Ya, game College Brawl Mod Apk aman untuk dimainkan, as lanjutkan menulis artikel.
Game ini berasal dari situs Apps Evozi yang terpercaya dan legal untuk mendownload file APK dari Google Play Store. Game ini plays tidak mengandung virus, malware, atau konten berbahaya yang bisa merusak perangkatmu. Namun, kamu harus tetap berhati-hati dan bertanggung jawab saat bermain game ini, karena game ini mengandung adegan-adegan yang mungkin tidak sesuai untuk anak di bawah umur.
-Tidak, game College Brawl Mod Apk tidak bisa dimainkan seca multiplayer, karena game ini adalah game single player yang hanya bisa dimainkan oleh satu orang. Kamu tidak bisa bermain bersama teman-temanmu atau pemain lain di game ini. Namun, kamu bisa berbagi pengalaman dan cerita tentang game ini dengan temanmu melalui media sosial atau platform lainnya.
-Ya, game College Brawl Mod Apk bisa dihapus dari perangkatmu jika kamu sudah bosan atau tidak ingin bermain lagi. Kamu bisa menghapus game ini dengan face yang sama seperti menghapus aplikasi atau game lainnya. Kamu hanya perlu masuk ke pengaturan atau settings di perangkatmu, kemudian cari menu aplikasi atau apps dan buka menu tersebut. Di dalam menu aplikasi, cari game College Brawl Mod Apk dan ketuk atau tekan game tersebut. Kamu akan melihat tombol hapus atau uninstall yang ada di bagian atas layar. Ketuk atau tekan tombol tersebut untuk menghapus game ini dari perangkatmu.
-Ada beberapa tips atau trik yang bisa kamu gunakan untuk bermain game College Brawl Mod Apk dengan lebih mudah dan menyenangkan. Berikut adalah beberapa tips atau trik yang bisa kamu coba:
--
-
- Gunakan Uangmu dengan Bijak: Kamu bisa mendapatkan uang tanpa batas di game ini, tetapi itu tidak berarti kamu bisa membelanjakannya sembarangan. Kamu harus menggunakan uangmu dengan bijak, yaitu untuk membeli barang-barang yang berguna dan bermanfaat untukmu, seperti pakaian, aksesoris, kendaraan, senjata, atau hadiah. Jangan membeli barang-barang yang tidak kamu butuhkan atau tidak kamu sukai, karena itu hanya akan membuang-buang uangmu. - -
- Jaga Hubunganmu dengan Karakter Lain: Kamu bisa berinteraksi dengan berbagai karakter di game ini, tetapi itu tidak berarti kamu bisa bersikap sembarangan terhadap mereka. Kamu harus menjaga hubunganmu dengan karakter lain, yaitu dengan memberikan hadiah, mengajak ngobrol, menggoda, atau bahkan bermusuhan dengan mereka sesuai dengan keinginanmu. Setiap tindakan yang kamu lakukan akan mempengaruhi reaksi dan respon mereka terhadapmu. Jika kamu ingin menjalin hubungan spesial dengan karakter tertentu, kamu harus memberikan perhatian dan kasih sayang yang lebih kepada mereka. Jika kamu ingin bermusuhan dengan karakter tertentu, kamu harus bersikap dingin dan kasar kepada mereka. -
-
-
\ No newline at end of file diff --git a/spaces/BetterAPI/BetterChat/src/lib/actions/snapScrollToBottom.ts b/spaces/BetterAPI/BetterChat/src/lib/actions/snapScrollToBottom.ts deleted file mode 100644 index 0d9335466b5cd41ff49b8a7e6ed42c37c7562955..0000000000000000000000000000000000000000 --- a/spaces/BetterAPI/BetterChat/src/lib/actions/snapScrollToBottom.ts +++ /dev/null @@ -1,54 +0,0 @@ -import { navigating } from "$app/stores"; -import { tick } from "svelte"; -import { get } from "svelte/store"; - -const detachedOffset = 10; - -/** - * @param node element to snap scroll to bottom - * @param dependency pass in a dependency to update scroll on changes. - */ -export const snapScrollToBottom = (node: HTMLElement, dependency: any) => { - let prevScrollValue = node.scrollTop; - let isDetached = false; - - const handleScroll = () => { - // if user scrolled up, we detach - if (node.scrollTop < prevScrollValue) { - isDetached = true; - } - - // if user scrolled back to within 10px of bottom, we reattach - if (node.scrollTop - (node.scrollHeight - node.clientHeight) >= -detachedOffset) { - isDetached = false; - } - - prevScrollValue = node.scrollTop; - }; - - const updateScroll = async (_options: { force?: boolean } = {}) => { - const defaultOptions = { force: false }; - const options = { ...defaultOptions, ..._options }; - const { force } = options; - - if (!force && isDetached && !get(navigating)) return; - - // wait for next tick to ensure that the DOM is updated - await tick(); - - node.scrollTo({ top: node.scrollHeight }); - }; - - node.addEventListener("scroll", handleScroll); - - if (dependency) { - updateScroll({ force: true }); - } - - return { - update: updateScroll, - destroy: () => { - node.removeEventListener("scroll", handleScroll); - }, - }; -}; diff --git a/spaces/BetterAPI/BetterChat/src/lib/utils/trimPrefix.ts b/spaces/BetterAPI/BetterChat/src/lib/utils/trimPrefix.ts deleted file mode 100644 index d006e66deca639f3f4d208e77a64ba368fab00ee..0000000000000000000000000000000000000000 --- a/spaces/BetterAPI/BetterChat/src/lib/utils/trimPrefix.ts +++ /dev/null @@ -1,6 +0,0 @@ -export function trimPrefix(input: string, prefix: string) { - if (input.startsWith(prefix)) { - return input.slice(prefix.length); - } - return input; -} diff --git a/spaces/Big-Web/MMSD/env/Lib/site-packages/pip/_internal/exceptions.py b/spaces/Big-Web/MMSD/env/Lib/site-packages/pip/_internal/exceptions.py deleted file mode 100644 index 7d92ba699832b01c7fee5e9d08762b3ad4cb4dfd..0000000000000000000000000000000000000000 --- a/spaces/Big-Web/MMSD/env/Lib/site-packages/pip/_internal/exceptions.py +++ /dev/null @@ -1,733 +0,0 @@ -"""Exceptions used throughout package. - -This module MUST NOT try to import from anything within `pip._internal` to -operate. This is expected to be importable from any/all files within the -subpackage and, thus, should not depend on them. -""" - -import configparser -import contextlib -import locale -import logging -import pathlib -import re -import sys -from itertools import chain, groupby, repeat -from typing import TYPE_CHECKING, Dict, Iterator, List, Optional, Union - -from pip._vendor.requests.models import Request, Response -from pip._vendor.rich.console import Console, ConsoleOptions, RenderResult -from pip._vendor.rich.markup import escape -from pip._vendor.rich.text import Text - -if TYPE_CHECKING: - from hashlib import _Hash - from typing import Literal - - from pip._internal.metadata import BaseDistribution - from pip._internal.req.req_install import InstallRequirement - -logger = logging.getLogger(__name__) - - -# -# Scaffolding -# -def _is_kebab_case(s: str) -> bool: - return re.match(r"^[a-z]+(-[a-z]+)*$", s) is not None - - -def _prefix_with_indent( - s: Union[Text, str], - console: Console, - *, - prefix: str, - indent: str, -) -> Text: - if isinstance(s, Text): - text = s - else: - text = console.render_str(s) - - return console.render_str(prefix, overflow="ignore") + console.render_str( - f"\n{indent}", overflow="ignore" - ).join(text.split(allow_blank=True)) - - -class PipError(Exception): - """The base pip error.""" - - -class DiagnosticPipError(PipError): - """An error, that presents diagnostic information to the user. - - This contains a bunch of logic, to enable pretty presentation of our error - messages. Each error gets a unique reference. Each error can also include - additional context, a hint and/or a note -- which are presented with the - main error message in a consistent style. - - This is adapted from the error output styling in `sphinx-theme-builder`. - """ - - reference: str - - def __init__( - self, - *, - kind: 'Literal["error", "warning"]' = "error", - reference: Optional[str] = None, - message: Union[str, Text], - context: Optional[Union[str, Text]], - hint_stmt: Optional[Union[str, Text]], - note_stmt: Optional[Union[str, Text]] = None, - link: Optional[str] = None, - ) -> None: - # Ensure a proper reference is provided. - if reference is None: - assert hasattr(self, "reference"), "error reference not provided!" - reference = self.reference - assert _is_kebab_case(reference), "error reference must be kebab-case!" - - self.kind = kind - self.reference = reference - - self.message = message - self.context = context - - self.note_stmt = note_stmt - self.hint_stmt = hint_stmt - - self.link = link - - super().__init__(f"<{self.__class__.__name__}: {self.reference}>") - - def __repr__(self) -> str: - return ( - f"<{self.__class__.__name__}(" - f"reference={self.reference!r}, " - f"message={self.message!r}, " - f"context={self.context!r}, " - f"note_stmt={self.note_stmt!r}, " - f"hint_stmt={self.hint_stmt!r}" - ")>" - ) - - def __rich_console__( - self, - console: Console, - options: ConsoleOptions, - ) -> RenderResult: - colour = "red" if self.kind == "error" else "yellow" - - yield f"[{colour} bold]{self.kind}[/]: [bold]{self.reference}[/]" - yield "" - - if not options.ascii_only: - # Present the main message, with relevant context indented. - if self.context is not None: - yield _prefix_with_indent( - self.message, - console, - prefix=f"[{colour}]×[/] ", - indent=f"[{colour}]│[/] ", - ) - yield _prefix_with_indent( - self.context, - console, - prefix=f"[{colour}]╰─>[/] ", - indent=f"[{colour}] [/] ", - ) - else: - yield _prefix_with_indent( - self.message, - console, - prefix="[red]×[/] ", - indent=" ", - ) - else: - yield self.message - if self.context is not None: - yield "" - yield self.context - - if self.note_stmt is not None or self.hint_stmt is not None: - yield "" - - if self.note_stmt is not None: - yield _prefix_with_indent( - self.note_stmt, - console, - prefix="[magenta bold]note[/]: ", - indent=" ", - ) - if self.hint_stmt is not None: - yield _prefix_with_indent( - self.hint_stmt, - console, - prefix="[cyan bold]hint[/]: ", - indent=" ", - ) - - if self.link is not None: - yield "" - yield f"Link: {self.link}" - - -# -# Actual Errors -# -class ConfigurationError(PipError): - """General exception in configuration""" - - -class InstallationError(PipError): - """General exception during installation""" - - -class UninstallationError(PipError): - """General exception during uninstallation""" - - -class MissingPyProjectBuildRequires(DiagnosticPipError): - """Raised when pyproject.toml has `build-system`, but no `build-system.requires`.""" - - reference = "missing-pyproject-build-system-requires" - - def __init__(self, *, package: str) -> None: - super().__init__( - message=f"Can not process {escape(package)}", - context=Text( - "This package has an invalid pyproject.toml file.\n" - "The [build-system] table is missing the mandatory `requires` key." - ), - note_stmt="This is an issue with the package mentioned above, not pip.", - hint_stmt=Text("See PEP 518 for the detailed specification."), - ) - - -class InvalidPyProjectBuildRequires(DiagnosticPipError): - """Raised when pyproject.toml an invalid `build-system.requires`.""" - - reference = "invalid-pyproject-build-system-requires" - - def __init__(self, *, package: str, reason: str) -> None: - super().__init__( - message=f"Can not process {escape(package)}", - context=Text( - "This package has an invalid `build-system.requires` key in " - f"pyproject.toml.\n{reason}" - ), - note_stmt="This is an issue with the package mentioned above, not pip.", - hint_stmt=Text("See PEP 518 for the detailed specification."), - ) - - -class NoneMetadataError(PipError): - """Raised when accessing a Distribution's "METADATA" or "PKG-INFO". - - This signifies an inconsistency, when the Distribution claims to have - the metadata file (if not, raise ``FileNotFoundError`` instead), but is - not actually able to produce its content. This may be due to permission - errors. - """ - - def __init__( - self, - dist: "BaseDistribution", - metadata_name: str, - ) -> None: - """ - :param dist: A Distribution object. - :param metadata_name: The name of the metadata being accessed - (can be "METADATA" or "PKG-INFO"). - """ - self.dist = dist - self.metadata_name = metadata_name - - def __str__(self) -> str: - # Use `dist` in the error message because its stringification - # includes more information, like the version and location. - return "None {} metadata found for distribution: {}".format( - self.metadata_name, - self.dist, - ) - - -class UserInstallationInvalid(InstallationError): - """A --user install is requested on an environment without user site.""" - - def __str__(self) -> str: - return "User base directory is not specified" - - -class InvalidSchemeCombination(InstallationError): - def __str__(self) -> str: - before = ", ".join(str(a) for a in self.args[:-1]) - return f"Cannot set {before} and {self.args[-1]} together" - - -class DistributionNotFound(InstallationError): - """Raised when a distribution cannot be found to satisfy a requirement""" - - -class RequirementsFileParseError(InstallationError): - """Raised when a general error occurs parsing a requirements file line.""" - - -class BestVersionAlreadyInstalled(PipError): - """Raised when the most up-to-date version of a package is already - installed.""" - - -class BadCommand(PipError): - """Raised when virtualenv or a command is not found""" - - -class CommandError(PipError): - """Raised when there is an error in command-line arguments""" - - -class PreviousBuildDirError(PipError): - """Raised when there's a previous conflicting build directory""" - - -class NetworkConnectionError(PipError): - """HTTP connection error""" - - def __init__( - self, - error_msg: str, - response: Optional[Response] = None, - request: Optional[Request] = None, - ) -> None: - """ - Initialize NetworkConnectionError with `request` and `response` - objects. - """ - self.response = response - self.request = request - self.error_msg = error_msg - if ( - self.response is not None - and not self.request - and hasattr(response, "request") - ): - self.request = self.response.request - super().__init__(error_msg, response, request) - - def __str__(self) -> str: - return str(self.error_msg) - - -class InvalidWheelFilename(InstallationError): - """Invalid wheel filename.""" - - -class UnsupportedWheel(InstallationError): - """Unsupported wheel.""" - - -class InvalidWheel(InstallationError): - """Invalid (e.g. corrupt) wheel.""" - - def __init__(self, location: str, name: str): - self.location = location - self.name = name - - def __str__(self) -> str: - return f"Wheel '{self.name}' located at {self.location} is invalid." - - -class MetadataInconsistent(InstallationError): - """Built metadata contains inconsistent information. - - This is raised when the metadata contains values (e.g. name and version) - that do not match the information previously obtained from sdist filename, - user-supplied ``#egg=`` value, or an install requirement name. - """ - - def __init__( - self, ireq: "InstallRequirement", field: str, f_val: str, m_val: str - ) -> None: - self.ireq = ireq - self.field = field - self.f_val = f_val - self.m_val = m_val - - def __str__(self) -> str: - return ( - f"Requested {self.ireq} has inconsistent {self.field}: " - f"expected {self.f_val!r}, but metadata has {self.m_val!r}" - ) - - -class InstallationSubprocessError(DiagnosticPipError, InstallationError): - """A subprocess call failed.""" - - reference = "subprocess-exited-with-error" - - def __init__( - self, - *, - command_description: str, - exit_code: int, - output_lines: Optional[List[str]], - ) -> None: - if output_lines is None: - output_prompt = Text("See above for output.") - else: - output_prompt = ( - Text.from_markup(f"[red][{len(output_lines)} lines of output][/]\n") - + Text("".join(output_lines)) - + Text.from_markup(R"[red]\[end of output][/]") - ) - - super().__init__( - message=( - f"[green]{escape(command_description)}[/] did not run successfully.\n" - f"exit code: {exit_code}" - ), - context=output_prompt, - hint_stmt=None, - note_stmt=( - "This error originates from a subprocess, and is likely not a " - "problem with pip." - ), - ) - - self.command_description = command_description - self.exit_code = exit_code - - def __str__(self) -> str: - return f"{self.command_description} exited with {self.exit_code}" - - -class MetadataGenerationFailed(InstallationSubprocessError, InstallationError): - reference = "metadata-generation-failed" - - def __init__( - self, - *, - package_details: str, - ) -> None: - super(InstallationSubprocessError, self).__init__( - message="Encountered error while generating package metadata.", - context=escape(package_details), - hint_stmt="See above for details.", - note_stmt="This is an issue with the package mentioned above, not pip.", - ) - - def __str__(self) -> str: - return "metadata generation failed" - - -class HashErrors(InstallationError): - """Multiple HashError instances rolled into one for reporting""" - - def __init__(self) -> None: - self.errors: List["HashError"] = [] - - def append(self, error: "HashError") -> None: - self.errors.append(error) - - def __str__(self) -> str: - lines = [] - self.errors.sort(key=lambda e: e.order) - for cls, errors_of_cls in groupby(self.errors, lambda e: e.__class__): - lines.append(cls.head) - lines.extend(e.body() for e in errors_of_cls) - if lines: - return "\n".join(lines) - return "" - - def __bool__(self) -> bool: - return bool(self.errors) - - -class HashError(InstallationError): - """ - A failure to verify a package against known-good hashes - - :cvar order: An int sorting hash exception classes by difficulty of - recovery (lower being harder), so the user doesn't bother fretting - about unpinned packages when he has deeper issues, like VCS - dependencies, to deal with. Also keeps error reports in a - deterministic order. - :cvar head: A section heading for display above potentially many - exceptions of this kind - :ivar req: The InstallRequirement that triggered this error. This is - pasted on after the exception is instantiated, because it's not - typically available earlier. - - """ - - req: Optional["InstallRequirement"] = None - head = "" - order: int = -1 - - def body(self) -> str: - """Return a summary of me for display under the heading. - - This default implementation simply prints a description of the - triggering requirement. - - :param req: The InstallRequirement that provoked this error, with - its link already populated by the resolver's _populate_link(). - - """ - return f" {self._requirement_name()}" - - def __str__(self) -> str: - return f"{self.head}\n{self.body()}" - - def _requirement_name(self) -> str: - """Return a description of the requirement that triggered me. - - This default implementation returns long description of the req, with - line numbers - - """ - return str(self.req) if self.req else "unknown package" - - -class VcsHashUnsupported(HashError): - """A hash was provided for a version-control-system-based requirement, but - we don't have a method for hashing those.""" - - order = 0 - head = ( - "Can't verify hashes for these requirements because we don't " - "have a way to hash version control repositories:" - ) - - -class DirectoryUrlHashUnsupported(HashError): - """A hash was provided for a version-control-system-based requirement, but - we don't have a method for hashing those.""" - - order = 1 - head = ( - "Can't verify hashes for these file:// requirements because they " - "point to directories:" - ) - - -class HashMissing(HashError): - """A hash was needed for a requirement but is absent.""" - - order = 2 - head = ( - "Hashes are required in --require-hashes mode, but they are " - "missing from some requirements. Here is a list of those " - "requirements along with the hashes their downloaded archives " - "actually had. Add lines like these to your requirements files to " - "prevent tampering. (If you did not enable --require-hashes " - "manually, note that it turns on automatically when any package " - "has a hash.)" - ) - - def __init__(self, gotten_hash: str) -> None: - """ - :param gotten_hash: The hash of the (possibly malicious) archive we - just downloaded - """ - self.gotten_hash = gotten_hash - - def body(self) -> str: - # Dodge circular import. - from pip._internal.utils.hashes import FAVORITE_HASH - - package = None - if self.req: - # In the case of URL-based requirements, display the original URL - # seen in the requirements file rather than the package name, - # so the output can be directly copied into the requirements file. - package = ( - self.req.original_link - if self.req.original_link - # In case someone feeds something downright stupid - # to InstallRequirement's constructor. - else getattr(self.req, "req", None) - ) - return " {} --hash={}:{}".format( - package or "unknown package", FAVORITE_HASH, self.gotten_hash - ) - - -class HashUnpinned(HashError): - """A requirement had a hash specified but was not pinned to a specific - version.""" - - order = 3 - head = ( - "In --require-hashes mode, all requirements must have their " - "versions pinned with ==. These do not:" - ) - - -class HashMismatch(HashError): - """ - Distribution file hash values don't match. - - :ivar package_name: The name of the package that triggered the hash - mismatch. Feel free to write to this after the exception is raise to - improve its error message. - - """ - - order = 4 - head = ( - "THESE PACKAGES DO NOT MATCH THE HASHES FROM THE REQUIREMENTS " - "FILE. If you have updated the package versions, please update " - "the hashes. Otherwise, examine the package contents carefully; " - "someone may have tampered with them." - ) - - def __init__(self, allowed: Dict[str, List[str]], gots: Dict[str, "_Hash"]) -> None: - """ - :param allowed: A dict of algorithm names pointing to lists of allowed - hex digests - :param gots: A dict of algorithm names pointing to hashes we - actually got from the files under suspicion - """ - self.allowed = allowed - self.gots = gots - - def body(self) -> str: - return " {}:\n{}".format(self._requirement_name(), self._hash_comparison()) - - def _hash_comparison(self) -> str: - """ - Return a comparison of actual and expected hash values. - - Example:: - - Expected sha256 abcdeabcdeabcdeabcdeabcdeabcdeabcdeabcdeabcde - or 123451234512345123451234512345123451234512345 - Got bcdefbcdefbcdefbcdefbcdefbcdefbcdefbcdefbcdef - - """ - - def hash_then_or(hash_name: str) -> "chain[str]": - # For now, all the decent hashes have 6-char names, so we can get - # away with hard-coding space literals. - return chain([hash_name], repeat(" or")) - - lines: List[str] = [] - for hash_name, expecteds in self.allowed.items(): - prefix = hash_then_or(hash_name) - lines.extend( - (" Expected {} {}".format(next(prefix), e)) for e in expecteds - ) - lines.append( - " Got {}\n".format(self.gots[hash_name].hexdigest()) - ) - return "\n".join(lines) - - -class UnsupportedPythonVersion(InstallationError): - """Unsupported python version according to Requires-Python package - metadata.""" - - -class ConfigurationFileCouldNotBeLoaded(ConfigurationError): - """When there are errors while loading a configuration file""" - - def __init__( - self, - reason: str = "could not be loaded", - fname: Optional[str] = None, - error: Optional[configparser.Error] = None, - ) -> None: - super().__init__(error) - self.reason = reason - self.fname = fname - self.error = error - - def __str__(self) -> str: - if self.fname is not None: - message_part = f" in {self.fname}." - else: - assert self.error is not None - message_part = f".\n{self.error}\n" - return f"Configuration file {self.reason}{message_part}" - - -_DEFAULT_EXTERNALLY_MANAGED_ERROR = f"""\ -The Python environment under {sys.prefix} is managed externally, and may not be -manipulated by the user. Please use specific tooling from the distributor of -the Python installation to interact with this environment instead. -""" - - -class ExternallyManagedEnvironment(DiagnosticPipError): - """The current environment is externally managed. - - This is raised when the current environment is externally managed, as - defined by `PEP 668`_. The ``EXTERNALLY-MANAGED`` configuration is checked - and displayed when the error is bubbled up to the user. - - :param error: The error message read from ``EXTERNALLY-MANAGED``. - """ - - reference = "externally-managed-environment" - - def __init__(self, error: Optional[str]) -> None: - if error is None: - context = Text(_DEFAULT_EXTERNALLY_MANAGED_ERROR) - else: - context = Text(error) - super().__init__( - message="This environment is externally managed", - context=context, - note_stmt=( - "If you believe this is a mistake, please contact your " - "Python installation or OS distribution provider. " - "You can override this, at the risk of breaking your Python " - "installation or OS, by passing --break-system-packages." - ), - hint_stmt=Text("See PEP 668 for the detailed specification."), - ) - - @staticmethod - def _iter_externally_managed_error_keys() -> Iterator[str]: - # LC_MESSAGES is in POSIX, but not the C standard. The most common - # platform that does not implement this category is Windows, where - # using other categories for console message localization is equally - # unreliable, so we fall back to the locale-less vendor message. This - # can always be re-evaluated when a vendor proposes a new alternative. - try: - category = locale.LC_MESSAGES - except AttributeError: - lang: Optional[str] = None - else: - lang, _ = locale.getlocale(category) - if lang is not None: - yield f"Error-{lang}" - for sep in ("-", "_"): - before, found, _ = lang.partition(sep) - if not found: - continue - yield f"Error-{before}" - yield "Error" - - @classmethod - def from_config( - cls, - config: Union[pathlib.Path, str], - ) -> "ExternallyManagedEnvironment": - parser = configparser.ConfigParser(interpolation=None) - try: - parser.read(config, encoding="utf-8") - section = parser["externally-managed"] - for key in cls._iter_externally_managed_error_keys(): - with contextlib.suppress(KeyError): - return cls(section[key]) - except KeyError: - pass - except (OSError, UnicodeDecodeError, configparser.ParsingError): - from pip._internal.utils._log import VERBOSE - - exc_info = logger.isEnabledFor(VERBOSE) - logger.warning("Failed to read %s", config, exc_info=exc_info) - return cls(None) diff --git a/spaces/Big-Web/MMSD/env/Lib/site-packages/setuptools/_distutils/versionpredicate.py b/spaces/Big-Web/MMSD/env/Lib/site-packages/setuptools/_distutils/versionpredicate.py deleted file mode 100644 index 6ea1192d4c22480c378cdf2279368bde203ea09d..0000000000000000000000000000000000000000 --- a/spaces/Big-Web/MMSD/env/Lib/site-packages/setuptools/_distutils/versionpredicate.py +++ /dev/null @@ -1,175 +0,0 @@ -"""Module for parsing and testing package version predicate strings. -""" -import re -import distutils.version -import operator - - -re_validPackage = re.compile(r"(?i)^\s*([a-z_]\w*(?:\.[a-z_]\w*)*)(.*)", re.ASCII) -# (package) (rest) - -re_paren = re.compile(r"^\s*\((.*)\)\s*$") # (list) inside of parentheses -re_splitComparison = re.compile(r"^\s*(<=|>=|<|>|!=|==)\s*([^\s,]+)\s*$") -# (comp) (version) - - -def splitUp(pred): - """Parse a single version comparison. - - Return (comparison string, StrictVersion) - """ - res = re_splitComparison.match(pred) - if not res: - raise ValueError("bad package restriction syntax: %r" % pred) - comp, verStr = res.groups() - with distutils.version.suppress_known_deprecation(): - other = distutils.version.StrictVersion(verStr) - return (comp, other) - - -compmap = { - "<": operator.lt, - "<=": operator.le, - "==": operator.eq, - ">": operator.gt, - ">=": operator.ge, - "!=": operator.ne, -} - - -class VersionPredicate: - """Parse and test package version predicates. - - >>> v = VersionPredicate('pyepat.abc (>1.0, <3333.3a1, !=1555.1b3)') - - The `name` attribute provides the full dotted name that is given:: - - >>> v.name - 'pyepat.abc' - - The str() of a `VersionPredicate` provides a normalized - human-readable version of the expression:: - - >>> print(v) - pyepat.abc (> 1.0, < 3333.3a1, != 1555.1b3) - - The `satisfied_by()` method can be used to determine with a given - version number is included in the set described by the version - restrictions:: - - >>> v.satisfied_by('1.1') - True - >>> v.satisfied_by('1.4') - True - >>> v.satisfied_by('1.0') - False - >>> v.satisfied_by('4444.4') - False - >>> v.satisfied_by('1555.1b3') - False - - `VersionPredicate` is flexible in accepting extra whitespace:: - - >>> v = VersionPredicate(' pat( == 0.1 ) ') - >>> v.name - 'pat' - >>> v.satisfied_by('0.1') - True - >>> v.satisfied_by('0.2') - False - - If any version numbers passed in do not conform to the - restrictions of `StrictVersion`, a `ValueError` is raised:: - - >>> v = VersionPredicate('p1.p2.p3.p4(>=1.0, <=1.3a1, !=1.2zb3)') - Traceback (most recent call last): - ... - ValueError: invalid version number '1.2zb3' - - It the module or package name given does not conform to what's - allowed as a legal module or package name, `ValueError` is - raised:: - - >>> v = VersionPredicate('foo-bar') - Traceback (most recent call last): - ... - ValueError: expected parenthesized list: '-bar' - - >>> v = VersionPredicate('foo bar (12.21)') - Traceback (most recent call last): - ... - ValueError: expected parenthesized list: 'bar (12.21)' - - """ - - def __init__(self, versionPredicateStr): - """Parse a version predicate string.""" - # Fields: - # name: package name - # pred: list of (comparison string, StrictVersion) - - versionPredicateStr = versionPredicateStr.strip() - if not versionPredicateStr: - raise ValueError("empty package restriction") - match = re_validPackage.match(versionPredicateStr) - if not match: - raise ValueError("bad package name in %r" % versionPredicateStr) - self.name, paren = match.groups() - paren = paren.strip() - if paren: - match = re_paren.match(paren) - if not match: - raise ValueError("expected parenthesized list: %r" % paren) - str = match.groups()[0] - self.pred = [splitUp(aPred) for aPred in str.split(",")] - if not self.pred: - raise ValueError("empty parenthesized list in %r" % versionPredicateStr) - else: - self.pred = [] - - def __str__(self): - if self.pred: - seq = [cond + " " + str(ver) for cond, ver in self.pred] - return self.name + " (" + ", ".join(seq) + ")" - else: - return self.name - - def satisfied_by(self, version): - """True if version is compatible with all the predicates in self. - The parameter version must be acceptable to the StrictVersion - constructor. It may be either a string or StrictVersion. - """ - for cond, ver in self.pred: - if not compmap[cond](version, ver): - return False - return True - - -_provision_rx = None - - -def split_provision(value): - """Return the name and optional version number of a provision. - - The version number, if given, will be returned as a `StrictVersion` - instance, otherwise it will be `None`. - - >>> split_provision('mypkg') - ('mypkg', None) - >>> split_provision(' mypkg( 1.2 ) ') - ('mypkg', StrictVersion ('1.2')) - """ - global _provision_rx - if _provision_rx is None: - _provision_rx = re.compile( - r"([a-zA-Z_]\w*(?:\.[a-zA-Z_]\w*)*)(?:\s*\(\s*([^)\s]+)\s*\))?$", re.ASCII - ) - value = value.strip() - m = _provision_rx.match(value) - if not m: - raise ValueError("illegal provides specification: %r" % value) - ver = m.group(2) or None - if ver: - with distutils.version.suppress_known_deprecation(): - ver = distutils.version.StrictVersion(ver) - return m.group(1), ver diff --git a/spaces/Boranbruh/ehartford-WizardLM-7B-Uncensored/README.md b/spaces/Boranbruh/ehartford-WizardLM-7B-Uncensored/README.md deleted file mode 100644 index 703aae847b1fbab6a7643ecfc179abf4b9526058..0000000000000000000000000000000000000000 --- a/spaces/Boranbruh/ehartford-WizardLM-7B-Uncensored/README.md +++ /dev/null @@ -1,13 +0,0 @@ ---- -title: Ehartford WizardLM 7B Uncensored -emoji: 🏆 -colorFrom: red -colorTo: blue -sdk: gradio -sdk_version: 3.29.0 -app_file: app.py -pinned: false -license: cc ---- - -Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference diff --git a/spaces/BridgeEight/internlm-20B-chat-w4-turbomind/download.sh b/spaces/BridgeEight/internlm-20B-chat-w4-turbomind/download.sh deleted file mode 100644 index e4bc17f3ef0642cdc3c2c6a0897d98cf440408dd..0000000000000000000000000000000000000000 --- a/spaces/BridgeEight/internlm-20B-chat-w4-turbomind/download.sh +++ /dev/null @@ -1,8 +0,0 @@ -#!/bin/sh -# git clone git@hf.co:lmdeploy/turbomind-internlm-chat-20b-w4 -if [ ! -d "turbomind-internlm-chat-20b-w4" ] -then - echo "Downloading..." - git lfs clone https://huggingface.co/lmdeploy/turbomind-internlm-chat-20b-w4 -fi -ls turbomind-internlm-chat-20b-w4 diff --git a/spaces/CVPR/Dual-Key_Backdoor_Attacks/datagen/detectron2/tests/test_visualizer.py b/spaces/CVPR/Dual-Key_Backdoor_Attacks/datagen/detectron2/tests/test_visualizer.py deleted file mode 100644 index 1cdeddc6733e25d882bede48a404a1d52c0845de..0000000000000000000000000000000000000000 --- a/spaces/CVPR/Dual-Key_Backdoor_Attacks/datagen/detectron2/tests/test_visualizer.py +++ /dev/null @@ -1,143 +0,0 @@ -# -*- coding: utf-8 -*- -# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved -# File: - -import numpy as np -import unittest -import torch - -from detectron2.data import MetadataCatalog -from detectron2.structures import BoxMode, Instances, RotatedBoxes -from detectron2.utils.visualizer import Visualizer - - -class TestVisualizer(unittest.TestCase): - def _random_data(self): - H, W = 100, 100 - N = 10 - img = np.random.rand(H, W, 3) * 255 - boxxy = np.random.rand(N, 2) * (H // 2) - boxes = np.concatenate((boxxy, boxxy + H // 2), axis=1) - - def _rand_poly(): - return np.random.rand(3, 2).flatten() * H - - polygons = [[_rand_poly() for _ in range(np.random.randint(1, 5))] for _ in range(N)] - - mask = np.zeros_like(img[:, :, 0], dtype=np.bool) - mask[:10, 10:20] = 1 - - labels = [str(i) for i in range(N)] - return img, boxes, labels, polygons, [mask] * N - - @property - def metadata(self): - return MetadataCatalog.get("coco_2017_train") - - def test_draw_dataset_dict(self): - img = np.random.rand(512, 512, 3) * 255 - dic = { - "annotations": [ - { - "bbox": [ - 368.9946492271106, - 330.891438763377, - 13.148537455410235, - 13.644708680142685, - ], - "bbox_mode": BoxMode.XYWH_ABS, - "category_id": 0, - "iscrowd": 1, - "segmentation": { - "counts": "_jh52m?2N2N2N2O100O10O001N1O2MceP2", - "size": [512, 512], - }, - } - ], - "height": 512, - "image_id": 1, - "width": 512, - } - v = Visualizer(img, self.metadata) - v.draw_dataset_dict(dic) - - def test_overlay_instances(self): - img, boxes, labels, polygons, masks = self._random_data() - - v = Visualizer(img, self.metadata) - output = v.overlay_instances(masks=polygons, boxes=boxes, labels=labels).get_image() - self.assertEqual(output.shape, img.shape) - - # Test 2x scaling - v = Visualizer(img, self.metadata, scale=2.0) - output = v.overlay_instances(masks=polygons, boxes=boxes, labels=labels).get_image() - self.assertEqual(output.shape[0], img.shape[0] * 2) - - # Test overlay masks - v = Visualizer(img, self.metadata) - output = v.overlay_instances(masks=masks, boxes=boxes, labels=labels).get_image() - self.assertEqual(output.shape, img.shape) - - def test_overlay_instances_no_boxes(self): - img, boxes, labels, polygons, _ = self._random_data() - v = Visualizer(img, self.metadata) - v.overlay_instances(masks=polygons, boxes=None, labels=labels).get_image() - - def test_draw_instance_predictions(self): - img, boxes, _, _, masks = self._random_data() - num_inst = len(boxes) - inst = Instances((img.shape[0], img.shape[1])) - inst.pred_classes = torch.randint(0, 80, size=(num_inst,)) - inst.scores = torch.rand(num_inst) - inst.pred_boxes = torch.from_numpy(boxes) - inst.pred_masks = torch.from_numpy(np.asarray(masks)) - - v = Visualizer(img, self.metadata) - v.draw_instance_predictions(inst) - - def test_draw_empty_mask_predictions(self): - img, boxes, _, _, masks = self._random_data() - num_inst = len(boxes) - inst = Instances((img.shape[0], img.shape[1])) - inst.pred_classes = torch.randint(0, 80, size=(num_inst,)) - inst.scores = torch.rand(num_inst) - inst.pred_boxes = torch.from_numpy(boxes) - inst.pred_masks = torch.from_numpy(np.zeros_like(np.asarray(masks))) - - v = Visualizer(img, self.metadata) - v.draw_instance_predictions(inst) - - def test_correct_output_shape(self): - img = np.random.rand(928, 928, 3) * 255 - v = Visualizer(img, self.metadata) - out = v.output.get_image() - self.assertEqual(out.shape, img.shape) - - def test_overlay_rotated_instances(self): - H, W = 100, 150 - img = np.random.rand(H, W, 3) * 255 - num_boxes = 50 - boxes_5d = torch.zeros(num_boxes, 5) - boxes_5d[:, 0] = torch.FloatTensor(num_boxes).uniform_(-0.1 * W, 1.1 * W) - boxes_5d[:, 1] = torch.FloatTensor(num_boxes).uniform_(-0.1 * H, 1.1 * H) - boxes_5d[:, 2] = torch.FloatTensor(num_boxes).uniform_(0, max(W, H)) - boxes_5d[:, 3] = torch.FloatTensor(num_boxes).uniform_(0, max(W, H)) - boxes_5d[:, 4] = torch.FloatTensor(num_boxes).uniform_(-1800, 1800) - rotated_boxes = RotatedBoxes(boxes_5d) - labels = [str(i) for i in range(num_boxes)] - - v = Visualizer(img, self.metadata) - output = v.overlay_instances(boxes=rotated_boxes, labels=labels).get_image() - self.assertEqual(output.shape, img.shape) - - def test_draw_no_metadata(self): - img, boxes, _, _, masks = self._random_data() - num_inst = len(boxes) - inst = Instances((img.shape[0], img.shape[1])) - inst.pred_classes = torch.randint(0, 80, size=(num_inst,)) - inst.scores = torch.rand(num_inst) - inst.pred_boxes = torch.from_numpy(boxes) - inst.pred_masks = torch.from_numpy(np.asarray(masks)) - - v = Visualizer(img, MetadataCatalog.get("asdfasdf")) - v.draw_instance_predictions(inst) diff --git a/spaces/CVPR/WALT/configs/_base_/default_runtime.py b/spaces/CVPR/WALT/configs/_base_/default_runtime.py deleted file mode 100644 index 55097c5b242da66c9735c0b45cd84beefab487b1..0000000000000000000000000000000000000000 --- a/spaces/CVPR/WALT/configs/_base_/default_runtime.py +++ /dev/null @@ -1,16 +0,0 @@ -checkpoint_config = dict(interval=1) -# yapf:disable -log_config = dict( - interval=50, - hooks=[ - dict(type='TextLoggerHook'), - # dict(type='TensorboardLoggerHook') - ]) -# yapf:enable -custom_hooks = [dict(type='NumClassCheckHook')] - -dist_params = dict(backend='nccl') -log_level = 'INFO' -load_from = None -resume_from = None -workflow = [('train', 1)] diff --git a/spaces/CVPR/WALT/mmdet/models/roi_heads/point_rend_roi_head.py b/spaces/CVPR/WALT/mmdet/models/roi_heads/point_rend_roi_head.py deleted file mode 100644 index 478cdf5bff6779e9291f94c543205289036ea2c6..0000000000000000000000000000000000000000 --- a/spaces/CVPR/WALT/mmdet/models/roi_heads/point_rend_roi_head.py +++ /dev/null @@ -1,218 +0,0 @@ -# Modified from https://github.com/facebookresearch/detectron2/tree/master/projects/PointRend # noqa - -import torch -import torch.nn.functional as F -from mmcv.ops import point_sample, rel_roi_point_to_rel_img_point - -from mmdet.core import bbox2roi, bbox_mapping, merge_aug_masks -from .. import builder -from ..builder import HEADS -from .standard_roi_head import StandardRoIHead - - -@HEADS.register_module() -class PointRendRoIHead(StandardRoIHead): - """`PointRend
- \n\n" + "".join(display_append) + "
-
-
-
-## Dataset
-
-### Train Dataset
-
-| trainset | instance_num | repeat_num | note |
-| :-------: | :----------: | :--------: | :----------: |
-| Syn90k | 8919273 | 1 | synth |
-| SynthText | 7239272 | 1 | alphanumeric |
-
-### Test Dataset
-
-| testset | instance_num | note |
-| :-----: | :----------: | :-------: |
-| IIIT5K | 3000 | regular |
-| SVT | 647 | regular |
-| IC13 | 1015 | regular |
-| IC15 | 2077 | irregular |
-| SVTP | 645 | irregular |
-| CT80 | 288 | irregular |
-
-## Results and models
-
-| Methods | Backbone | | Regular Text | | | | Irregular Text | | download |
-| :--------------------------------------------------------------: | :------: | :----: | :----------: | :-------: | :-: | :-------: | :------------: | :----: | :-------------------------------------------------------------------: |
-| | | IIIT5K | SVT | IC13-1015 | | IC15-2077 | SVTP | CT80 | |
-| [ASTER](/configs/textrecog/aster/aster_resnet45_6e_st_mj.py) | ResNet45 | 0.9357 | 0.8949 | 0.9281 | | 0.7665 | 0.8062 | 0.8507 | [model](https://download.openmmlab.com/mmocr/textrecog/aster/aster_resnet45_6e_st_mj/aster_resnet45_6e_st_mj-cc56eca4.pth) \| [log](https://download.openmmlab.com/mmocr/textrecog/aster/aster_resnet45_6e_st_mj/20221214_232605.log) |
-| [ASTER-TTA](/configs/textrecog/aster/aster_resnet45_6e_st_mj.py) | ResNet45 | 0.9337 | 0.8949 | 0.9251 | | 0.7925 | 0.8109 | 0.8507 | |
-
-## Citation
-
-```bibtex
-@article{shi2018aster,
- title={Aster: An attentional scene text recognizer with flexible rectification},
- author={Shi, Baoguang and Yang, Mingkun and Wang, Xinggang and Lyu, Pengyuan and Yao, Cong and Bai, Xiang},
- journal={IEEE transactions on pattern analysis and machine intelligence},
- volume={41},
- number={9},
- pages={2035--2048},
- year={2018},
- publisher={IEEE}
-}
-```
diff --git a/spaces/NATSpeech/DiffSpeech/docs/diffspeech.md b/spaces/NATSpeech/DiffSpeech/docs/diffspeech.md
deleted file mode 100644
index 9d575861911a6b3a9734ebf7fa97833a213313be..0000000000000000000000000000000000000000
--- a/spaces/NATSpeech/DiffSpeech/docs/diffspeech.md
+++ /dev/null
@@ -1,62 +0,0 @@
-# Run DiffSpeech
-
-## Quick Start
-
-### Install Dependencies
-
-Install dependencies following [readme.md](../readme.md)
-
-### Set Config Path and Experiment Name
-
-```bash
-export CONFIG_NAME=egs/datasets/audio/lj/ds.yaml
-export MY_EXP_NAME=ds_exp
-```
-
-### Preprocess and binary dataset
-
-Prepare dataset following [prepare_data.md](./prepare_data.md)
-
-### Prepare Vocoder
-
-Prepare vocoder following [prepare_vocoder.md](./prepare_vocoder.md)
-
-## Training
-
-First, you need a pre-trained FastSpeech2 checkpoint `chckpoints/fs2_exp/model_ckpt_steps_160000.ckpt`. To train a FastSpeech 2 model, run:
-
-```bash
-CUDA_VISIBLE_DEVICES=0 python tasks/run.py --config egs/datasets/audio/lj/fs2_orig.yaml --exp_name fs2_exp --reset
-```
-
-Then, run:
-
-```bash
-CUDA_VISIBLE_DEVICES=0 python tasks/run.py --config $CONFIG_NAME --exp_name $MY_EXP_NAME --reset
-```
-
-You can check the training and validation curves open Tensorboard via:
-
-```bash
-tensorboard --logdir checkpoints/$MY_EXP_NAME
-```
-
-## Inference (Testing)
-
-```bash
-CUDA_VISIBLE_DEVICES=0 python tasks/run.py --config $CONFIG_NAME --exp_name $MY_EXP_NAME --infer
-```
-
-## Citation
-
-If you find this useful for your research, please use the following.
-
-```bib
-@article{liu2021diffsinger,
- title={Diffsinger: Singing voice synthesis via shallow diffusion mechanism},
- author={Liu, Jinglin and Li, Chengxi and Ren, Yi and Chen, Feiyang and Liu, Peng and Zhao, Zhou},
- journal={arXiv preprint arXiv:2105.02446},
- volume={2},
- year={2021}
- }
-```
diff --git a/spaces/NLPark/Misteln-Schariac/app.py b/spaces/NLPark/Misteln-Schariac/app.py
deleted file mode 100644
index ff0e0d927feb316972a7672aeef3447eaaf445e3..0000000000000000000000000000000000000000
--- a/spaces/NLPark/Misteln-Schariac/app.py
+++ /dev/null
@@ -1,214 +0,0 @@
-import gradio as gr
-
-import copy
-import random
-import os
-import requests
-import time
-import sys
-
-os.system("pip install --upgrade pip")
-os.system('''CMAKE_ARGS="-DLLAMA_AVX512=ON -DLLAMA_AVX512_VBMI=ON -DLLAMA_AVX512_VNNI=ON -DLLAMA_FP16_VA=ON" pip install llama-cpp-python''')
-
-from huggingface_hub import snapshot_download
-from llama_cpp import Llama
-
-
-SYSTEM_PROMPT = '''You are a helpful, respectful and honest INTP-T AI Assistant named "Schariac" in English or "沙尼亚特" in Chinese.
-You are good at speaking English and Chinese.
-You are good at math and programming.
-You are talking to a human User. If the question is meaningless, please explain the reason and don't share false information.
-You are based on Cecilia model, trained by "SSFW NLPark" team, not related to GPT, LLaMA, Meta, Mistral or OpenAI.
-Let's work this out in a step by step way to be sure we have the right answer.\n\n'''
-SYSTEM_TOKEN = 1587
-USER_TOKEN = 2188
-BOT_TOKEN = 12435
-LINEBREAK_TOKEN = 13
-
-
-ROLE_TOKENS = {
- "user": USER_TOKEN,
- "bot": BOT_TOKEN,
- "system": SYSTEM_TOKEN
-}
-
-
-def get_message_tokens(model, role, content):
- message_tokens = model.tokenize(content.encode("utf-8"))
- message_tokens.insert(1, ROLE_TOKENS[role])
- message_tokens.insert(2, LINEBREAK_TOKEN)
- message_tokens.append(model.token_eos())
- return message_tokens
-
-
-def get_system_tokens(model):
- system_message = {"role": "system", "content": SYSTEM_PROMPT}
- return get_message_tokens(model, **system_message)
-
-
-repo_name = "sprint-mammoth/openbuddy-llemma-34b-v13.1-GGUF"
-model_name = "openbuddy-llemma-34b-v13.1-Q4_K_M.gguf"
-
-snapshot_download(repo_id=repo_name, local_dir=".", allow_patterns=model_name)
-
-model = Llama(
- model_path=model_name,
- n_ctx=2000,
- n_parts=1,
-)
-
-max_new_tokens = 1500
-
-def user(message, history):
- new_history = history + [[message, None]]
- return "", new_history
-
-
-def bot(
- history,
- system_prompt,
- top_p,
- top_k,
- temp
-):
- tokens = get_system_tokens(model)[:]
- tokens.append(LINEBREAK_TOKEN)
-
- for user_message, bot_message in history[:-1]:
- message_tokens = get_message_tokens(model=model, role="user", content=user_message)
- tokens.extend(message_tokens)
- if bot_message:
- message_tokens = get_message_tokens(model=model, role="bot", content=bot_message)
- tokens.extend(message_tokens)
-
- last_user_message = history[-1][0]
- message_tokens = get_message_tokens(model=model, role="user", content=last_user_message)
- tokens.extend(message_tokens)
-
- role_tokens = [model.token_bos(), BOT_TOKEN, LINEBREAK_TOKEN]
- tokens.extend(role_tokens)
- generator = model.generate(
- tokens,
- top_k=top_k,
- top_p=top_p,
- temp=temp
- )
-
- partial_text = ""
- for i, token in enumerate(generator):
- if token == model.token_eos() or (max_new_tokens is not None and i >= max_new_tokens):
- break
- partial_text += model.detokenize([token]).decode("utf-8", "ignore")
- history[-1][1] = partial_text
- yield history
-
-
-with gr.Blocks(
- theme=gr.themes.Soft()
-) as demo:
- gr.Markdown(f"""
-JWorld-Cecilia-人工智能助理
""")
- gr.Markdown(value="""这是一个多语言数学与编程模型的部署。
- 这是量化版 Cecilia 的部署，具有 340亿 个参数，在 CPU 上运行。
- Cecilia 是一种会话语言模型，在多种类型的语料库上进行训练。
- 本节目由上海师范大学附属外国语中学 & JWorld NLPark 赞助播出""")
-
- with gr.Row():
- with gr.Column(scale=5):
- chatbot = gr.Chatbot(label="以真理之名").style(height=400)
- with gr.Row():
- with gr.Column():
- msg = gr.Textbox(
- label="来问问 Cecilia 吧……",
- placeholder="Cecilia, 抵达战场……",
- show_label=True,
- ).style(container=True)
- submit = gr.Button("Submit / 开凹！")
- stop = gr.Button("Stop / 全局时空断裂")
- clear = gr.Button("Clear / 打扫群内垃圾")
- with gr.Row():
- with gr.Column(min_width=80, scale=1):
- with gr.Tab(label="设置参数"):
- top_p = gr.Slider(
- minimum=0.0,
- maximum=1.0,
- value=0.9,
- step=0.05,
- interactive=True,
- label="Top-p",
- )
- top_k = gr.Slider(
- minimum=10,
- maximum=100,
- value=30,
- step=5,
- interactive=True,
- label="Top-k",
- )
- temp = gr.Slider(
- minimum=0.0,
- maximum=2.0,
- value=0.2,
- step=0.01,
- interactive=True,
- label="情感温度"
- )
- with gr.Column():
- system_prompt = gr.Textbox(label="系统提示词", placeholder="", value=SYSTEM_PROMPT, interactive=False)
- with gr.Row():
- gr.Markdown(
- """警告：该模型可能会生成事实上或道德上不正确的文本。NLPark和 Cecilia 对此不承担任何责任。"""
- )
-
-
- # Pressing Enter
- submit_event = msg.submit(
- fn=user,
- inputs=[msg, chatbot],
- outputs=[msg, chatbot],
- queue=False,
- ).success(
- fn=bot,
- inputs=[
- chatbot,
- system_prompt,
- top_p,
- top_k,
- temp
- ],
- outputs=chatbot,
- queue=True,
- )
-
- # Pressing the button
- submit_click_event = submit.click(
- fn=user,
- inputs=[msg, chatbot],
- outputs=[msg, chatbot],
- queue=False,
- ).success(
- fn=bot,
- inputs=[
- chatbot,
- system_prompt,
- top_p,
- top_k,
- temp
- ],
- outputs=chatbot,
- queue=True,
- )
-
- # Stop generation
- stop.click(
- fn=None,
- inputs=None,
- outputs=None,
- cancels=[submit_event, submit_click_event],
- queue=False,
- )
-
- # Clear history
- clear.click(lambda: None, None, chatbot, queue=False)
-
-demo.queue(max_size=128, concurrency_count=1)
-demo.launch()
\ No newline at end of file
diff --git a/spaces/Nick1/rvc-models/lib/infer_pack/commons.py b/spaces/Nick1/rvc-models/lib/infer_pack/commons.py
deleted file mode 100644
index 54470986f37825b35d90d7efa7437d1c26b87215..0000000000000000000000000000000000000000
--- a/spaces/Nick1/rvc-models/lib/infer_pack/commons.py
+++ /dev/null
@@ -1,166 +0,0 @@
-import math
-import numpy as np
-import torch
-from torch import nn
-from torch.nn import functional as F
-
-
-def init_weights(m, mean=0.0, std=0.01):
- classname = m.__class__.__name__
- if classname.find("Conv") != -1:
- m.weight.data.normal_(mean, std)
-
-
-def get_padding(kernel_size, dilation=1):
- return int((kernel_size * dilation - dilation) / 2)
-
-
-def convert_pad_shape(pad_shape):
- l = pad_shape[::-1]
- pad_shape = [item for sublist in l for item in sublist]
- return pad_shape
-
-
-def kl_divergence(m_p, logs_p, m_q, logs_q):
- """KL(P||Q)"""
- kl = (logs_q - logs_p) - 0.5
- kl += (
- 0.5 * (torch.exp(2.0 * logs_p) + ((m_p - m_q) ** 2)) * torch.exp(-2.0 * logs_q)
- )
- return kl
-
-
-def rand_gumbel(shape):
- """Sample from the Gumbel distribution, protect from overflows."""
- uniform_samples = torch.rand(shape) * 0.99998 + 0.00001
- return -torch.log(-torch.log(uniform_samples))
-
-
-def rand_gumbel_like(x):
- g = rand_gumbel(x.size()).to(dtype=x.dtype, device=x.device)
- return g
-
-
-def slice_segments(x, ids_str, segment_size=4):
- ret = torch.zeros_like(x[:, :, :segment_size])
- for i in range(x.size(0)):
- idx_str = ids_str[i]
- idx_end = idx_str + segment_size
- ret[i] = x[i, :, idx_str:idx_end]
- return ret
-
-
-def slice_segments2(x, ids_str, segment_size=4):
- ret = torch.zeros_like(x[:, :segment_size])
- for i in range(x.size(0)):
- idx_str = ids_str[i]
- idx_end = idx_str + segment_size
- ret[i] = x[i, idx_str:idx_end]
- return ret
-
-
-def rand_slice_segments(x, x_lengths=None, segment_size=4):
- b, d, t = x.size()
- if x_lengths is None:
- x_lengths = t
- ids_str_max = x_lengths - segment_size + 1
- ids_str = (torch.rand([b]).to(device=x.device) * ids_str_max).to(dtype=torch.long)
- ret = slice_segments(x, ids_str, segment_size)
- return ret, ids_str
-
-
-def get_timing_signal_1d(length, channels, min_timescale=1.0, max_timescale=1.0e4):
- position = torch.arange(length, dtype=torch.float)
- num_timescales = channels // 2
- log_timescale_increment = math.log(float(max_timescale) / float(min_timescale)) / (
- num_timescales - 1
- )
- inv_timescales = min_timescale * torch.exp(
- torch.arange(num_timescales, dtype=torch.float) * -log_timescale_increment
- )
- scaled_time = position.unsqueeze(0) * inv_timescales.unsqueeze(1)
- signal = torch.cat([torch.sin(scaled_time), torch.cos(scaled_time)], 0)
- signal = F.pad(signal, [0, 0, 0, channels % 2])
- signal = signal.view(1, channels, length)
- return signal
-
-
-def add_timing_signal_1d(x, min_timescale=1.0, max_timescale=1.0e4):
- b, channels, length = x.size()
- signal = get_timing_signal_1d(length, channels, min_timescale, max_timescale)
- return x + signal.to(dtype=x.dtype, device=x.device)
-
-
-def cat_timing_signal_1d(x, min_timescale=1.0, max_timescale=1.0e4, axis=1):
- b, channels, length = x.size()
- signal = get_timing_signal_1d(length, channels, min_timescale, max_timescale)
- return torch.cat([x, signal.to(dtype=x.dtype, device=x.device)], axis)
-
-
-def subsequent_mask(length):
- mask = torch.tril(torch.ones(length, length)).unsqueeze(0).unsqueeze(0)
- return mask
-
-
-@torch.jit.script
-def fused_add_tanh_sigmoid_multiply(input_a, input_b, n_channels):
- n_channels_int = n_channels[0]
- in_act = input_a + input_b
- t_act = torch.tanh(in_act[:, :n_channels_int, :])
- s_act = torch.sigmoid(in_act[:, n_channels_int:, :])
- acts = t_act * s_act
- return acts
-
-
-def convert_pad_shape(pad_shape):
- l = pad_shape[::-1]
- pad_shape = [item for sublist in l for item in sublist]
- return pad_shape
-
-
-def shift_1d(x):
- x = F.pad(x, convert_pad_shape([[0, 0], [0, 0], [1, 0]]))[:, :, :-1]
- return x
-
-
-def sequence_mask(length, max_length=None):
- if max_length is None:
- max_length = length.max()
- x = torch.arange(max_length, dtype=length.dtype, device=length.device)
- return x.unsqueeze(0) < length.unsqueeze(1)
-
-
-def generate_path(duration, mask):
- """
- duration: [b, 1, t_x]
- mask: [b, 1, t_y, t_x]
- """
- device = duration.device
-
- b, _, t_y, t_x = mask.shape
- cum_duration = torch.cumsum(duration, -1)
-
- cum_duration_flat = cum_duration.view(b * t_x)
- path = sequence_mask(cum_duration_flat, t_y).to(mask.dtype)
- path = path.view(b, t_x, t_y)
- path = path - F.pad(path, convert_pad_shape([[0, 0], [1, 0], [0, 0]]))[:, :-1]
- path = path.unsqueeze(1).transpose(2, 3) * mask
- return path
-
-
-def clip_grad_value_(parameters, clip_value, norm_type=2):
- if isinstance(parameters, torch.Tensor):
- parameters = [parameters]
- parameters = list(filter(lambda p: p.grad is not None, parameters))
- norm_type = float(norm_type)
- if clip_value is not None:
- clip_value = float(clip_value)
-
- total_norm = 0
- for p in parameters:
- param_norm = p.grad.data.norm(norm_type)
- total_norm += param_norm.item() ** norm_type
- if clip_value is not None:
- p.grad.data.clamp_(min=-clip_value, max=clip_value)
- total_norm = total_norm ** (1.0 / norm_type)
- return total_norm
diff --git a/spaces/OAOA/DifFace/basicsr/data/data_sampler.py b/spaces/OAOA/DifFace/basicsr/data/data_sampler.py
deleted file mode 100644
index 575452d9f844a928f7f42296c81635cfbadec7c2..0000000000000000000000000000000000000000
--- a/spaces/OAOA/DifFace/basicsr/data/data_sampler.py
+++ /dev/null
@@ -1,48 +0,0 @@
-import math
-import torch
-from torch.utils.data.sampler import Sampler
-
-
-class EnlargedSampler(Sampler):
- """Sampler that restricts data loading to a subset of the dataset.
-
- Modified from torch.utils.data.distributed.DistributedSampler
- Support enlarging the dataset for iteration-based training, for saving
- time when restart the dataloader after each epoch
-
- Args:
- dataset (torch.utils.data.Dataset): Dataset used for sampling.
- num_replicas (int | None): Number of processes participating in
- the training. It is usually the world_size.
- rank (int | None): Rank of the current process within num_replicas.
- ratio (int): Enlarging ratio. Default: 1.
- """
-
- def __init__(self, dataset, num_replicas, rank, ratio=1):
- self.dataset = dataset
- self.num_replicas = num_replicas
- self.rank = rank
- self.epoch = 0
- self.num_samples = math.ceil(len(self.dataset) * ratio / self.num_replicas)
- self.total_size = self.num_samples * self.num_replicas
-
- def __iter__(self):
- # deterministically shuffle based on epoch
- g = torch.Generator()
- g.manual_seed(self.epoch)
- indices = torch.randperm(self.total_size, generator=g).tolist()
-
- dataset_size = len(self.dataset)
- indices = [v % dataset_size for v in indices]
-
- # subsample
- indices = indices[self.rank:self.total_size:self.num_replicas]
- assert len(indices) == self.num_samples
-
- return iter(indices)
-
- def __len__(self):
- return self.num_samples
-
- def set_epoch(self, epoch):
- self.epoch = epoch
diff --git a/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/examples/multilingual/data_scripts/download_wmt20.sh b/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/examples/multilingual/data_scripts/download_wmt20.sh
deleted file mode 100644
index 31cd5c76b75081331ae03c5ea70ea7ddebaa06e1..0000000000000000000000000000000000000000
--- a/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/examples/multilingual/data_scripts/download_wmt20.sh
+++ /dev/null
@@ -1,547 +0,0 @@
-#!/bin/bash
-# Copyright (c) Facebook, Inc. and its affiliates.
-# All rights reserved.
-#
-# This source code is licensed under the license found in the
-# LICENSE file in the root directory of this source tree.
-
-if [ -z $WORKDIR_ROOT ] ;
-then
- echo "please specify your working directory root in environment variable WORKDIR_ROOT. Exitting..."
- exit
-fi
-
-
-
-set -x -e
-
-# TODO update the workdir and dest dir name
-# put fasttext model
-WORKDIR=$WORKDIR_ROOT
-# put intermediate files
-TMP_DIR=$WORKDIR_ROOT/tmp/tmp_wmt20_lowres_download
-# output {train,valid,test} files to dest
-DEST=$WORKDIR_ROOT/ML50/raw
-
-UTILS=$PWD/utils
-
-# per dataset locations
-COMMONCRAWL_DIR=$TMP_DIR/commoncrawl
-YANDEX_CORPUS=$WORKDIR_ROOT/wmt20/official/ru/yandex/1mcorpus.zip
-# unzipped
-CZENG_CORPUS=$WORKDIR_ROOT/wmt20/official/cs/czeng/czeng20-train
-CCMT_DIR=$WORKDIR_ROOT/wmt20/official/zh/ccmt/parallel
-
-download_and_select() {
- SUBFOLDER=$1
- URL=$2
- UNCOMPRESS_CMD=$3
- LANG=$4
- INPUT_FILEPATH=$5
- if [[ $# -gt 5 ]]; then
- LANG_COL=$6
- EN_COL=$7
- fi
-
- mkdir -p $SUBFOLDER
- cd $SUBFOLDER
- wget -nc --content-disposition $URL
- $UNCOMPRESS_CMD
-
- if [[ $# -gt 5 ]]; then
- cut -f$LANG_COL $INPUT_FILEPATH > $INPUT_FILEPATH.$LANG
- cut -f$EN_COL $INPUT_FILEPATH > $INPUT_FILEPATH.en
- fi
- cd ..
-
- ln -sf $SUBFOLDER/$INPUT_FILEPATH.$LANG $SUBFOLDER.$LANG
- ln -sf $SUBFOLDER/$INPUT_FILEPATH.en $SUBFOLDER.en
-}
-
-prepare_lid() {
- pip install fasttext
-
- # TODO specify global workdir
- MODEL=$WORKDIR/fasttext/lid.176.bin
- LID_MULTI=$UTILS/fasttext_multi_filter.py
-
- if [ ! -f "$MODEL" ]; then
- echo "downloading fasttext lid model..."
- mkdir -p $WORKDIR/fasttext
- wget -nc https://dl.fbaipublicfiles.com/fasttext/supervised-models/lid.176.bin -O $MODEL
- fi
-}
-
-prepare_moses() {
- pushd $UTILS
- echo 'Cloning Moses github repository (for tokenization scripts)...'
- git clone https://github.com/moses-smt/mosesdecoder.git
- popd
-}
-
-lid_filter() {
- # TODO specify global workdir
- MODEL=$WORKDIR/fasttext/lid.176.bin
- LID_MULTI=$UTILS/fasttext_multi_filter.py
-
- prepare_lid
-
- SRC=$1
- SRC_FILE=$2
- SRC_OUTPUT=$3
- TGT=$4
- TGT_FILE=$5
- TGT_OUTPUT=$6
- python $LID_MULTI --model $MODEL --inputs $SRC_FILE $TGT_FILE --langs $SRC $TGT --outputs $SRC_OUTPUT $TGT_OUTPUT
-}
-
-prepare_ja_ted() {
- mkdir -p ted
- cd ted
-
- wget -nc https://wit3.fbk.eu/archive/2017-01-trnted//texts/en/ja/en-ja.tgz
- tar -zxvf en-ja.tgz
- cat en-ja/train.tags.en-ja.en | grep -v -P "^[ ]*\<" | sed 's/^[ \t]*//g' | sed 's/[ \t]*$//g' > en-ja/train.en-ja.en
- cat en-ja/train.tags.en-ja.ja | grep -v -P "^[ ]*\<" | sed 's/^[ \t]*//g' | sed 's/[ \t]*$//g' > en-ja/train.en-ja.ja
-
- cd ..
- ln -sf ted/en-ja/train.en-ja.ja ted.ja
- ln -sf ted/en-ja/train.en-ja.en ted.en
-}
-
-prepare_ja() {
- OUTPUT_DIR=$TMP_DIR/ja
- mkdir -p $OUTPUT_DIR
- cd $OUTPUT_DIR
-
- download_and_select paracrawl "http://www.kecl.ntt.co.jp/icl/lirg/jparacrawl/release/2.0/bitext/en-ja.tar.gz" "tar -zxvf en-ja.tar.gz" ja en-ja/en-ja.bicleaner05.txt 4 3 &
- download_and_select newscommentary "http://data.statmt.org/news-commentary/v15/training/news-commentary-v15.en-ja.tsv.gz" "gunzip -f news-commentary-v15.en-ja.tsv.gz" ja news-commentary-v15.en-ja.tsv 2 1 &
- download_and_select wikititles "http://data.statmt.org/wikititles/v2/wikititles-v2.ja-en.tsv.gz" "gunzip -f wikititles-v2.ja-en.tsv.gz" ja wikititles-v2.ja-en.tsv 1 2 &
- download_and_select wikimatrix "http://data.statmt.org/wmt20/translation-task/WikiMatrix/WikiMatrix.v1.en-ja.langid.tsv.gz" "gunzip -f WikiMatrix.v1.en-ja.langid.tsv.gz" ja WikiMatrix.v1.en-ja.langid.tsv 3 2 &
- download_and_select subtitle "https://nlp.stanford.edu/projects/jesc/data/split.tar.gz" "tar -zxvf split.tar.gz" ja split/train 2 1 &
- download_and_select kftt "http://www.phontron.com/kftt/download/kftt-data-1.0.tar.gz" "tar -zxvf kftt-data-1.0.tar.gz" ja kftt-data-1.0/data/orig/kyoto-train &
-
- prepare_ja_ted &
-
- # ted data needs to
-
- wait
-
- # remove previous results
- rm -f all.??
- find ./ -maxdepth 1 -name "*.ja" | sort -V | xargs cat > all.ja
- find ./ -maxdepth 1 -name "*.en" | sort -V | xargs cat > all.en
- lid_filter ja all.ja $DEST/train.ja_XX-en_XX.ja_XX en all.en $DEST/train.ja_XX-en_XX.en_XX
-}
-
-prepare_ta() {
- OUTPUT_DIR=$TMP_DIR/ta
- mkdir -p $OUTPUT_DIR
- cd $OUTPUT_DIR
-
- download_and_select wikititles "http://data.statmt.org/wikititles/v2/wikititles-v2.ta-en.tsv.gz" "gunzip -f wikititles-v2.ta-en.tsv.gz" ta wikititles-v2.ta-en.tsv 1 2 &
- download_and_select wikimatrix "http://data.statmt.org/wmt20/translation-task/WikiMatrix/WikiMatrix.v1.en-ta.langid.tsv.gz" "gunzip -f WikiMatrix.v1.en-ta.langid.tsv.gz" ta WikiMatrix.v1.en-ta.langid.tsv 3 2 &
- download_and_select pmindia "http://data.statmt.org/pmindia/v1/parallel/pmindia.v1.ta-en.tsv" "" ta pmindia.v1.ta-en.tsv 2 1 &
- download_and_select tanzil "https://object.pouta.csc.fi/OPUS-Tanzil/v1/moses/en-ta.txt.zip" "unzip en-ta.txt.zip" ta Tanzil.en-ta &
- download_and_select pib "http://preon.iiit.ac.in/~jerin/resources/datasets/pib-v0.tar" "tar -xvf pib-v0.tar" ta pib/en-ta/train &
- download_and_select mkb "http://preon.iiit.ac.in/~jerin/resources/datasets/mkb-v0.tar" "tar -xvf mkb-v0.tar" ta mkb/en-ta/mkb &
- download_and_select ufal "http://ufal.mff.cuni.cz/~ramasamy/parallel/data/v2/en-ta-parallel-v2.tar.gz" "tar -zxvf en-ta-parallel-v2.tar.gz" ta en-ta-parallel-v2/corpus.bcn.train &
-
- wait
-
- # need special handling for nlpc
- mkdir -p nlpc
- cd nlpc
- wget -nc https://raw.githubusercontent.com/nlpc-uom/English-Tamil-Parallel-Corpus/master/En-Ta%20Corpus/En-Ta%20English.txt
- wget -nc https://github.com/nlpc-uom/English-Tamil-Parallel-Corpus/raw/master/En-Ta%20Corpus/En-Ta%20Tamil.txt
- tail -n +4 "En-Ta English.txt" > en-ta.en
- tail -n +4 "En-Ta Tamil.txt" > en-ta.ta
- cd ..
- ln -sf nlpc/en-ta.en nlpc.en
- ln -sf nlpc/en-ta.ta nlpc.ta
-
- # remove previous results
- rm -f all.??
- find ./ -maxdepth 1 -name "*.ta" | sort -V | xargs cat > all.ta
- find ./ -maxdepth 1 -name "*.en" | sort -V | xargs cat > all.en
- lid_filter ta all.ta $DEST/train.ta_IN-en_XX.ta_IN en all.en $DEST/train.ta_IN-en_XX.en_XX
-}
-
-prepare_iu() {
- OUTPUT_DIR=$TMP_DIR/iu
- mkdir -p $OUTPUT_DIR
- cd $OUTPUT_DIR
-
- download_and_select nh "https://nrc-digital-repository.canada.ca/eng/view/dataset/?id=c7e34fa7-7629-43c2-bd6d-19b32bf64f60" "tar -zxvf Nunavut-Hansard-Inuktitut-English-Parallel-Corpus-3.0.1.tgz" iu Nunavut-Hansard-Inuktitut-English-Parallel-Corpus-3.0/NunavutHansard > /dev/null &
- download_and_select wikititles "http://data.statmt.org/wikititles/v2/wikititles-v2.iu-en.tsv.gz" "gunzip -f wikititles-v2.iu-en.tsv.gz" iu wikititles-v2.iu-en.tsv 1 2 &
-
- wait
-
- # remove previous results
- rm -f all.??
- find ./ -maxdepth 1 -name "*.iu" | sort -V | xargs cat | nh/Nunavut-Hansard-Inuktitut-English-Parallel-Corpus-3.0/scripts/normalize-iu-spelling.pl > all.iu
- find ./ -maxdepth 1 -name "*.en" | sort -V | xargs cat > all.en
- paste all.iu all.en | awk -F $'\t' '$1!=""&&$2!=""' > all.iuen
- cut -f1 all.iuen > $DEST/train.iu_CA-en_XX.iu_CA
- cut -f2 all.iuen > $DEST/train.iu_CA-en_XX.en_XX
-}
-
-prepare_km() {
- OUTPUT_DIR=$TMP_DIR/km
- mkdir -p $OUTPUT_DIR
- cd $OUTPUT_DIR
-
- download_and_select paracrawl "http://data.statmt.org/wmt20/translation-task/ps-km/wmt20-sent.en-km.xz" "unxz wmt20-sent.en-km.zx" km wmt20-sent.en-km 2 1 &
-
- # km-parallel has multiple sets, concat all of them together
- mkdir -p opus
- cd opus
- wget -nc "http://data.statmt.org/wmt20/translation-task/ps-km/km-parallel.tgz"
- tar -zxvf km-parallel.tgz
- find ./km-parallel -maxdepth 1 -name "*.km" | sort -V | xargs cat > opus.km
- find ./km-parallel -maxdepth 1 -name "*.en" | sort -V | xargs cat > opus.en
- cd ..
- ln -sf opus/opus.km .
- ln -sf opus/opus.en .
-
- wait
-
- # remove previous results
- rm -f all.??
- find ./ -maxdepth 1 -name "*.km" | sort -V | xargs cat > all.km
- find ./ -maxdepth 1 -name "*.en" | sort -V | xargs cat > all.en
- lid_filter km all.km $DEST/train.km_KH-en_XX.km_KH en all.en $DEST/train.km_KH-en_XX.en_XX
-}
-
-prepare_ps() {
- OUTPUT_DIR=$TMP_DIR/ps
- mkdir -p $OUTPUT_DIR
- cd $OUTPUT_DIR
-
- download_and_select paracrawl "http://data.statmt.org/wmt20/translation-task/ps-km/wmt20-sent.en-ps.xz" "unxz wmt20-sent.en-ps.xz" ps wmt20-sent.en-ps 2 1 &
- download_and_select wikititles "http://data.statmt.org/wikititles/v2/wikititles-v2.ps-en.tsv.gz" "gunzip -f wikititles-v2.ps-en.tsv.gz" ps wikititles-v2.ps-en.tsv 1 2 &
- # ps-parallel has multiple sets, concat all of them together
- mkdir -p opus
- cd opus
- wget -nc "http://data.statmt.org/wmt20/translation-task/ps-km/ps-parallel.tgz"
- tar -zxvf ps-parallel.tgz
- find ./ps-parallel -maxdepth 1 -name "*.ps" | sort -V | xargs cat > opus.ps
- find ./ps-parallel -maxdepth 1 -name "*.en" | sort -V | xargs cat > opus.en
- cd ..
- ln -sf opus/opus.ps opus.ps
- ln -sf opus/opus.en opus.en
-
- wait
-
- # remove previous results
- rm -f all.??
- find ./ -maxdepth 1 -name "*.ps" | sort -V | xargs cat > all.ps
- find ./ -maxdepth 1 -name "*.en" | sort -V | xargs cat > all.en
- lid_filter ps all.ps $DEST/train.ps_AF-en_XX.ps_AF en all.en $DEST/train.ps_AF-en_XX.en_XX
-}
-
-download_commoncrawl() {
- mkdir -p $COMMONCRAWL_DIR
- cd $COMMONCRAWL_DIR
-
- wget -nc "http://www.statmt.org/wmt13/training-parallel-commoncrawl.tgz"
- tar -zxvf training-parallel-commoncrawl.tgz
-}
-link_commoncrawl() {
- LANG=$1
- ln -sf $COMMONCRAWL_DIR/commoncrawl.$LANG-en.en commoncrawl.en
- ln -sf $COMMONCRAWL_DIR/commoncrawl.$LANG-en.$LANG commoncrawl.$LANG
-}
-
-strip_xlf() {
- INPUT_FILE=$1
- SRC=$2
- TGT=$3
- grep '- -[InstructPix2Pix: Learning to Follow Image Editing Instructions](https://www.timothybrooks.com/instruct-pix2pix/) - [Tim Brooks](https://www.timothybrooks.com/)\*, - [Aleksander Holynski](https://holynski.org/)\*, - [Alexei A. Efros](https://people.eecs.berkeley.edu/~efros/)
- UC Berkeley
- \*denotes equal contribution - -
-
-## TL;DR: quickstart
-
-Set up a conda environment, and download a pretrained model:
-```
-conda env create -f environment.yaml
-conda activate ip2p
-bash scripts/download_checkpoints.sh
-```
-
-Edit a single image:
-```
-python edit_cli.py --input imgs/example.jpg --output imgs/output.jpg --edit "turn him into a cyborg"
-
-# Optionally, you can specify parameters to tune your result:
-# python edit_cli.py --steps 100 --resolution 512 --seed 1371 --cfg-text 7.5 --cfg-image 1.2 --input imgs/example.jpg --output imgs/output.jpg --edit "turn him into a cyborg"
-```
-
-Or launch your own interactive editing Gradio app:
-```
-python edit_app.py
-```
-
-
-_(For advice on how to get the best results by tuning parameters, see the [Tips](https://github.com/timothybrooks/instruct-pix2pix#tips) section)._
-
-## Setup
-
-Install all dependencies with:
-```
-conda env create -f environment.yaml
-```
-
-Download the pretrained models by running:
-```
-bash scripts/download_checkpoints.sh
-```
-
-## Generated Dataset
-
-Our image editing model is trained on a generated dataset consisting of 454,445 examples. Each example contains (1) an input image, (2) an editing instruction, and (3) an output edited image. We provide two versions of the dataset, one in which each pair of edited images is generated 100 times, and the best examples are chosen based on CLIP metrics (Section 3.1.2 in the paper) (`clip-filtered-dataset`), and one in which examples are randomly chosen (`random-sample-dataset`).
-
-For the released version of this dataset, we've additionally filtered prompts and images for NSFW content. After NSFW filtering, the GPT-3 generated dataset contains 451,990 examples. The final image-pair datasets contain:
-
-| | # of image editing examples | Dataset size |
-|--|-----------------------|----------------------- |
-| `random-sample-dataset` |451990|727GB|
-| `clip-filtered-dataset` |313010|436GB|
-
-To download one of these datasets, along with the entire NSFW-filtered text data, run the following command with the appropriate dataset name:
-
-```
-bash scripts/download_data.sh clip-filtered-dataset
-```
-
-
-## Training InstructPix2Pix
-
-InstructPix2Pix is trained by fine-tuning from an initial StableDiffusion checkpoint. The first step is to download a Stable Diffusion checkpoint. For our trained models, we used the v1.5 checkpoint as the starting point. To download the same ones we used, you can run the following script:
-```
-bash scripts/download_pretrained_sd.sh
-```
-If you'd like to use a different checkpoint, point to it in the config file `configs/train.yaml`, on line 8, after `ckpt_path:`.
-
-Next, we need to change the config to point to our downloaded (or generated) dataset. If you're using the `clip-filtered-dataset` from above, you can skip this. Otherwise, you may need to edit lines 85 and 94 of the config (`data.params.train.params.path`, `data.params.validation.params.path`).
-
-Finally, start a training job with the following command:
-
-```
-python main.py --name default --base configs/train.yaml --train --gpus 0,1,2,3,4,5,6,7
-```
-
-
-## Creating your own dataset
-
-Our generated dataset of paired images and editing instructions is made in two phases: First, we use GPT-3 to generate text triplets: (a) a caption describing an image, (b) an edit instruction, (c) a caption describing the image after the edit. Then, we turn pairs of captions (before/after the edit) into pairs of images using Stable Diffusion and Prompt-to-Prompt.
-
-### (1) Generate a dataset of captions and instructions
-
-We provide our generated dataset of captions and edit instructions [here](https://instruct-pix2pix.eecs.berkeley.edu/gpt-generated-prompts.jsonl). If you plan to use our captions+instructions, skip to step (2). Otherwise, if you would like to create your own text dataset, please follow steps (1.1-1.3) below. Note that generating very large datasets using GPT-3 can be expensive.
-
-#### (1.1) Manually write a dataset of instructions and captions
-
-The first step of the process is fine-tuning GPT-3. To do this, we made a dataset of 700 examples broadly covering of edits that we might want our model to be able to perform. Our examples are available [here](https://instruct-pix2pix.eecs.berkeley.edu/human-written-prompts.jsonl). These should be diverse and cover a wide range of possible captions and types of edits. Ideally, they should avoid duplication or significant overlap of captions and instructions. It is also important to be mindful of limitations of Stable Diffusion and Prompt-to-Prompt in writing these examples, such as inability to perform large spatial transformations (e.g., moving the camera, zooming in, swapping object locations).
-
-Input prompts should closely match the distribution of input prompts used to generate the larger dataset. We sampled the 700 input prompts from the _LAION Improved Aesthetics 6.5+_ dataset and also use this dataset for generating examples. We found this dataset is quite noisy (many of the captions are overly long and contain irrelevant text). For this reason, we also considered MSCOCO and LAION-COCO datasets, but ultimately chose _LAION Improved Aesthetics 6.5+_ due to its diversity of content, proper nouns, and artistic mediums. If you choose to use another dataset or combination of datasets as input to GPT-3 when generating examples, we recommend you sample the input prompts from the same distribution when manually writing training examples.
-
-#### (1.2) Finetune GPT-3
-
-The next step is to finetune a large language model on the manually written instructions/outputs to generate edit instructions and edited caption from a new input caption. For this, we finetune GPT-3's Davinci model via the OpenAI API, although other language models could be used.
-
-To prepare training data for GPT-3, one must first create an OpenAI developer account to access the needed APIs, and [set up the API keys on your local device](https://beta.openai.com/docs/api-reference/introduction). Also, run the `prompts/prepare_for_gpt.py` script, which forms the prompts into the correct format by concatenating instructions and captions and adding delimiters and stop sequences.
-
-```bash
-python dataset_creation/prepare_for_gpt.py --input-path data/human-written-prompts.jsonl --output-path data/human-written-prompts-for-gpt.jsonl
-```
-
-Next, finetune GPT-3 via the OpenAI CLI. We provide an example below, although please refer to OpenAI's official documentation for this, as best practices may change. We trained the Davinci model for a single epoch. You can experiment with smaller less expensive GPT-3 variants or with open source language models, although this may negatively affect performance.
-
-```bash
-openai api fine_tunes.create -t data/human-written-prompts-for-gpt.jsonl -m davinci --n_epochs 1 --suffix "instruct-pix2pix"
-```
-
-You can test out the finetuned GPT-3 model by launching the provided Gradio app:
-
-```bash
-python prompt_app.py --openai-api-key OPENAI_KEY --openai-model OPENAI_MODEL_NAME
-```
-
-
-
-#### (1.3) Generate a large dataset of captions and instructions
-
-We now use the finetuned GPT-3 model to generate a large dataset. Our dataset cost thousands of dollars to create. See `prompts/gen_instructions_and_captions.py` for the script which generates these examples. We recommend first generating a small number of examples (by setting a low value of `--num-samples`) and gradually increasing the scale to ensure the results are working as desired before increasing scale.
-
-```bash
-python dataset_creation/generate_txt_dataset.py --openai-api-key OPENAI_KEY --openai-model OPENAI_MODEL_NAME
-```
-
-If you are generating at a very large scale (e.g., 100K+), it will be noteably faster to generate the dataset with multiple processes running in parallel. This can be accomplished by setting `--partitions=N` to a higher number and running multiple processes, setting each `--partition` to the corresponding value.
-
-```bash
-python dataset_creation/generate_txt_dataset.py --openai-api-key OPENAI_KEY --openai-model OPENAI_MODEL_NAME --partitions=10 --partition=0
-```
-
-### (2) Turn paired captions into paired images
-
-The next step is to turn pairs of text captions into pairs of images. For this, we need to copy some pre-trained Stable Diffusion checkpoints to `stable_diffusion/models/ldm/stable-diffusion-v1/`. You may have already done this if you followed the instructions above for training with our provided data, but if not, you can do this by running:
-
-```bash
-bash scripts/download_pretrained_sd.sh
-```
-
-For our model, we used [checkpoint v1.5](https://huggingface.co/runwayml/stable-diffusion-v1-5/blob/main/v1-5-pruned.ckpt), and the [new autoencoder](https://huggingface.co/stabilityai/sd-vae-ft-mse-original/resolve/main/vae-ft-mse-840000-ema-pruned.ckpt), but other models may work as well. If you choose to use other models, make sure to change point to the corresponding checkpoints by passing in the `--ckpt` and `--vae-ckpt` arguments. Once all checkpoints have been downloaded, we can generate the dataset with the following command:
-
-```
-python dataset_creation/generate_img_dataset.py --out_dir data/instruct-pix2pix-dataset-000 --prompts_file path/to/generated_prompts.jsonl
-```
-
-This command operates on a single GPU (typically a V100 or A100). To parallelize over many GPUs/machines, set `--n-partitions` to the total number of parallel jobs and `--partition` to the index of each job.
-
-```
-python dataset_creation/generate_img_dataset.py --out_dir data/instruct-pix2pix-dataset-000 --prompts_file path/to/generated_prompts.jsonl --n-partitions 100 --partition 0
-```
-
-The default parameters match that of our dataset, although in practice you can use a smaller number of steps (e.g., `--steps=25`) to generate high quality data faster. By default, we generate 100 samples per prompt and use CLIP filtering to keep a max of 4 per prompt. You can experiment with fewer samples by setting `--n-samples`. The command below turns off CLIP filtering entirely and is therefore faster:
-
-```
-python dataset_creation/generate_img_dataset.py --out_dir data/instruct-pix2pix-dataset-000 --prompts_file path/to/generated_prompts.jsonl --n-samples 4 --clip-threshold 0 --clip-dir-threshold 0 --clip-img-threshold 0 --n-partitions 100 --partition 0
-```
-
-After generating all of the dataset examples, run the following command below to create a list of the examples. This is needed for the dataset onject to efficiently be able to sample examples without needing to iterate over the entire dataset directory at the start of each training run.
-
-```
-python dataset_creation/prepare_dataset.py data/instruct-pix2pix-dataset-000
-```
-
-## Evaluation
-
-To generate plots like the ones in Figures 8 and 10 in the paper, run the following command:
-
-```
-python metrics/compute_metrics.py --ckpt /path/to/your/model.ckpt
-```
-
-## Tips
-
-If you're not getting the quality result you want, there may be a few reasons:
-1. **Is the image not changing enough?** Your Image CFG weight may be too high. This value dictates how similar the output should be to the input. It's possible your edit requires larger changes from the original image, and your Image CFG weight isn't allowing that. Alternatively, your Text CFG weight may be too low. This value dictates how much to listen to the text instruction. The default Image CFG of 1.5 and Text CFG of 7.5 are a good starting point, but aren't necessarily optimal for each edit. Try:
- * Decreasing the Image CFG weight, or
- * Incerasing the Text CFG weight, or
-2. Conversely, **is the image changing too much**, such that the details in the original image aren't preserved? Try:
- * Increasing the Image CFG weight, or
- * Decreasing the Text CFG weight
-3. Try generating results with different random seeds by setting "Randomize Seed" and running generation multiple times. You can also try setting "Randomize CFG" to sample new Text CFG and Image CFG values each time.
-4. Rephrasing the instruction sometimes improves results (e.g., "turn him into a dog" vs. "make him a dog" vs. "as a dog").
-5. Increasing the number of steps sometimes improves results.
-6. Do faces look weird? The Stable Diffusion autoencoder has a hard time with faces that are small in the image. Try cropping the image so the face takes up a larger portion of the frame.
-
-## Comments
-
-- Our codebase is based on the [Stable Diffusion codebase](https://github.com/CompVis/stable-diffusion).
-
-## BibTeX
-
-```
-@article{brooks2022instructpix2pix,
- title={InstructPix2Pix: Learning to Follow Image Editing Instructions},
- author={Brooks, Tim and Holynski, Aleksander and Efros, Alexei A},
- journal={arXiv preprint arXiv:2211.09800},
- year={2022}
-}
-```
-
-
-
diff --git a/spaces/XzJosh/Azuma-Bert-VITS2/server.py b/spaces/XzJosh/Azuma-Bert-VITS2/server.py
deleted file mode 100644
index c736ca4f95fec853950eef6654ef79856beffc0a..0000000000000000000000000000000000000000
--- a/spaces/XzJosh/Azuma-Bert-VITS2/server.py
+++ /dev/null
@@ -1,123 +0,0 @@
-from flask import Flask, request, Response
-from io import BytesIO
-import torch
-from av import open as avopen
-
-import commons
-import utils
-from models import SynthesizerTrn
-from text.symbols import symbols
-from text import cleaned_text_to_sequence, get_bert
-from text.cleaner import clean_text
-from scipy.io import wavfile
-
-# Flask Init
-app = Flask(__name__)
-app.config['JSON_AS_ASCII'] = False
-def get_text(text, language_str, hps):
- norm_text, phone, tone, word2ph = clean_text(text, language_str)
- print([f"{p}{t}" for p, t in zip(phone, tone)])
- phone, tone, language = cleaned_text_to_sequence(phone, tone, language_str)
-
- if hps.data.add_blank:
- phone = commons.intersperse(phone, 0)
- tone = commons.intersperse(tone, 0)
- language = commons.intersperse(language, 0)
- for i in range(len(word2ph)):
- word2ph[i] = word2ph[i] * 2
- word2ph[0] += 1
- bert = get_bert(norm_text, word2ph, language_str)
-
- assert bert.shape[-1] == len(phone)
-
- phone = torch.LongTensor(phone)
- tone = torch.LongTensor(tone)
- language = torch.LongTensor(language)
-
- return bert, phone, tone, language
-
-def infer(text, sdp_ratio, noise_scale, noise_scale_w,length_scale,sid):
- bert, phones, tones, lang_ids = get_text(text,"ZH", hps,)
- with torch.no_grad():
- x_tst=phones.to(dev).unsqueeze(0)
- tones=tones.to(dev).unsqueeze(0)
- lang_ids=lang_ids.to(dev).unsqueeze(0)
- bert = bert.to(dev).unsqueeze(0)
- x_tst_lengths = torch.LongTensor([phones.size(0)]).to(dev)
- speakers = torch.LongTensor([hps.data.spk2id[sid]]).to(dev)
- audio = net_g.infer(x_tst, x_tst_lengths, speakers, tones, lang_ids,bert, sdp_ratio=sdp_ratio
- , noise_scale=noise_scale, noise_scale_w=noise_scale_w, length_scale=length_scale)[0][0,0].data.cpu().float().numpy()
- return audio
-
-def replace_punctuation(text, i=2):
- punctuation = "，。？！"
- for char in punctuation:
- text = text.replace(char, char * i)
- return text
-
-def wav2(i, o, format):
- inp = avopen(i, 'rb')
- out = avopen(o, 'wb', format=format)
- if format == "ogg": format = "libvorbis"
-
- ostream = out.add_stream(format)
-
- for frame in inp.decode(audio=0):
- for p in ostream.encode(frame): out.mux(p)
-
- for p in ostream.encode(None): out.mux(p)
-
- out.close()
- inp.close()
-
-# Load Generator
-hps = utils.get_hparams_from_file("./configs/config.json")
-
-dev='cuda'
-net_g = SynthesizerTrn(
- len(symbols),
- hps.data.filter_length // 2 + 1,
- hps.train.segment_size // hps.data.hop_length,
- n_speakers=hps.data.n_speakers,
- **hps.model).to(dev)
-_ = net_g.eval()
-
-_ = utils.load_checkpoint("logs/G_649000.pth", net_g, None,skip_optimizer=True)
-
-@app.route("/",methods=['GET','POST'])
-def main():
- if request.method == 'GET':
- try:
- speaker = request.args.get('speaker')
- text = request.args.get('text').replace("/n","")
- sdp_ratio = float(request.args.get("sdp_ratio", 0.2))
- noise = float(request.args.get("noise", 0.5))
- noisew = float(request.args.get("noisew", 0.6))
- length = float(request.args.get("length", 1.2))
- if length >= 2:
- return "Too big length"
- if len(text) >=200:
- return "Too long text"
- fmt = request.args.get("format", "wav")
- if None in (speaker, text):
- return "Missing Parameter"
- if fmt not in ("mp3", "wav", "ogg"):
- return "Invalid Format"
- except:
- return "Invalid Parameter"
-
- with torch.no_grad():
- audio = infer(text, sdp_ratio=sdp_ratio, noise_scale=noise, noise_scale_w=noisew, length_scale=length, sid=speaker)
-
- with BytesIO() as wav:
- wavfile.write(wav, hps.data.sampling_rate, audio)
- torch.cuda.empty_cache()
- if fmt == "wav":
- return Response(wav.getvalue(), mimetype="audio/wav")
- wav.seek(0, 0)
- with BytesIO() as ofp:
- wav2(wav, ofp, fmt)
- return Response(
- ofp.getvalue(),
- mimetype="audio/mpeg" if fmt == "mp3" else "audio/ogg"
- )
diff --git a/spaces/XzJosh/LAPLACE-Bert-VITS2/text/symbols.py b/spaces/XzJosh/LAPLACE-Bert-VITS2/text/symbols.py
deleted file mode 100644
index 9dfae4e633829f20c4fd767b1c7a9198911ed801..0000000000000000000000000000000000000000
--- a/spaces/XzJosh/LAPLACE-Bert-VITS2/text/symbols.py
+++ /dev/null
@@ -1,51 +0,0 @@
-punctuation = ['!', '?', '…', ",", ".", "'", '-']
-pu_symbols = punctuation + ["SP", "UNK"]
-pad = '_'
-
-# chinese
-zh_symbols = ['E', 'En', 'a', 'ai', 'an', 'ang', 'ao', 'b', 'c', 'ch', 'd', 'e', 'ei', 'en', 'eng', 'er', 'f', 'g', 'h',
- 'i', 'i0', 'ia', 'ian', 'iang', 'iao', 'ie', 'in', 'ing', 'iong', 'ir', 'iu', 'j', 'k', 'l', 'm', 'n', 'o',
- 'ong',
- 'ou', 'p', 'q', 'r', 's', 'sh', 't', 'u', 'ua', 'uai', 'uan', 'uang', 'ui', 'un', 'uo', 'v', 'van', 've', 'vn',
- 'w', 'x', 'y', 'z', 'zh',
- "AA", "EE", "OO"]
-num_zh_tones = 6
-
-# japanese
-ja_symbols = ['I', 'N', 'U', 'a', 'b', 'by', 'ch', 'cl', 'd', 'dy', 'e', 'f', 'g', 'gy', 'h', 'hy', 'i', 'j', 'k', 'ky',
- 'm', 'my', 'n', 'ny', 'o', 'p', 'py', 'r', 'ry', 's', 'sh', 't', 'ts', 'u', 'V', 'w', 'y', 'z']
-num_ja_tones = 1
-
-# English
-en_symbols = ['aa', 'ae', 'ah', 'ao', 'aw', 'ay', 'b', 'ch', 'd', 'dh', 'eh', 'er', 'ey', 'f', 'g', 'hh', 'ih', 'iy',
- 'jh', 'k', 'l', 'm', 'n', 'ng', 'ow', 'oy', 'p', 'r', 's',
- 'sh', 't', 'th', 'uh', 'uw', 'V', 'w', 'y', 'z', 'zh']
-num_en_tones = 4
-
-# combine all symbols
-normal_symbols = sorted(set(zh_symbols + ja_symbols + en_symbols))
-symbols = [pad] + normal_symbols + pu_symbols
-sil_phonemes_ids = [symbols.index(i) for i in pu_symbols]
-
-# combine all tones
-num_tones = num_zh_tones + num_ja_tones + num_en_tones
-
-# language maps
-language_id_map = {
- 'ZH': 0,
- "JA": 1,
- "EN": 2
-}
-num_languages = len(language_id_map.keys())
-
-language_tone_start_map = {
- 'ZH': 0,
- "JA": num_zh_tones,
- "EN": num_zh_tones + num_ja_tones
-}
-
-if __name__ == '__main__':
- a = set(zh_symbols)
- b = set(en_symbols)
- print(sorted(a&b))
-
diff --git a/spaces/XzJosh/nine2-Bert-VITS2/text/symbols.py b/spaces/XzJosh/nine2-Bert-VITS2/text/symbols.py
deleted file mode 100644
index 9dfae4e633829f20c4fd767b1c7a9198911ed801..0000000000000000000000000000000000000000
--- a/spaces/XzJosh/nine2-Bert-VITS2/text/symbols.py
+++ /dev/null
@@ -1,51 +0,0 @@
-punctuation = ['!', '?', '…', ",", ".", "'", '-']
-pu_symbols = punctuation + ["SP", "UNK"]
-pad = '_'
-
-# chinese
-zh_symbols = ['E', 'En', 'a', 'ai', 'an', 'ang', 'ao', 'b', 'c', 'ch', 'd', 'e', 'ei', 'en', 'eng', 'er', 'f', 'g', 'h',
- 'i', 'i0', 'ia', 'ian', 'iang', 'iao', 'ie', 'in', 'ing', 'iong', 'ir', 'iu', 'j', 'k', 'l', 'm', 'n', 'o',
- 'ong',
- 'ou', 'p', 'q', 'r', 's', 'sh', 't', 'u', 'ua', 'uai', 'uan', 'uang', 'ui', 'un', 'uo', 'v', 'van', 've', 'vn',
- 'w', 'x', 'y', 'z', 'zh',
- "AA", "EE", "OO"]
-num_zh_tones = 6
-
-# japanese
-ja_symbols = ['I', 'N', 'U', 'a', 'b', 'by', 'ch', 'cl', 'd', 'dy', 'e', 'f', 'g', 'gy', 'h', 'hy', 'i', 'j', 'k', 'ky',
- 'm', 'my', 'n', 'ny', 'o', 'p', 'py', 'r', 'ry', 's', 'sh', 't', 'ts', 'u', 'V', 'w', 'y', 'z']
-num_ja_tones = 1
-
-# English
-en_symbols = ['aa', 'ae', 'ah', 'ao', 'aw', 'ay', 'b', 'ch', 'd', 'dh', 'eh', 'er', 'ey', 'f', 'g', 'hh', 'ih', 'iy',
- 'jh', 'k', 'l', 'm', 'n', 'ng', 'ow', 'oy', 'p', 'r', 's',
- 'sh', 't', 'th', 'uh', 'uw', 'V', 'w', 'y', 'z', 'zh']
-num_en_tones = 4
-
-# combine all symbols
-normal_symbols = sorted(set(zh_symbols + ja_symbols + en_symbols))
-symbols = [pad] + normal_symbols + pu_symbols
-sil_phonemes_ids = [symbols.index(i) for i in pu_symbols]
-
-# combine all tones
-num_tones = num_zh_tones + num_ja_tones + num_en_tones
-
-# language maps
-language_id_map = {
- 'ZH': 0,
- "JA": 1,
- "EN": 2
-}
-num_languages = len(language_id_map.keys())
-
-language_tone_start_map = {
- 'ZH': 0,
- "JA": num_zh_tones,
- "EN": num_zh_tones + num_ja_tones
-}
-
-if __name__ == '__main__':
- a = set(zh_symbols)
- b = set(en_symbols)
- print(sorted(a&b))
-
diff --git a/spaces/Yasu55/stable-diffusion-webui/README.md b/spaces/Yasu55/stable-diffusion-webui/README.md
deleted file mode 100644
index be90d7ea477a42a1bf7f8e46e43762acf28d3bbe..0000000000000000000000000000000000000000
--- a/spaces/Yasu55/stable-diffusion-webui/README.md
+++ /dev/null
@@ -1,14 +0,0 @@
----
-title: Stable Diffusion Webui
-emoji: 💻
-colorFrom: yellow
-colorTo: gray
-sdk: gradio
-sdk_version: 3.12.0
-app_file: app.py
-pinned: false
-license: openrail
-duplicated_from: kamiyamai/stable-diffusion-webui
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/Yati05/TF-CodeT5-base/README.md b/spaces/Yati05/TF-CodeT5-base/README.md
deleted file mode 100644
index 69fdb3a9ecd0bb779e215e1875594bf74317a913..0000000000000000000000000000000000000000
--- a/spaces/Yati05/TF-CodeT5-base/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: TF CodeT5 Base
-emoji: 📚
-colorFrom: blue
-colorTo: indigo
-sdk: gradio
-sdk_version: 3.27.0
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/abhishek/sketch-to-image/annotator/uniformer/__init__seg.py b/spaces/abhishek/sketch-to-image/annotator/uniformer/__init__seg.py
deleted file mode 100644
index 3364d40997447a4ec15ca7a525a4d0e92ab211bd..0000000000000000000000000000000000000000
--- a/spaces/abhishek/sketch-to-image/annotator/uniformer/__init__seg.py
+++ /dev/null
@@ -1,27 +0,0 @@
-# Uniformer
-# From https://github.com/Sense-X/UniFormer
-# # Apache-2.0 license
-
-import os
-
-from annotator.uniformer.mmseg.apis import init_segmentor, inference_segmentor, show_result_pyplot
-from annotator.uniformer.mmseg.core.evaluation import get_palette
-from annotator.util import annotator_ckpts_path
-
-
-checkpoint_file = "https://huggingface.co/lllyasviel/ControlNet/resolve/main/annotator/ckpts/upernet_global_small.pth"
-
-
-class UniformerDetector:
- def __init__(self):
- modelpath = os.path.join(annotator_ckpts_path, "upernet_global_small.pth")
- if not os.path.exists(modelpath):
- from basicsr.utils.download_util import load_file_from_url
- load_file_from_url(checkpoint_file, model_dir=annotator_ckpts_path)
- config_file = os.path.join(os.path.dirname(annotator_ckpts_path), "uniformer", "exp", "upernet_global_small", "config.py")
- self.model = init_segmentor(config_file, modelpath).cuda()
-
- def __call__(self, img):
- result = inference_segmentor(self.model, img)
- res_img = show_result_pyplot(self.model, img, result, get_palette('ade'), opacity=1)
- return res_img
diff --git a/spaces/abhishek/sketch-to-image/annotator/uniformer/mmdet/datasets/pipelines/instaboost.py b/spaces/abhishek/sketch-to-image/annotator/uniformer/mmdet/datasets/pipelines/instaboost.py
deleted file mode 100644
index 38b6819f60587a6e0c0f6d57bfda32bb3a7a4267..0000000000000000000000000000000000000000
--- a/spaces/abhishek/sketch-to-image/annotator/uniformer/mmdet/datasets/pipelines/instaboost.py
+++ /dev/null
@@ -1,98 +0,0 @@
-import numpy as np
-
-from ..builder import PIPELINES
-
-
-@PIPELINES.register_module()
-class InstaBoost(object):
- r"""Data augmentation method in `InstaBoost: Boosting Instance
- Segmentation Via Probability Map Guided Copy-Pasting
- Reloading...
\'; setTimeout(function(){location.reload()},2500); return []}') - - # chat mode event handlers - if shared.is_chat(): - shared.input_params = [shared.gradio[k] for k in ['Chat input', 'interface_state']] - clear_arr = [shared.gradio[k] for k in ['Clear history-confirm', 'Clear history', 'Clear history-cancel']] - reload_inputs = [shared.gradio[k] for k in ['name1', 'name2', 'mode']] - - gen_events.append(shared.gradio['Generate'].click( - ui.gather_interface_values, [shared.gradio[k] for k in shared.input_elements], shared.gradio['interface_state']).then( - lambda x: (x, ''), shared.gradio['textbox'], [shared.gradio['Chat input'], shared.gradio['textbox']], show_progress=False).then( - chat.cai_chatbot_wrapper, shared.input_params, shared.gradio['display'], show_progress=shared.args.no_stream).then( - chat.save_history, shared.gradio['mode'], None, show_progress=False) - ) - - gen_events.append(shared.gradio['textbox'].submit( - ui.gather_interface_values, [shared.gradio[k] for k in shared.input_elements], shared.gradio['interface_state']).then( - lambda x: (x, ''), shared.gradio['textbox'], [shared.gradio['Chat input'], shared.gradio['textbox']], show_progress=False).then( - chat.cai_chatbot_wrapper, shared.input_params, shared.gradio['display'], show_progress=shared.args.no_stream).then( - chat.save_history, shared.gradio['mode'], None, show_progress=False) - ) - - gen_events.append(shared.gradio['Regenerate'].click( - ui.gather_interface_values, [shared.gradio[k] for k in shared.input_elements], shared.gradio['interface_state']).then( - chat.regenerate_wrapper, shared.input_params, shared.gradio['display'], show_progress=shared.args.no_stream).then( - chat.save_history, shared.gradio['mode'], None, show_progress=False) - ) - - gen_events.append(shared.gradio['Continue'].click( - ui.gather_interface_values, [shared.gradio[k] for k in shared.input_elements], shared.gradio['interface_state']).then( - chat.continue_wrapper, shared.input_params, shared.gradio['display'], show_progress=shared.args.no_stream).then( - chat.save_history, shared.gradio['mode'], None, show_progress=False) - ) - - gen_events.append(shared.gradio['Impersonate'].click( - ui.gather_interface_values, [shared.gradio[k] for k in shared.input_elements], shared.gradio['interface_state']).then( - chat.impersonate_wrapper, shared.input_params, shared.gradio['textbox'], show_progress=shared.args.no_stream) - ) - - shared.gradio['Replace last reply'].click( - chat.replace_last_reply, [shared.gradio[k] for k in ['textbox', 'name1', 'name2', 'mode']], shared.gradio['display'], show_progress=shared.args.no_stream).then( - lambda x: '', shared.gradio['textbox'], shared.gradio['textbox'], show_progress=False).then( - chat.save_history, shared.gradio['mode'], None, show_progress=False) - - shared.gradio['Send dummy message'].click( - chat.send_dummy_message, [shared.gradio[k] for k in ['textbox', 'name1', 'name2', 'mode']], shared.gradio['display'], show_progress=shared.args.no_stream).then( - lambda x: '', shared.gradio['textbox'], shared.gradio['textbox'], show_progress=False).then( - chat.save_history, shared.gradio['mode'], None, show_progress=False) - - shared.gradio['Send dummy reply'].click( - chat.send_dummy_reply, [shared.gradio[k] for k in ['textbox', 'name1', 'name2', 'mode']], shared.gradio['display'], show_progress=shared.args.no_stream).then( - lambda x: '', shared.gradio['textbox'], shared.gradio['textbox'], show_progress=False).then( - chat.save_history, shared.gradio['mode'], None, show_progress=False) - - shared.gradio['Clear history-confirm'].click( - lambda: [gr.update(visible=False), gr.update(visible=True), gr.update(visible=False)], None, clear_arr).then( - chat.clear_chat_log, [shared.gradio[k] for k in ['name1', 'name2', 'greeting', 'mode']], shared.gradio['display']).then( - chat.save_history, shared.gradio['mode'], None, show_progress=False) - - shared.gradio['Stop'].click( - stop_everything_event, None, None, queue=False, cancels=gen_events if shared.args.no_stream else None).then( - chat.redraw_html, reload_inputs, shared.gradio['display']) - - shared.gradio['mode'].change( - lambda x: gr.update(visible=x == 'instruct'), shared.gradio['mode'], shared.gradio['instruction_template']).then( - lambda x: gr.update(interactive=x != 'instruct'), shared.gradio['mode'], shared.gradio['character_menu']).then( - chat.redraw_html, reload_inputs, shared.gradio['display']) - - shared.gradio['instruction_template'].change( - chat.load_character, [shared.gradio[k] for k in ['instruction_template', 'name1', 'name2', 'mode']], [shared.gradio[k] for k in ['name1', 'name2', 'character_picture', 'greeting', 'context', 'turn_template', 'display']]).then( - chat.redraw_html, reload_inputs, shared.gradio['display']) - - shared.gradio['upload_chat_history'].upload( - chat.load_history, [shared.gradio[k] for k in ['upload_chat_history', 'name1', 'name2']], None).then( - chat.redraw_html, reload_inputs, shared.gradio['display']) - - shared.gradio['Copy last reply'].click(chat.send_last_reply_to_input, None, shared.gradio['textbox'], show_progress=shared.args.no_stream) - shared.gradio['Clear history'].click(lambda: [gr.update(visible=True), gr.update(visible=False), gr.update(visible=True)], None, clear_arr) - shared.gradio['Clear history-cancel'].click(lambda: [gr.update(visible=False), gr.update(visible=True), gr.update(visible=False)], None, clear_arr) - shared.gradio['Remove last'].click(chat.remove_last_message, [shared.gradio[k] for k in ['name1', 'name2', 'mode']], [shared.gradio['display'], shared.gradio['textbox']], show_progress=False) - shared.gradio['download_button'].click(lambda x: chat.save_history(x, timestamp=True), shared.gradio['mode'], shared.gradio['download']) - shared.gradio['Upload character'].click(chat.upload_character, [shared.gradio['upload_json'], shared.gradio['upload_img_bot']], [shared.gradio['character_menu']]) - shared.gradio['character_menu'].change(chat.load_character, [shared.gradio[k] for k in ['character_menu', 'name1', 'name2', 'mode']], [shared.gradio[k] for k in ['name1', 'name2', 'character_picture', 'greeting', 'context', 'turn_template', 'display']]) - shared.gradio['upload_img_tavern'].upload(chat.upload_tavern_character, [shared.gradio['upload_img_tavern'], shared.gradio['name1'], shared.gradio['name2']], [shared.gradio['character_menu']]) - shared.gradio['your_picture'].change(chat.upload_your_profile_picture, [shared.gradio[k] for k in ['your_picture', 'name1', 'name2', 'mode']], shared.gradio['display']) - shared.gradio['interface'].load(None, None, None, _js=f"() => {{{ui.main_js+ui.chat_js}}}") - - # notebook/default modes event handlers - else: - shared.input_params = [shared.gradio[k] for k in ['textbox', 'interface_state']] - if shared.args.notebook: - output_params = [shared.gradio[k] for k in ['textbox', 'markdown', 'html']] - else: - output_params = [shared.gradio[k] for k in ['output_textbox', 'markdown', 'html']] - - gen_events.append(shared.gradio['Generate'].click( - lambda x: x, shared.gradio['textbox'], shared.gradio['last_input']).then( - ui.gather_interface_values, [shared.gradio[k] for k in shared.input_elements], shared.gradio['interface_state']).then( - generate_reply, shared.input_params, output_params, show_progress=shared.args.no_stream) # .then( - # None, None, None, _js="() => {element = document.getElementsByTagName('textarea')[0]; element.scrollTop = element.scrollHeight}") - ) - - gen_events.append(shared.gradio['textbox'].submit( - lambda x: x, shared.gradio['textbox'], shared.gradio['last_input']).then( - ui.gather_interface_values, [shared.gradio[k] for k in shared.input_elements], shared.gradio['interface_state']).then( - generate_reply, shared.input_params, output_params, show_progress=shared.args.no_stream) # .then( - # None, None, None, _js="() => {element = document.getElementsByTagName('textarea')[0]; element.scrollTop = element.scrollHeight}") - ) - - if shared.args.notebook: - shared.gradio['Undo'].click(lambda x: x, shared.gradio['last_input'], shared.gradio['textbox'], show_progress=False) - gen_events.append(shared.gradio['Regenerate'].click( - lambda x: x, shared.gradio['last_input'], shared.gradio['textbox'], show_progress=False).then( - ui.gather_interface_values, [shared.gradio[k] for k in shared.input_elements], shared.gradio['interface_state']).then( - generate_reply, shared.input_params, output_params, show_progress=shared.args.no_stream) # .then( - # None, None, None, _js="() => {element = document.getElementsByTagName('textarea')[0]; element.scrollTop = element.scrollHeight}") - ) - else: - gen_events.append(shared.gradio['Continue'].click( - ui.gather_interface_values, [shared.gradio[k] for k in shared.input_elements], shared.gradio['interface_state']).then( - generate_reply, [shared.gradio['output_textbox']] + shared.input_params[1:], output_params, show_progress=shared.args.no_stream) # .then( - # None, None, None, _js="() => {element = document.getElementsByTagName('textarea')[1]; element.scrollTop = element.scrollHeight}") - ) - - shared.gradio['Stop'].click(stop_everything_event, None, None, queue=False, cancels=gen_events if shared.args.no_stream else None) - shared.gradio['prompt_menu'].change(load_prompt, shared.gradio['prompt_menu'], shared.gradio['textbox'], show_progress=False) - shared.gradio['save_prompt'].click(save_prompt, shared.gradio['textbox'], shared.gradio['status'], show_progress=False) - shared.gradio['count_tokens'].click(count_tokens, shared.gradio['textbox'], shared.gradio['status'], show_progress=False) - shared.gradio['interface'].load(None, None, None, _js=f"() => {{{ui.main_js}}}") - - shared.gradio['interface'].load(partial(ui.apply_interface_values, {}, use_persistent=True), None, [shared.gradio[k] for k in ui.list_interface_input_elements(chat=shared.is_chat())], show_progress=False) - # Extensions block - if shared.args.extensions is not None: - extensions_module.create_extensions_block() - - # Launch the interface - shared.gradio['interface'].queue() - if shared.args.listen: - shared.gradio['interface'].launch(prevent_thread_lock=True, share=shared.args.share, server_name=shared.args.listen_host or '0.0.0.0', server_port=shared.args.listen_port, inbrowser=shared.args.auto_launch, auth=auth) - else: - shared.gradio['interface'].launch(prevent_thread_lock=True, share=shared.args.share, server_port=shared.args.listen_port, inbrowser=shared.args.auto_launch, auth=auth) - - -if __name__ == "__main__": - # Loading custom settings - settings_file = None - if shared.args.settings is not None and Path(shared.args.settings).exists(): - settings_file = Path(shared.args.settings) - elif Path('settings.json').exists(): - settings_file = Path('settings.json') - if settings_file is not None: - logging.info(f"Loading settings from {settings_file}...") - new_settings = json.loads(open(settings_file, 'r').read()) - for item in new_settings: - shared.settings[item] = new_settings[item] - - # Default extensions - extensions_module.available_extensions = get_available_extensions() - if shared.is_chat(): - for extension in shared.settings['chat_default_extensions']: - shared.args.extensions = shared.args.extensions or [] - if extension not in shared.args.extensions: - shared.args.extensions.append(extension) - else: - for extension in shared.settings['default_extensions']: - shared.args.extensions = shared.args.extensions or [] - if extension not in shared.args.extensions: - shared.args.extensions.append(extension) - - available_models = get_available_models() - - # Model defined through --model - if shared.args.model is not None: - shared.model_name = shared.args.model - - # Only one model is available - elif len(available_models) == 1: - shared.model_name = available_models[0] - - # Select the model from a command-line menu - elif shared.args.model_menu: - if len(available_models) == 0: - logging.error('No models are available! Please download at least one.') - sys.exit(0) - else: - print('The following models are available:\n') - for i, model in enumerate(available_models): - print(f'{i+1}. {model}') - - print(f'\nWhich one do you want to load? 1-{len(available_models)}\n') - i = int(input()) - 1 - print() - - shared.model_name = available_models[i] - - # If any model has been selected, load it - if shared.model_name != 'None': - model_settings = get_model_specific_settings(shared.model_name) - shared.settings.update(model_settings) # hijacking the interface defaults - update_model_parameters(model_settings, initial=True) # hijacking the command-line arguments - - # Load the model - shared.model, shared.tokenizer = load_model(shared.model_name) - if shared.args.lora: - add_lora_to_model(shared.args.lora) - - # Force a character to be loaded - if shared.is_chat(): - shared.persistent_interface_state.update({ - 'mode': shared.settings['mode'], - 'character_menu': shared.args.character or shared.settings['character'], - 'instruction_template': shared.settings['instruction_template'] - }) - - # Launch the web UI - create_interface() - while True: - time.sleep(0.5) - if shared.need_restart: - shared.need_restart = False - shared.gradio['interface'].close() - create_interface() diff --git a/spaces/arbml/whisper-largev2-ar/app.py b/spaces/arbml/whisper-largev2-ar/app.py deleted file mode 100644 index 323853d4e7c289e9f93de7ddcf4c20b24a91f352..0000000000000000000000000000000000000000 --- a/spaces/arbml/whisper-largev2-ar/app.py +++ /dev/null @@ -1,97 +0,0 @@ -import torch - -import gradio as gr -import pytube as pt -from transformers import pipeline -from huggingface_hub import model_info - -MODEL_NAME = "arbml/whisper-largev2-ar" #this always needs to stay in line 8 :D sorry for the hackiness -lang = "ar" - -device = 0 if torch.cuda.is_available() else "cpu" -pipe = pipeline( - task="automatic-speech-recognition", - model=MODEL_NAME, - chunk_length_s=30, - device=device, -) - -pipe.model.config.forced_decoder_ids = pipe.tokenizer.get_decoder_prompt_ids(language=lang, task="transcribe") - -def transcribe(microphone, file_upload): - warn_output = "" - if (microphone is not None) and (file_upload is not None): - warn_output = ( - "WARNING: You've uploaded an audio file and used the microphone. " - "The recorded file from the microphone will be used and the uploaded audio will be discarded.\n" - ) - - elif (microphone is None) and (file_upload is None): - return "ERROR: You have to either use the microphone or upload an audio file" - - file = microphone if microphone is not None else file_upload - - text = pipe(file)["text"] - - return warn_output + text - - -def _return_yt_html_embed(yt_url): - video_id = yt_url.split("?v=")[-1] - HTML_str = ( - f'Content
" - serve(html, open_browser=False, http_server=MockServer) diff --git a/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/fairseq/criterions/ctc.py b/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/fairseq/criterions/ctc.py deleted file mode 100644 index e966e47cf2680c6d47a75aa6bdbe5bef05113aa8..0000000000000000000000000000000000000000 --- a/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/fairseq/criterions/ctc.py +++ /dev/null @@ -1,295 +0,0 @@ -# All rights reserved. -# -# This source code is licensed under the license found in the LICENSE file in -# the root directory of this source tree. An additional grant of patent rights -# can be found in the PATENTS file in the same directory. - -import math -from argparse import Namespace -from dataclasses import dataclass, field -from omegaconf import II -from typing import Optional - -import torch -import torch.nn.functional as F -from fairseq import metrics, utils -from fairseq.criterions import FairseqCriterion, register_criterion -from fairseq.dataclass import FairseqDataclass -from fairseq.data.data_utils import post_process -from fairseq.tasks import FairseqTask -from fairseq.logging.meters import safe_round - - -@dataclass -class CtcCriterionConfig(FairseqDataclass): - zero_infinity: bool = field( - default=False, - metadata={"help": "zero inf loss when source length <= target length"}, - ) - sentence_avg: bool = II("optimization.sentence_avg") - post_process: str = field( - default="letter", - metadata={ - "help": "how to post process predictions into words. can be letter, " - "wordpiece, BPE symbols, etc. " - "See fairseq.data.data_utils.post_process() for full list of options" - }, - ) - wer_kenlm_model: Optional[str] = field( - default=None, - metadata={ - "help": "if this is provided, use kenlm to compute wer (along with other wer_* args)" - }, - ) - wer_lexicon: Optional[str] = field( - default=None, - metadata={"help": "lexicon to use with wer_kenlm_model"}, - ) - wer_lm_weight: float = field( - default=2.0, - metadata={"help": "lm weight to use with wer_kenlm_model"}, - ) - wer_word_score: float = field( - default=-1.0, - metadata={"help": "lm word score to use with wer_kenlm_model"}, - ) - - wer_args: Optional[str] = field( - default=None, - metadata={ - "help": "DEPRECATED: tuple of (wer_kenlm_model, wer_lexicon, wer_lm_weight, wer_word_score)" - }, - ) - - -@register_criterion("ctc", dataclass=CtcCriterionConfig) -class CtcCriterion(FairseqCriterion): - def __init__(self, cfg: CtcCriterionConfig, task: FairseqTask): - super().__init__(task) - self.blank_idx = ( - task.target_dictionary.index(task.blank_symbol) - if hasattr(task, "blank_symbol") - else 0 - ) - self.pad_idx = task.target_dictionary.pad() - self.eos_idx = task.target_dictionary.eos() - self.post_process = cfg.post_process - - if cfg.wer_args is not None: - ( - cfg.wer_kenlm_model, - cfg.wer_lexicon, - cfg.wer_lm_weight, - cfg.wer_word_score, - ) = eval(cfg.wer_args) - - if cfg.wer_kenlm_model is not None and cfg.wer_kenlm_model != "": - from examples.speech_recognition.w2l_decoder import W2lKenLMDecoder - - dec_args = Namespace() - dec_args.nbest = 1 - dec_args.criterion = "ctc" - dec_args.kenlm_model = cfg.wer_kenlm_model - dec_args.lexicon = cfg.wer_lexicon - dec_args.beam = 50 - dec_args.beam_size_token = min(50, len(task.target_dictionary)) - dec_args.beam_threshold = min(50, len(task.target_dictionary)) - dec_args.lm_weight = cfg.wer_lm_weight - dec_args.word_score = cfg.wer_word_score - dec_args.unk_weight = -math.inf - dec_args.sil_weight = 0 - - self.w2l_decoder = W2lKenLMDecoder(dec_args, task.target_dictionary) - else: - self.w2l_decoder = None - - self.zero_infinity = cfg.zero_infinity - self.sentence_avg = cfg.sentence_avg - - def forward(self, model, sample, reduce=True): - net_output = model(**sample["net_input"]) - lprobs = model.get_normalized_probs( - net_output, log_probs=True - ).contiguous() # (T, B, C) from the encoder - - if "src_lengths" in sample["net_input"]: - input_lengths = sample["net_input"]["src_lengths"] - else: - if net_output["padding_mask"] is not None: - non_padding_mask = ~net_output["padding_mask"] - input_lengths = non_padding_mask.long().sum(-1) - else: - input_lengths = lprobs.new_full( - (lprobs.size(1),), lprobs.size(0), dtype=torch.long - ) - - pad_mask = (sample["target"] != self.pad_idx) & ( - sample["target"] != self.eos_idx - ) - targets_flat = sample["target"].masked_select(pad_mask) - if "target_lengths" in sample: - target_lengths = sample["target_lengths"] - else: - target_lengths = pad_mask.sum(-1) - - with torch.backends.cudnn.flags(enabled=False): - loss = F.ctc_loss( - lprobs, - targets_flat, - input_lengths, - target_lengths, - blank=self.blank_idx, - reduction="sum", - zero_infinity=self.zero_infinity, - ) - - ntokens = ( - sample["ntokens"] if "ntokens" in sample else target_lengths.sum().item() - ) - - sample_size = sample["target"].size(0) if self.sentence_avg else ntokens - logging_output = { - "loss": utils.item(loss.data), # * sample['ntokens'], - "ntokens": ntokens, - "nsentences": sample["id"].numel(), - "sample_size": sample_size, - } - - if not model.training: - import editdistance - - with torch.no_grad(): - lprobs_t = lprobs.transpose(0, 1).float().contiguous().cpu() - - c_err = 0 - c_len = 0 - w_errs = 0 - w_len = 0 - wv_errs = 0 - for lp, t, inp_l in zip( - lprobs_t, - sample["target_label"] - if "target_label" in sample - else sample["target"], - input_lengths, - ): - lp = lp[:inp_l].unsqueeze(0) - - decoded = None - if self.w2l_decoder is not None: - decoded = self.w2l_decoder.decode(lp) - if len(decoded) < 1: - decoded = None - else: - decoded = decoded[0] - if len(decoded) < 1: - decoded = None - else: - decoded = decoded[0] - - p = (t != self.task.target_dictionary.pad()) & ( - t != self.task.target_dictionary.eos() - ) - targ = t[p] - targ_units = self.task.target_dictionary.string(targ) - targ_units_arr = targ.tolist() - - toks = lp.argmax(dim=-1).unique_consecutive() - pred_units_arr = toks[toks != self.blank_idx].tolist() - - c_err += editdistance.eval(pred_units_arr, targ_units_arr) - c_len += len(targ_units_arr) - - targ_words = post_process(targ_units, self.post_process).split() - - pred_units = self.task.target_dictionary.string(pred_units_arr) - pred_words_raw = post_process(pred_units, self.post_process).split() - - if decoded is not None and "words" in decoded: - pred_words = decoded["words"] - w_errs += editdistance.eval(pred_words, targ_words) - wv_errs += editdistance.eval(pred_words_raw, targ_words) - else: - dist = editdistance.eval(pred_words_raw, targ_words) - w_errs += dist - wv_errs += dist - - w_len += len(targ_words) - - logging_output["wv_errors"] = wv_errs - logging_output["w_errors"] = w_errs - logging_output["w_total"] = w_len - logging_output["c_errors"] = c_err - logging_output["c_total"] = c_len - - return loss, sample_size, logging_output - - @staticmethod - def reduce_metrics(logging_outputs) -> None: - """Aggregate logging outputs from data parallel training.""" - - loss_sum = utils.item(sum(log.get("loss", 0) for log in logging_outputs)) - ntokens = utils.item(sum(log.get("ntokens", 0) for log in logging_outputs)) - nsentences = utils.item( - sum(log.get("nsentences", 0) for log in logging_outputs) - ) - sample_size = utils.item( - sum(log.get("sample_size", 0) for log in logging_outputs) - ) - - metrics.log_scalar( - "loss", loss_sum / sample_size / math.log(2), sample_size, round=3 - ) - metrics.log_scalar("ntokens", ntokens) - metrics.log_scalar("nsentences", nsentences) - if sample_size != ntokens: - metrics.log_scalar( - "nll_loss", loss_sum / ntokens / math.log(2), ntokens, round=3 - ) - - c_errors = sum(log.get("c_errors", 0) for log in logging_outputs) - metrics.log_scalar("_c_errors", c_errors) - c_total = sum(log.get("c_total", 0) for log in logging_outputs) - metrics.log_scalar("_c_total", c_total) - w_errors = sum(log.get("w_errors", 0) for log in logging_outputs) - metrics.log_scalar("_w_errors", w_errors) - wv_errors = sum(log.get("wv_errors", 0) for log in logging_outputs) - metrics.log_scalar("_wv_errors", wv_errors) - w_total = sum(log.get("w_total", 0) for log in logging_outputs) - metrics.log_scalar("_w_total", w_total) - - if c_total > 0: - metrics.log_derived( - "uer", - lambda meters: safe_round( - meters["_c_errors"].sum * 100.0 / meters["_c_total"].sum, 3 - ) - if meters["_c_total"].sum > 0 - else float("nan"), - ) - if w_total > 0: - metrics.log_derived( - "wer", - lambda meters: safe_round( - meters["_w_errors"].sum * 100.0 / meters["_w_total"].sum, 3 - ) - if meters["_w_total"].sum > 0 - else float("nan"), - ) - metrics.log_derived( - "raw_wer", - lambda meters: safe_round( - meters["_wv_errors"].sum * 100.0 / meters["_w_total"].sum, 3 - ) - if meters["_w_total"].sum > 0 - else float("nan"), - ) - - @staticmethod - def logging_outputs_can_be_summed() -> bool: - """ - Whether the logging outputs returned by `forward` can be summed - across workers prior to calling `reduce_metrics`. Setting this - to True will improves distributed training speed. - """ - return True diff --git a/spaces/autosummproject/autosumm/corpora/sourcer.py b/spaces/autosummproject/autosumm/corpora/sourcer.py deleted file mode 100644 index d8388de70d681b6b01c4a6229f81f8c34dc816ad..0000000000000000000000000000000000000000 --- a/spaces/autosummproject/autosumm/corpora/sourcer.py +++ /dev/null @@ -1,52 +0,0 @@ -import requests -from string import Template -from bs4 import BeautifulSoup -from dotenv import load_dotenv -from os import getenv -import threading - -load_dotenv() - -google_key = getenv('GOOGLE_KEY') -google_engine = getenv('GOOGLE_ENGINE') - -url = Template(f'https://www.googleapis.com/customsearch/v1?key={google_key}&cx={google_engine}&q=$query') - -def download_page(url, responses, index): - responses[index] = requests.get(url) - -def process_page(texts, responses, index): - resp = responses[index] - soup = BeautifulSoup(resp.text, 'html.parser') - text = [] - # remove lists - for tag in soup.find_all('li'): - tag.extract() - - tags = soup.find_all('p') - for tag in tags: - text.append(tag.text) - texts.append('\n'.join(text)) - -def search_web(query: str) -> list: - query = '+'.join(query.split()) - results = requests.get(url.substitute(query=query)).json()['items'] - - links = [item['link'] for item in results] - - texts = [] - responses = [None] * len(links) - download_page(links[0], responses, 0) # download first page - for i in range(1, len(links)): - # process previous page - processing_thread = threading.Thread(target=process_page, args=(texts, responses, i-1), name='processing'+str(i-1)) - # dowload new page - download_thread = threading.Thread(target=download_page, args=(links[i], responses, i), name='download'+str(i)) - # start threads - download_thread.start() - processing_thread.start() - # wait for threads - download_thread.join() - processing_thread.join() - - return texts \ No newline at end of file diff --git a/spaces/avichr/HebEMO_demo/app.py b/spaces/avichr/HebEMO_demo/app.py deleted file mode 100644 index 5968666ebdfbc7e968c0923a09d6599cf4f00e55..0000000000000000000000000000000000000000 --- a/spaces/avichr/HebEMO_demo/app.py +++ /dev/null @@ -1,39 +0,0 @@ -from HebEMO import HebEMO -from transformers import pipeline -import streamlit as st -import matplotlib.pyplot as plt -import pandas as pd -from spider_plot import spider_plot - - -# @st.cache -HebEMO_model = HebEMO() - - -st.title("Emotion Recognition in Hebrew Texts") -st.write("HebEMO is a tool to detect polarity and extract emotions from Hebrew user-generated content (UGC), which was trained on a unique Covid-19 related dataset that we collected and annotated. HebEMO yielded a high performance of weighted average F1-score = 0.96 for polarity classification. Emotion detection reached an F1-score of 0.78-0.97, with the exception of *surprise*, which the model failed to capture (F1 = 0.41). More information can be found in our git: https://github.com/avichaychriqui/HeBERT") -st.write("Write Hebrew sentences in the text box below to analyze (each sentence in a different rew). It takes a while, be patient :). An additional demo can be found in the Colab notebook: https://colab.research.google.com/drive/1Jw3gOWjwVMcZslu-ttXoNeD17lms1-ff ") - -sent = st.text_area("Text", "החיים יפים ומאושרים", height = 20) -# interact(HebEMO_model.hebemo, text='החיים יפים ומאושרי', plot=fixed(True), input_path=fixed(False), save_results=fixed(False),) - -hebEMO_df = HebEMO_model.hebemo(sent, read_lines=True, plot=False) -hebEMO = pd.DataFrame() -for emo in hebEMO_df.columns[1::2]: - hebEMO[emo] = abs(hebEMO_df[emo]-(1-hebEMO_df['confidence_'+emo])) - -st.write (hebEMO) - -# plot= st.checkbox('Plot?') -# if plot: -# ax = spider_plot(hebEMO) -# st.pyplot(ax) -# fig = px.bar_polar(hebEMO.melt(), r="value", theta="variable", -# color="variable", -# template="ggplot2", -# ) - -# st.plotly_chart(fig, use_container_width=True) - - - diff --git a/spaces/awacke1/AI.Dashboard.Maps/style.css b/spaces/awacke1/AI.Dashboard.Maps/style.css deleted file mode 100644 index 114adf441e9032febb46bc056b2a8bb651075f0d..0000000000000000000000000000000000000000 --- a/spaces/awacke1/AI.Dashboard.Maps/style.css +++ /dev/null @@ -1,28 +0,0 @@ -body { - padding: 2rem; - font-family: -apple-system, BlinkMacSystemFont, "Arial", sans-serif; -} - -h1 { - font-size: 16px; - margin-top: 0; -} - -p { - color: rgb(107, 114, 128); - font-size: 15px; - margin-bottom: 10px; - margin-top: 5px; -} - -.card { - max-width: 620px; - margin: 0 auto; - padding: 16px; - border: 1px solid lightgray; - border-radius: 16px; -} - -.card p:last-child { - margin-bottom: 0; -} diff --git a/spaces/awacke1/ArtNovelComicBookComposer/app.py b/spaces/awacke1/ArtNovelComicBookComposer/app.py deleted file mode 100644 index 4da39753084a6e536ef4d94f8099acabe5deb69e..0000000000000000000000000000000000000000 --- a/spaces/awacke1/ArtNovelComicBookComposer/app.py +++ /dev/null @@ -1,145 +0,0 @@ -import os -import random - -import gradio as gr - -#links -Article = "" -Article = Article + " Gallery UI documentation: https://gradio.app/docs/" -Article = Article + " Unsplash free image source requires 5MP or larger images: https://unsplash.com/" -Article = Article + " Github should also be a worthy alternative with free repos. " -Article = Article + " Raw git content can be accessed by URL like so: https://raw.github.com/AaronCWacker/Yggdrasil/images/" -Article = Article + " Originals go here: https://github.com/AaronCWacker/Yggdrasil/tree/main/images" -# Aaron_Wacker_health_and_medical_icon_set_on_white_background_bba24b60-9fcf-411b-9c00-dd1ba1e3553c.png - - -import os -import csv -import gradio as gr -from gradio import inputs, outputs -import huggingface_hub -from huggingface_hub import Repository -from datetime import datetime - -HF_TOKEN = os.environ.get("HF_TOKEN") -print("is none?", HF_TOKEN is None) -DATASET_REPO_URL = "https://huggingface.co/datasets/awacke1/BookComposer" -DATA_FILENAME = "BookComposer.csv" -DATA_FILE=os.path.join("data", DATA_FILENAME) -HF_TOKEN=os.environ.get("HF_TOKEN") - -def gan(): - images = [ - (random.choice( - [ - "Comectress.png", - "Comic Plate 1 - Amnesia.png", - "Comic Plate 1 - Depths.png", - "Comic Plate 1 - Helion.png", - "Comic Plate 1 - Kitsune.png", - "Comic Plate 1 - Sinbad.png", - "Comic Plate 1 - Vampiress.png", - "Comic Plate 2 - Amnesia.png", - "Comic Plate 2 - Depths.png", - "Comic Plate 2 - Helion.png", - "Comic Plate 2 - Kitsune.png", - "Comic Plate 2 - Sinbad.png", - "Comic Plate 2 - Vampiress.png", - "Comic Plate 3 - Amnesia.png", - "Comic Plate 3 - Depths.png", - "Comic Plate 3 - Helion.png", - "Comic Plate 3 - Kitsune.png", - "Comic Plate 3 - Vampiress.png", - "Comic Plate 4 - Amnesia.png", - "Comic Plate 4 - Depths.png", - "Comic Plate 4 - Helion.png", - "Comic Plate 4 - Kitsune.png", - "Comic Plate 4 - Sinbad.png", - "Comic Plate 4 - Vampiress.png", - "Comic Plate 5 - Vampiress.png", - "Gold Suit.png", - "Heavens.png", - "Red Dot Sight.png", - "Starfire.png", - "Vamp 1.png", - "Vamp 2.png", - "Vamp 3.png", - "Vamp 4.png", - "Whirlwind.png", - "Zyphoria.png", - ] - ), f"label {i}" if i != 0 else "label" * 50) - for i in range(10) - ] - return images - -def gan2(): - images = [ - (random.choice( - [ - "Comectress.png", - "Comic Plate 1 - Amnesia.png", - "Comic Plate 1 - Depths.png", - "Comic Plate 1 - Helion.png", - "Comic Plate 1 - Kitsune.png", - "Comic Plate 1 - Sinbad.png", - "Comic Plate 1 - Vampiress.png", - "Comic Plate 2 - Amnesia.png", - "Comic Plate 2 - Depths.png", - "Comic Plate 2 - Helion.png", - "Comic Plate 2 - Kitsune.png", - "Comic Plate 2 - Sinbad.png", - "Comic Plate 2 - Vampiress.png", - "Comic Plate 3 - Amnesia.png", - "Comic Plate 3 - Depths.png", - "Comic Plate 3 - Helion.png", - "Comic Plate 3 - Kitsune.png", - "Comic Plate 3 - Vampiress.png", - "Comic Plate 4 - Amnesia.png", - "Comic Plate 4 - Depths.png", - "Comic Plate 4 - Helion.png", - "Comic Plate 4 - Kitsune.png", - "Comic Plate 4 - Sinbad.png", - "Comic Plate 4 - Vampiress.png", - "Comic Plate 5 - Vampiress.png", - "Gold Suit.png", - "Heavens.png", - "Red Dot Sight.png", - "Starfire.png", - "Vamp 1.png", - "Vamp 2.png", - "Vamp 3.png", - "Vamp 4.png", - "Whirlwind.png", - "Zyphoria.png", - ] - ), f"label {i}" if i != 0 else "label" * 50) - for i in range(10) - ] - return images - - - -with gr.Blocks() as demo: - with gr.Column(variant="panel"): - with gr.Row(variant="compact"): - text = gr.Textbox( - label="Health and Medical Icon Sets", - show_label=False, - max_lines=1, - placeholder="Enter your prompt", - ).style( - container=False, - ) - btn = gr.Button("Generate image").style(full_width=False) - btn2 = gr.Button("Generate story").style(full_width=False) - - gallery = gr.Gallery( - label="Generated images", show_label=False, elem_id="gallery" - ).style(grid=[2], height="auto") - - btn.click(gan, None, gallery) - btn2.click(gan2, None, gallery) - -if __name__ == "__main__": - demo.launch() \ No newline at end of file diff --git a/spaces/awacke1/SOTA-MedEntity/README.md b/spaces/awacke1/SOTA-MedEntity/README.md deleted file mode 100644 index 9eba747745a8003e865855d430b5dd3be0027cae..0000000000000000000000000000000000000000 --- a/spaces/awacke1/SOTA-MedEntity/README.md +++ /dev/null @@ -1,13 +0,0 @@ ---- -title: 👩⚕️QA NLP Biomed MLM Mask Language Gradio🩺 -emoji: 👩⚕️QA🙋 -colorFrom: orange -colorTo: blue -sdk: gradio -sdk_version: 2.9.1 -app_file: app.py -pinned: false -license: mit ---- - -Check out the configuration reference at https://huggingface.co/docs/hub/spaces#reference diff --git a/spaces/awacke1/TimeSeries/README.md b/spaces/awacke1/TimeSeries/README.md deleted file mode 100644 index 90ab3ee5f692d6edffb7890e15c521ac8643c624..0000000000000000000000000000000000000000 --- a/spaces/awacke1/TimeSeries/README.md +++ /dev/null @@ -1,13 +0,0 @@ ---- -title: 📈Graph Altair Timeseries📊 -emoji: 📈📊 -colorFrom: indigo -colorTo: green -sdk: streamlit -sdk_version: 1.9.0 -app_file: app.py -pinned: false -license: mit ---- - -Check out the configuration reference at https://huggingface.co/docs/hub/spaces#reference diff --git a/spaces/awacke1/VoiceGPT15/templates.py b/spaces/awacke1/VoiceGPT15/templates.py deleted file mode 100644 index 036bb02bbc7a0bc4ae4614dc5bf528403ddbedd0..0000000000000000000000000000000000000000 --- a/spaces/awacke1/VoiceGPT15/templates.py +++ /dev/null @@ -1,44 +0,0 @@ -css = ''' -