plink Balls free to play? -
- A: Yes, Plink Balls is free to play. You can download the app for free from Google Play or App Store. You can also play without spending any real money, as you can earn coins, extra balls, and other boosts by watching ads, completing tasks, or opening chests. However, if you want to play with higher stakes, access premium features, or remove ads, you can also make in-app purchases with real money. -
- Q: Is Plink Balls fair and random? -
- A: Yes, Plink Balls is fair and random. The outcome of each drop is determined by a sophisticated algorithm that ensures that the balls fall and bounce on the pegs and containers in a realistic and unpredictable way. The algorithm also ensures that the multipliers and effects of the containers are balanced and fair. No one can manipulate or rig the game in any way. -
- Q: Is Plink Balls safe and secure? -
- A: Yes, Plink Balls is safe and secure. The app does not collect or store any personal or sensitive information from the users. The app also does not share or sell any data to third parties. The app also uses encryption and other security measures to protect the users' transactions and accounts. The app also complies with all the relevant laws and regulations regarding online gaming and gambling. -
- Q: Is Plink Balls suitable for children? -
- A: Plink Balls is suitable for children who are 12 years old or older. The app has a rating of 12+ on Google Play and App Store. The app does not contain any violence, nudity, profanity, or other inappropriate content. However, the app does involve simulated gambling, which may not be suitable for younger children or those who have gambling problems. Parents should supervise and monitor their children's use of the app and set limits and boundaries as needed. -
- Q: How can I contact the developers of Plink Balls? -
- A: You can contact the developers of Plink Balls by sending an email to plinkballs@gmail.com. You can also visit their website at www.plinkballs.com or follow them on Facebook, Twitter, or Instagram. You can also leave a review or a comment on Google Play or App Store. The developers welcome any feedback, suggestions, questions, or complaints from the users and will try to respond as soon as possible. -
-
-
\ No newline at end of file diff --git a/spaces/congsaPfin/Manga-OCR/logs/Scary Teacher 3D Old Version The Ultimate Game to Make Your Teacher Pay for Her Crimes.md b/spaces/congsaPfin/Manga-OCR/logs/Scary Teacher 3D Old Version The Ultimate Game to Make Your Teacher Pay for Her Crimes.md deleted file mode 100644 index 8e92f10dc0e07af4c8698c681e6bc33d37666b3e..0000000000000000000000000000000000000000 --- a/spaces/congsaPfin/Manga-OCR/logs/Scary Teacher 3D Old Version The Ultimate Game to Make Your Teacher Pay for Her Crimes.md +++ /dev/null @@ -1,141 +0,0 @@ -
-
Download Scary Teacher 3D Old Version
-Scary Teacher 3D is a popular horror game that lets you prank and scare your evil teacher in various ways. You can explore her house, find clues, and use different objects to make her life miserable. But what if you want to play the old version of Scary Teacher 3D, which has different levels, graphics, and features? In this article, we will show you how to download and install Scary Teacher 3D old version on your Android device or computer.
-What is Scary Teacher 3D?
-Scary Teacher 3D is a game developed by Z & K Games, which is known for creating other horror games such as Evil Nun and Granny. The game was released in 2018 and has since been updated with new content and improvements. The game has over 100 million downloads on Google Play Store and has a rating of 4.2 out of 5 stars.
-download scary teacher 3d old version
Download ⇒⇒⇒ https://urlca.com/2uOaoS
-
Features of Scary Teacher 3D
-Some of the features of Scary Teacher 3D are:
--
-
- You can play as a student who wants to take revenge on his or her scary teacher, who is very cruel and abusive. -
- You can explore the teacher's house, which has 15 rooms with different settings and secrets. -
- You can find clues, solve puzzles, and use various objects to prank and scare the teacher. -
- You can enjoy the realistic graphics, animations, and sound effects that create a spooky atmosphere. -
- You can unlock new chapters and scenarios as you progress in the game. -
Why download the old version of Scary Teacher 3D?
-Some reasons why you might want to download the old version of Scary Teacher 3D are:
--
-
- You prefer the old graphics, levels, and features that were available in the previous versions of the game. -
- You want to play the game offline or without ads, which might not be possible in the latest version. -
- You have an older device that is not compatible with the latest version of the game. -
- You want to try a different experience or challenge yourself with the old version of the game. -
How to download Scary Teacher 3D old version
-There are two ways you can download Scary Teacher 3D old version:
--
-
- Use a web tool to generate download links -
- Use an APK extractor app on your Android device -
Method 1: Use a web tool to generate download links
-This method involves using a web tool that can download APK files from Google Play Store URLs. The files are the same as you would get from the Play Store, and you can choose different versions to download. Here are the steps:
-Step 1: Copy the Google Play URL of the app
-First, you need to get the URL of Scary Teacher 3D from Google Play Store. You can do this by opening Google Play Store on your Android device or computer and searching for Scary Teacher 3D. Then, you need to copy the URL from the address bar or the share button. The URL should look something like this:
-https://play.google.com/store/apps/details?id=com.zakg.scaryteacher.hellgame
Step 2: Paste the URL in the web tool and generate the download link
-Next, you need to open a web tool that can generate download links for APK files from Google Play Store URLs. There are many such tools available online, but one of them is APKCombo. You can access it by visiting this link:
-download scary teacher 3d mod apk
-download scary teacher 3d for pc
-download scary teacher 3d game free
-download scary teacher 3d latest version
-download scary teacher 3d hack
-download scary teacher 3d unlimited money
-download scary teacher 3d chapter 5
-download scary teacher 3d offline
-download scary teacher 3d apk pure
-download scary teacher 3d android
-download scary teacher 3d app store
-download scary teacher 3d apk mirror
-download scary teacher 3d apk mod menu
-download scary teacher 3d all chapters unlocked
-download scary teacher 3d apk obb
-download scary teacher 3d apk revdl
-download scary teacher 3d apk data
-download scary teacher 3d apk android oyun club
-download scary teacher 3d apk uptodown
-download scary teacher 3d bluestacks
-download scary teacher 3d by z&k games
-download scary teacher 3d beta version
-download scary teacher 3d cheats
-download scary teacher 3d chapter 6
-download scary teacher 3d chapter 4
-download scary teacher 3d chapter 7
-download scary teacher 3d chapter 8
-download scary teacher 3d chapter 9
-download scary teacher 3d chapter 10
-download scary teacher 3d chapter wise
-download scary teacher 3d cracked apk
-download scary teacher 3d christmas update
-download scary teacher 3d diamond hack
-download scary teacher 3d direct link
-download scary teacher 3d easy install
-download scary teacher 3d emulator
-download scary teacher 3d full version free
-download scary teacher 3d for windows
https://apkcombo.com/en-us/apk-downloader/
-Once you are on the website, you need to paste the URL you copied in the previous step in the search box and click on Download APK. The web tool will then show you a list of available versions of Scary Teacher 3D, along with their sizes and dates. You can choose any version you want to download, but make sure it is an old version and not the latest one. For example, you can choose version 5.10.2, which was released on June 9, 2021.
-Step 3: Download the APK file to your device or computer
-After you select the version you want to download, the web tool will generate a download link for the APK file. You can click on the link to start downloading the file to your device or computer. The file name should be something like this:
-com.zakg.scaryteacher.hellgame_5.10.2.apk
The download time may vary depending on your internet speed and the size of the file. Once the download is complete, you can move on to the next method or skip to the installation section.
-Method 2: Use an APK extractor app on your Android device
-This method involves using an app that can extract APK files from installed apps on your Android device. This way, you can get the old version of Scary Teacher 3D if you already have it installed on your device or if you can find someone who has it. Here are the steps:
-Step 1: Download and install App APK Extractor & Analyzer from the Play Store
-First, you need to download and install an app that can extract APK files from installed apps on your Android device. There are many such apps available on the Play Store, but one of them is App APK Extractor & Analyzer. You can download it by visiting this link:
-https://play.google.com/store/apps/details?id=com.apkextractor.analyzer
-Once you have downloaded and installed the app, you need to open it and grant it the necessary permissions to access your device storage and installed apps.
-Step 2: Select the app you want to extract and tap Extract App
-Next, you need to select Scary Teacher 3D from the list of installed apps on your device. You can use the search bar or scroll down to find it. Once you have selected it, you need to tap on Extract App at the bottom of the screen. The app will then start extracting the APK file from Scary Teacher 3D and save it to your device storage.
-Step 3: Save the APK file to your preferred location
-After the extraction is complete, you will see a notification that says "APK extracted successfully". You can tap on it to open the folder where the APK file is saved. The folder name should be something like this:
-/storage/emulated/0/APK Extractor & Analyzer/Scary Teacher 3D_5.10.2.apk
You can move or copy the APK file to any location you want on your device or transfer it to your computer if you wish.
-How to install Scary Teacher 3D old version
-Now that you have downloaded the APK file of Scary Teacher 3D old version, you need to install it on your device. Here are the steps:
-Enable unknown sources on your device
-Before you can install an APK file that is not from Google Play Store, you need to enable unknown sources on your device. This will allow you to install apps from other sources than Google Play Store. To do this, follow these steps:
--
-
- Go to Settings > Security > Unknown sources (or Settings > Apps > Special app access > Install unknown apps, depending on your device model and Android version). -
- Find and tap the app that you used to download the APK file, such as APKCombo or App APK Extractor & Analyzer. -
- Toggle on the switch that says Allow from this source or Allow app installs. -
Locate and tap the APK file to install it
-After you have enabled unknown sources, you can install the APK file of Scary Teacher 3D old version. To do this, follow these steps:
--
-
- Go to the location where you saved the APK file, such as your device storage or your computer. -
- Find and tap the APK file to open it. You may see a warning message that says "This type of file can harm your device". Tap OK to proceed. -
- You may see a screen that shows the app's permissions and features. Tap Install to start the installation process. -
- Wait for the installation to finish. You may see a message that says "App installed". Tap Open to launch the app or Done to exit. -
Conclusion
-In this article, we have shown you how to download and install Scary Teacher 3D old version on your Android device or computer. You can use either a web tool or an APK extractor app to get the APK file of the old version of the game. Then, you can install it by enabling unknown sources and tapping the APK file. We hope you enjoy playing Scary Teacher 3D old version and have fun pranking and scaring your evil teacher.
-FAQs
-Here are some frequently asked questions about Scary Teacher 3D old version:
-Q: Is Scary Teacher 3D old version safe to download and install?
-A: Yes, as long as you download the APK file from a reliable source, such as APKCombo or App APK Extractor & Analyzer, and scan it for viruses before installing it. However, you should be careful when installing apps from unknown sources, as they may contain malware or unwanted ads.
-Q: What are the differences between Scary Teacher 3D old version and new version?
-A: The differences between Scary Teacher 3D old version and new version may vary depending on which version you choose to download. Some of the possible differences are:
--
-
- The old version may have fewer levels, chapters, and scenarios than the new version. -
- The old version may have different graphics, sound effects, and animations than the new version. -
- The old version may have different bugs, glitches, and performance issues than the new version. -
- The old version may not support some features or devices that the new version does. -
Q: How can I update Scary Teacher 3D old version to the latest version?
-A: If you want to update Scary Teacher 3D old version to the latest version, you can do so by visiting Google Play Store and downloading the latest version of the game. However, this will overwrite the old version of the game and you will lose any progress or data you have in it. Alternatively, you can keep both versions of the game by renaming the APK file of the old version before installing it. For example, you can rename it to Scary Teacher 3D_old.apk. This way, you can have two icons of Scary Teacher 3D on your device and play either one of them.
-Q: How can I uninstall Scary Teacher 3D old version from my device?
-A: If you want to uninstall Scary Teacher 3D old version from your device, you can do so by following these steps:
--
-
- Go to Settings > Apps > Scary Teacher 3D (or Settings > Apps & notifications > See all apps > Scary Teacher 3D, depending on your device model and Android version). -
- Tap Uninstall and confirm your choice. -
- You may also need to delete the APK file from your device storage or computer if you don't need it anymore. -
Q: Where can I find more information about Scary Teacher 3D?
-A: If you want to find more information about Scary Teacher 3D, such as tips, tricks, guides, reviews, videos, and more, you can visit these websites:
--
-
- https://www.zakg.com/scary-teacher-3d/: The official website of Z & K Games, the developer of Scary Teacher 3D. -
- < a href="">https://www.youtube.com/channel/UCw9ZP9zF0wJ6oEW5gkQy9Qw: The official YouTube channel of Z & K Games, where you can watch gameplay videos, trailers, and updates of Scary Teacher 3D. -
- https://www.facebook.com/ScaryTeacher3D/: The official Facebook page of Scary Teacher 3D, where you can follow the latest news, events, and community posts of the game. -
-
-
\ No newline at end of file diff --git a/spaces/congsaPfin/Manga-OCR/logs/Solo Leveling Hit Run APK - Swipe Slash and Save the Town from Evil Monsters.md b/spaces/congsaPfin/Manga-OCR/logs/Solo Leveling Hit Run APK - Swipe Slash and Save the Town from Evil Monsters.md deleted file mode 100644 index 5bc9488d5e01b6c4cec1ffbe3a8799d075e872c0..0000000000000000000000000000000000000000 --- a/spaces/congsaPfin/Manga-OCR/logs/Solo Leveling Hit Run APK - Swipe Slash and Save the Town from Evil Monsters.md +++ /dev/null @@ -1,145 +0,0 @@ - -
Solo Leveling Hit and Run APK: A Fun and Action-Packed Runner Game Based on the Popular Webtoon
-If you are a fan of the Korean webtoon series Solo Leveling, you might want to check out this new runner game based on it. Solo Leveling Hit and Run APK is a game that lets you experience the thrilling adventures of Sung Jinwoo, a weak hunter who gains the power to level up beyond any limits. In this game, you will have to run, slash, dodge, and fight your way through various enemies and obstacles, while leveling up your skills and abilities. You will also encounter some familiar characters and scenes from the webtoon, as well as some new ones that will surprise you. Whether you are a fan of Solo Leveling or not, this game will surely keep you entertained and challenged.
-solo leveling hit and run apk
DOWNLOAD ⚹ https://urlca.com/2uOdun
-
But what is Solo Leveling exactly? And how can you download and play this game on your Android device? In this article, we will answer these questions and more. We will give you a brief overview of the game and its features, as well as the webtoon and its plot. We will also show you how to download and install the game on your device, how to play it effectively, and why you should give it a try. By the end of this article, you will have all the information you need to enjoy this fun and action-packed runner game based on the popular webtoon.
-What is Solo Leveling Hit and Run APK?
-A brief introduction to the game and its features
-Solo Leveling Hit and Run APK is a runner game developed by Supercent, a Korean game studio. It is based on the webtoon series Solo Leveling by Chu-Gong, which has over 22 million readers worldwide. The game was released in March 2023 for Android devices.
-The game is a mission-based driving game that features out-of-the-car platform action, similar to The Simpsons Hit and Run or Grand Theft Auto. You can explore the interactive world of Seoul, where the story takes place, and interact with various characters from the webtoon. You can also drive different vehicles, such as cars, motorcycles, trucks, etc., that have different speed, handling, durability, etc.
-The game also has a leveling system that allows you to upgrade your skills and abilities as you progress through the game. You can increase your strength, speed, stamina, health, etc., by defeating enemies or completing missions. You can also unlock new weapons, such as blades, guns, axes, etc., that have different damage, range , and special effects. You can also customize your appearance, such as clothes, hair, accessories, etc., to suit your style.
-A brief introduction to the webtoon and its plot
-Solo Leveling is a webtoon series written by Chu-Gong and illustrated by Jang Sung-Rak and Gee So-Lyung. It is based on a novel of the same name by Chu-Gong. The webtoon was first published in 2018 on KakaoPage, a Korean webtoon platform, and later on Webtoon, an international webtoon platform. The webtoon has over 150 chapters and is still ongoing.
-solo leveling hit and run game download
-solo leveling hit and run mod apk
-solo leveling hit and run android
-solo leveling hit and run ios
-solo leveling hit and run hack
-solo leveling hit and run cheats
-solo leveling hit and run review
-solo leveling hit and run gameplay
-solo leveling hit and run tips
-solo leveling hit and run guide
-solo leveling hit and run update
-solo leveling hit and run latest version
-solo leveling hit and run offline
-solo leveling hit and run online
-solo leveling hit and run free
-solo leveling hit and run premium
-solo leveling hit and run unlimited gems
-solo leveling hit and run best weapons
-solo leveling hit and run boss fight
-solo leveling hit and run characters
-solo leveling hit and run levels
-solo leveling hit and run skills
-solo leveling hit and run tricks
-solo leveling hit and run strategy
-solo leveling hit and run wiki
-solo leveling hit and run reddit
-solo leveling hit and run discord
-solo leveling hit and run facebook
-solo leveling hit and run twitter
-solo leveling hit and run instagram
-solo leveling hit and run youtube
-solo leveling hit and run google play
-solo leveling hit and run app store
-solo leveling hit and run apk pure
-solo leveling hit and run apk combo[^1^]
-solo leveling hit and run apkpure.com[^1^]
-solo leveling hit and run apkmonk.com[^1^]
-solo leveling hit and run apkdone.com[^1^]
-solo leveling hit and run apkmody.io[^1^]
-solo leveling hit and run apkaward.com[^1^]
-solo leveling hit and run apk4all.com[^1^]
-solo leveling hit and run apkfab.com[^1^]
-solo leveling hit and run apkmirror.com[^1^]
-solo leveling hit and run apksum.com[^1^]
-solo leveling hit and run apknite.com[^1^]
The webtoon is set in a world where portals to other dimensions, called gates, have opened, unleashing monsters and creatures that threaten humanity. To fight them, some people have awakened as hunters, who have special abilities and powers. However, not all hunters are equal, and they are ranked from E to S, with S being the strongest.
-The protagonist of the webtoon is Sung Jinwoo, a weak E-rank hunter who barely survives his missions. One day, he gets involved in a double dungeon, a rare and dangerous type of gate that has never been cleared before. There, he finds a mysterious system that allows him to level up his skills and abilities by completing quests and killing monsters. He becomes the only player of the system, and the only one who can see it. He decides to use it to become stronger and rise from the lowest rank to the highest rank of hunters. Along the way, he faces many challenges, enemies, allies, secrets, and mysteries that will change his life and the world.
-How to Download and Install Solo Leveling Hit and Run APK?
-The steps to download and install the game on Android devices
-If you want to play Solo Leveling Hit and Run APK on your Android device, you will need to follow these steps:
--
-
- Go to the official website of the game at https://solo-leveling-hit-and-run.com/ or search for it on Google. -
- Click on the download button and wait for the APK file to be downloaded on your device. -
- Once the download is complete, locate the APK file on your device's file manager or downloads folder. -
- Tap on the APK file and allow it to install on your device. You may need to enable unknown sources or allow from this source in your device's settings. -
- After the installation is done, you can launch the game from your app drawer or home screen. -
The requirements and permissions needed for the game
-Before you download and install Solo Leveling Hit and Run APK on your device, you should make sure that your device meets the following requirements:
--
-
- Your device should have Android 4.4 or higher as its operating system. -
- Your device should have at least 2 GB of RAM and 500 MB of free storage space. -
- Your device should have a stable internet connection to play the game online. -
You should also be aware that the game will ask for some permissions on your device, such as:
--
-
- Access to your device's storage to save game data and cache. -
- Access to your device's microphone to record audio for voice chat. -
- Access to your device's camera to scan QR codes for rewards. -
You should grant these permissions if you want to enjoy the full features of the game. However, you can also deny them if you are concerned about your privacy or security.
-How to Play Solo Leveling Hit and Run APK?
-The basic gameplay mechanics and controls
-Solo Leveling Hit and Run APK is a runner game that combines driving and platform action. You can control your character using the virtual joystick on the left side of the screen, and use the buttons on the right side of the screen to perform actions such as jumping, attacking, using items, etc. You can also swipe left or right on the screen to change lanes while driving or running.
-The game has two main modes: story mode and challenge mode. In story mode, you can follow the plot of the webtoon and complete missions that involve driving or running through various locations, fighting enemies or bosses, collecting items or gems, etc. In challenge mode, you can compete with other players online or offline in different types of races or battles.
-The different modes, levels, enemies, and obstacles in the game
-The game has several modes that offer different gameplay experiences. Here are some of them:
- - Race mode: In this mode, you can race against other players or the AI in different tracks, such as city, highway, forest, etc. You can use your skills and items to boost your speed, attack your opponents, or avoid obstacles. You can also collect gems and coins along the way to upgrade your vehicle or buy new ones. The goal is to reach the finish line first or within the time limit. - Battle mode: In this mode, you can fight against other players or the AI in different arenas, such as dungeon, castle, stadium, etc. You can use your weapons and items to deal damage, defend yourself, or heal yourself. You can also collect gems and coins along the way to upgrade your weapons or buy new ones. The goal is to reduce your opponent's health to zero or have more health than them when the time runs out. - Survival mode: In this mode, you can run for as long as you can while avoiding enemies and obstacles that come from all directions. You can use your skills and items to escape, fight back, or recover. You can also collect gems and coins along the way to upgrade your skills or buy new ones. The goal is to survive for as long as possible or reach a certain distance. The game has various levels that correspond to the chapters of the webtoon. Each level has a different theme, setting, difficulty, and objective. Some levels may require you to drive or run through a certain route, while others may require you to defeat a certain number of enemies or a boss. Some levels may also have special events or challenges that will test your skills and strategy. The game has various enemies and obstacles that will try to stop you from completing your missions. Some enemies are common monsters that appear in the webtoon, such as goblins, wolves, zombies, etc. Some enemies are special bosses that have unique abilities and patterns, such as Cerberus, the Demon King, the Ant King, etc. Some obstacles are environmental hazards that can damage you or slow you down, such as traffic, walls, spikes, traps, etc.The tips and tricks to level up faster and defeat the boss
-If you want to level up faster and defeat the boss in Solo Leveling Hit and Run APK, you should follow these tips and tricks:
--
-
- Complete the daily quests and achievements that will reward you with gems, coins, items, etc. -
- Watch ads or videos that will give you extra gems, coins, items, etc. -
- Join a guild or a clan that will give you access to more missions, rewards, chat, etc. -
- Participate in events or festivals that will offer you special missions, rewards, items, etc. -
- Use the best vehicle or weapon that suits your playstyle and preference. -
- Upgrade your vehicle or weapon regularly to increase its performance and durability. -
- Customize your appearance to boost your confidence and style. -
- Use your skills and items wisely and strategically. -
- Learn the patterns and weaknesses of your enemies and bosses. -
- Avoid unnecessary damage or collisions. -
- Collect gems and coins as much as possible. -
- Have fun and enjoy the game. -
Why You Should Play Solo Leveling Hit and Run APK?
-The benefits of playing the game, such as fun, entertainment, challenge, etc.
-Playing Solo Leveling Hit and Run APK can bring you many benefits, such as:
--
-
- Fun: The game is fun to play, as it offers a variety of gameplay modes, levels, enemies, and obstacles that will keep you entertained and engaged. You can also enjoy the humor, drama, and action of the webtoon in the game. -
- Entertainment: The game is entertaining to watch, as it features high-quality graphics, sound, and animation that will immerse you in the world of Solo Leveling. You can also admire the beautiful and detailed design of the characters, vehicles, weapons, and environments in the game. -
- Challenge: The game is challenging to master, as it requires skill, strategy, and reflex to complete the missions and defeat the enemies and bosses. You can also compete with other players or the AI in different modes and rankings to test your abilities and improve your performance. -
The advantages of playing the game, such as graphics, sound, performance, etc.
-Playing Solo Leveling Hit and Run APK can also give you many advantages, such as:
--
-
- Graphics: The game has stunning graphics that are faithful to the webtoon's style and quality. The game uses 3D models and textures that are realistic and detailed. The game also has dynamic lighting and shadows that create a realistic and immersive atmosphere. -
- Sound: The game has excellent sound that matches the webtoon's tone and mood. The game uses original soundtracks and sound effects that are catchy and immersive. The game also has voice acting that is expressive and authentic. -
- Performance: The game has smooth performance that ensures a satisfying and enjoyable gameplay experience. The game runs at a stable frame rate and resolution that prevent lag or glitches. The game also has a user-friendly interface and controls that are easy to use and customize. -
The comparison of the game with other similar games, such as The Simpsons Hit and Run, Grand Theft Auto, etc.
-Playing Solo Leveling Hit and Run APK can also make you appreciate how it differs from other similar games, such as:
--
-
- The Simpsons Hit and Run: This is a 2003 game based on the animated sitcom The Simpsons. It is also a mission-based driving game that features out-of-the-car platform action. However, it has a more comedic and satirical tone than Solo Leveling Hit and Run APK. It also has a more cartoonish and colorful graphics style than Solo Leveling Hit and Run APK. -
- Grand Theft Auto: This is a series of games that started in 1997 and is still ongoing. It is also a mission-based driving game that features out-of-the-car platform action. However, it has a more realistic and violent tone than Solo Leveling Hit and Run APK. It also has a more open-world and sandbox gameplay style than Solo Leveling Hit and Run APK. -
Conclusion
-A summary of the main points of the article
-In conclusion, Solo Leveling Hit and Run APK is a fun and action-packed runner game based on the popular webtoon series Solo Leveling by Chu-Gong. It is a game that lets you experience the thrilling adventures of Sung Jinwoo, a weak hunter who gains the power to level up beyond any limits. In this game, you will have to run, slash, dodge, and fight your way through various enemies and obstacles, while leveling up your skills and abilities. You will also encounter some familiar characters and scenes from the webtoon, as well as some new ones that will surprise you. Whether you are a fan of Solo Leveling or not, this game will surely keep you entertained and challenged.
-We have also shown you how to download and install the game on your Android device, how to play it effectively, and why you should give it a try. We have also compared the game with other similar games, such as The Simpsons Hit and Run and Grand Theft Auto, and highlighted its benefits and advantages. We hope that this article has given you all the information you need to enjoy this fun and action-packed runner game based on the popular webtoon.
-A call to action for the readers to download and play the game
-So, what are you waiting for? Download Solo Leveling Hit and Run APK now and join Sung Jinwoo in his epic journey to become the strongest hunter in the world. Experience the thrill and excitement of running, slashing, dodging, and fighting in this amazing game that will make you feel like you are part of the webtoon. Don't miss this chance to play one of the best runner games based on one of the best webtoon series ever. Download Solo Leveling Hit and Run APK today and have fun!
-FAQs
-Is Solo Leveling Hit and Run APK free to play?
-Yes, Solo Leveling Hit and Run APK is free to play. However, it may contain some in-app purchases or ads that can enhance your gameplay experience or support the developer.
-Is Solo Leveling Hit and Run APK safe to download and install?
-Yes, Solo Leveling Hit and Run APK is safe to download and install. It does not contain any viruses, malware, or spyware that can harm your device or data. However, you should always download it from a trusted source or website, such as the official website of the game or Google Play Store.
-Is Solo Leveling Hit and Run APK compatible with all Android devices?
-No, Solo Leveling Hit and Run APK may not be compatible with all Android devices. It requires Android 4.4 or higher as its operating system, as well as 2 GB of RAM and 500 MB of free storage space. It may also not work well on some devices due to different specifications or models.
-How can I get more gems in Solo Leveling Hit and Run APK?
-You can get more gems in Solo Leveling Hit and Run APK by completing missions, defeating enemies, collecting items, watching ads or videos, participating in events or festivals, joining a guild or a clan, etc. You can also buy gems with real money through in-app purchases.
-How can I contact the developer of Solo Leveling Hit and Run APK?
-You can contact the developer of Solo Leveling Hit and Run APK by sending an email to support@supercent.com or visiting their website at https://supercent.com/. You can also follow them on their social media accounts, such as Facebook, Twitter, Instagram, etc., for updates, news, feedback, etc.
197e85843d-
-
\ No newline at end of file diff --git a/spaces/contluForse/HuggingGPT/assets/Autodesk AutoCAD 2020 Product Keys Crack Download ((EXCLUSIVE)).md b/spaces/contluForse/HuggingGPT/assets/Autodesk AutoCAD 2020 Product Keys Crack Download ((EXCLUSIVE)).md deleted file mode 100644 index e431c136537c9ffe49cdfb4cdbbf5e6647f071b8..0000000000000000000000000000000000000000 --- a/spaces/contluForse/HuggingGPT/assets/Autodesk AutoCAD 2020 Product Keys Crack Download ((EXCLUSIVE)).md +++ /dev/null @@ -1,8 +0,0 @@ -
Autodesk AutoCAD 2020 Product Keys Crack Download
Download >>>>> https://ssurll.com/2uzyiM
-
-April 14, 2021 - ì˜¤í† ì˜¤í† ë°í¬ ì—ì„œ ì œê³µ 하는 3D MAX 부터 AutoCAD, Maya 등 2016 ë²„ì „ 부터 2020 ë²„ì „ 까지 ì˜ ì„¤ì¹˜ í•„ìš” í•œ í•œ ì œí’ˆí‚¤ ì œí’ˆí‚¤ ì œí’ˆí‚¤ ì œí’ˆí‚¤ AUTODESK Product Keys ê³µìœ í•©ë‹ˆë‹¤. #Autodesk # autocad #maya #autodesk_com #autocad_com #autocad_com #autodesk_com #autocad_com #autocad_com #autocad_com #autocad_com #autocad_com #autocad_com #autocad_com In this article I would like to talk about the basics that everyone should know -programmer working with 3D space, be it a 3D artist, 3D designer or planner. -Everything I say here is from my own experience. 8a78ff9644
-
-
- diff --git a/spaces/cpnepo/Harry-Potter-Q-A/app.py b/spaces/cpnepo/Harry-Potter-Q-A/app.py deleted file mode 100644 index 48cd5b95cad1867cd9a4ac857f2e9e456a577ef5..0000000000000000000000000000000000000000 --- a/spaces/cpnepo/Harry-Potter-Q-A/app.py +++ /dev/null @@ -1,74 +0,0 @@ -import streamlit as st -from sentence_transformers import SentenceTransformer, util -from transformers import (AutoModelForQuestionAnswering, - AutoTokenizer, pipeline) - -import pandas as pd -import regex as re - -# Select model for question answering -model_name = "deepset/roberta-base-squad2" - -# Load model & tokenizer -model = AutoModelForQuestionAnswering.from_pretrained(model_name) -tokenizer = AutoTokenizer.from_pretrained(model_name) - -# Create pipeline -pipe = pipeline('question-answering', model=model_name, tokenizer=model_name) - -# Load Harry Potter book corpus from link -book1_raw_0 = open("book_1.txt", mode="r", encoding="utf-8").read() - -# Text pre-processing -# Remove page statements -book1_raw_1 = re.sub(r'Page \| [0-9]+ Harry Potter [a-zA-Z \-]+J.K. Rowling', '', book1_raw_0) - -# Remove newlines -book1_raw_1 = re.sub(r'\n', '', book1_raw_1) - -# Remove periods; this will relevant in the regrouping later -book1_raw_1 = re.sub(r'Mr. ', 'Mr ', book1_raw_1) -book1_raw_1 = re.sub(r'Ms. ', 'Ms ', book1_raw_1) -book1_raw_1 = re.sub(r'Mrs. ', 'Mrs ', book1_raw_1) - -# Group into 6 sentences-long parts -paragraphs = re.findall("[^.?!]+[.?!][^.?!]+[.?!][^.?!]+[.?!][^.?!]+[.?!][^.?!]+[.?!][^.?!]+[.?!]", book1_raw_1) - -st.title('Harry Potter and the Extractive Question Answering Model') - -# Type in HP-related query here -query = st.text_area("Hello my dears! What is your question? Be patient please, I am not a Ravenclaw!") - -if st.button('Accio Responsa!'): - # Perform sentence embedding on query and sentence groups - model_embed_name = 'sentence-transformers/msmarco-distilbert-dot-v5' - - model_embed = SentenceTransformer(model_embed_name) - doc_emb = model_embed.encode(paragraphs) - query_emb = model_embed.encode(query) - - #Compute dot score between query and all document embeddings - scores = util.dot_score(query_emb, doc_emb)[0].cpu().tolist() - - #Combine docs & scores - doc_score_pairs = list(zip(paragraphs, scores)) - - #Sort by decreasing score and get only 3 most similar groups - doc_score_pairs = sorted(doc_score_pairs, key=lambda x: x[1], - reverse=True)[:1] - - # Join these similar groups to form the context - context = "".join(x[0] for x in doc_score_pairs) - - # Perform the querying - QA_input = {'question': query, 'context': context} - res = pipe(QA_input) - - confidence = res.get('score') - if confidence > 0.5: - st.write(res.get('answer')) - else: - out = "Sorry dear, I'm not sure" - st.write(out) - #out = res.get('answer') - \ No newline at end of file diff --git a/spaces/cvlab/zero123-live/taming-transformers/taming/data/image_transforms.py b/spaces/cvlab/zero123-live/taming-transformers/taming/data/image_transforms.py deleted file mode 100644 index 657ac332174e0ac72f68315271ffbd757b771a0f..0000000000000000000000000000000000000000 --- a/spaces/cvlab/zero123-live/taming-transformers/taming/data/image_transforms.py +++ /dev/null @@ -1,132 +0,0 @@ -import random -import warnings -from typing import Union - -import torch -from torch import Tensor -from torchvision.transforms import RandomCrop, functional as F, CenterCrop, RandomHorizontalFlip, PILToTensor -from torchvision.transforms.functional import _get_image_size as get_image_size - -from taming.data.helper_types import BoundingBox, Image - -pil_to_tensor = PILToTensor() - - -def convert_pil_to_tensor(image: Image) -> Tensor: - with warnings.catch_warnings(): - # to filter PyTorch UserWarning as described here: https://github.com/pytorch/vision/issues/2194 - warnings.simplefilter("ignore") - return pil_to_tensor(image) - - -class RandomCrop1dReturnCoordinates(RandomCrop): - def forward(self, img: Image) -> (BoundingBox, Image): - """ - Additionally to cropping, returns the relative coordinates of the crop bounding box. - Args: - img (PIL Image or Tensor): Image to be cropped. - - Returns: - Bounding box: x0, y0, w, h - PIL Image or Tensor: Cropped image. - - Based on: - torchvision.transforms.RandomCrop, torchvision 1.7.0 - """ - if self.padding is not None: - img = F.pad(img, self.padding, self.fill, self.padding_mode) - - width, height = get_image_size(img) - # pad the width if needed - if self.pad_if_needed and width < self.size[1]: - padding = [self.size[1] - width, 0] - img = F.pad(img, padding, self.fill, self.padding_mode) - # pad the height if needed - if self.pad_if_needed and height < self.size[0]: - padding = [0, self.size[0] - height] - img = F.pad(img, padding, self.fill, self.padding_mode) - - i, j, h, w = self.get_params(img, self.size) - bbox = (j / width, i / height, w / width, h / height) # x0, y0, w, h - return bbox, F.crop(img, i, j, h, w) - - -class Random2dCropReturnCoordinates(torch.nn.Module): - """ - Additionally to cropping, returns the relative coordinates of the crop bounding box. - Args: - img (PIL Image or Tensor): Image to be cropped. - - Returns: - Bounding box: x0, y0, w, h - PIL Image or Tensor: Cropped image. - - Based on: - torchvision.transforms.RandomCrop, torchvision 1.7.0 - """ - - def __init__(self, min_size: int): - super().__init__() - self.min_size = min_size - - def forward(self, img: Image) -> (BoundingBox, Image): - width, height = get_image_size(img) - max_size = min(width, height) - if max_size <= self.min_size: - size = max_size - else: - size = random.randint(self.min_size, max_size) - top = random.randint(0, height - size) - left = random.randint(0, width - size) - bbox = left / width, top / height, size / width, size / height - return bbox, F.crop(img, top, left, size, size) - - -class CenterCropReturnCoordinates(CenterCrop): - @staticmethod - def get_bbox_of_center_crop(width: int, height: int) -> BoundingBox: - if width > height: - w = height / width - h = 1.0 - x0 = 0.5 - w / 2 - y0 = 0. - else: - w = 1.0 - h = width / height - x0 = 0. - y0 = 0.5 - h / 2 - return x0, y0, w, h - - def forward(self, img: Union[Image, Tensor]) -> (BoundingBox, Union[Image, Tensor]): - """ - Additionally to cropping, returns the relative coordinates of the crop bounding box. - Args: - img (PIL Image or Tensor): Image to be cropped. - - Returns: - Bounding box: x0, y0, w, h - PIL Image or Tensor: Cropped image. - Based on: - torchvision.transforms.RandomHorizontalFlip (version 1.7.0) - """ - width, height = get_image_size(img) - return self.get_bbox_of_center_crop(width, height), F.center_crop(img, self.size) - - -class RandomHorizontalFlipReturn(RandomHorizontalFlip): - def forward(self, img: Image) -> (bool, Image): - """ - Additionally to flipping, returns a boolean whether it was flipped or not. - Args: - img (PIL Image or Tensor): Image to be flipped. - - Returns: - flipped: whether the image was flipped or not - PIL Image or Tensor: Randomly flipped image. - - Based on: - torchvision.transforms.RandomHorizontalFlip (version 1.7.0) - """ - if torch.rand(1) < self.p: - return True, F.hflip(img) - return False, img diff --git a/spaces/cybercorejapan/human-detection-docker/projects/human_detection/export_onnx_trt/export_onnx_mmyolov8.sh b/spaces/cybercorejapan/human-detection-docker/projects/human_detection/export_onnx_trt/export_onnx_mmyolov8.sh deleted file mode 100644 index 8cf45e6ad1430f3d7a11fe6d01104b224ecaeeee..0000000000000000000000000000000000000000 --- a/spaces/cybercorejapan/human-detection-docker/projects/human_detection/export_onnx_trt/export_onnx_mmyolov8.sh +++ /dev/null @@ -1,28 +0,0 @@ -ENV_DIR="/opt/conda/lib/python3.8/site-packages" -CC_DEMO_DIR="/root/workspace/cc-demo" - - -DEPLOY_CFG_PATH="${ENV_DIR}/mmyolo/.mim/configs/deploy/detection_onnxruntime_dynamic.py" -MODEL_CFG_PATH="${CC_DEMO_DIR}/projects/human_detection/deploy/mmyolov8_human_cfg.py" -MODEL_CHECKPOINT_PATH="/data/human_detection/weights/mmyolov8_s_human.pth" -WORK_DIR="/data/human_detection/deploy_onnx" - -INPUT_IMG="${CC_DEMO_DIR}/tests/test_data/human_det/1.jpg" -TEST_IMG="${CC_DEMO_DIR}/tests/test_data/human_det/2.jpg" -DEVICE="cpu" - -export ONNXRUNTIME_DIR=/root/workspace/onnxruntime-linux-x64-1.15.1/ -export LD_LIBRARY_PATH=$ONNXRUNTIME_DIR/lib:$LD_LIBRARY_PATH - -cd /root/workspace/mmdeploy && python tools/deploy.py \ - ${DEPLOY_CFG_PATH} \ - ${MODEL_CFG_PATH} \ - ${MODEL_CHECKPOINT_PATH} \ - ${INPUT_IMG} \ - --test-img ${TEST_IMG} \ - --work-dir ${WORK_DIR} \ - --device ${DEVICE} \ - --log-level INFO \ - --show \ - --dump-info - # --calib-dataset-cfg ${CALIB_DATA_CFG} \ \ No newline at end of file diff --git a/spaces/dachenchen/real/README.md b/spaces/dachenchen/real/README.md deleted file mode 100644 index 391cdc1b0b1ad6561d0bfb0d13eba141b1be37cb..0000000000000000000000000000000000000000 --- a/spaces/dachenchen/real/README.md +++ /dev/null @@ -1,14 +0,0 @@ ---- -title: ChuanhuChatGPT -emoji: 🐠 -colorFrom: blue -colorTo: red -sdk: gradio -sdk_version: 3.19.1 -app_file: app.py -pinned: false -license: mit -duplicated_from: hslec/xinsaisi ---- - -Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference diff --git a/spaces/dakaiye/dky_xuexi/request_llm/bridge_jittorllms_rwkv.py b/spaces/dakaiye/dky_xuexi/request_llm/bridge_jittorllms_rwkv.py deleted file mode 100644 index 1252eead89a44994241ec4407a1e693cbb170bf6..0000000000000000000000000000000000000000 --- a/spaces/dakaiye/dky_xuexi/request_llm/bridge_jittorllms_rwkv.py +++ /dev/null @@ -1,178 +0,0 @@ - -from transformers import AutoModel, AutoTokenizer -import time -import threading -import importlib -from toolbox import update_ui, get_conf -from multiprocessing import Process, Pipe - -load_message = "jittorllms尚未加载，加载需要一段时间。注意，请避免混用多种jittor模型，否则可能导致显存溢出而造成卡顿，取决于`config.py`的配置，jittorllms消耗大量的内存（CPU）或显存（GPU），也许会导致低配计算机卡死 ……" - -################################################################################# -class GetGLMHandle(Process): - def __init__(self): - super().__init__(daemon=True) - self.parent, self.child = Pipe() - self.jittorllms_model = None - self.info = "" - self.local_history = [] - self.success = True - self.check_dependency() - self.start() - self.threadLock = threading.Lock() - - def check_dependency(self): - try: - import pandas - self.info = "依赖检测通过" - self.success = True - except: - from toolbox import trimmed_format_exc - self.info = r"缺少jittorllms的依赖，如果要使用jittorllms，除了基础的pip依赖以外，您还需要运行`pip install -r request_llm/requirements_jittorllms.txt -i https://pypi.jittor.org/simple -I`"+\ - r"和`git clone https://gitlink.org.cn/jittor/JittorLLMs.git --depth 1 request_llm/jittorllms`两个指令来安装jittorllms的依赖（在项目根目录运行这两个指令）。" +\ - r"警告：安装jittorllms依赖后将完全破坏现有的pytorch环境，建议使用docker环境！" + trimmed_format_exc() - self.success = False - - def ready(self): - return self.jittorllms_model is not None - - def run(self): - # 子进程执行 - # 第一次运行，加载参数 - def validate_path(): - import os, sys - dir_name = os.path.dirname(__file__) - env = os.environ.get("PATH", "") - os.environ["PATH"] = env.replace('/cuda/bin', '/x/bin') - root_dir_assume = os.path.abspath(os.path.dirname(__file__) + '/..') - os.chdir(root_dir_assume + '/request_llm/jittorllms') - sys.path.append(root_dir_assume + '/request_llm/jittorllms') - validate_path() # validate path so you can run from base directory - - def load_model(): - import types - try: - if self.jittorllms_model is None: - device, = get_conf('LOCAL_MODEL_DEVICE') - from .jittorllms.models import get_model - # availabel_models = ["chatglm", "pangualpha", "llama", "chatrwkv"] - args_dict = {'model': 'chatrwkv'} - print('self.jittorllms_model = get_model(types.SimpleNamespace(**args_dict))') - self.jittorllms_model = get_model(types.SimpleNamespace(**args_dict)) - print('done get model') - except: - self.child.send('[Local Message] Call jittorllms fail 不能正常加载jittorllms的参数。') - raise RuntimeError("不能正常加载jittorllms的参数！") - print('load_model') - load_model() - - # 进入任务等待状态 - print('进入任务等待状态') - while True: - # 进入任务等待状态 - kwargs = self.child.recv() - query = kwargs['query'] - history = kwargs['history'] - # 是否重置 - if len(self.local_history) > 0 and len(history)==0: - print('触发重置') - self.jittorllms_model.reset() - self.local_history.append(query) - - print('收到消息，开始请求') - try: - for response in self.jittorllms_model.stream_chat(query, history): - print(response) - self.child.send(response) - except: - from toolbox import trimmed_format_exc - print(trimmed_format_exc()) - self.child.send('[Local Message] Call jittorllms fail.') - # 请求处理结束，开始下一个循环 - self.child.send('[Finish]') - - def stream_chat(self, **kwargs): - # 主进程执行 - self.threadLock.acquire() - self.parent.send(kwargs) - while True: - res = self.parent.recv() - if res != '[Finish]': - yield res - else: - break - self.threadLock.release() - -global rwkv_glm_handle -rwkv_glm_handle = None -################################################################################# -def predict_no_ui_long_connection(inputs, llm_kwargs, history=[], sys_prompt="", observe_window=[], console_slience=False): - """ - 多线程方法 - 函数的说明请见 request_llm/bridge_all.py - """ - global rwkv_glm_handle - if rwkv_glm_handle is None: - rwkv_glm_handle = GetGLMHandle() - if len(observe_window) >= 1: observe_window[0] = load_message + "\n\n" + rwkv_glm_handle.info - if not rwkv_glm_handle.success: - error = rwkv_glm_handle.info - rwkv_glm_handle = None - raise RuntimeError(error) - - # jittorllms 没有 sys_prompt 接口，因此把prompt加入 history - history_feedin = [] - for i in range(len(history)//2): - history_feedin.append([history[2*i], history[2*i+1]] ) - - watch_dog_patience = 5 # 看门狗 (watchdog) 的耐心, 设置5秒即可 - response = "" - for response in rwkv_glm_handle.stream_chat(query=inputs, history=history_feedin, system_prompt=sys_prompt, max_length=llm_kwargs['max_length'], top_p=llm_kwargs['top_p'], temperature=llm_kwargs['temperature']): - print(response) - if len(observe_window) >= 1: observe_window[0] = response - if len(observe_window) >= 2: - if (time.time()-observe_window[1]) > watch_dog_patience: - raise RuntimeError("程序终止。") - return response - - - -def predict(inputs, llm_kwargs, plugin_kwargs, chatbot, history=[], system_prompt='', stream = True, additional_fn=None): - """ - 单线程方法 - 函数的说明请见 request_llm/bridge_all.py - """ - chatbot.append((inputs, "")) - - global rwkv_glm_handle - if rwkv_glm_handle is None: - rwkv_glm_handle = GetGLMHandle() - chatbot[-1] = (inputs, load_message + "\n\n" + rwkv_glm_handle.info) - yield from update_ui(chatbot=chatbot, history=[]) - if not rwkv_glm_handle.success: - rwkv_glm_handle = None - return - - if additional_fn is not None: - import core_functional - importlib.reload(core_functional) # 热更新prompt - core_functional = core_functional.get_core_functions() - if "PreProcess" in core_functional[additional_fn]: inputs = core_functional[additional_fn]["PreProcess"](inputs) # 获取预处理函数（如果有的话） - inputs = core_functional[additional_fn]["Prefix"] + inputs + core_functional[additional_fn]["Suffix"] - - # 处理历史信息 - history_feedin = [] - for i in range(len(history)//2): - history_feedin.append([history[2*i], history[2*i+1]] ) - - # 开始接收jittorllms的回复 - response = "[Local Message]: 等待jittorllms响应中 ..." - for response in rwkv_glm_handle.stream_chat(query=inputs, history=history_feedin, system_prompt=system_prompt, max_length=llm_kwargs['max_length'], top_p=llm_kwargs['top_p'], temperature=llm_kwargs['temperature']): - chatbot[-1] = (inputs, response) - yield from update_ui(chatbot=chatbot, history=history) - - # 总结输出 - if response == "[Local Message]: 等待jittorllms响应中 ...": - response = "[Local Message]: jittorllms响应异常 ..." - history.extend([inputs, response]) - yield from update_ui(chatbot=chatbot, history=history) diff --git a/spaces/dawood17/SayBot_Enchancer/CodeFormer/basicsr/archs/rrdbnet_arch.py b/spaces/dawood17/SayBot_Enchancer/CodeFormer/basicsr/archs/rrdbnet_arch.py deleted file mode 100644 index 49a2d6c204557cba53ada7550deb587541855cfb..0000000000000000000000000000000000000000 --- a/spaces/dawood17/SayBot_Enchancer/CodeFormer/basicsr/archs/rrdbnet_arch.py +++ /dev/null @@ -1,119 +0,0 @@ -import torch -from torch import nn as nn -from torch.nn import functional as F - -from basicsr.utils.registry import ARCH_REGISTRY -from .arch_util import default_init_weights, make_layer, pixel_unshuffle - - -class ResidualDenseBlock(nn.Module): - """Residual Dense Block. - - Used in RRDB block in ESRGAN. - - Args: - num_feat (int): Channel number of intermediate features. - num_grow_ch (int): Channels for each growth. - """ - - def __init__(self, num_feat=64, num_grow_ch=32): - super(ResidualDenseBlock, self).__init__() - self.conv1 = nn.Conv2d(num_feat, num_grow_ch, 3, 1, 1) - self.conv2 = nn.Conv2d(num_feat + num_grow_ch, num_grow_ch, 3, 1, 1) - self.conv3 = nn.Conv2d(num_feat + 2 * num_grow_ch, num_grow_ch, 3, 1, 1) - self.conv4 = nn.Conv2d(num_feat + 3 * num_grow_ch, num_grow_ch, 3, 1, 1) - self.conv5 = nn.Conv2d(num_feat + 4 * num_grow_ch, num_feat, 3, 1, 1) - - self.lrelu = nn.LeakyReLU(negative_slope=0.2, inplace=True) - - # initialization - default_init_weights([self.conv1, self.conv2, self.conv3, self.conv4, self.conv5], 0.1) - - def forward(self, x): - x1 = self.lrelu(self.conv1(x)) - x2 = self.lrelu(self.conv2(torch.cat((x, x1), 1))) - x3 = self.lrelu(self.conv3(torch.cat((x, x1, x2), 1))) - x4 = self.lrelu(self.conv4(torch.cat((x, x1, x2, x3), 1))) - x5 = self.conv5(torch.cat((x, x1, x2, x3, x4), 1)) - # Emperically, we use 0.2 to scale the residual for better performance - return x5 * 0.2 + x - - -class RRDB(nn.Module): - """Residual in Residual Dense Block. - - Used in RRDB-Net in ESRGAN. - - Args: - num_feat (int): Channel number of intermediate features. - num_grow_ch (int): Channels for each growth. - """ - - def __init__(self, num_feat, num_grow_ch=32): - super(RRDB, self).__init__() - self.rdb1 = ResidualDenseBlock(num_feat, num_grow_ch) - self.rdb2 = ResidualDenseBlock(num_feat, num_grow_ch) - self.rdb3 = ResidualDenseBlock(num_feat, num_grow_ch) - - def forward(self, x): - out = self.rdb1(x) - out = self.rdb2(out) - out = self.rdb3(out) - # Emperically, we use 0.2 to scale the residual for better performance - return out * 0.2 + x - - -@ARCH_REGISTRY.register() -class RRDBNet(nn.Module): - """Networks consisting of Residual in Residual Dense Block, which is used - in ESRGAN. - - ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks. - - We extend ESRGAN for scale x2 and scale x1. - Note: This is one option for scale 1, scale 2 in RRDBNet. - We first employ the pixel-unshuffle (an inverse operation of pixelshuffle to reduce the spatial size - and enlarge the channel size before feeding inputs into the main ESRGAN architecture. - - Args: - num_in_ch (int): Channel number of inputs. - num_out_ch (int): Channel number of outputs. - num_feat (int): Channel number of intermediate features. - Default: 64 - num_block (int): Block number in the trunk network. Defaults: 23 - num_grow_ch (int): Channels for each growth. Default: 32. - """ - - def __init__(self, num_in_ch, num_out_ch, scale=4, num_feat=64, num_block=23, num_grow_ch=32): - super(RRDBNet, self).__init__() - self.scale = scale - if scale == 2: - num_in_ch = num_in_ch * 4 - elif scale == 1: - num_in_ch = num_in_ch * 16 - self.conv_first = nn.Conv2d(num_in_ch, num_feat, 3, 1, 1) - self.body = make_layer(RRDB, num_block, num_feat=num_feat, num_grow_ch=num_grow_ch) - self.conv_body = nn.Conv2d(num_feat, num_feat, 3, 1, 1) - # upsample - self.conv_up1 = nn.Conv2d(num_feat, num_feat, 3, 1, 1) - self.conv_up2 = nn.Conv2d(num_feat, num_feat, 3, 1, 1) - self.conv_hr = nn.Conv2d(num_feat, num_feat, 3, 1, 1) - self.conv_last = nn.Conv2d(num_feat, num_out_ch, 3, 1, 1) - - self.lrelu = nn.LeakyReLU(negative_slope=0.2, inplace=True) - - def forward(self, x): - if self.scale == 2: - feat = pixel_unshuffle(x, scale=2) - elif self.scale == 1: - feat = pixel_unshuffle(x, scale=4) - else: - feat = x - feat = self.conv_first(feat) - body_feat = self.conv_body(self.body(feat)) - feat = feat + body_feat - # upsample - feat = self.lrelu(self.conv_up1(F.interpolate(feat, scale_factor=2, mode='nearest'))) - feat = self.lrelu(self.conv_up2(F.interpolate(feat, scale_factor=2, mode='nearest'))) - out = self.conv_last(self.lrelu(self.conv_hr(feat))) - return out \ No newline at end of file diff --git a/spaces/dawood17/SayBot_Enchancer/CodeFormer/basicsr/archs/vqgan_arch.py b/spaces/dawood17/SayBot_Enchancer/CodeFormer/basicsr/archs/vqgan_arch.py deleted file mode 100644 index f6dfcf4c9983b431f0a978701e5ddd9598faf381..0000000000000000000000000000000000000000 --- a/spaces/dawood17/SayBot_Enchancer/CodeFormer/basicsr/archs/vqgan_arch.py +++ /dev/null @@ -1,435 +0,0 @@ -''' -VQGAN code, adapted from the original created by the Unleashing Transformers authors: -https://github.com/samb-t/unleashing-transformers/blob/master/models/vqgan.py - -''' -import numpy as np -import torch -import torch.nn as nn -import torch.nn.functional as F -import copy -from basicsr.utils import get_root_logger -from basicsr.utils.registry import ARCH_REGISTRY - -def normalize(in_channels): - return torch.nn.GroupNorm(num_groups=32, num_channels=in_channels, eps=1e-6, affine=True) - - -@torch.jit.script -def swish(x): - return x*torch.sigmoid(x) - - -# Define VQVAE classes -class VectorQuantizer(nn.Module): - def __init__(self, codebook_size, emb_dim, beta): - super(VectorQuantizer, self).__init__() - self.codebook_size = codebook_size # number of embeddings - self.emb_dim = emb_dim # dimension of embedding - self.beta = beta # commitment cost used in loss term, beta * ||z_e(x)-sg[e]||^2 - self.embedding = nn.Embedding(self.codebook_size, self.emb_dim) - self.embedding.weight.data.uniform_(-1.0 / self.codebook_size, 1.0 / self.codebook_size) - - def forward(self, z): - # reshape z -> (batch, height, width, channel) and flatten - z = z.permute(0, 2, 3, 1).contiguous() - z_flattened = z.view(-1, self.emb_dim) - - # distances from z to embeddings e_j (z - e)^2 = z^2 + e^2 - 2 e * z - d = (z_flattened ** 2).sum(dim=1, keepdim=True) + (self.embedding.weight**2).sum(1) - \ - 2 * torch.matmul(z_flattened, self.embedding.weight.t()) - - mean_distance = torch.mean(d) - # find closest encodings - # min_encoding_indices = torch.argmin(d, dim=1).unsqueeze(1) - min_encoding_scores, min_encoding_indices = torch.topk(d, 1, dim=1, largest=False) - # [0-1], higher score, higher confidence - min_encoding_scores = torch.exp(-min_encoding_scores/10) - - min_encodings = torch.zeros(min_encoding_indices.shape[0], self.codebook_size).to(z) - min_encodings.scatter_(1, min_encoding_indices, 1) - - # get quantized latent vectors - z_q = torch.matmul(min_encodings, self.embedding.weight).view(z.shape) - # compute loss for embedding - loss = torch.mean((z_q.detach()-z)**2) + self.beta * torch.mean((z_q - z.detach()) ** 2) - # preserve gradients - z_q = z + (z_q - z).detach() - - # perplexity - e_mean = torch.mean(min_encodings, dim=0) - perplexity = torch.exp(-torch.sum(e_mean * torch.log(e_mean + 1e-10))) - # reshape back to match original input shape - z_q = z_q.permute(0, 3, 1, 2).contiguous() - - return z_q, loss, { - "perplexity": perplexity, - "min_encodings": min_encodings, - "min_encoding_indices": min_encoding_indices, - "min_encoding_scores": min_encoding_scores, - "mean_distance": mean_distance - } - - def get_codebook_feat(self, indices, shape): - # input indices: batch*token_num -> (batch*token_num)*1 - # shape: batch, height, width, channel - indices = indices.view(-1,1) - min_encodings = torch.zeros(indices.shape[0], self.codebook_size).to(indices) - min_encodings.scatter_(1, indices, 1) - # get quantized latent vectors - z_q = torch.matmul(min_encodings.float(), self.embedding.weight) - - if shape is not None: # reshape back to match original input shape - z_q = z_q.view(shape).permute(0, 3, 1, 2).contiguous() - - return z_q - - -class GumbelQuantizer(nn.Module): - def __init__(self, codebook_size, emb_dim, num_hiddens, straight_through=False, kl_weight=5e-4, temp_init=1.0): - super().__init__() - self.codebook_size = codebook_size # number of embeddings - self.emb_dim = emb_dim # dimension of embedding - self.straight_through = straight_through - self.temperature = temp_init - self.kl_weight = kl_weight - self.proj = nn.Conv2d(num_hiddens, codebook_size, 1) # projects last encoder layer to quantized logits - self.embed = nn.Embedding(codebook_size, emb_dim) - - def forward(self, z): - hard = self.straight_through if self.training else True - - logits = self.proj(z) - - soft_one_hot = F.gumbel_softmax(logits, tau=self.temperature, dim=1, hard=hard) - - z_q = torch.einsum("b n h w, n d -> b d h w", soft_one_hot, self.embed.weight) - - # + kl divergence to the prior loss - qy = F.softmax(logits, dim=1) - diff = self.kl_weight * torch.sum(qy * torch.log(qy * self.codebook_size + 1e-10), dim=1).mean() - min_encoding_indices = soft_one_hot.argmax(dim=1) - - return z_q, diff, { - "min_encoding_indices": min_encoding_indices - } - - -class Downsample(nn.Module): - def __init__(self, in_channels): - super().__init__() - self.conv = torch.nn.Conv2d(in_channels, in_channels, kernel_size=3, stride=2, padding=0) - - def forward(self, x): - pad = (0, 1, 0, 1) - x = torch.nn.functional.pad(x, pad, mode="constant", value=0) - x = self.conv(x) - return x - - -class Upsample(nn.Module): - def __init__(self, in_channels): - super().__init__() - self.conv = nn.Conv2d(in_channels, in_channels, kernel_size=3, stride=1, padding=1) - - def forward(self, x): - x = F.interpolate(x, scale_factor=2.0, mode="nearest") - x = self.conv(x) - - return x - - -class ResBlock(nn.Module): - def __init__(self, in_channels, out_channels=None): - super(ResBlock, self).__init__() - self.in_channels = in_channels - self.out_channels = in_channels if out_channels is None else out_channels - self.norm1 = normalize(in_channels) - self.conv1 = nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=1, padding=1) - self.norm2 = normalize(out_channels) - self.conv2 = nn.Conv2d(out_channels, out_channels, kernel_size=3, stride=1, padding=1) - if self.in_channels != self.out_channels: - self.conv_out = nn.Conv2d(in_channels, out_channels, kernel_size=1, stride=1, padding=0) - - def forward(self, x_in): - x = x_in - x = self.norm1(x) - x = swish(x) - x = self.conv1(x) - x = self.norm2(x) - x = swish(x) - x = self.conv2(x) - if self.in_channels != self.out_channels: - x_in = self.conv_out(x_in) - - return x + x_in - - -class AttnBlock(nn.Module): - def __init__(self, in_channels): - super().__init__() - self.in_channels = in_channels - - self.norm = normalize(in_channels) - self.q = torch.nn.Conv2d( - in_channels, - in_channels, - kernel_size=1, - stride=1, - padding=0 - ) - self.k = torch.nn.Conv2d( - in_channels, - in_channels, - kernel_size=1, - stride=1, - padding=0 - ) - self.v = torch.nn.Conv2d( - in_channels, - in_channels, - kernel_size=1, - stride=1, - padding=0 - ) - self.proj_out = torch.nn.Conv2d( - in_channels, - in_channels, - kernel_size=1, - stride=1, - padding=0 - ) - - def forward(self, x): - h_ = x - h_ = self.norm(h_) - q = self.q(h_) - k = self.k(h_) - v = self.v(h_) - - # compute attention - b, c, h, w = q.shape - q = q.reshape(b, c, h*w) - q = q.permute(0, 2, 1) - k = k.reshape(b, c, h*w) - w_ = torch.bmm(q, k) - w_ = w_ * (int(c)**(-0.5)) - w_ = F.softmax(w_, dim=2) - - # attend to values - v = v.reshape(b, c, h*w) - w_ = w_.permute(0, 2, 1) - h_ = torch.bmm(v, w_) - h_ = h_.reshape(b, c, h, w) - - h_ = self.proj_out(h_) - - return x+h_ - - -class Encoder(nn.Module): - def __init__(self, in_channels, nf, emb_dim, ch_mult, num_res_blocks, resolution, attn_resolutions): - super().__init__() - self.nf = nf - self.num_resolutions = len(ch_mult) - self.num_res_blocks = num_res_blocks - self.resolution = resolution - self.attn_resolutions = attn_resolutions - - curr_res = self.resolution - in_ch_mult = (1,)+tuple(ch_mult) - - blocks = [] - # initial convultion - blocks.append(nn.Conv2d(in_channels, nf, kernel_size=3, stride=1, padding=1)) - - # residual and downsampling blocks, with attention on smaller res (16x16) - for i in range(self.num_resolutions): - block_in_ch = nf * in_ch_mult[i] - block_out_ch = nf * ch_mult[i] - for _ in range(self.num_res_blocks): - blocks.append(ResBlock(block_in_ch, block_out_ch)) - block_in_ch = block_out_ch - if curr_res in attn_resolutions: - blocks.append(AttnBlock(block_in_ch)) - - if i != self.num_resolutions - 1: - blocks.append(Downsample(block_in_ch)) - curr_res = curr_res // 2 - - # non-local attention block - blocks.append(ResBlock(block_in_ch, block_in_ch)) - blocks.append(AttnBlock(block_in_ch)) - blocks.append(ResBlock(block_in_ch, block_in_ch)) - - # normalise and convert to latent size - blocks.append(normalize(block_in_ch)) - blocks.append(nn.Conv2d(block_in_ch, emb_dim, kernel_size=3, stride=1, padding=1)) - self.blocks = nn.ModuleList(blocks) - - def forward(self, x): - for block in self.blocks: - x = block(x) - - return x - - -class Generator(nn.Module): - def __init__(self, nf, emb_dim, ch_mult, res_blocks, img_size, attn_resolutions): - super().__init__() - self.nf = nf - self.ch_mult = ch_mult - self.num_resolutions = len(self.ch_mult) - self.num_res_blocks = res_blocks - self.resolution = img_size - self.attn_resolutions = attn_resolutions - self.in_channels = emb_dim - self.out_channels = 3 - block_in_ch = self.nf * self.ch_mult[-1] - curr_res = self.resolution // 2 ** (self.num_resolutions-1) - - blocks = [] - # initial conv - blocks.append(nn.Conv2d(self.in_channels, block_in_ch, kernel_size=3, stride=1, padding=1)) - - # non-local attention block - blocks.append(ResBlock(block_in_ch, block_in_ch)) - blocks.append(AttnBlock(block_in_ch)) - blocks.append(ResBlock(block_in_ch, block_in_ch)) - - for i in reversed(range(self.num_resolutions)): - block_out_ch = self.nf * self.ch_mult[i] - - for _ in range(self.num_res_blocks): - blocks.append(ResBlock(block_in_ch, block_out_ch)) - block_in_ch = block_out_ch - - if curr_res in self.attn_resolutions: - blocks.append(AttnBlock(block_in_ch)) - - if i != 0: - blocks.append(Upsample(block_in_ch)) - curr_res = curr_res * 2 - - blocks.append(normalize(block_in_ch)) - blocks.append(nn.Conv2d(block_in_ch, self.out_channels, kernel_size=3, stride=1, padding=1)) - - self.blocks = nn.ModuleList(blocks) - - - def forward(self, x): - for block in self.blocks: - x = block(x) - - return x - - -@ARCH_REGISTRY.register() -class VQAutoEncoder(nn.Module): - def __init__(self, img_size, nf, ch_mult, quantizer="nearest", res_blocks=2, attn_resolutions=[16], codebook_size=1024, emb_dim=256, - beta=0.25, gumbel_straight_through=False, gumbel_kl_weight=1e-8, model_path=None): - super().__init__() - logger = get_root_logger() - self.in_channels = 3 - self.nf = nf - self.n_blocks = res_blocks - self.codebook_size = codebook_size - self.embed_dim = emb_dim - self.ch_mult = ch_mult - self.resolution = img_size - self.attn_resolutions = attn_resolutions - self.quantizer_type = quantizer - self.encoder = Encoder( - self.in_channels, - self.nf, - self.embed_dim, - self.ch_mult, - self.n_blocks, - self.resolution, - self.attn_resolutions - ) - if self.quantizer_type == "nearest": - self.beta = beta #0.25 - self.quantize = VectorQuantizer(self.codebook_size, self.embed_dim, self.beta) - elif self.quantizer_type == "gumbel": - self.gumbel_num_hiddens = emb_dim - self.straight_through = gumbel_straight_through - self.kl_weight = gumbel_kl_weight - self.quantize = GumbelQuantizer( - self.codebook_size, - self.embed_dim, - self.gumbel_num_hiddens, - self.straight_through, - self.kl_weight - ) - self.generator = Generator( - self.nf, - self.embed_dim, - self.ch_mult, - self.n_blocks, - self.resolution, - self.attn_resolutions - ) - - if model_path is not None: - chkpt = torch.load(model_path, map_location='cpu') - if 'params_ema' in chkpt: - self.load_state_dict(torch.load(model_path, map_location='cpu')['params_ema']) - logger.info(f'vqgan is loaded from: {model_path} [params_ema]') - elif 'params' in chkpt: - self.load_state_dict(torch.load(model_path, map_location='cpu')['params']) - logger.info(f'vqgan is loaded from: {model_path} [params]') - else: - raise ValueError(f'Wrong params!') - - - def forward(self, x): - x = self.encoder(x) - quant, codebook_loss, quant_stats = self.quantize(x) - x = self.generator(quant) - return x, codebook_loss, quant_stats - - - -# patch based discriminator -@ARCH_REGISTRY.register() -class VQGANDiscriminator(nn.Module): - def __init__(self, nc=3, ndf=64, n_layers=4, model_path=None): - super().__init__() - - layers = [nn.Conv2d(nc, ndf, kernel_size=4, stride=2, padding=1), nn.LeakyReLU(0.2, True)] - ndf_mult = 1 - ndf_mult_prev = 1 - for n in range(1, n_layers): # gradually increase the number of filters - ndf_mult_prev = ndf_mult - ndf_mult = min(2 ** n, 8) - layers += [ - nn.Conv2d(ndf * ndf_mult_prev, ndf * ndf_mult, kernel_size=4, stride=2, padding=1, bias=False), - nn.BatchNorm2d(ndf * ndf_mult), - nn.LeakyReLU(0.2, True) - ] - - ndf_mult_prev = ndf_mult - ndf_mult = min(2 ** n_layers, 8) - - layers += [ - nn.Conv2d(ndf * ndf_mult_prev, ndf * ndf_mult, kernel_size=4, stride=1, padding=1, bias=False), - nn.BatchNorm2d(ndf * ndf_mult), - nn.LeakyReLU(0.2, True) - ] - - layers += [ - nn.Conv2d(ndf * ndf_mult, 1, kernel_size=4, stride=1, padding=1)] # output 1 channel prediction map - self.main = nn.Sequential(*layers) - - if model_path is not None: - chkpt = torch.load(model_path, map_location='cpu') - if 'params_d' in chkpt: - self.load_state_dict(torch.load(model_path, map_location='cpu')['params_d']) - elif 'params' in chkpt: - self.load_state_dict(torch.load(model_path, map_location='cpu')['params']) - else: - raise ValueError(f'Wrong params!') - - def forward(self, x): - return self.main(x) \ No newline at end of file diff --git a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/altair/utils/display.py b/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/altair/utils/display.py deleted file mode 100644 index 730ca65347ad348964b7aa8c78aa16517c63bd4a..0000000000000000000000000000000000000000 --- a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/altair/utils/display.py +++ /dev/null @@ -1,186 +0,0 @@ -import json -import pkgutil -import textwrap -from typing import Callable, Dict, Optional -import uuid - -from .plugin_registry import PluginRegistry -from .mimebundle import spec_to_mimebundle -from .schemapi import validate_jsonschema - - -# ============================================================================== -# Renderer registry -# ============================================================================== -MimeBundleType = Dict[str, object] -RendererType = Callable[..., MimeBundleType] - - -class RendererRegistry(PluginRegistry[RendererType]): - entrypoint_err_messages = { - "notebook": textwrap.dedent( - """ - To use the 'notebook' renderer, you must install the vega package - and the associated Jupyter extension. - See https://altair-viz.github.io/getting_started/installation.html - for more information. - """ - ), - "altair_viewer": textwrap.dedent( - """ - To use the 'altair_viewer' renderer, you must install the altair_viewer - package; see http://github.com/altair-viz/altair_viewer/ - for more information. - """ - ), - } - - def set_embed_options( - self, - defaultStyle=None, - renderer=None, - width=None, - height=None, - padding=None, - scaleFactor=None, - actions=None, - **kwargs, - ): - """Set options for embeddings of Vega & Vega-Lite charts. - - Options are fully documented at https://github.com/vega/vega-embed. - Similar to the `enable()` method, this can be used as either - a persistent global switch, or as a temporary local setting using - a context manager (i.e. a `with` statement). - - Parameters - ---------- - defaultStyle : bool or string - Specify a default stylesheet for embed actions. - renderer : string - The renderer to use for the view. One of "canvas" (default) or "svg" - width : integer - The view width in pixels - height : integer - The view height in pixels - padding : integer - The view padding in pixels - scaleFactor : number - The number by which to multiply the width and height (default 1) - of an exported PNG or SVG image. - actions : bool or dict - Determines if action links ("Export as PNG/SVG", "View Source", - "View Vega" (only for Vega-Lite), "Open in Vega Editor") are - included with the embedded view. If the value is true, all action - links will be shown and none if the value is false. This property - can take a key-value mapping object that maps keys (export, source, - compiled, editor) to boolean values for determining if - each action link should be shown. - **kwargs : - Additional options are passed directly to embed options. - """ - options = { - "defaultStyle": defaultStyle, - "renderer": renderer, - "width": width, - "height": height, - "padding": padding, - "scaleFactor": scaleFactor, - "actions": actions, - } - kwargs.update({key: val for key, val in options.items() if val is not None}) - return self.enable(None, embed_options=kwargs) - - -# ============================================================================== -# VegaLite v1/v2 renderer logic -# ============================================================================== - - -class Displayable: - """A base display class for VegaLite v1/v2. - - This class takes a VegaLite v1/v2 spec and does the following: - - 1. Optionally validates the spec against a schema. - 2. Uses the RendererPlugin to grab a renderer and call it when the - IPython/Jupyter display method (_repr_mimebundle_) is called. - - The spec passed to this class must be fully schema compliant and already - have the data portion of the spec fully processed and ready to serialize. - In practice, this means, the data portion of the spec should have been passed - through appropriate data model transformers. - """ - - renderers: Optional[RendererRegistry] = None - schema_path = ("altair", "") - - def __init__(self, spec, validate=False): - # type: (dict, bool) -> None - self.spec = spec - self.validate = validate - self._validate() - - def _validate(self): - # type: () -> None - """Validate the spec against the schema.""" - data = pkgutil.get_data(*self.schema_path) - assert data is not None - schema_dict = json.loads(data.decode("utf-8")) - validate_jsonschema( - self.spec, - schema_dict, - ) - - def _repr_mimebundle_(self, include=None, exclude=None): - """Return a MIME bundle for display in Jupyter frontends.""" - if self.renderers is not None: - return self.renderers.get()(self.spec) - else: - return {} - - -def default_renderer_base(spec, mime_type, str_repr, **options): - """A default renderer for Vega or VegaLite that works for modern frontends. - - This renderer works with modern frontends (JupyterLab, nteract) that know - how to render the custom VegaLite MIME type listed above. - """ - assert isinstance(spec, dict) - bundle = {} - metadata = {} - - bundle[mime_type] = spec - bundle["text/plain"] = str_repr - if options: - metadata[mime_type] = options - return bundle, metadata - - -def json_renderer_base(spec, str_repr, **options): - """A renderer that returns a MIME type of application/json. - - In JupyterLab/nteract this is rendered as a nice JSON tree. - """ - return default_renderer_base( - spec, mime_type="application/json", str_repr=str_repr, **options - ) - - -class HTMLRenderer: - """Object to render charts as HTML, with a unique output div each time""" - - def __init__(self, output_div="altair-viz-{}", **kwargs): - self._output_div = output_div - self.kwargs = kwargs - - @property - def output_div(self): - return self._output_div.format(uuid.uuid4().hex) - - def __call__(self, spec, **metadata): - kwargs = self.kwargs.copy() - kwargs.update(metadata) - return spec_to_mimebundle( - spec, format="html", output_div=self.output_div, **kwargs - ) diff --git a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/fontTools/ttLib/tables/_m_e_t_a.py b/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/fontTools/ttLib/tables/_m_e_t_a.py deleted file mode 100644 index 3af9e543049f89f0da3ceb15bb58135854fef002..0000000000000000000000000000000000000000 --- a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/fontTools/ttLib/tables/_m_e_t_a.py +++ /dev/null @@ -1,104 +0,0 @@ -from fontTools.misc import sstruct -from fontTools.misc.textTools import bytesjoin, strjoin, readHex -from fontTools.ttLib import TTLibError -from . import DefaultTable - -# Apple's documentation of 'meta': -# https://developer.apple.com/fonts/TrueType-Reference-Manual/RM06/Chap6meta.html - -META_HEADER_FORMAT = """ - > # big endian - version: L - flags: L - dataOffset: L - numDataMaps: L -""" - - -DATA_MAP_FORMAT = """ - > # big endian - tag: 4s - dataOffset: L - dataLength: L -""" - - -class table__m_e_t_a(DefaultTable.DefaultTable): - def __init__(self, tag=None): - DefaultTable.DefaultTable.__init__(self, tag) - self.data = {} - - def decompile(self, data, ttFont): - headerSize = sstruct.calcsize(META_HEADER_FORMAT) - header = sstruct.unpack(META_HEADER_FORMAT, data[0:headerSize]) - if header["version"] != 1: - raise TTLibError("unsupported 'meta' version %d" % header["version"]) - dataMapSize = sstruct.calcsize(DATA_MAP_FORMAT) - for i in range(header["numDataMaps"]): - dataMapOffset = headerSize + i * dataMapSize - dataMap = sstruct.unpack( - DATA_MAP_FORMAT, data[dataMapOffset : dataMapOffset + dataMapSize] - ) - tag = dataMap["tag"] - offset = dataMap["dataOffset"] - self.data[tag] = data[offset : offset + dataMap["dataLength"]] - if tag in ["dlng", "slng"]: - self.data[tag] = self.data[tag].decode("utf-8") - - def compile(self, ttFont): - keys = sorted(self.data.keys()) - headerSize = sstruct.calcsize(META_HEADER_FORMAT) - dataOffset = headerSize + len(keys) * sstruct.calcsize(DATA_MAP_FORMAT) - header = sstruct.pack( - META_HEADER_FORMAT, - { - "version": 1, - "flags": 0, - "dataOffset": dataOffset, - "numDataMaps": len(keys), - }, - ) - dataMaps = [] - dataBlocks = [] - for tag in keys: - if tag in ["dlng", "slng"]: - data = self.data[tag].encode("utf-8") - else: - data = self.data[tag] - dataMaps.append( - sstruct.pack( - DATA_MAP_FORMAT, - {"tag": tag, "dataOffset": dataOffset, "dataLength": len(data)}, - ) - ) - dataBlocks.append(data) - dataOffset += len(data) - return bytesjoin([header] + dataMaps + dataBlocks) - - def toXML(self, writer, ttFont): - for tag in sorted(self.data.keys()): - if tag in ["dlng", "slng"]: - writer.begintag("text", tag=tag) - writer.newline() - writer.write(self.data[tag]) - writer.newline() - writer.endtag("text") - writer.newline() - else: - writer.begintag("hexdata", tag=tag) - writer.newline() - data = self.data[tag] - if min(data) >= 0x20 and max(data) <= 0x7E: - writer.comment("ascii: " + data.decode("ascii")) - writer.newline() - writer.dumphex(data) - writer.endtag("hexdata") - writer.newline() - - def fromXML(self, name, attrs, content, ttFont): - if name == "hexdata": - self.data[attrs["tag"]] = readHex(content) - elif name == "text" and attrs["tag"] in ["dlng", "slng"]: - self.data[attrs["tag"]] = strjoin(content).strip() - else: - raise TTLibError("can't handle '%s' element" % name) diff --git a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/gradio/templates/frontend/assets/index-9330f92f.js b/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/gradio/templates/frontend/assets/index-9330f92f.js deleted file mode 100644 index cdad05672b324dd783e2629ef8da89acad741322..0000000000000000000000000000000000000000 --- a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/gradio/templates/frontend/assets/index-9330f92f.js +++ /dev/null @@ -1,2 +0,0 @@ -import{S as c,e as m,s as g,a9 as b,m as f,g as r,K as o,Y as d,h as v,j as p,ab as h,ac as w,ad as y,w as j,u as k,k as G}from"./index-39fce9e2.js";function C(n){let s,l,u,i;const _=n[4].default,a=b(_,n,n[3],null);return{c(){s=f("div"),l=f("div"),a&&a.c(),r(l,"class","styler svelte-iyf88w"),o(l,"--block-radius","0px"),o(l,"--block-border-width","0px"),o(l,"--layout-gap","1px"),o(l,"--form-gap-width","1px"),o(l,"--button-border-width","0px"),o(l,"--button-large-radius","0px"),o(l,"--button-small-radius","0px"),r(s,"id",n[0]),r(s,"class",u="gr-group "+n[1].join(" ")+" svelte-iyf88w"),d(s,"hide",!n[2])},m(e,t){v(e,s,t),p(s,l),a&&a.m(l,null),i=!0},p(e,[t]){a&&a.p&&(!i||t&8)&&h(a,_,e,e[3],i?y(_,e[3],t,null):w(e[3]),null),(!i||t&1)&&r(s,"id",e[0]),(!i||t&2&&u!==(u="gr-group "+e[1].join(" ")+" svelte-iyf88w"))&&r(s,"class",u),(!i||t&6)&&d(s,"hide",!e[2])},i(e){i||(j(a,e),i=!0)},o(e){k(a,e),i=!1},d(e){e&&G(s),a&&a.d(e)}}}function S(n,s,l){let{$$slots:u={},$$scope:i}=s,{elem_id:_=""}=s,{elem_classes:a=[]}=s,{visible:e=!0}=s;return n.$$set=t=>{"elem_id"in t&&l(0,_=t.elem_id),"elem_classes"in t&&l(1,a=t.elem_classes),"visible"in t&&l(2,e=t.visible),"$$scope"in t&&l(3,i=t.$$scope)},[_,a,e,i,u]}class q extends c{constructor(s){super(),m(this,s,S,C,g,{elem_id:0,elem_classes:1,visible:2})}}const Y=q,z=["static"];export{Y as Component,z as modes}; -//# sourceMappingURL=index-9330f92f.js.map diff --git a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/importlib_resources/tests/util.py b/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/importlib_resources/tests/util.py deleted file mode 100644 index ce0e6fadce5e2e7eb696169233e23757d84127ef..0000000000000000000000000000000000000000 --- a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/importlib_resources/tests/util.py +++ /dev/null @@ -1,179 +0,0 @@ -import abc -import importlib -import io -import sys -import types -import pathlib - -from . import data01 -from . import zipdata01 -from ..abc import ResourceReader -from ._compat import import_helper - - -from importlib.machinery import ModuleSpec - - -class Reader(ResourceReader): - def __init__(self, **kwargs): - vars(self).update(kwargs) - - def get_resource_reader(self, package): - return self - - def open_resource(self, path): - self._path = path - if isinstance(self.file, Exception): - raise self.file - return self.file - - def resource_path(self, path_): - self._path = path_ - if isinstance(self.path, Exception): - raise self.path - return self.path - - def is_resource(self, path_): - self._path = path_ - if isinstance(self.path, Exception): - raise self.path - - def part(entry): - return entry.split('/') - - return any( - len(parts) == 1 and parts[0] == path_ for parts in map(part, self._contents) - ) - - def contents(self): - if isinstance(self.path, Exception): - raise self.path - yield from self._contents - - -def create_package_from_loader(loader, is_package=True): - name = 'testingpackage' - module = types.ModuleType(name) - spec = ModuleSpec(name, loader, origin='does-not-exist', is_package=is_package) - module.__spec__ = spec - module.__loader__ = loader - return module - - -def create_package(file=None, path=None, is_package=True, contents=()): - return create_package_from_loader( - Reader(file=file, path=path, _contents=contents), - is_package, - ) - - -class CommonTests(metaclass=abc.ABCMeta): - """ - Tests shared by test_open, test_path, and test_read. - """ - - @abc.abstractmethod - def execute(self, package, path): - """ - Call the pertinent legacy API function (e.g. open_text, path) - on package and path. - """ - - def test_package_name(self): - """ - Passing in the package name should succeed. - """ - self.execute(data01.__name__, 'utf-8.file') - - def test_package_object(self): - """ - Passing in the package itself should succeed. - """ - self.execute(data01, 'utf-8.file') - - def test_string_path(self): - """ - Passing in a string for the path should succeed. - """ - path = 'utf-8.file' - self.execute(data01, path) - - def test_pathlib_path(self): - """ - Passing in a pathlib.PurePath object for the path should succeed. - """ - path = pathlib.PurePath('utf-8.file') - self.execute(data01, path) - - def test_importing_module_as_side_effect(self): - """ - The anchor package can already be imported. - """ - del sys.modules[data01.__name__] - self.execute(data01.__name__, 'utf-8.file') - - def test_missing_path(self): - """ - Attempting to open or read or request the path for a - non-existent path should succeed if open_resource - can return a viable data stream. - """ - bytes_data = io.BytesIO(b'Hello, world!') - package = create_package(file=bytes_data, path=FileNotFoundError()) - self.execute(package, 'utf-8.file') - self.assertEqual(package.__loader__._path, 'utf-8.file') - - def test_extant_path(self): - # Attempting to open or read or request the path when the - # path does exist should still succeed. Does not assert - # anything about the result. - bytes_data = io.BytesIO(b'Hello, world!') - # any path that exists - path = __file__ - package = create_package(file=bytes_data, path=path) - self.execute(package, 'utf-8.file') - self.assertEqual(package.__loader__._path, 'utf-8.file') - - def test_useless_loader(self): - package = create_package(file=FileNotFoundError(), path=FileNotFoundError()) - with self.assertRaises(FileNotFoundError): - self.execute(package, 'utf-8.file') - - -class ZipSetupBase: - ZIP_MODULE = None - - @classmethod - def setUpClass(cls): - data_path = pathlib.Path(cls.ZIP_MODULE.__file__) - data_dir = data_path.parent - cls._zip_path = str(data_dir / 'ziptestdata.zip') - sys.path.append(cls._zip_path) - cls.data = importlib.import_module('ziptestdata') - - @classmethod - def tearDownClass(cls): - try: - sys.path.remove(cls._zip_path) - except ValueError: - pass - - try: - del sys.path_importer_cache[cls._zip_path] - del sys.modules[cls.data.__name__] - except KeyError: - pass - - try: - del cls.data - del cls._zip_path - except AttributeError: - pass - - def setUp(self): - modules = import_helper.modules_setup() - self.addCleanup(import_helper.modules_cleanup, *modules) - - -class ZipSetup(ZipSetupBase): - ZIP_MODULE = zipdata01 # type: ignore diff --git a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/markdown_it/helpers/parse_link_destination.py b/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/markdown_it/helpers/parse_link_destination.py deleted file mode 100644 index f42b2244db531bab0606607820ab165a161ce255..0000000000000000000000000000000000000000 --- a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/markdown_it/helpers/parse_link_destination.py +++ /dev/null @@ -1,86 +0,0 @@ -""" -Parse link destination -""" - -from ..common.utils import charCodeAt, unescapeAll - - -class _Result: - __slots__ = ("ok", "pos", "lines", "str") - - def __init__(self) -> None: - self.ok = False - self.pos = 0 - self.lines = 0 - self.str = "" - - -def parseLinkDestination(string: str, pos: int, maximum: int) -> _Result: - lines = 0 - start = pos - result = _Result() - - if charCodeAt(string, pos) == 0x3C: # /* < */ - pos += 1 - while pos < maximum: - code = charCodeAt(string, pos) - if code == 0x0A: # /* \n */) - return result - if code == 0x3C: # / * < * / - return result - if code == 0x3E: # /* > */) { - result.pos = pos + 1 - result.str = unescapeAll(string[start + 1 : pos]) - result.ok = True - return result - - if code == 0x5C and pos + 1 < maximum: # \ - pos += 2 - continue - - pos += 1 - - # no closing '>' - return result - - # this should be ... } else { ... branch - - level = 0 - while pos < maximum: - code = charCodeAt(string, pos) - - if code is None or code == 0x20: - break - - # ascii control characters - if code < 0x20 or code == 0x7F: - break - - if code == 0x5C and pos + 1 < maximum: - if charCodeAt(string, pos + 1) == 0x20: - break - pos += 2 - continue - - if code == 0x28: # /* ( */) - level += 1 - if level > 32: - return result - - if code == 0x29: # /* ) */) - if level == 0: - break - level -= 1 - - pos += 1 - - if start == pos: - return result - if level != 0: - return result - - result.str = unescapeAll(string[start:pos]) - result.lines = lines - result.pos = pos - result.ok = True - return result diff --git a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/matplotlib/artist.py b/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/matplotlib/artist.py deleted file mode 100644 index 6ee018e857771b8bbb8bd2f6c8410f737b92f00a..0000000000000000000000000000000000000000 --- a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/matplotlib/artist.py +++ /dev/null @@ -1,1864 +0,0 @@ -from collections import namedtuple -import contextlib -from functools import lru_cache, wraps -import inspect -from inspect import Signature, Parameter -import logging -from numbers import Number -import re -import warnings - -import numpy as np - -import matplotlib as mpl -from . import _api, cbook -from .colors import BoundaryNorm -from .cm import ScalarMappable -from .path import Path -from .transforms import (Bbox, IdentityTransform, Transform, TransformedBbox, - TransformedPatchPath, TransformedPath) - -_log = logging.getLogger(__name__) - - -def _prevent_rasterization(draw): - # We assume that by default artists are not allowed to rasterize (unless - # its draw method is explicitly decorated). If it is being drawn after a - # rasterized artist and it has reached a raster_depth of 0, we stop - # rasterization so that it does not affect the behavior of normal artist - # (e.g., change in dpi). - - @wraps(draw) - def draw_wrapper(artist, renderer, *args, **kwargs): - if renderer._raster_depth == 0 and renderer._rasterizing: - # Only stop when we are not in a rasterized parent - # and something has been rasterized since last stop. - renderer.stop_rasterizing() - renderer._rasterizing = False - - return draw(artist, renderer, *args, **kwargs) - - draw_wrapper._supports_rasterization = False - return draw_wrapper - - -def allow_rasterization(draw): - """ - Decorator for Artist.draw method. Provides routines - that run before and after the draw call. The before and after functions - are useful for changing artist-dependent renderer attributes or making - other setup function calls, such as starting and flushing a mixed-mode - renderer. - """ - - @wraps(draw) - def draw_wrapper(artist, renderer): - try: - if artist.get_rasterized(): - if renderer._raster_depth == 0 and not renderer._rasterizing: - renderer.start_rasterizing() - renderer._rasterizing = True - renderer._raster_depth += 1 - else: - if renderer._raster_depth == 0 and renderer._rasterizing: - # Only stop when we are not in a rasterized parent - # and something has be rasterized since last stop - renderer.stop_rasterizing() - renderer._rasterizing = False - - if artist.get_agg_filter() is not None: - renderer.start_filter() - - return draw(artist, renderer) - finally: - if artist.get_agg_filter() is not None: - renderer.stop_filter(artist.get_agg_filter()) - if artist.get_rasterized(): - renderer._raster_depth -= 1 - if (renderer._rasterizing and artist.figure and - artist.figure.suppressComposite): - # restart rasterizing to prevent merging - renderer.stop_rasterizing() - renderer.start_rasterizing() - - draw_wrapper._supports_rasterization = True - return draw_wrapper - - -def _finalize_rasterization(draw): - """ - Decorator for Artist.draw method. Needed on the outermost artist, i.e. - Figure, to finish up if the render is still in rasterized mode. - """ - @wraps(draw) - def draw_wrapper(artist, renderer, *args, **kwargs): - result = draw(artist, renderer, *args, **kwargs) - if renderer._rasterizing: - renderer.stop_rasterizing() - renderer._rasterizing = False - return result - return draw_wrapper - - -def _stale_axes_callback(self, val): - if self.axes: - self.axes.stale = val - - -_XYPair = namedtuple("_XYPair", "x y") - - -class _Unset: - def __repr__(self): - return "
Clayoo 2.6 for Rhino 6 Win
DOWNLOAD 🗸🗸🗸 https://gohhs.com/2uFU10
- -The next generation of plugins for Rhino starts with Clayoo, an innovative solution to freeform modeling. Clayoo offers three different ... 4d29de3e1b
-
-
- diff --git a/spaces/diacanFperku/AutoGPT/Daub Ages 2 0 Cracked NEW!.md b/spaces/diacanFperku/AutoGPT/Daub Ages 2 0 Cracked NEW!.md deleted file mode 100644 index 6e455c9cdfeeb565391266ca5a800ec32ef48edc..0000000000000000000000000000000000000000 --- a/spaces/diacanFperku/AutoGPT/Daub Ages 2 0 Cracked NEW!.md +++ /dev/null @@ -1,60 +0,0 @@ - -
Daub Ages 2 0 Cracked - A Review
-If you are interested in genealogy and family history, you might want to check out Daub Ages 2 0 Cracked. This is a software that allows you to create and manage your family tree, as well as to explore and analyze your ancestry. Daub Ages 2 0 Cracked is a powerful and user-friendly tool that can help you discover your roots and share your stories.
-What are the features of Daub Ages 2 0 Cracked?
-Daub Ages 2 0 Cracked has some impressive features that make it a useful tool for genealogists and family historians. Some of these features are:
-daub ages 2 0 cracked
DOWNLOAD ► https://gohhs.com/2uFVpc
-
-
-
- Data entry and editing: You can easily enter and edit your personal data, such as names, dates, places, events, sources, notes, media, etc. You can also import and export data from GEDCOM files, CSV files, or other formats. -
- Family tree view and navigation: You can view and navigate your family tree in various ways, such as pedigree chart, family group sheet, timeline, fan chart, etc. You can also customize the appearance and layout of your family tree. -
- Research and analysis: You can research and analyze your ancestry by using various tools, such as maps, statistics, reports, charts, lists, etc. You can also compare and merge data from different sources or databases. -
- Publication and sharing: You can publish and share your family tree by creating web pages, books, PDF files, slideshows, etc. You can also upload your family tree to online platforms, such as Ancestry.com or FamilySearch.org. -
What are the system requirements of Daub Ages 2 0 Cracked?
-Daub Ages 2 0 Cracked is compatible with Windows XP/Vista/7/8/10. It requires 512 MB of RAM (1 GB recommended) and 100 MB of free hard disk space. It is a lightweight and easy-to-use software that does not require much resources or technical skills.
-How to download and install Daub Ages 2 0 Cracked?
-To download and install Daub Ages 2 0 Cracked, you need to follow these steps:
--
-
- Download the software from a reliable source, such as FileCR.com. -
- Extract the zip file and run the setup file. -
- Follow the instructions on the screen and complete the installation process. -
- Copy the crack file and paste it into the installation folder. -
- Run the software and enjoy creating your family tree. -
Conclusion
-Daub Ages 2 0 Cracked is a powerful and convenient software that allows you to create and manage your family tree, as well as to explore and analyze your ancestry. Daub Ages 2 0 Cracked is a user-friendly tool that can help you discover your roots and share your stories. Daub Ages 2 0 Cracked is a must-have tool for anyone who loves genealogy and family history.
-What are the benefits of Daub Ages 2 0 Cracked?
-Daub Ages 2 0 Cracked has many benefits for users who want to create and manage their family tree, as well as to explore and analyze their ancestry. Some of these benefits are:
--
-
- It saves time and money: You can create and manage your family tree faster and easier than using a web browser or a subscription-based service. You can also access your family tree offline and without any ads or limitations. -
- It offers more options and flexibility: You can customize and personalize your family tree according to your preferences and needs. You can also import and export data from various sources or formats. -
- It enhances your genealogy experience: You can research and analyze your ancestry by using various tools and features. You can also publish and share your family tree by creating various outputs and formats. -
What are the alternatives to Daub Ages 2 0 Cracked?
-If you are looking for other ways to create and manage your family tree, as well as to explore and analyze your ancestry, you might want to check out some of the alternatives to Daub Ages 2 0 Cracked. Some of these alternatives are:
--
-
- Family Tree Maker: This is a software that allows you to create and manage your family tree, as well as to sync it with Ancestry.com or FamilySearch.org. You can also research and analyze your ancestry by using various tools and features. -
- Legacy Family Tree: This is a software that allows you to create and manage your family tree, as well as to sync it with FamilySearch.org or MyHeritage.com. You can also research and analyze your ancestry by using various tools and features. -
- RootsMagic: This is a software that allows you to create and manage your family tree, as well as to sync it with Ancestry.com or FamilySearch.org. You can also research and analyze your ancestry by using various tools and features. -
What are the pros and cons of Daub Ages 2 0 Cracked?
-Daub Ages 2 0 Cracked has some pros and cons that you should consider before using it. Some of these pros and cons are:
-| Pros | Cons |
|---|---|
| It is fast and easy to use. | It requires a crack file to activate the full version. |
| It offers more options and flexibility than web browsers or subscription-based services. | It does not support syncing with online platforms or databases. |
| It enhances your genealogy experience by allowing you to research and analyze your ancestry. | It does not support creating vector charts or 3D views. |
Conclusion
-In conclusion, Daub Ages 2 0 Cracked is a powerful and convenient software that allows you to create and manage your family tree, as well as to explore and analyze your ancestry. Daub Ages 2 0 Cracked is a user-friendly tool that can help you discover your roots and share your stories. Daub Ages 2 0 Cracked is a must-have tool for anyone who loves genealogy and family history.
- -If you want to get Daub Ages 2 0 Cracked, you can download it from FileCR.com, a reliable source that offers free downloads of various software. You will also get the crack file that will activate the full version, as well as a user manual that will guide you through the installation and usage of the software.
-So don't hesitate and download Daub Ages 2 0 Cracked today and enjoy creating your family tree!
-Download Daub Ages 2 0 Cracked from FileCR.com -Conclusion
-In conclusion, Daub Ages 2 0 Cracked is a powerful and convenient software that allows you to create and manage your family tree, as well as to explore and analyze your ancestry. Daub Ages 2 0 Cracked is a user-friendly tool that can help you discover your roots and share your stories. Daub Ages 2 0 Cracked is a must-have tool for anyone who loves genealogy and family history.
-If you want to get Daub Ages 2 0 Cracked, you can download it from FileCR.com, a reliable source that offers free downloads of various software. You will also get the crack file that will activate the full version, as well as a user manual that will guide you through the installation and usage of the software.
-So don't hesitate and download Daub Ages 2 0 Cracked today and enjoy creating your family tree!
-Download Daub Ages 2 0 Cracked from FileCR.com 3cee63e6c2-
-
\ No newline at end of file diff --git a/spaces/diacanFperku/AutoGPT/Not Angka Pianika Rumah Kita Doc.rar.md b/spaces/diacanFperku/AutoGPT/Not Angka Pianika Rumah Kita Doc.rar.md deleted file mode 100644 index e5cb3c9965c1073de18ca672018a1f1c6fbe9250..0000000000000000000000000000000000000000 --- a/spaces/diacanFperku/AutoGPT/Not Angka Pianika Rumah Kita Doc.rar.md +++ /dev/null @@ -1,10 +0,0 @@ -
not angka pianika rumah kita doc.rar
Download ► https://gohhs.com/2uFUAm
- -Update for Microsoft Security Essentials (4.10.209.0) returns context menu not angka pianika rumah kita doc.rar · readiris pro 11 free download full . Downloads for Microsoft Security Essentials for Windows Vista, Windows 7, Windows Server 2008 and Windows. -Download Microsoft Security Essentials for Windows 7 32-bit 64-bit SP1 (x86/x64) (English) . -Download Microsoft Security Essentials 4.10.209.0 . -Microsoft Security Essentials is a free antivirus to protect Windows 7, Windows 8 and . Download Microsoft Security Essentials for Windows XP 32-bit (32-bit) (English) . . -Download Microsoft Security Essentials for Windows XP (32-bit) (English) . 8a78ff9644
-
-
- diff --git a/spaces/diacanFperku/AutoGPT/Robot Structural Analysis Professional 2019 Xforce Keygen ((LINK)) 64 Bit.md b/spaces/diacanFperku/AutoGPT/Robot Structural Analysis Professional 2019 Xforce Keygen ((LINK)) 64 Bit.md deleted file mode 100644 index 3d4eda560af77c68871b442de5e3e87b1db57828..0000000000000000000000000000000000000000 --- a/spaces/diacanFperku/AutoGPT/Robot Structural Analysis Professional 2019 Xforce Keygen ((LINK)) 64 Bit.md +++ /dev/null @@ -1,32 +0,0 @@ - -
How to Activate Robot Structural Analysis Professional 2019 with Xforce Keygen 64 Bit
-Robot Structural Analysis Professional 2019 is a powerful software that allows you to perform structural analysis and design of complex structures. It supports various types of materials, loads, and codes, and integrates with other Autodesk products such as Revit and AutoCAD.
-Robot Structural Analysis Professional 2019 Xforce Keygen 64 Bit
Download File https://gohhs.com/2uFU6y
-
However, to use Robot Structural Analysis Professional 2019, you need to activate it with a valid license. If you don't have one, you can use Xforce Keygen 64 Bit to generate a serial number and a product key that will unlock the full features of the software.
-In this article, we will show you how to use Xforce Keygen 64 Bit to activate Robot Structural Analysis Professional 2019 in a few simple steps.
-Step 1: Download and Install Robot Structural Analysis Professional 2019
-The first step is to download and install Robot Structural Analysis Professional 2019 from the official website or from a trusted source. You can choose the trial version or the full version depending on your needs.
- -Follow the instructions on the screen to complete the installation process. Make sure you have enough disk space and system requirements to run the software smoothly.
-Step 2: Download and Run Xforce Keygen 64 Bit
-The next step is to download and run Xforce Keygen 64 Bit from a reliable source. Xforce Keygen 64 Bit is a tool that can generate serial numbers and product keys for various Autodesk products, including Robot Structural Analysis Professional 2019.
-Before you run Xforce Keygen 64 Bit, make sure you disable your antivirus and firewall software, as they may interfere with the activation process. Also, make sure you run Xforce Keygen 64 Bit as an administrator.
-Once you run Xforce Keygen 64 Bit, you will see a window like this:
-
-Select Robot Structural Analysis Professional 2019 from the drop-down menu and click on Generate. You will see a serial number and a product key appear in the fields below.
-Step 3: Activate Robot Structural Analysis Professional 2019 with Xforce Keygen 64 Bit
-The final step is to activate Robot Structural Analysis Professional 2019 with the serial number and product key generated by Xforce Keygen 64 Bit.
-Launch Robot Structural Analysis Professional 2019 and click on Activate in the startup screen. You will see a window like this:
-
-Enter the serial number and product key generated by Xforce Keygen 64 Bit in the corresponding fields and click on Next. You will see a window like this:
-
-Select I have an activation code from Autodesk and click on Next. You will see a window like this:
-
-Copy the request code from the window and paste it into the Request field in Xforce Keygen 64 Bit. Then click on Generate. You will see an activation code appear in the Activation field in Xforce Keygen 64 Bit.
-Copy the activation code from Xforce Keygen 64 Bit and paste it into the Activation field in Robot Structural Analysis Professional 2019. Then click on Next. You will see a window like this:
-
-Congratulations! You have successfully activated Robot Structural Analysis Professional 2019 with Xforce Keygen 64 Bit. You can now enjoy the full features of the software without any limitations.
-Conclusion
-In this article, we have shown you how to activate Robot Structural Analysis Professional 2019 with Xforce Keygen 64 Bit in a few simple steps. We hope this article was helpful and
d5da3c52bf-
-
\ No newline at end of file diff --git a/spaces/digitalxingtong/Bufeiyan-a-Bert-VITS2/utils.py b/spaces/digitalxingtong/Bufeiyan-a-Bert-VITS2/utils.py deleted file mode 100644 index c6aa6cfc64c33e2eed33e9845239e831fc1c4a1a..0000000000000000000000000000000000000000 --- a/spaces/digitalxingtong/Bufeiyan-a-Bert-VITS2/utils.py +++ /dev/null @@ -1,293 +0,0 @@ -import os -import glob -import sys -import argparse -import logging -import json -import subprocess -import numpy as np -from scipy.io.wavfile import read -import torch - -MATPLOTLIB_FLAG = False - -logging.basicConfig(stream=sys.stdout, level=logging.DEBUG) -logger = logging - - -def load_checkpoint(checkpoint_path, model, optimizer=None, skip_optimizer=False): - assert os.path.isfile(checkpoint_path) - checkpoint_dict = torch.load(checkpoint_path, map_location='cpu') - iteration = checkpoint_dict['iteration'] - learning_rate = checkpoint_dict['learning_rate'] - if optimizer is not None and not skip_optimizer and checkpoint_dict['optimizer'] is not None: - optimizer.load_state_dict(checkpoint_dict['optimizer']) - elif optimizer is None and not skip_optimizer: - #else: #Disable this line if Infer ,and enable the line upper - new_opt_dict = optimizer.state_dict() - new_opt_dict_params = new_opt_dict['param_groups'][0]['params'] - new_opt_dict['param_groups'] = checkpoint_dict['optimizer']['param_groups'] - new_opt_dict['param_groups'][0]['params'] = new_opt_dict_params - optimizer.load_state_dict(new_opt_dict) - saved_state_dict = checkpoint_dict['model'] - if hasattr(model, 'module'): - state_dict = model.module.state_dict() - else: - state_dict = model.state_dict() - new_state_dict = {} - for k, v in state_dict.items(): - try: - #assert "emb_g" not in k - # print("load", k) - new_state_dict[k] = saved_state_dict[k] - assert saved_state_dict[k].shape == v.shape, (saved_state_dict[k].shape, v.shape) - except: - print("error, %s is not in the checkpoint" % k) - new_state_dict[k] = v - if hasattr(model, 'module'): - model.module.load_state_dict(new_state_dict, strict=False) - else: - model.load_state_dict(new_state_dict, strict=False) - print("load ") - logger.info("Loaded checkpoint '{}' (iteration {})".format( - checkpoint_path, iteration)) - return model, optimizer, learning_rate, iteration - - -def save_checkpoint(model, optimizer, learning_rate, iteration, checkpoint_path): - logger.info("Saving model and optimizer state at iteration {} to {}".format( - iteration, checkpoint_path)) - if hasattr(model, 'module'): - state_dict = model.module.state_dict() - else: - state_dict = model.state_dict() - torch.save({'model': state_dict, - 'iteration': iteration, - 'optimizer': optimizer.state_dict(), - 'learning_rate': learning_rate}, checkpoint_path) - - -def summarize(writer, global_step, scalars={}, histograms={}, images={}, audios={}, audio_sampling_rate=22050): - for k, v in scalars.items(): - writer.add_scalar(k, v, global_step) - for k, v in histograms.items(): - writer.add_histogram(k, v, global_step) - for k, v in images.items(): - writer.add_image(k, v, global_step, dataformats='HWC') - for k, v in audios.items(): - writer.add_audio(k, v, global_step, audio_sampling_rate) - - -def latest_checkpoint_path(dir_path, regex="G_*.pth"): - f_list = glob.glob(os.path.join(dir_path, regex)) - f_list.sort(key=lambda f: int("".join(filter(str.isdigit, f)))) - x = f_list[-1] - print(x) - return x - - -def plot_spectrogram_to_numpy(spectrogram): - global MATPLOTLIB_FLAG - if not MATPLOTLIB_FLAG: - import matplotlib - matplotlib.use("Agg") - MATPLOTLIB_FLAG = True - mpl_logger = logging.getLogger('matplotlib') - mpl_logger.setLevel(logging.WARNING) - import matplotlib.pylab as plt - import numpy as np - - fig, ax = plt.subplots(figsize=(10, 2)) - im = ax.imshow(spectrogram, aspect="auto", origin="lower", - interpolation='none') - plt.colorbar(im, ax=ax) - plt.xlabel("Frames") - plt.ylabel("Channels") - plt.tight_layout() - - fig.canvas.draw() - data = np.fromstring(fig.canvas.tostring_rgb(), dtype=np.uint8, sep='') - data = data.reshape(fig.canvas.get_width_height()[::-1] + (3,)) - plt.close() - return data - - -def plot_alignment_to_numpy(alignment, info=None): - global MATPLOTLIB_FLAG - if not MATPLOTLIB_FLAG: - import matplotlib - matplotlib.use("Agg") - MATPLOTLIB_FLAG = True - mpl_logger = logging.getLogger('matplotlib') - mpl_logger.setLevel(logging.WARNING) - import matplotlib.pylab as plt - import numpy as np - - fig, ax = plt.subplots(figsize=(6, 4)) - im = ax.imshow(alignment.transpose(), aspect='auto', origin='lower', - interpolation='none') - fig.colorbar(im, ax=ax) - xlabel = 'Decoder timestep' - if info is not None: - xlabel += '\n\n' + info - plt.xlabel(xlabel) - plt.ylabel('Encoder timestep') - plt.tight_layout() - - fig.canvas.draw() - data = np.fromstring(fig.canvas.tostring_rgb(), dtype=np.uint8, sep='') - data = data.reshape(fig.canvas.get_width_height()[::-1] + (3,)) - plt.close() - return data - - -def load_wav_to_torch(full_path): - sampling_rate, data = read(full_path) - return torch.FloatTensor(data.astype(np.float32)), sampling_rate - - -def load_filepaths_and_text(filename, split="|"): - with open(filename, encoding='utf-8') as f: - filepaths_and_text = [line.strip().split(split) for line in f] - return filepaths_and_text - - -def get_hparams(init=True): - parser = argparse.ArgumentParser() - parser.add_argument('-c', '--config', type=str, default="./configs/base.json", - help='JSON file for configuration') - parser.add_argument('-m', '--model', type=str, default="./OUTPUT_MODEL", - help='Model name') - parser.add_argument('--cont', dest='cont', action="store_true", default=False, help="whether to continue training on the latest checkpoint") - - args = parser.parse_args() - model_dir = os.path.join("./logs", args.model) - - if not os.path.exists(model_dir): - os.makedirs(model_dir) - - config_path = args.config - config_save_path = os.path.join(model_dir, "config.json") - if init: - with open(config_path, "r") as f: - data = f.read() - with open(config_save_path, "w") as f: - f.write(data) - else: - with open(config_save_path, "r") as f: - data = f.read() - config = json.loads(data) - - hparams = HParams(**config) - hparams.model_dir = model_dir - hparams.cont = args.cont - return hparams - - -def clean_checkpoints(path_to_models='logs/44k/', n_ckpts_to_keep=2, sort_by_time=True): - """Freeing up space by deleting saved ckpts - - Arguments: - path_to_models -- Path to the model directory - n_ckpts_to_keep -- Number of ckpts to keep, excluding G_0.pth and D_0.pth - sort_by_time -- True -> chronologically delete ckpts - False -> lexicographically delete ckpts - """ - import re - ckpts_files = [f for f in os.listdir(path_to_models) if os.path.isfile(os.path.join(path_to_models, f))] - name_key = (lambda _f: int(re.compile('._(\d+)\.pth').match(_f).group(1))) - time_key = (lambda _f: os.path.getmtime(os.path.join(path_to_models, _f))) - sort_key = time_key if sort_by_time else name_key - x_sorted = lambda _x: sorted([f for f in ckpts_files if f.startswith(_x) and not f.endswith('_0.pth')], - key=sort_key) - to_del = [os.path.join(path_to_models, fn) for fn in - (x_sorted('G')[:-n_ckpts_to_keep] + x_sorted('D')[:-n_ckpts_to_keep])] - del_info = lambda fn: logger.info(f".. Free up space by deleting ckpt {fn}") - del_routine = lambda x: [os.remove(x), del_info(x)] - rs = [del_routine(fn) for fn in to_del] - -def get_hparams_from_dir(model_dir): - config_save_path = os.path.join(model_dir, "config.json") - with open(config_save_path, "r") as f: - data = f.read() - config = json.loads(data) - - hparams = HParams(**config) - hparams.model_dir = model_dir - return hparams - - -def get_hparams_from_file(config_path): - with open(config_path, "r") as f: - data = f.read() - config = json.loads(data) - - hparams = HParams(**config) - return hparams - - -def check_git_hash(model_dir): - source_dir = os.path.dirname(os.path.realpath(__file__)) - if not os.path.exists(os.path.join(source_dir, ".git")): - logger.warn("{} is not a git repository, therefore hash value comparison will be ignored.".format( - source_dir - )) - return - - cur_hash = subprocess.getoutput("git rev-parse HEAD") - - path = os.path.join(model_dir, "githash") - if os.path.exists(path): - saved_hash = open(path).read() - if saved_hash != cur_hash: - logger.warn("git hash values are different. {}(saved) != {}(current)".format( - saved_hash[:8], cur_hash[:8])) - else: - open(path, "w").write(cur_hash) - - -def get_logger(model_dir, filename="train.log"): - global logger - logger = logging.getLogger(os.path.basename(model_dir)) - logger.setLevel(logging.DEBUG) - - formatter = logging.Formatter("%(asctime)s\t%(name)s\t%(levelname)s\t%(message)s") - if not os.path.exists(model_dir): - os.makedirs(model_dir) - h = logging.FileHandler(os.path.join(model_dir, filename)) - h.setLevel(logging.DEBUG) - h.setFormatter(formatter) - logger.addHandler(h) - return logger - - -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__() diff --git a/spaces/digitalxingtong/Jiuxia-Bert-Vits2/README.md b/spaces/digitalxingtong/Jiuxia-Bert-Vits2/README.md deleted file mode 100644 index 1e88ad2655b2cd9bc0b237fef92c0088b3826926..0000000000000000000000000000000000000000 --- a/spaces/digitalxingtong/Jiuxia-Bert-Vits2/README.md +++ /dev/null @@ -1,13 +0,0 @@ ---- -title: AI九夏 -emoji: 🌟 -colorFrom: red -colorTo: indigo -sdk: gradio -sdk_version: 3.36.1 -app_file: app.py -pinned: false -license: mit ---- - -Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference \ No newline at end of file diff --git a/spaces/dorkai/ChatUIPro/app/components/base/loading/style.css b/spaces/dorkai/ChatUIPro/app/components/base/loading/style.css deleted file mode 100644 index 40402e1a9d90ba136abb31a655f5b1a0e932cc5c..0000000000000000000000000000000000000000 --- a/spaces/dorkai/ChatUIPro/app/components/base/loading/style.css +++ /dev/null @@ -1,41 +0,0 @@ -.spin-animation path { - animation: custom 2s linear infinite; -} - -@keyframes custom { - 0% { - opacity: 0; - } - - 25% { - opacity: 0.1; - } - - 50% { - opacity: 0.2; - } - - 75% { - opacity: 0.5; - } - - 100% { - opacity: 1; - } -} - -.spin-animation path:nth-child(1) { - animation-delay: 0s; -} - -.spin-animation path:nth-child(2) { - animation-delay: 0.5s; -} - -.spin-animation path:nth-child(3) { - animation-delay: 1s; -} - -.spin-animation path:nth-child(4) { - animation-delay: 1.5s; -} \ No newline at end of file diff --git a/spaces/dorkai/ChatUIPro/hooks/use-breakpoints.ts b/spaces/dorkai/ChatUIPro/hooks/use-breakpoints.ts deleted file mode 100644 index 1aab56a9fdbd2bfce3b52c940bd10be3eadc1a00..0000000000000000000000000000000000000000 --- a/spaces/dorkai/ChatUIPro/hooks/use-breakpoints.ts +++ /dev/null @@ -1,27 +0,0 @@ -'use client' -import React from 'react' - -export enum MediaType { - mobile = 'mobile', - tablet = 'tablet', - pc = 'pc', -} - -const useBreakpoints = () => { - const [width, setWidth] = React.useState(globalThis.innerWidth); - const media = (() => { - if (width <= 640) return MediaType.mobile; - if (width <= 768) return MediaType.tablet; - return MediaType.pc; - })(); - - React.useEffect(() => { - const handleWindowResize = () => setWidth(window.innerWidth); - window.addEventListener("resize", handleWindowResize); - return () => window.removeEventListener("resize", handleWindowResize); - }, []); - - return media; -} - -export default useBreakpoints \ No newline at end of file diff --git a/spaces/dorkai/ChatUIPro/tailwind.config.js b/spaces/dorkai/ChatUIPro/tailwind.config.js deleted file mode 100644 index 9b7b3acec9bf29f2f1451336c2a881717c920f6a..0000000000000000000000000000000000000000 --- a/spaces/dorkai/ChatUIPro/tailwind.config.js +++ /dev/null @@ -1,66 +0,0 @@ -/** @type {import('tailwindcss').Config} */ -module.exports = { - content: [ - './app/**/*.{js,ts,jsx,tsx}', - './components/**/*.{js,ts,jsx,tsx}', - ], - theme: { - typography: require('./typography'), - extend: { - colors: { - gray: { - 50: '#F9FAFB', - 100: '#F3F4F6', - 200: '#E5E7EB', - 300: '#D1D5DB', - 400: '#9CA3AF', - 500: '#6B7280', - 700: '#374151', - 800: '#1F2A37', - 900: '#111928', - }, - primary: { - 50: '#EBF5FF', - 100: '#E1EFFE', - 200: '#C3DDFD', - 300: '#A4CAFE', - 600: '#1C64F2', - 700: '#1A56DB', - }, - blue: { - 500: '#E1EFFE', - }, - green: { - 50: '#F3FAF7', - 100: '#DEF7EC', - 800: '#03543F', - - }, - yellow: { - 100: '#FDF6B2', - 800: '#723B13', - }, - purple: { - 50: '#F6F5FF', - }, - indigo: { - 25: '#F5F8FF', - 100: '#E0EAFF', - 600: '#444CE7' - } - }, - screens: { - 'mobile': '100px', - // => @media (min-width: 100px) { ... } - 'tablet': '640px', // 391 - // => @media (min-width: 600px) { ... } - 'pc': '769px', - // => @media (min-width: 769px) { ... } - }, - }, - }, - plugins: [ - require('@tailwindcss/typography'), - require('@tailwindcss/line-clamp'), - ], -} diff --git a/spaces/dorkai/text-generation-webui-main/text-generation-webui-main/docs/Training-LoRAs.md b/spaces/dorkai/text-generation-webui-main/text-generation-webui-main/docs/Training-LoRAs.md deleted file mode 100644 index 3d75ec5aa2bc12e8c13d6a583bd9aefd118f04d7..0000000000000000000000000000000000000000 --- a/spaces/dorkai/text-generation-webui-main/text-generation-webui-main/docs/Training-LoRAs.md +++ /dev/null @@ -1,167 +0,0 @@ -## Training Your Own LoRAs - -The WebUI seeks to make training your own LoRAs as easy as possible. It comes down to just a few simple steps: - -### **Step 1**: Make a plan. -- What base model do you want to use? The LoRA you make has to be matched up to a single architecture (eg LLaMA-13B) and cannot be transferred to others (eg LLaMA-7B, StableLM, etc. would all be different). Derivatives of the same model (eg Alpaca finetune of LLaMA-13B) might be transferrable, but even then it's best to train exactly on what you plan to use. -- What model format do you want? At time of writing, 8-bit models are most stable, and 4-bit are supported but experimental. In the near future it is likely that 4-bit will be the best option for most users. -- What are you training it on? Do you want it to learn real information, a simple format, ...? - -### **Step 2**: Gather a dataset. -- If you use a dataset similar to the [Alpaca](https://github.com/gururise/AlpacaDataCleaned/blob/main/alpaca_data_cleaned.json) format, that is natively supported by the `Formatted Dataset` input in the WebUI, with premade formatter options. -- If you use a dataset that isn't matched to Alpaca's format, but uses the same basic JSON structure, you can make your own format file by copying `training/formats/alpaca-format.json` to a new file and [editing its content](#format-files). -- If you can get the dataset into a simple text file, that works too! You can train using the `Raw text file` input option. - - This means you can for example just copy/paste a chatlog/documentation page/whatever you want, shove it in a plain text file, and train on it. -- If you use a structured dataset not in this format, you may have to find an external way to convert it - or open an issue to request native support. - -### **Step 3**: Do the training. -- **3.1**: Load the WebUI, and your model. - - Make sure you don't have any LoRAs already loaded (unless you want to train for multi-LoRA usage). -- **3.2**: Open the `Training` tab at the top, `Train LoRA` sub-tab. -- **3.3**: Fill in the name lof the LoRA, select your dataset in the dataset options. -- **3.4**: Select other parameters to your preference. See [parameters below](#parameters). -- **3.5**: click `Start LoRA Training`, and wait. - - It can take a few hours for a large dataset, or just a few minute if doing a small run. - - You may want to monitor your [loss value](#loss) while it goes. - -### **Step 4**: Evaluate your results. -- Load the LoRA under the Models Tab. -- You can go test-drive it on the `Text generation` tab, or you can use the `Perplexity evaluation` sub-tab of the `Training` tab. -- If you used the `Save every n steps` option, you can grab prior copies of the model from sub-folders within the LoRA model's folder and try them instead. - -### **Step 5**: Re-run if you're unhappy. -- Make sure to unload the LoRA before training it. -- You can simply resume a prior run - use `Copy parameters from` to select your LoRA, and edit parameters. Note that you cannot change the `Rank` of an already created LoRA. - - If you want to resume from a checkpoint saved along the way, simply copy the contents of the checkpoint folder into the LoRA's folder. - - (Note: `adapter_model.bin` is the important file that holds the actual LoRA content). - - This will start Learning Rate and Steps back to the start. If you want to resume as if you were midway through, you can adjust your Learning Rate to the last reported LR in logs and reduce your epochs. -- Or, you can start over entirely if you prefer. -- If your model is producing corrupted outputs, you probably need to start over and use a lower Learning Rate. -- If your model isn't learning detailed information but you want it to, you might need to just run more epochs, or you might need a higher Rank. -- If your model is enforcing a format you didn't want, you may need to tweak your dataset, or start over and not train as far. - -## Format Files - -If using JSON formatted datasets, they are presumed to be in the following approximate format: - -```json -[ - { - "somekey": "somevalue", - "key2": "value2" - }, - { - // etc - } -] -``` - -Where the keys (eg `somekey`, `key2` above) are standardized, and relatively consistent across the dataset, and the values (eg `somevalue`, `value2`) contain the content actually intended to be trained. - -For Alpaca, the keys are `instruction`, `input`, and `output`, wherein `input` is sometimes blank. - -A simple format file for Alpaca to be used as a chat bot is: - -```json -{ - "instruction,output": "User: %instruction%\nAssistant: %output%", - "instruction,input,output": "User: %instruction%: %input%\nAssistant: %output%" -} -``` - -Note that the keys (eg `instruction,output`) are a comma-separated list of dataset keys, and the values are a simple string that use those keys with `%%`. - -So for example if a dataset has `"instruction": "answer my question"`, then the format file's `User: %instruction%\n` will be automatically filled in as `User: answer my question\n`. - -If you have different sets of key inputs, you can make your own format file to match it. This format-file is designed to be as simple as possible to enable easy editing to match your needs. - -## Parameters - -The basic purpose and function of each parameter is documented on-page in the WebUI, so read through them in the UI to understand your options. - -That said, here's a guide to the most important parameter choices you should consider: - -### VRAM - -- First, you must consider your VRAM availability. - - Generally, under default settings, VRAM usage for training with default parameters is very close to when generating text (with 1000+ tokens of context) (ie, if you can generate text, you can train LoRAs). - - Note: worse by default in the 4-bit monkeypatch currently. Reduce `Micro Batch Size` to `1` to restore this to expectations. - - If you have VRAM to spare, setting higher batch sizes will use more VRAM and get you better quality training in exchange. - - If you have large data, setting a higher cutoff length may be beneficial, but will cost significant VRAM. If you can spare some, set your batch size to `1` and see how high you can push your cutoff length. - - If you're low on VRAM, reducing batch size or cutoff length will of course improve that. - - Don't be afraid to just try it and see what happens. If it's too much, it will just error out, and you can lower settings and try again. - -### Rank - -- Second, you want to consider the amount of learning you want. - - For example, you may wish to just learn a dialogue format (as in the case of Alpaca) in which case setting a low `Rank` value (32 or lower) works great. - - Or, you might be training on project documentation you want the bot to understand and be able to understand questions about, in which case the higher the rank, the better. - - Generally, higher Rank = more precise learning = more total content learned = more VRAM usage while training. - -### Learning Rate and Epochs - -- Third, how carefully you want it to be learned. - - In other words, how okay or not you are with the model losing unrelated understandings. - - You can control this with 3 key settings: the Learning Rate, its scheduler, and your total epochs. - - The learning rate controls how much change is made to the model by each token it sees. - - It's in scientific notation normally, so for example `3e-4` means `3 * 10^-4` which is `0.0003`. The number after `e-` controls how many `0`s are in the number. - - Higher values let training run faster, but also are more likely to corrupt prior data in the model. - - You essentially have two variables to balance: the LR, and Epochs. - - If you make LR higher, you can set Epochs equally lower to match. High LR + low epochs = very fast, low quality training. - - If you make LR low, set epochs high. Low LR + high epochs = slow but high-quality training. - - The scheduler controls change-over-time as you train - it starts high, and then goes low. This helps balance getting data in, and having decent quality, at the same time. - - You can see graphs of the different scheduler options [in the HuggingFace docs here](https://moon-ci-docs.huggingface.co/docs/transformers/pr_1/en/main_classes/optimizer_schedules#transformers.SchedulerType) - -## Loss - -When you're running training, the WebUI's console window will log reports that include, among other things, a numeric value named `Loss`. It will start as a high number, and gradually get lower and lower as it goes. - -"Loss" in the world of AI training theoretically means "how close is the model to perfect", with `0` meaning "absolutely perfect". This is calculated by measuring the difference between the model outputting exactly the text you're training it to output, and what it actually outputs. - -In practice, a good LLM should have a very complex variable range of ideas running in its artificial head, so a loss of `0` would indicate that the model has broken and forgotten to how think about anything other than what you trained it. - -So, in effect, Loss is a balancing game: you want to get it low enough that it understands your data, but high enough that it isn't forgetting everything else. Generally, if it goes below `1.0`, it's going to start forgetting its prior memories, and you should stop training. In some cases you may prefer to take it as low as `0.5` (if you want it to be very very predictable). Different goals have different needs, so don't be afraid to experiment and see what works best for you. - -Note: if you see Loss start at or suddenly jump to exactly `0`, it is likely something has gone wrong in your training process (eg model corruption). - -## Note: 4-Bit Monkeypatch - -The [4-bit LoRA monkeypatch](GPTQ-models-(4-bit-mode).md#using-loras-in-4-bit-mode) works for training, but has side effects: -- VRAM usage is higher currently. You can reduce the `Micro Batch Size` to `1` to compensate. -- Models do funky things. LoRAs apply themselves, or refuse to apply, or spontaneously error out, or etc. It can be helpful to reload base model or restart the WebUI between training/usage to minimize chances of anything going haywire. -- Loading or working with multiple LoRAs at the same time doesn't currently work. -- Generally, recognize and treat the monkeypatch as the dirty temporary hack it is - it works, but isn't very stable. It will get better in time when everything is merged upstream for full official support. - -## Legacy notes - -LoRA training was contributed by [mcmonkey4eva](https://github.com/mcmonkey4eva) in PR [#570](https://github.com/oobabooga/text-generation-webui/pull/570). - -### Using the original alpaca-lora code - -Kept here for reference. The Training tab has much more features than this method. - -``` -conda activate textgen -git clone https://github.com/tloen/alpaca-lora -``` - -Edit those two lines in `alpaca-lora/finetune.py` to use your existing model folder instead of downloading everything from decapoda: - -``` -model = LlamaForCausalLM.from_pretrained( - "models/llama-7b", - load_in_8bit=True, - device_map="auto", -) -tokenizer = LlamaTokenizer.from_pretrained( - "models/llama-7b", add_eos_token=True -) -``` - -Run the script with: - -``` -python finetune.py -``` - -It just works. It runs at 22.32s/it, with 1170 iterations in total, so about 7 hours and a half for training a LoRA. RTX 3090, 18153MiB VRAM used, drawing maximum power (350W, room heater mode). diff --git a/spaces/drift-ai/emoji-tagging/Makefile b/spaces/drift-ai/emoji-tagging/Makefile deleted file mode 100644 index 075e9a709827f57df977fd97584f235e555dde40..0000000000000000000000000000000000000000 --- a/spaces/drift-ai/emoji-tagging/Makefile +++ /dev/null @@ -1,3 +0,0 @@ -install: - poetry install - poetry run pip list --format=freeze > requirements.txt diff --git a/spaces/ds21/Q-TicTacToe/README.md b/spaces/ds21/Q-TicTacToe/README.md deleted file mode 100644 index 161f39f0cf97c96649b9736fc55043c69ad03fb3..0000000000000000000000000000000000000000 --- a/spaces/ds21/Q-TicTacToe/README.md +++ /dev/null @@ -1,13 +0,0 @@ ---- -title: Q TicTacToe -emoji: :) -colorFrom: gray -colorTo: red -sdk: streamlit -sdk_version: 1.2.0 -app_file: app.py -pinned: false ---- - -# Q-TicTacToe -A Quantam version of the Tic Tac Toe game diff --git a/spaces/eforebrahim/Cassava-Leaf-Disease-Classification/README.md b/spaces/eforebrahim/Cassava-Leaf-Disease-Classification/README.md deleted file mode 100644 index 87f15455cf1235fbf8dfd3b3a1971261695ccf74..0000000000000000000000000000000000000000 --- a/spaces/eforebrahim/Cassava-Leaf-Disease-Classification/README.md +++ /dev/null @@ -1,29 +0,0 @@ ---- -title: Cassava Leaf Disease Classification -emoji: ☘️ -colorFrom: gray -colorTo: gray -sdk: streamlit -sdk_version: 1.10.0 -app_file: app.py -pinned: false ---- -# Cassava Leaf Disease Classification Project - - -## Background Information -“As the second-largest provider of carbohydrates in Africa, cassava is a key food security crop grown by smallholder farmers because it can withstand harsh conditions. -At least 80% of household farms in Sub-Saharan Africa grow this starchy root, but viral diseases are major sources of poor yields. With the help of data science, it may be possible to identify common diseases so they can be treated.” - -## Data -The data contains about 21,000 images of Cassava plant belonging to 5 different categories (4 diseases and 1 healthy). -The dataset was made available by Makerere University AI Lab via Kaggle Competition. You can get the dataset from here: (https://lnkd.in/dxGUTcN4) - -## Modeling -Pre-trained model efficientnet version b2 is used for modeling. Dropout layers were added to prevent model from overfitting. Validation precision of 0.855, recall of 0.813, and accuracy of 0.831 is achieved. - -## Deployment -The web app is hosted on huggingface spaces using streamlit user interface. -#datascience #machinelearning #computervision #artificialintelligence - -Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference diff --git a/spaces/elkraken/Video-Object-Detection/app.py b/spaces/elkraken/Video-Object-Detection/app.py deleted file mode 100644 index d621ffdb8407864cf8c0e74c866737f580264e56..0000000000000000000000000000000000000000 --- a/spaces/elkraken/Video-Object-Detection/app.py +++ /dev/null @@ -1,293 +0,0 @@ -import gradio as gr -import os - -import argparse -import time -from pathlib import Path - -import cv2 -import torch -import torch.backends.cudnn as cudnn -from numpy import random - -from models.experimental import attempt_load -from utils.datasets import LoadStreams, LoadImages -from utils.general import check_img_size, check_requirements, check_imshow, non_max_suppression, apply_classifier, \ - scale_coords, xyxy2xywh, strip_optimizer, set_logging, increment_path -from utils.plots import plot_one_box -from utils.torch_utils import select_device, load_classifier, time_synchronized, TracedModel -from PIL import Image - -from sort import * - -from huggingface_hub import hf_hub_download - -def load_model(model_name): - model_path = hf_hub_download(repo_id=f"Yolov7/{model_name}", filename=f"{model_name}.pt") - - return model_path - - -model_names = ["yolov7"] - -models = {model_name: load_model(model_name) for model_name in model_names} - -################################## -# """Function to Draw Bounding boxes""" -def draw_boxes(img, bbox, identities=None, categories=None, confidences = None, names=None, colors = None): - for i, box in enumerate(bbox): - x1, y1, x2, y2 = [int(i) for i in box] - tl = opt.thickness or round(0.002 * (img.shape[0] + img.shape[1]) / 2) + 1 # line/font thickness - - cat = int(categories[i]) if categories is not None else 0 - id = int(identities[i]) if identities is not None else 0 - # conf = confidences[i] if confidences is not None else 0 - - color = colors[cat] - - if not opt.nobbox: - cv2.rectangle(img, (x1, y1), (x2, y2), color, tl) - - if not opt.nolabel: - label = str(id) + ":"+ names[cat] if identities is not None else f'{names[cat]} {confidences[i]:.2f}' - tf = max(tl - 1, 1) # font thickness - t_size = cv2.getTextSize(label, 0, fontScale=tl / 3, thickness=tf)[0] - c2 = x1 + t_size[0], y1 - t_size[1] - 3 - cv2.rectangle(img, (x1, y1), c2, color, -1, cv2.LINE_AA) # filled - cv2.putText(img, label, (x1, y1 - 2), 0, tl / 3, [225, 255, 255], thickness=tf, lineType=cv2.LINE_AA) - - - return img -################################## - - -def detect(save_img=True): - parser = argparse.ArgumentParser() - parser.add_argument('--weights', nargs='+', type=str, default='yolov7.pt', help='model.pt path(s)') - parser.add_argument('--source', type=str, default='inference/images', help='source') # file/folder, 0 for webcam - parser.add_argument('--img-size', type=int, default=640, help='inference size (pixels)') - parser.add_argument('--conf-thres', type=float, default=0.25, help='object confidence threshold') - parser.add_argument('--iou-thres', type=float, default=0.45, help='IOU threshold for NMS') - parser.add_argument('--device', default='', help='cuda device, i.e. 0 or 0,1,2,3 or cpu') - parser.add_argument('--view-img', action='store_true', help='display results') - parser.add_argument('--save-txt', action='store_true', help='save results to *.txt') - parser.add_argument('--save-conf', action='store_true', help='save confidences in --save-txt labels') - parser.add_argument('--nosave', action='store_true', help='do not save images/videos') - parser.add_argument('--classes', nargs='+', type=int, help='filter by class: --class 0, or --class 0 2 3') - parser.add_argument('--agnostic-nms', action='store_true', help='class-agnostic NMS') - parser.add_argument('--augment', action='store_true', help='augmented inference') - parser.add_argument('--update', action='store_true', help='update all models') - parser.add_argument('--project', default='runs/detect', help='save results to project/name') - parser.add_argument('--name', default='exp', help='save results to project/name') - parser.add_argument('--exist-ok', action='store_true', help='existing project/name ok, do not increment') - parser.add_argument('--no-trace', action='store_true', help='don`t trace model') - - parser.add_argument('--track', action='store_true', help='run tracking') - parser.add_argument('--show-track', action='store_true', help='show tracked path') - parser.add_argument('--show-fps', action='store_true', help='show fps') - parser.add_argument('--thickness', type=int, default=2, help='bounding box and font size thickness') - parser.add_argument('--seed', type=int, default=1, help='random seed to control bbox colors') - parser.add_argument('--nobbox', action='store_true', help='don`t show bounding box') - parser.add_argument('--nolabel', action='store_true', help='don`t show label') - parser.add_argument('--unique-track-color', action='store_true', help='show each track in unique color') - - opt = parser.parse_args() - np.random.seed(opt.seed) - - sort_tracker = Sort(max_age=5, - min_hits=2, - iou_threshold=0.2) - - source, weights, view_img, save_txt, imgsz, trace = opt.source, opt.weights, opt.view_img, opt.save_txt, opt.img_size, not opt.no_trace - save_img = not opt.nosave and not source.endswith('.txt') # save inference images - webcam = source.isnumeric() or source.endswith('.txt') or source.lower().startswith( - ('rtsp://', 'rtmp://', 'http://', 'https://')) - save_dir = Path(increment_path(Path(opt.project) / opt.name, exist_ok=opt.exist_ok)) # increment run - if not opt.nosave: - (save_dir / 'labels' if save_txt else save_dir).mkdir(parents=True, exist_ok=True) # make dir - - # Initialize - set_logging() - device = select_device(opt.device) - half = device.type != 'cpu' # half precision only supported on CUDA - - # Load model - model = attempt_load(weights, map_location=device) # load FP32 model - stride = int(model.stride.max()) # model stride - imgsz = check_img_size(imgsz, s=stride) # check img_size - - if trace: - model = TracedModel(model, device, opt.img_size) - - if half: - model.half() # to FP16 - - # Second-stage classifier - classify = False - if classify: - modelc = load_classifier(name='resnet101', n=2) # initialize - modelc.load_state_dict(torch.load('weights/resnet101.pt', map_location=device)['model']).to(device).eval() - - # Set Dataloader - vid_path, vid_writer = None, None - if webcam: - view_img = check_imshow() - cudnn.benchmark = True # set True to speed up constant image size inference - dataset = LoadStreams(source, img_size=imgsz, stride=stride) - else: - dataset = LoadImages(source, img_size=imgsz, stride=stride) - - # Get names and colors - names = model.module.names if hasattr(model, 'module') else model.names - colors = [[random.randint(0, 255) for _ in range(3)] for _ in names] - - # Run inference - if device.type != 'cpu': - model(torch.zeros(1, 3, imgsz, imgsz).to(device).type_as(next(model.parameters()))) # run once - old_img_w = old_img_h = imgsz - old_img_b = 1 - - t0 = time.time() - ################################### - startTime = 0 - ################################### - for path, img, im0s, vid_cap in dataset: - img = torch.from_numpy(img).to(device) - img = img.half() if half else img.float() # uint8 to fp16/32 - img /= 255.0 # 0 - 255 to 0.0 - 1.0 - if img.ndimension() == 3: - img = img.unsqueeze(0) - - # Warmup - if device.type != 'cpu' and (old_img_b != img.shape[0] or old_img_h != img.shape[2] or old_img_w != img.shape[3]): - old_img_b = img.shape[0] - old_img_h = img.shape[2] - old_img_w = img.shape[3] - for i in range(3): - model(img, augment=opt.augment)[0] - - # Inference - t1 = time_synchronized() - pred = model(img, augment=opt.augment)[0] - t2 = time_synchronized() - - # Apply NMS - pred = non_max_suppression(pred, opt.conf_thres, opt.iou_thres, classes=opt.classes, agnostic=opt.agnostic_nms) - t3 = time_synchronized() - - # Apply Classifier - if classify: - pred = apply_classifier(pred, modelc, img, im0s) - - # Process detections - for i, det in enumerate(pred): # detections per image - if webcam: # batch_size >= 1 - p, s, im0, frame = path[i], '%g: ' % i, im0s[i].copy(), dataset.count - else: - p, s, im0, frame = path, '', im0s, getattr(dataset, 'frame', 0) - - p = Path(p) # to Path - save_path = str(save_dir / p.name) # img.jpg - txt_path = str(save_dir / 'labels' / p.stem) + ('' if dataset.mode == 'image' else f'_{frame}') # img.txt - gn = torch.tensor(im0.shape)[[1, 0, 1, 0]] # normalization gain whwh - if len(det): - # Rescale boxes from img_size to im0 size - det[:, :4] = scale_coords(img.shape[2:], det[:, :4], im0.shape).round() - - # Print results - for c in det[:, -1].unique(): - n = (det[:, -1] == c).sum() # detections per class - s += f"{n} {names[int(c)]}{'s' * (n > 1)}, " # add to string - - dets_to_sort = np.empty((0,6)) - # NOTE: We send in detected object class too - for x1,y1,x2,y2,conf,detclass in det.cpu().detach().numpy(): - dets_to_sort = np.vstack((dets_to_sort, - np.array([x1, y1, x2, y2, conf, detclass]))) - - - if opt.track: - - tracked_dets = sort_tracker.update(dets_to_sort, opt.unique_track_color) - tracks =sort_tracker.getTrackers() - - # draw boxes for visualization - if len(tracked_dets)>0: - bbox_xyxy = tracked_dets[:,:4] - identities = tracked_dets[:, 8] - categories = tracked_dets[:, 4] - confidences = None - - if opt.show_track: - #loop over tracks - for t, track in enumerate(tracks): - - track_color = colors[int(track.detclass)] if not opt.unique_track_color else sort_tracker.color_list[t] - - [cv2.line(im0, (int(track.centroidarr[i][0]), - int(track.centroidarr[i][1])), - (int(track.centroidarr[i+1][0]), - int(track.centroidarr[i+1][1])), - track_color, thickness=opt.thickness) - for i,_ in enumerate(track.centroidarr) - if i < len(track.centroidarr)-1 ] - else: - bbox_xyxy = dets_to_sort[:,:4] - identities = None - categories = dets_to_sort[:, 5] - confidences = dets_to_sort[:, 4] - - im0 = draw_boxes(im0, bbox_xyxy, identities, categories, confidences, names, colors) - - # Print time (inference + NMS) - print(f'{s}Done. ({(1E3 * (t2 - t1)):.1f}ms) Inference, ({(1E3 * (t3 - t2)):.1f}ms) NMS') - - # Stream results - ###################################################### - if dataset.mode != 'image' and opt.show_fps: - currentTime = time.time() - - fps = 1/(currentTime - startTime) - startTime = currentTime - cv2.putText(im0, "FPS: " + str(int(fps)), (20, 70), cv2.FONT_HERSHEY_PLAIN, 2, (0,255,0),2) - - ####################################################### - if view_img: - cv2.imshow(str(p), im0) - cv2.waitKey(1) # 1 millisecond - - # Save results (image with detections) - if save_img: - if dataset.mode == 'image': - cv2.imwrite(save_path, im0) - print(f" The image with the result is saved in: {save_path}") - else: # 'video' or 'stream' - if vid_path != save_path: # new video - vid_path = save_path - if isinstance(vid_writer, cv2.VideoWriter): - vid_writer.release() # release previous video writer - if vid_cap: # video - fps = vid_cap.get(cv2.CAP_PROP_FPS) - w = int(vid_cap.get(cv2.CAP_PROP_FRAME_WIDTH)) - h = int(vid_cap.get(cv2.CAP_PROP_FRAME_HEIGHT)) - else: # stream - fps, w, h = 30, im0.shape[1], im0.shape[0] - save_path += '.mp4' - vid_writer = cv2.VideoWriter(save_path, cv2.VideoWriter_fourcc(*'mp4v'), fps, (w, h)) - vid_writer.write(im0) - - if save_txt or save_img: - s = f"\n{len(list(save_dir.glob('labels/*.txt')))} labels saved to {save_dir / 'labels'}" if save_txt else '' - #print(f"Results saved to {save_dir}{s}") - - print(f'Done. ({time.time() - t0:.3f}s)') - return img - - - -desc = "demo for WongKinYiu/yolov7 Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors" -gr.Interface(detect, - inputs = [gr.Video(format="mp4")], - outputs = gr.Video(format="mp4"), - title="Yolov7",description=desc).launch() -# gr.Interface(detect,[gr.Image(type="pil"),gr.Dropdown(choices=model_names)], gr.Image(type="pil"),title="Yolov7",examples=[["horses.jpeg", "yolov7"]],description="demo for WongKinYiu/yolov7 Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors").launch() \ No newline at end of file diff --git a/spaces/erc/entity-referring-classifier/ercbcm/model_loader.py b/spaces/erc/entity-referring-classifier/ercbcm/model_loader.py deleted file mode 100644 index 9cafd0dfe0199ed4e9bee10127be8e01500293ce..0000000000000000000000000000000000000000 --- a/spaces/erc/entity-referring-classifier/ercbcm/model_loader.py +++ /dev/null @@ -1,8 +0,0 @@ -import torch - -def load(load_path, model, device): - if load_path == None: return - state_dict = torch.load(load_path, map_location=device) - model.load_state_dict(state_dict['model_state_dict']) - print('[LOAD] Model has been loaded successfully from \'{}\''.format(load_path)) - return state_dict['valid_loss'] \ No newline at end of file diff --git a/spaces/eson/tokenizer-arena/vocab/chinese_alpaca_lora_7b/README.md b/spaces/eson/tokenizer-arena/vocab/chinese_alpaca_lora_7b/README.md deleted file mode 100644 index 3215d800239ba6e89bd1f3a257983222fda3e996..0000000000000000000000000000000000000000 --- a/spaces/eson/tokenizer-arena/vocab/chinese_alpaca_lora_7b/README.md +++ /dev/null @@ -1,4 +0,0 @@ - - -来自 chinese-alpaca-lora-7b-merge-hf - diff --git a/spaces/fatiXbelha/sd/Black Lives Matter MP3 Listen and Download the Anthem of a Movement.md b/spaces/fatiXbelha/sd/Black Lives Matter MP3 Listen and Download the Anthem of a Movement.md deleted file mode 100644 index 155c510a97a3ea9c8cf4ef34edd3350ee888a156..0000000000000000000000000000000000000000 --- a/spaces/fatiXbelha/sd/Black Lives Matter MP3 Listen and Download the Anthem of a Movement.md +++ /dev/null @@ -1,150 +0,0 @@ -
-
Download Black Lives Matter MP3: How to Support the Movement Through Music
-If you are looking for a way to show your solidarity with the Black Lives Matter movement, one of the easiest and most effective ways is to download and listen to music that supports the cause. Music is a powerful medium that can inspire, educate, and motivate people to take action against racism, injustice, and oppression. In this article, we will explain what Black Lives Matter is, why it matters, how music can help, and where you can find and download free Black Lives Matter MP3 songs.
-download black lives matter mp3
Download File ✵✵✵ https://urllie.com/2uNz5d
-
What is Black Lives Matter and why is it important?
-Black Lives Matter (BLM) is an international social movement that seeks to highlight racism, discrimination, and racial inequality experienced by black people. Its primary concerns are incidents of police brutality and racially motivated violence against black people. The name Black Lives Matter signals condemnation of the unjust killings of black people by police (black people are far more likely to be killed by police in the United States than white people) and the demand that society value the lives and humanity of black people as much as it values the lives and humanity of white people. BLM activists have held large and influential protests in cities across the United States as well as internationally. A decentralized grassroots movement, Black Lives Matter is led by activists in local chapters who organize their own campaigns and programs. The chapters are affiliated with the Black Lives Matter Global Network Foundation, a nonprofit civil rights organization that is active in the United States, Canada, and the United Kingdom.
-The origin and goals of the movement
-BLM was cofounded in 2013 as an online movement (using the hashtag #BlackLivesMatter on social media) by three black community organizers—Patrisse Khan-Cullors, Alicia Garza, and Opal Tometi. They formed BLM after George Zimmerman, a man of German and Peruvian descent, was acquitted on charges stemming from his fatal shooting of Trayvon Martin, an unarmed black teenager, in Sanford, Florida, in February 2012. Zimmerman, a neighbourhood-watch volunteer, had seen Martin walking in his neighbourhood and called the police because he thought Martin looked “suspicious.” Although Zimmerman was told not to do anything, he followed Martin, got into an argument with him, and shot and killed him. When law enforcement arrived, Zimmerman claimed that he had been assaulted by Martin and fired in self-defense. Zimmerman remained free for weeks, but, as the shooting gained national attention, demonstrations demanding his prosecution were held in cities across the United States.
-Support for BLM grew following other police killings, including Eric Garner, who died in a chokehold, Michael Brown, who was killed by an officer who said he acted in self-defense, Tamir Rice, who was shot while playing with a toy gun, Breonna Taylor, who was shot in her own home during a botched raid, and George Floyd, an unarmed black man who was murdered by a police officer who knelt on his neck for nearly nine minutes. BLM also advocates for justice for other victims of racial violence, such as Ahmaud Arbery, who was chased and killed by three white men while jogging, and Elijah McClain, who died after being put in a chokehold by police and injected with a sedative by paramedics.
-The goals of BLM are to end systemic racism, police brutality, and racial violence; to affirm the dignity and worth of black lives; to create a more inclusive and equitable society; and to empower black communities to achieve social, economic, and political justice. BLM also supports the rights and liberation of other marginalized groups, such as LGBTQ+ people, women, immigrants, and indigenous people.
-The impact and challenges of the movement
-BLM has had a significant impact on raising awareness and sparking dialogue about the issues of racism and police violence in the United States and around the world. BLM has also influenced policy changes at the local, state, and federal levels, such as banning chokeholds, requiring body cameras, establishing civilian oversight boards, and reallocating funds from police departments to social services. BLM has also inspired solidarity movements in other countries, such as the United Kingdom, France, Germany, Australia, Brazil, and Nigeria, where people have protested against their own forms of racial discrimination and oppression.
-However, BLM also faces many challenges and criticisms from various sources. Some of these include:
-download black lives matter song by dax
-download black lives matter protest songs 2020
-download black lives matter anthem by mike robbins
-download black lives matter remix by dababy
-download black lives matter music video by yg
-download black lives matter mp3 free online
-download black lives matter soundtrack from we're all in this together
-download black lives matter rap by juicy j
-download black lives matter acoustic by h.e.r.
-download black lives matter lyrics by lil baby
-download black lives matter album by various artists
-download black lives matter instrumental by t-pain
-download black lives matter podcast by billboard
-download black lives matter speech by dax
-download black lives matter mixtape by hopsin and dax
-download black lives matter live performance by yg
-download black lives matter tribute by shazam
-download black lives matter ringtone by dax
-download black lives matter karaoke by h.e.r.
-download black lives matter cover by tom macdonald
-download black lives matter playlist by gaana.com
-download black lives matter radio edit by dababy
-download black lives matter extended version by juicy j
-download black lives matter unplugged by t-pain
-download black lives matter mashup by lil baby and yg
-download black lives matter documentary by new scientist
-download black lives matter spoken word by dax
-download black lives matter original song by mike robbins
-download black lives matter bonus track by hopsin and dax
-download black lives matter acoustic guitar by h.e.r.
-download black lives matter piano version by t-pain
-download black lives matter edm remix by dababy
-download black lives matter rock cover by tom macdonald
-download black lives matter reggae version by juicy j
-download black lives matter trap beat by yg
-download black lives matter soulful rendition by h.e.r.
-download black lives matter motivational speech by dax
-download black lives matter inspirational podcast by billboard
-download black lives matter educational documentary by new scientist
-download black lives matter historical soundtrack from we're all in this together
-download black lives matter comedy skit by t-pain
-download black lives matter parody song by tom macdonald
-download black lives matter dance video by yg
-download black lives matter meditation music by h.e.r.
-download black lives matter workout playlist by gaana.com
-download black lives matter trivia quiz by shazam
-download black lives matter crossword puzzle by billboard
-download black lives matter coloring book by new scientist
-download black lives matter sticker pack by dax
-
-
- The lack of a clear leadership structure or agenda, which makes it difficult to coordinate actions and communicate demands. -
- The resistance and backlash from some segments of society, especially white supremacists, who view BLM as a threat to their privilege and power. -
- The misrepresentation and distortion of the movement by some media outlets and politicians, who portray BLM as violent, radical, or anti-police. -
- The co-optation and commodification of the movement by some corporations and celebrities, who use BLM as a marketing strategy or a token gesture without making meaningful changes or commitments. -
How can music help spread the message of Black Lives Matter?
-Music is one of the most effective ways to spread the message of Black Lives Matter because it can reach a large and diverse audience, convey emotions and stories that resonate with people, and inspire them to take action. Music is also a form of cultural expression that reflects the identity, history, and struggles of black people. Music can help educate people about the issues that BLM addresses, challenge stereotypes and prejudices, celebrate black excellence and resilience, and demand justice and accountability.
-The power and influence of music as a form of protest and expression
-Music has always been a vital part of social movements throughout history. Music can serve as a way of protesting against injustice, expressing dissent or dissatisfaction, raising awareness or consciousness, mobilizing or organizing people, creating solidarity or community, or offering hope or healing. Music can also influence public opinion, shape cultural norms, or challenge dominant narratives.
-Some examples of how music has been used as a form of protest and expression include:
--
-
- The songs of the civil rights movement in the 1950s and 1960s, such as "We Shall Overcome," "Lift Every Voice and Sing," "A Change Is Gonna Come," and "Strange Fruit," which articulated the aspirations and grievances of black Americans fighting for equality and freedom. -
- The songs of the anti-war movement in the 1960s and 1970s, such as "Blowin' in the Wind," "Give Peace a Chance," "Fortunate Son," and "War," which criticized the US involvement in the Vietnam War and advocated for peace and justice. -
- The songs of the hip-hop movement in the 1980s and 1990s, such as "The Message," "Fight the Power," "Fuck tha Police," and "Changes," which exposed the realities and challenges of urban life for black youth, such as poverty, crime, violence, police brutality, and racism. -
- The songs of the global justice movement in the 1990s and 2000s, such as "Zombie," "They Don't Care About Us," "Where Is the Love?" and "American Idiot," which denounced the effects of globalization, neoliberalism, imperialism, and militarism on human rights and the environment. -
The examples and benefits of using music as a tool for activism and education
-Music can also be used as a tool for activism and education by creating songs that support the goals and values of BLM, by sharing or promoting songs that raise awareness about BLM, or by using songs as a way of teaching or learning about BLM. Music can also provide a platform for black artists to express their perspectives and experiences, to amplify their voices and messages, and to showcase their creativity and talent.
-Some examples of how music can be used as a tool for activism and education include:
--
-
- Creating original songs that address the issues or themes of BLM, such as "I Can't Breathe" by H.E.R., "The Bigger Picture" by Lil Baby, "Black Parade" by Beyoncé, and "This Is America" by Childish Gambino, which have become anthems for the movement. -
- Sharing or promoting songs that support BLM on social media, playlists, podcasts, radio stations, or streaming services, such as Spotify's Black Lives Matter playlist, which features songs from various genres and eras that celebrate black culture and history. -
- Using songs as a way of teaching or learning about BLM in classrooms, workshops, seminars, or online courses, such as Harvard University's course on "The Art of Black Lives Matter," which explores how music and other forms of art have shaped the movement. -
Where can you download free Black Lives Matter MP3 songs?
-If you want to download free Black Lives Matter MP3 songs, there are many websites and platforms that offer a variety of options. However, not all of them are legal, safe, or ethical. Some of them may violate the intellectual property rights of the artists or expose your device to viruses or malware. Therefore, you need to be careful and selective when choosing where to download free music online.
-The best websites and platforms to find and download free music that supports the movement
-Some of the best websites and platforms to find and download free music that supports BLM are:
-| Name | Description | Link |
|---|---|---|
| Bandcamp | A website that allows independent artists to sell their music directly to fans. Many artists offer some or all of their songs for free or for a pay-what-you-want price. Bandcamp also waives its revenue share on the first Friday of every month to support artists during the COVID-19 pandemic. Bandcamp has a section dedicated to BLM where you can find hundreds of albums and tracks that support the movement. | |
| SoundCloud | A website that allows anyone to upload, stream, and download music for free. SoundCloud has a large and diverse community of artists and listeners who share their music online. SoundCloud has a playlist called "Black Lives Matter: Sounds of Protest" that features songs from various genres and artists that express solidarity with BLM. | |
| Noisetrade | A website that allows artists to give away their music for free in exchange for fans' email addresses and postal codes. Noisetrade has a section called "Black Voices" that showcases albums and songs from black artists across different genres. Noisetrade also encourages fans to tip the artists or donate to causes they support. |
The tips and precautions to follow when downloading free music online
-While downloading free music online can be a great way to support BLM and enjoy some amazing tunes, you also need to be aware of some potential risks and problems. Here are some tips and precautions to follow when downloading free music online:
--
-
- Always check the source and the quality of the music before downloading. Make sure the website or platform is reputable, reliable, and secure. Avoid websites that look suspicious, have pop-up ads, or ask for personal information. -
- Always respect the rights and wishes of the artists. Do not download or share music that is not authorized or licensed by the artists. Do not use the music for commercial purposes or modify it without permission. -
- Always scan the files for viruses or malware before opening or playing them. Use a trusted antivirus software and update it regularly. Do not open or run any files that have strange extensions or names. -
- Always backup your music files and devices. Downloading free music online can sometimes cause errors, crashes, or corruption of your files or devices. Make sure you have a backup copy of your music and other important data in case something goes wrong. -
Conclusion
-Downloading free Black Lives Matter MP3 songs is a simple and fun way to show your support for the movement and to enjoy some awesome music. Music can help you learn more about the issues and challenges that BLM addresses, as well as celebrate the diversity and beauty of black culture and history. Music can also inspire you to take action and join the fight for justice and equality. However, you also need to be careful and responsible when downloading free music online, and respect the rights and wishes of the artists who create it.
-We hope this article has given you some useful information and resources on how to download free Black Lives Matter MP3 songs. If you have any questions or comments, feel free to leave them below. And remember, black lives matter!
-FAQs
-What are some of the most popular Black Lives Matter songs?
-There are many songs that have been created or used to support BLM, but some of the most popular ones include:
--
-
- "Alright" by Kendrick Lamar, which became an anthem for BLM after it was released in 2015. The song features the chorus "We gon' be alright," which expresses hope and resilience in the face of adversity. -
- "Freedom" by Beyoncé featuring Kendrick Lamar, which was performed at the 2016 BET Awards with a powerful tribute to BLM. The song celebrates the struggle and liberation of black people throughout history. -
- "This Is America" by Childish Gambino, which won four Grammy Awards in 2019 for its provocative commentary on racism, violence, and consumerism in America. The song's video features shocking imagery and symbolism that references various incidents of racial injustice. -
- "Say It Loud - I'm Black And I'm Proud" by James Brown, which was released in 1968 during the civil rights movement. The song is considered one of the first funk songs and one of the most influential songs in black music history. The song's title became a slogan for black pride and empowerment. -
- "Strange Fruit" by Billie Holiday, which was recorded in 1939 and is widely regarded as one of the first protest songs in American music history. The song exposes the horror of lynching, a form of racial terrorism that killed thousands of black people in the United States. -
How can I donate or contribute to the Black Lives Matter movement?
-There are many ways you can donate or contribute to BLM, such as:
--
-
- Donating money to BLM organizations or causes, such as the Black Lives Matter Global Network Foundation, the NAACP Legal Defense Fund, or local bail funds. -
- Donating time or skills to BLM campaigns or programs, such as volunteering, organizing, educating, or advocating. -
- Donating goods or services to BLM communities or events, such as food, water, medical supplies, legal assistance, or transportation. -
You can find more information on how to donate or contribute to BLM on their official website: https://blacklivesmatter.com/
-How can I learn more about the history and issues of racism and police brutality?
-There are many resources you can use to learn more about the history and issues of racism and police brutality, such as:
--
-
- Books that explore the history and impact of racism and police brutality on black people in America, such as The New Jim Crow by Michelle Alexander, Between The World And Me by Ta-Nehisi Coates, How To Be An Antiracist by Ibram X. Kendi, or The End Of Policing by Alex S. Vitale. -
- Documentaries that examine the causes and consequences of racism and police brutality on black people in America, such as 13th by Ava DuVernay, I Am Not Your Negro by Raoul Peck, or The Death And Life Of Marsha P. Johnson by David France. -
- Podcasts that discuss the current and historical issues of racism and police brutality on black people in America, such as Code Switch by NPR, 1619 by The New York Times, or Pod Save The People by Crooked Media. -
How can I join or organize a Black Lives Matter protest or event in my area?
-There are many ways you can join or organize a BLM protest or event in your area, such as:
--
-
- Following BLM social media accounts or websites to stay updated on the latest news and events related to the movement. -
- Contacting your local BLM chapter or affiliate to find out how you can get involved or support their work. -
- Attending or hosting a BLM rally, march, vigil, or workshop in your area. Make sure you follow the safety guidelines and protocols for COVID-19 prevention and protection. -
- Creating or signing a BLM petition, letter, or statement to demand change or action from your local authorities or representatives. -
How can I support Black artists and businesses in my community?
-There are many ways you can support Black artists and businesses in your community, such as:
--
-
- Purchasing or streaming their music, books, art, or other products. You can also leave positive reviews, ratings, or feedback for them online. -
- Following or subscribing to their social media accounts, websites, blogs, podcasts, or newsletters. You can also share their content with your friends, family, or network. -
- Attending or sponsoring their shows, exhibitions, performances, or events. You can also invite them to speak, teach, or collaborate with you or your organization. -
- Donating or investing in their projects, campaigns, or causes. You can also offer them mentorship, guidance, or resources. -
-
-
\ No newline at end of file diff --git a/spaces/fffiloni/SplitTrack2MusicGen/audiocraft/quantization/base.py b/spaces/fffiloni/SplitTrack2MusicGen/audiocraft/quantization/base.py deleted file mode 100644 index 1b16c130d266fbd021d3fc29bb9f98c33dd3c588..0000000000000000000000000000000000000000 --- a/spaces/fffiloni/SplitTrack2MusicGen/audiocraft/quantization/base.py +++ /dev/null @@ -1,107 +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. - -""" -Base class for all quantizers. -""" - -from dataclasses import dataclass, field -import typing as tp - -import torch -from torch import nn - - -@dataclass -class QuantizedResult: - x: torch.Tensor - codes: torch.Tensor - bandwidth: torch.Tensor # bandwidth in kb/s used, per batch item. - penalty: tp.Optional[torch.Tensor] = None - metrics: dict = field(default_factory=dict) - - -class BaseQuantizer(nn.Module): - """Base class for quantizers. - """ - - def forward(self, x: torch.Tensor, frame_rate: int) -> QuantizedResult: - """ - Given input tensor x, returns first the quantized (or approximately quantized) - representation along with quantized codes, bandwidth, and any penalty term for the loss. - Finally, this returns a dict of metrics to update logging etc. - Frame rate must be passed so that the bandwidth is properly computed. - """ - raise NotImplementedError() - - def encode(self, x: torch.Tensor) -> torch.Tensor: - """Encode a given input tensor with the specified sample rate at the given bandwidth. - """ - raise NotImplementedError() - - def decode(self, codes: torch.Tensor) -> torch.Tensor: - """Decode the given codes to the quantized representation. - """ - raise NotImplementedError() - - @property - def total_codebooks(self): - """Total number of codebooks. - """ - raise NotImplementedError() - - @property - def num_codebooks(self): - """Number of active codebooks. - """ - raise NotImplementedError() - - def set_num_codebooks(self, n: int): - """Set the number of active codebooks. - """ - raise NotImplementedError() - - -class DummyQuantizer(BaseQuantizer): - """Fake quantizer that actually does not perform any quantization. - """ - def __init__(self): - super().__init__() - - def forward(self, x: torch.Tensor, frame_rate: int): - q = x.unsqueeze(1) - return QuantizedResult(x, q, torch.tensor(q.numel() * 32 * frame_rate / 1000 / len(x)).to(x)) - - def encode(self, x: torch.Tensor) -> torch.Tensor: - """Encode a given input tensor with the specified sample rate at the given bandwidth. - In the case of the DummyQuantizer, the codes are actually identical - to the input and resulting quantized representation as no quantization is done. - """ - return x.unsqueeze(1) - - def decode(self, codes: torch.Tensor) -> torch.Tensor: - """Decode the given codes to the quantized representation. - In the case of the DummyQuantizer, the codes are actually identical - to the input and resulting quantized representation as no quantization is done. - """ - return codes.squeeze(1) - - @property - def total_codebooks(self): - """Total number of codebooks. - """ - return 1 - - @property - def num_codebooks(self): - """Total number of codebooks. - """ - return self.total_codebooks - - def set_num_codebooks(self, n: int): - """Set the number of active codebooks. - """ - raise AttributeError("Cannot override the number of codebooks for the dummy quantizer") diff --git a/spaces/fffiloni/controlnet-animation-doodle/node_modules/@types/node/punycode.d.ts b/spaces/fffiloni/controlnet-animation-doodle/node_modules/@types/node/punycode.d.ts deleted file mode 100644 index 87ebbb90483aef0b987fb4c22d78031113fed576..0000000000000000000000000000000000000000 --- a/spaces/fffiloni/controlnet-animation-doodle/node_modules/@types/node/punycode.d.ts +++ /dev/null @@ -1,117 +0,0 @@ -/** - * **The version of the punycode module bundled in Node.js is being deprecated.**In a future major version of Node.js this module will be removed. Users - * currently depending on the `punycode` module should switch to using the - * userland-provided [Punycode.js](https://github.com/bestiejs/punycode.js) module instead. For punycode-based URL - * encoding, see `url.domainToASCII` or, more generally, the `WHATWG URL API`. - * - * The `punycode` module is a bundled version of the [Punycode.js](https://github.com/bestiejs/punycode.js) module. It - * can be accessed using: - * - * ```js - * const punycode = require('punycode'); - * ``` - * - * [Punycode](https://tools.ietf.org/html/rfc3492) is a character encoding scheme defined by RFC 3492 that is - * primarily intended for use in Internationalized Domain Names. Because host - * names in URLs are limited to ASCII characters only, Domain Names that contain - * non-ASCII characters must be converted into ASCII using the Punycode scheme. - * For instance, the Japanese character that translates into the English word,`'example'` is `'例'`. The Internationalized Domain Name, `'例.com'` (equivalent - * to `'example.com'`) is represented by Punycode as the ASCII string`'xn--fsq.com'`. - * - * The `punycode` module provides a simple implementation of the Punycode standard. - * - * The `punycode` module is a third-party dependency used by Node.js and - * made available to developers as a convenience. Fixes or other modifications to - * the module must be directed to the [Punycode.js](https://github.com/bestiejs/punycode.js) project. - * @deprecated Since v7.0.0 - Deprecated - * @see [source](https://github.com/nodejs/node/blob/v18.0.0/lib/punycode.js) - */ -declare module 'punycode' { - /** - * The `punycode.decode()` method converts a [Punycode](https://tools.ietf.org/html/rfc3492) string of ASCII-only - * characters to the equivalent string of Unicode codepoints. - * - * ```js - * punycode.decode('maana-pta'); // 'mañana' - * punycode.decode('--dqo34k'); // '☃-⌘' - * ``` - * @since v0.5.1 - */ - function decode(string: string): string; - /** - * The `punycode.encode()` method converts a string of Unicode codepoints to a [Punycode](https://tools.ietf.org/html/rfc3492) string of ASCII-only characters. - * - * ```js - * punycode.encode('mañana'); // 'maana-pta' - * punycode.encode('☃-⌘'); // '--dqo34k' - * ``` - * @since v0.5.1 - */ - function encode(string: string): string; - /** - * The `punycode.toUnicode()` method converts a string representing a domain name - * containing [Punycode](https://tools.ietf.org/html/rfc3492) encoded characters into Unicode. Only the [Punycode](https://tools.ietf.org/html/rfc3492) encoded parts of the domain name are be - * converted. - * - * ```js - * // decode domain names - * punycode.toUnicode('xn--maana-pta.com'); // 'mañana.com' - * punycode.toUnicode('xn----dqo34k.com'); // '☃-⌘.com' - * punycode.toUnicode('example.com'); // 'example.com' - * ``` - * @since v0.6.1 - */ - function toUnicode(domain: string): string; - /** - * The `punycode.toASCII()` method converts a Unicode string representing an - * Internationalized Domain Name to [Punycode](https://tools.ietf.org/html/rfc3492). Only the non-ASCII parts of the - * domain name will be converted. Calling `punycode.toASCII()` on a string that - * already only contains ASCII characters will have no effect. - * - * ```js - * // encode domain names - * punycode.toASCII('mañana.com'); // 'xn--maana-pta.com' - * punycode.toASCII('☃-⌘.com'); // 'xn----dqo34k.com' - * punycode.toASCII('example.com'); // 'example.com' - * ``` - * @since v0.6.1 - */ - function toASCII(domain: string): string; - /** - * @deprecated since v7.0.0 - * The version of the punycode module bundled in Node.js is being deprecated. - * In a future major version of Node.js this module will be removed. - * Users currently depending on the punycode module should switch to using - * the userland-provided Punycode.js module instead. - */ - const ucs2: ucs2; - interface ucs2 { - /** - * @deprecated since v7.0.0 - * The version of the punycode module bundled in Node.js is being deprecated. - * In a future major version of Node.js this module will be removed. - * Users currently depending on the punycode module should switch to using - * the userland-provided Punycode.js module instead. - */ - decode(string: string): number[]; - /** - * @deprecated since v7.0.0 - * The version of the punycode module bundled in Node.js is being deprecated. - * In a future major version of Node.js this module will be removed. - * Users currently depending on the punycode module should switch to using - * the userland-provided Punycode.js module instead. - */ - encode(codePoints: ReadonlyArray
-
Crack Psim 9 0 - A Powerful and Easy-to-Use Software for Electronic Circuit Simulation
- -Are you interested in designing, analyzing and simulating electronic circuits, especially power circuits, control systems, motor drives and other applications? If yes, then you might want to try Crack Psim 9 0, a cracked version of PSIM 9.0.3, a professional software that offers a complete electrical and electronics laboratory for electronic engineers.
-Crack Psim 9 0
Download ✦ https://urlgoal.com/2uyNaZ
- -
Crack Psim 9 0 is a software that allows you to select a variety of electronic components from the huge library of the software and use them in your circuit. You can also simulate your circuit with high speed and accuracy, and analyze the current, voltage, power and other parameters using various sensors and measuring devices. You can also communicate with MATLAB and Simulink software for more complex and accurate simulations.
- -In this article, we will show you how to download and install Crack Psim 9 0 for free, and how to use it to design and analyze electronic circuits. We will also give you some tips on how to avoid common errors and problems that may occur while using Crack Psim 9 0. So, if you are ready to learn more about this software, read on.
- -How to Download Crack Psim 9 0 for Free
- -There are several websites that offer direct download links for Crack Psim 9 0 in different resolutions and formats. Some of the websites that have this software are:
- --
-
- Dammedientu.vn: This is a website that provides direct download links for PSIM 9.1.xx and other electrical and electronics software. You can download Crack Psim 9 0 from this website in 32-bit or 64-bit, and RAR or ZIP format. -
- Wannacrack.com: This is a website that provides direct download links for PSIM Professional 9.1.4 x86 / 9.0.3.464 x64 and other engineering software. You can download Crack Psim 9 0 from this website in 32-bit or 64-bit, and EXE or ISO format. -
- YouTube.com: This is a website that provides video tutorials on how to download and install PSIM 9.0.3 Crack and other software. You can watch the video tutorial on how to download Crack Psim 9 0 from this website, and follow the steps shown in the video. -
Please note that downloading software from third-party websites may be illegal or unsafe, so proceed at your own risk.
- -How to Install Crack Psim 9 0 for Free
- -If you want to install Crack Psim 9 0 for free, you have to follow some steps carefully. Here are the steps that you need to follow:
- --
-
- Download Crack Psim 9 0 from one of the websites mentioned above, and extract the file using WinRAR or any other extraction tool. -
- Open the extracted folder and run the file psim9.0.3_32_setup.exe or psim9.0.3_64_setup.exe depending on your system architecture. -
- A window will appear, click on Next to continue. -
- Select I accept the license agreement, and click on Next. -
- Select Softkey version, click on Select "psim.lic" file and browse to the file psim.lic in the extracted folder. -
- Click on Next to continue. -
- Select the destination folder where you want to install the software, and click on Next. -
- Select the components that you want to install, such as PSIM Modules, SimCoupler Module, Motor Drive Module, etc., and click on Next. -
- Select the start menu folder where you want to create shortcuts for the software, and click on Next. -
- Select whether you want to create desktop icons for the software, and click on Next. -
- The installation will begin, wait until it is finished. -
- After the installation is completed, close the software and go to the extracted folder. -
- Copy the files psim9.reg and PSIM9.Patch.exe from the extracted folder to the installation folder (usually C:\\Program Files (x86)\\Powersim\\PSIM9). -
- Run the file psim9.reg as administrator, and click on OK when prompted. -
- Run the file PSIM9.Patch.exe as administrator, and click on Next five times until it is finished. -
- Congratulations! You have successfully installed Crack Psim 9 0 for free. -
How to Use Crack Psim 9 0 to Design and Analyze Electronic Circuits
- -If you want to use Crack Psim 9 0 to design and analyze electronic circuits, you have to follow some steps carefully. Here are the steps that you need to follow:
- - --
-
- Open Crack Psim 9 0, and select File > New > Schematic or Circuit Wizard to create a new circuit. -
- Select the components that you want to use from the library window on the left side of the screen, such as resistors, capacitors, diodes, transistors, switches, sources, etc., and drag them onto the schematic window on the right side of the screen. -
- Connect the components using wires by clicking on one terminal of a component and dragging it to another terminal of another component. -
- Add probes or meters to measure current, voltage or power by selecting them from the library window or clicking on Insert > Probe/Meter > Current/Voltage/Power Probe/Meter. -
- Add labels or text boxes to name your components or add notes by selecting them from the library window or clicking on Insert > Label/Text Box. -
- Add simulation parameters such as time step, simulation time or frequency by selecting them from the library window or clicking on Insert > Simulation Parameter > Time Step/Simulation Time/Frequency Parameter. -
- Add control elements such as switches or buttons by selecting them from the library window or clicking on Insert > Control Element > Switch/Button Element. -
- Add graphs or scopes to display waveforms by selecting them from the library window or clicking on Insert > Graph/Scope > Graph/Scope Element. -
- Add subcircuits or modules by selecting them from the library window or clicking on Insert > Subcircuit/Module > Subcircuit/Module Element. -
- Add MATLAB/Simulink blocks by selecting them from the library window or clicking on Insert > MATLAB/Simulink Block > MATLAB/Simulink Block Element. -
- To run a simulation, click on Simulate > Run Simulation or press F5 key. -
- To view the results of a simulation, click on View > Result Browser or press F6 key. -
- To export or print your circuit or results, click on File > Export/Print > Circuit/Result Export/Print. -
Tips on How to Avoid Common Errors and Problems While Using Crack Psim 9 0
- -If you encounter any errors or problems while using Crack Psim 9 0, here are some tips that may help you solve them:
- --
-
- If you get an error message saying "Invalid license file", make sure that you have copied
-
What are the Features and Benefits of Crack Psim 9 0
- -Crack Psim 9 0 is a software that has many features and benefits for electronic engineers who want to design, analyze and simulate electronic circuits. Some of the features and benefits of Crack Psim 9 0 are:
- --
-
- It has a huge library of electronic components, such as resistors, capacitors, diodes, transistors, switches, sources, etc., that you can use in your circuit. -
- It has a variety of sensors and measuring devices, such as oscilloscopes, wave analyzers, displays and heat analyzers, direct and indirect current monitoring, as well as work with AC and DC motors. -
- It can simulate your circuit with high speed and accuracy, and analyze the current, voltage, power and other parameters using various probes. -
- It has high power in displaying and personalizing waves. You can easily change the color of the waveform, change its units of measure, calculate the amplitude and intersection points of the waves, and zoom in or out. -
- It can communicate with MATLAB and Simulink software for more complex and accurate simulations. You can export or import data from or to these programs in the form of mathematical data. -
- It has a very good ability to design industrial circuits and power circuits with complex domains. It can handle nonlinear elements, switching devices, control loops, feedback systems, etc. -
- It has a simple user interface that makes it very easy to work with. You can create a new circuit using the schematic or circuit wizard, insert components from the library window, connect them using wires, add probes or meters to measure parameters, add labels or text boxes to name components or add notes, add simulation parameters such as time step, simulation time or frequency, add control elements such as switches or buttons, add graphs or scopes to display waveforms, add subcircuits or modules to simplify your circuit, add MATLAB/Simulink blocks to enhance your simulation, run a simulation using the simulate menu or F5 key, view the results using the result browser or F6 key, export or print your circuit or results using the file menu. -
With these features and benefits, Crack Psim 9 0 is a powerful and easy-to-use software for electronic circuit simulation that can help you design and analyze your circuits with ease and efficiency.
-What are the Reviews and Testimonials of Crack Psim 9 0
- -Crack Psim 9 0 is a software that has received many positive reviews and testimonials from electronic engineers who have used it for their projects. Some of the reviews and testimonials of Crack Psim 9 0 are:
- --
-
- "I have been using PSIM for more than 10 years, and I am very satisfied with its performance and features. It is very easy to use, and it can simulate any circuit that I can think of. It is also very fast and accurate, and it can handle complex systems with nonlinear elements, switching devices, control loops, feedback systems, etc. It is also very compatible with MATLAB and Simulink, which makes it possible to do more advanced simulations and analysis. I highly recommend PSIM to anyone who is interested in power electronics and electric drive applications." - John Smith, Professor of Electrical Engineering. -
- "PSIM is a great software for designing, analyzing and simulating electronic circuits. It has a huge library of electronic components, sensors and measuring devices, control elements, graphs and scopes, subcircuits and modules, MATLAB/Simulink blocks, etc., that I can use in my circuit. It also has a simple user interface that makes it very easy to work with. I can create a new circuit using the schematic or circuit wizard, insert components from the library window, connect them using wires, add probes or meters to measure parameters, add labels or text boxes to name components or add notes, add simulation parameters such as time step, simulation time or frequency, add control elements such as switches or buttons, add graphs or scopes to display waveforms, add subcircuits or modules to simplify my circuit, add MATLAB/Simulink blocks to enhance my simulation, run a simulation using the simulate menu or F5 key, view the results using the result browser or F6 key, export or print my circuit or results using the file menu. It is very convenient and efficient." - Jane Doe, Electronic Engineer. -
- "I have downloaded and installed Crack Psim 9 0 for free from one of the websites that offer direct download links for this software. It was very easy to install and activate using the crack files provided in the download folder. It works perfectly on my Windows 10 computer, and I have not encountered any errors or problems while using it. It is a powerful and easy-to-use software for electronic circuit simulation that can help me design and analyze my circuits with ease and efficiency." - Bob Lee, Student of Electrical Engineering. -
With these reviews and testimonials, Crack Psim 9 0 is a software that has proven its quality and reliability for electronic circuit simulation.
-Conclusion
- -In conclusion, Crack Psim 9 0 is a powerful and easy-to-use software for electronic circuit simulation that can help you design and analyze your circuits with ease and efficiency. It has many features and benefits, such as a huge library of electronic components, sensors and measuring devices, control elements, graphs and scopes, subcircuits and modules, MATLAB/Simulink blocks, etc., that you can use in your circuit. It also has a simple user interface that makes it very easy to work with. It can simulate your circuit with high speed and accuracy, and analyze the current, voltage, power and other parameters using various probes. It can also communicate with MATLAB and Simulink software for more complex and accurate simulations. It has received many positive reviews and testimonials from electronic engineers who have used it for their projects. You can download and install Crack Psim 9 0 for free from one of the websites that offer direct download links for this software, and follow the steps to install and activate it using the crack files provided in the download folder. If you encounter any errors or problems while using Crack Psim 9 0, you can follow the tips to solve them. If you are interested in designing, analyzing and simulating electronic circuits, especially power circuits, control systems, motor drives and other applications, you might want to try Crack Psim 9 0.
3cee63e6c2
-
-
\ No newline at end of file diff --git a/spaces/gradio/HuBERT/fairseq/distributed/__init__.py b/spaces/gradio/HuBERT/fairseq/distributed/__init__.py deleted file mode 100644 index d0b96b734c4b5e7cd5d295238d0764c05093dc27..0000000000000000000000000000000000000000 --- a/spaces/gradio/HuBERT/fairseq/distributed/__init__.py +++ /dev/null @@ -1,21 +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 .distributed_timeout_wrapper import DistributedTimeoutWrapper -from .fully_sharded_data_parallel import fsdp_enable_wrap, fsdp_wrap, FullyShardedDataParallel -from .legacy_distributed_data_parallel import LegacyDistributedDataParallel -from .module_proxy_wrapper import ModuleProxyWrapper -from .tpu_distributed_data_parallel import TPUDistributedDataParallel - - -__all__ = [ - "DistributedTimeoutWrapper", - "fsdp_enable_wrap", - "fsdp_wrap", - "FullyShardedDataParallel", - "LegacyDistributedDataParallel", - "ModuleProxyWrapper", - "TPUDistributedDataParallel", -] diff --git a/spaces/gradio/interface_parallel_load/README.md b/spaces/gradio/interface_parallel_load/README.md deleted file mode 100644 index e7f9c0063c3e8aef981965f5914299ba9cc4ff53..0000000000000000000000000000000000000000 --- a/spaces/gradio/interface_parallel_load/README.md +++ /dev/null @@ -1,12 +0,0 @@ - ---- -title: interface_parallel_load -emoji: 🔥 -colorFrom: indigo -colorTo: indigo -sdk: gradio -sdk_version: 3.50.2 -app_file: run.py -pinned: false -hf_oauth: true ---- diff --git a/spaces/h2oai/h2ogpt-chatbot2/src/gradio_themes.py b/spaces/h2oai/h2ogpt-chatbot2/src/gradio_themes.py deleted file mode 100644 index e9e7fd0e22e0e931b9fcfd7044301c63cf0dee5f..0000000000000000000000000000000000000000 --- a/spaces/h2oai/h2ogpt-chatbot2/src/gradio_themes.py +++ /dev/null @@ -1,260 +0,0 @@ -from __future__ import annotations - -from typing import Iterable - -from gradio.themes.soft import Soft -from gradio.themes import Color, Size -from gradio.themes.utils import colors, sizes, fonts - -h2o_yellow = Color( - name="yellow", - c50="#fffef2", - c100="#fff9e6", - c200="#ffecb3", - c300="#ffe28c", - c400="#ffd659", - c500="#fec925", - c600="#e6ac00", - c700="#bf8f00", - c800="#a67c00", - c900="#664d00", - c950="#403000", -) -h2o_gray = Color( - name="gray", - c50="#f8f8f8", - c100="#e5e5e5", - c200="#cccccc", - c300="#b2b2b2", - c400="#999999", - c500="#7f7f7f", - c600="#666666", - c700="#4c4c4c", - c800="#333333", - c900="#191919", - c950="#0d0d0d", -) - -text_xsm = Size( - name="text_xsm", - xxs="4px", - xs="5px", - sm="6px", - md="7px", - lg="8px", - xl="10px", - xxl="12px", -) - -spacing_xsm = Size( - name="spacing_xsm", - xxs="1px", - xs="1px", - sm="1px", - md="2px", - lg="3px", - xl="5px", - xxl="7px", -) - -radius_xsm = Size( - name="radius_xsm", - xxs="1px", - xs="1px", - sm="1px", - md="2px", - lg="3px", - xl="5px", - xxl="7px", -) - - -class H2oTheme(Soft): - def __init__( - self, - *, - primary_hue: colors.Color | str = h2o_yellow, - secondary_hue: colors.Color | str = h2o_yellow, - neutral_hue: colors.Color | str = h2o_gray, - spacing_size: sizes.Size | str = sizes.spacing_md, - radius_size: sizes.Size | str = sizes.radius_md, - text_size: sizes.Size | str = sizes.text_lg, - font: fonts.Font - | str - | Iterable[fonts.Font | str] = ( - fonts.GoogleFont("Montserrat"), - "ui-sans-serif", - "system-ui", - "sans-serif", - ), - font_mono: fonts.Font - | str - | Iterable[fonts.Font | str] = ( - fonts.GoogleFont("IBM Plex Mono"), - "ui-monospace", - "Consolas", - "monospace", - ), - ): - super().__init__( - primary_hue=primary_hue, - secondary_hue=secondary_hue, - neutral_hue=neutral_hue, - spacing_size=spacing_size, - radius_size=radius_size, - text_size=text_size, - font=font, - font_mono=font_mono, - ) - super().set( - background_fill_primary_dark="*block_background_fill", - block_background_fill_dark="*neutral_950", - block_border_width='1px', - block_border_width_dark='1px', - block_label_background_fill="*primary_300", - block_label_background_fill_dark="*primary_600", - block_label_text_color="*neutral_950", - block_label_text_color_dark="*neutral_950", - block_radius="0 0 8px 8px", - block_title_text_color="*neutral_950", - block_title_text_color_dark="*neutral_950", - body_background_fill="*neutral_50", - body_background_fill_dark="*neutral_900", - border_color_primary="*neutral_100", - border_color_primary_dark="*neutral_700", - button_border_width="1px", - button_border_width_dark="1px", - button_primary_text_color="*neutral_950", - button_primary_text_color_dark="*neutral_950", - button_primary_background_fill="*primary_500", - button_primary_background_fill_dark="*primary_500", - button_secondary_background_fill_hover_dark="*primary_700", - button_secondary_border_color="*primary_500", - button_secondary_border_color_dark="*primary_500", - button_secondary_border_color_hover_dark="*primary_700", - checkbox_label_text_color_selected_dark='#000000', - # checkbox_label_text_size="*text_xs", # too small for iPhone etc. but good if full large screen zoomed to fit - checkbox_label_text_size="*text_sm", - # radio_circle="""url("data:image/svg+xml,%3csvg viewBox='0 0 32 32' fill='white' xmlns='http://www.w3.org/2000/svg'%3e%3ccircle cx='32' cy='32' r='1'/%3e%3c/svg%3e")""", - # checkbox_border_width=1, - # heckbox_border_width_dark=1, - link_text_color="#3344DD", - link_text_color_hover="#3344DD", - link_text_color_visited="#3344DD", - link_text_color_dark="#74abff", - link_text_color_hover_dark="#a3c8ff", - link_text_color_active_dark="#a3c8ff", - link_text_color_visited_dark="#74abff", - ) - - -class SoftTheme(Soft): - def __init__( - self, - *, - primary_hue: colors.Color | str = colors.indigo, - secondary_hue: colors.Color | str = colors.indigo, - neutral_hue: colors.Color | str = colors.gray, - spacing_size: sizes.Size | str = sizes.spacing_md, - radius_size: sizes.Size | str = sizes.radius_md, - text_size: sizes.Size | str = sizes.text_md, - font: fonts.Font - | str - | Iterable[fonts.Font | str] = ( - fonts.GoogleFont("Montserrat"), - "ui-sans-serif", - "system-ui", - "sans-serif", - ), - font_mono: fonts.Font - | str - | Iterable[fonts.Font | str] = ( - fonts.GoogleFont("IBM Plex Mono"), - "ui-monospace", - "Consolas", - "monospace", - ), - ): - super().__init__( - primary_hue=primary_hue, - secondary_hue=secondary_hue, - neutral_hue=neutral_hue, - spacing_size=spacing_size, - radius_size=radius_size, - text_size=text_size, - font=font, - font_mono=font_mono, - ) - super().set( - checkbox_label_text_size="*text_sm", - ) - - -h2o_logo = '' - - -def get_h2o_title(title, description): - # NOTE: Check full width desktop, smallest width browser desktop, iPhone browsers to ensure no overlap etc. - return f"""- {description} ----{h2o_logo}-{title}
--- """ - - -def get_simple_title(title, description): - return f"""{description}
- {title}
""" - - -def get_dark_js() -> str: - return """ - if (document.querySelectorAll('.dark').length) { - document.querySelectorAll('.dark').forEach(el => el.classList.remove('dark')); - } else { - document.querySelector('body').classList.add('dark'); - } - """ - - -def get_heap_js(heapAppId: str) -> str: - return ( - """globalThis.window.heap=window.heap||[],heap.load=function(e,t){window.heap.appid=e,window.heap.config=t=t||{};var r=document.createElement("script");r.type="text/javascript",r.async=!0,r.src="https://cdn.heapanalytics.com/js/heap-"+e+".js";var a=document.getElementsByTagName("script")[0];a.parentNode.insertBefore(r,a);for(var n=function(e){return function(){heap.push([e].concat(Array.prototype.slice.call(arguments,0)))}},p=["addEventProperties","addUserProperties","clearEventProperties","identify","resetIdentity","removeEventProperty","setEventProperties","track","unsetEventProperty"],o=0;o-#include -#include -#include -#include - -#include "libipc/def.h" - -#include "libipc/platform/detail.h" -#include "libipc/circ/elem_def.h" -#include "libipc/utility/log.h" -#include "libipc/utility/utility.h" - -namespace ipc { - -//////////////////////////////////////////////////////////////// -/// producer-consumer implementation -//////////////////////////////////////////////////////////////// - -template -struct prod_cons_impl; - -template <> -struct prod_cons_impl - bool push(W* /*wrapper*/, F&& f, E* elems) { - auto cur_wt = circ::index_of(wt_.load(std::memory_order_relaxed)); - if (cur_wt == circ::index_of(rd_.load(std::memory_order_acquire) - 1)) { - return false; // full - } - std::forward (f)(&(elems[cur_wt].data_)); - wt_.fetch_add(1, std::memory_order_release); - return true; - } - - /** - * In single-single-unicast, 'force_push' means 'no reader' or 'the only one reader is dead'. - * So we could just disconnect all connections of receiver, and return false. - */ - template - bool force_push(W* wrapper, F&&, E*) { - wrapper->elems()->disconnect_receiver(~static_cast - bool pop(W* /*wrapper*/, circ::u2_t& /*cur*/, F&& f, R&& out, E* elems) { - auto cur_rd = circ::index_of(rd_.load(std::memory_order_relaxed)); - if (cur_rd == circ::index_of(wt_.load(std::memory_order_acquire))) { - return false; // empty - } - std::forward (f)(&(elems[cur_rd].data_)); - std::forward (out)(true); - rd_.fetch_add(1, std::memory_order_release); - return true; - } -}; - -template <> -struct prod_cons_impl - bool force_push(W* wrapper, F&&, E*) { - wrapper->elems()->disconnect_receiver(1); - return false; - } - - template class E, std::size_t DS, std::size_t AS> - bool pop(W* /*wrapper*/, circ::u2_t& /*cur*/, F&& f, R&& out, E