diff --git a/env-llmeval/lib/python3.10/site-packages/filelock/__init__.py b/env-llmeval/lib/python3.10/site-packages/filelock/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..006299d2188c5d642cf78cf19305e88c95ee01cb --- /dev/null +++ b/env-llmeval/lib/python3.10/site-packages/filelock/__init__.py @@ -0,0 +1,52 @@ +""" +A platform independent file lock that supports the with-statement. + +.. autodata:: filelock.__version__ + :no-value: + +""" + +from __future__ import annotations + +import sys +import warnings +from typing import TYPE_CHECKING + +from ._api import AcquireReturnProxy, BaseFileLock +from ._error import Timeout +from ._soft import SoftFileLock +from ._unix import UnixFileLock, has_fcntl +from ._windows import WindowsFileLock +from .version import version + +#: version of the project as a string +__version__: str = version + + +if sys.platform == "win32": # pragma: win32 cover + _FileLock: type[BaseFileLock] = WindowsFileLock +else: # pragma: win32 no cover # noqa: PLR5501 + if has_fcntl: + _FileLock: type[BaseFileLock] = UnixFileLock + else: + _FileLock = SoftFileLock + if warnings is not None: + warnings.warn("only soft file lock is available", stacklevel=2) + +if TYPE_CHECKING: + FileLock = SoftFileLock +else: + #: Alias for the lock, which should be used for the current platform. + FileLock = _FileLock + + +__all__ = [ + "AcquireReturnProxy", + "BaseFileLock", + "FileLock", + "SoftFileLock", + "Timeout", + "UnixFileLock", + "WindowsFileLock", + "__version__", +] diff --git a/env-llmeval/lib/python3.10/site-packages/filelock/__pycache__/_error.cpython-310.pyc b/env-llmeval/lib/python3.10/site-packages/filelock/__pycache__/_error.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..4044520cccc72f0396ca87deeeb36ff99d140341 Binary files /dev/null and b/env-llmeval/lib/python3.10/site-packages/filelock/__pycache__/_error.cpython-310.pyc differ diff --git a/env-llmeval/lib/python3.10/site-packages/filelock/__pycache__/_util.cpython-310.pyc b/env-llmeval/lib/python3.10/site-packages/filelock/__pycache__/_util.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..4d9c9ba2c47c67ce8012a92fd07c80bd6347d3c8 Binary files /dev/null and b/env-llmeval/lib/python3.10/site-packages/filelock/__pycache__/_util.cpython-310.pyc differ diff --git a/env-llmeval/lib/python3.10/site-packages/filelock/__pycache__/version.cpython-310.pyc b/env-llmeval/lib/python3.10/site-packages/filelock/__pycache__/version.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..64ccdb91fcdc7926069ba767034a26b30b6a48d7 Binary files /dev/null and b/env-llmeval/lib/python3.10/site-packages/filelock/__pycache__/version.cpython-310.pyc differ diff --git a/env-llmeval/lib/python3.10/site-packages/filelock/_api.py b/env-llmeval/lib/python3.10/site-packages/filelock/_api.py new file mode 100644 index 0000000000000000000000000000000000000000..210b8c4138d70ac989401d9ca3fd6ab03e36c6eb --- /dev/null +++ b/env-llmeval/lib/python3.10/site-packages/filelock/_api.py @@ -0,0 +1,341 @@ +from __future__ import annotations + +import contextlib +import logging +import os +import time +import warnings +from abc import ABC, abstractmethod +from dataclasses import dataclass +from threading import local +from typing import TYPE_CHECKING, Any +from weakref import WeakValueDictionary + +from ._error import Timeout + +if TYPE_CHECKING: + import sys + from types import TracebackType + + if sys.version_info >= (3, 11): # pragma: no cover (py311+) + from typing import Self + else: # pragma: no cover ( None: + self.lock = lock + + def __enter__(self) -> BaseFileLock: + return self.lock + + def __exit__( + self, + exc_type: type[BaseException] | None, + exc_value: BaseException | None, + traceback: TracebackType | None, + ) -> None: + self.lock.release() + + +@dataclass +class FileLockContext: + """A dataclass which holds the context for a ``BaseFileLock`` object.""" + + # The context is held in a separate class to allow optional use of thread local storage via the + # ThreadLocalFileContext class. + + #: The path to the lock file. + lock_file: str + + #: The default timeout value. + timeout: float + + #: The mode for the lock files + mode: int + + #: The file descriptor for the *_lock_file* as it is returned by the os.open() function, not None when lock held + lock_file_fd: int | None = None + + #: The lock counter is used for implementing the nested locking mechanism. + lock_counter: int = 0 # When the lock is acquired is increased and the lock is only released, when this value is 0 + + +class ThreadLocalFileContext(FileLockContext, local): + """A thread local version of the ``FileLockContext`` class.""" + + +class BaseFileLock(ABC, contextlib.ContextDecorator): + """Abstract base class for a file lock object.""" + + _instances: WeakValueDictionary[str, BaseFileLock] + + def __new__( # noqa: PLR0913 + cls, + lock_file: str | os.PathLike[str], + timeout: float = -1, + mode: int = 0o644, + thread_local: bool = True, # noqa: ARG003, FBT001, FBT002 + *, + is_singleton: bool = False, + **kwargs: dict[str, Any], # capture remaining kwargs for subclasses # noqa: ARG003 + ) -> Self: + """Create a new lock object or if specified return the singleton instance for the lock file.""" + if not is_singleton: + return super().__new__(cls) + + instance = cls._instances.get(str(lock_file)) + if not instance: + instance = super().__new__(cls) + cls._instances[str(lock_file)] = instance + elif timeout != instance.timeout or mode != instance.mode: + msg = "Singleton lock instances cannot be initialized with differing arguments" + raise ValueError(msg) + + return instance # type: ignore[return-value] # https://github.com/python/mypy/issues/15322 + + def __init_subclass__(cls, **kwargs: dict[str, Any]) -> None: + """Setup unique state for lock subclasses.""" + super().__init_subclass__(**kwargs) + cls._instances = WeakValueDictionary() + + def __init__( # noqa: PLR0913 + self, + lock_file: str | os.PathLike[str], + timeout: float = -1, + mode: int = 0o644, + thread_local: bool = True, # noqa: FBT001, FBT002 + *, + is_singleton: bool = False, + ) -> None: + """ + Create a new lock object. + + :param lock_file: path to the file + :param timeout: default timeout when acquiring the lock, in seconds. It will be used as fallback value in \ + the acquire method, if no timeout value (``None``) is given. If you want to disable the timeout, set it \ + to a negative value. A timeout of 0 means that there is exactly one attempt to acquire the file lock. + :param mode: file permissions for the lockfile + :param thread_local: Whether this object's internal context should be thread local or not. If this is set to \ + ``False`` then the lock will be reentrant across threads. + :param is_singleton: If this is set to ``True`` then only one instance of this class will be created \ + per lock file. This is useful if you want to use the lock object for reentrant locking without needing \ + to pass the same object around. + + """ + self._is_thread_local = thread_local + self._is_singleton = is_singleton + + # Create the context. Note that external code should not work with the context directly and should instead use + # properties of this class. + kwargs: dict[str, Any] = { + "lock_file": os.fspath(lock_file), + "timeout": timeout, + "mode": mode, + } + self._context: FileLockContext = (ThreadLocalFileContext if thread_local else FileLockContext)(**kwargs) + + def is_thread_local(self) -> bool: + """:return: a flag indicating if this lock is thread local or not""" + return self._is_thread_local + + @property + def is_singleton(self) -> bool: + """:return: a flag indicating if this lock is singleton or not""" + return self._is_singleton + + @property + def lock_file(self) -> str: + """:return: path to the lock file""" + return self._context.lock_file + + @property + def timeout(self) -> float: + """ + :return: the default timeout value, in seconds + + .. versionadded:: 2.0.0 + """ + return self._context.timeout + + @timeout.setter + def timeout(self, value: float | str) -> None: + """ + Change the default timeout value. + + :param value: the new value, in seconds + + """ + self._context.timeout = float(value) + + @property + def mode(self) -> int: + """:return: the file permissions for the lockfile""" + return self._context.mode + + @abstractmethod + def _acquire(self) -> None: + """If the file lock could be acquired, self._context.lock_file_fd holds the file descriptor of the lock file.""" + raise NotImplementedError + + @abstractmethod + def _release(self) -> None: + """Releases the lock and sets self._context.lock_file_fd to None.""" + raise NotImplementedError + + @property + def is_locked(self) -> bool: + """ + + :return: A boolean indicating if the lock file is holding the lock currently. + + .. versionchanged:: 2.0.0 + + This was previously a method and is now a property. + """ + return self._context.lock_file_fd is not None + + @property + def lock_counter(self) -> int: + """:return: The number of times this lock has been acquired (but not yet released).""" + return self._context.lock_counter + + def acquire( + self, + timeout: float | None = None, + poll_interval: float = 0.05, + *, + poll_intervall: float | None = None, + blocking: bool = True, + ) -> AcquireReturnProxy: + """ + Try to acquire the file lock. + + :param timeout: maximum wait time for acquiring the lock, ``None`` means use the default :attr:`~timeout` is and + if ``timeout < 0``, there is no timeout and this method will block until the lock could be acquired + :param poll_interval: interval of trying to acquire the lock file + :param poll_intervall: deprecated, kept for backwards compatibility, use ``poll_interval`` instead + :param blocking: defaults to True. If False, function will return immediately if it cannot obtain a lock on the + first attempt. Otherwise, this method will block until the timeout expires or the lock is acquired. + :raises Timeout: if fails to acquire lock within the timeout period + :return: a context object that will unlock the file when the context is exited + + .. code-block:: python + + # You can use this method in the context manager (recommended) + with lock.acquire(): + pass + + # Or use an equivalent try-finally construct: + lock.acquire() + try: + pass + finally: + lock.release() + + .. versionchanged:: 2.0.0 + + This method returns now a *proxy* object instead of *self*, + so that it can be used in a with statement without side effects. + + """ + # Use the default timeout, if no timeout is provided. + if timeout is None: + timeout = self._context.timeout + + if poll_intervall is not None: + msg = "use poll_interval instead of poll_intervall" + warnings.warn(msg, DeprecationWarning, stacklevel=2) + poll_interval = poll_intervall + + # Increment the number right at the beginning. We can still undo it, if something fails. + self._context.lock_counter += 1 + + lock_id = id(self) + lock_filename = self.lock_file + start_time = time.perf_counter() + try: + while True: + if not self.is_locked: + _LOGGER.debug("Attempting to acquire lock %s on %s", lock_id, lock_filename) + self._acquire() + if self.is_locked: + _LOGGER.debug("Lock %s acquired on %s", lock_id, lock_filename) + break + if blocking is False: + _LOGGER.debug("Failed to immediately acquire lock %s on %s", lock_id, lock_filename) + raise Timeout(lock_filename) # noqa: TRY301 + if 0 <= timeout < time.perf_counter() - start_time: + _LOGGER.debug("Timeout on acquiring lock %s on %s", lock_id, lock_filename) + raise Timeout(lock_filename) # noqa: TRY301 + msg = "Lock %s not acquired on %s, waiting %s seconds ..." + _LOGGER.debug(msg, lock_id, lock_filename, poll_interval) + time.sleep(poll_interval) + except BaseException: # Something did go wrong, so decrement the counter. + self._context.lock_counter = max(0, self._context.lock_counter - 1) + raise + return AcquireReturnProxy(lock=self) + + def release(self, force: bool = False) -> None: # noqa: FBT001, FBT002 + """ + Releases the file lock. Please note, that the lock is only completely released, if the lock counter is 0. + Also note, that the lock file itself is not automatically deleted. + + :param force: If true, the lock counter is ignored and the lock is released in every case/ + + """ + if self.is_locked: + self._context.lock_counter -= 1 + + if self._context.lock_counter == 0 or force: + lock_id, lock_filename = id(self), self.lock_file + + _LOGGER.debug("Attempting to release lock %s on %s", lock_id, lock_filename) + self._release() + self._context.lock_counter = 0 + _LOGGER.debug("Lock %s released on %s", lock_id, lock_filename) + + def __enter__(self) -> Self: + """ + Acquire the lock. + + :return: the lock object + + """ + self.acquire() + return self + + def __exit__( + self, + exc_type: type[BaseException] | None, + exc_value: BaseException | None, + traceback: TracebackType | None, + ) -> None: + """ + Release the lock. + + :param exc_type: the exception type if raised + :param exc_value: the exception value if raised + :param traceback: the exception traceback if raised + + """ + self.release() + + def __del__(self) -> None: + """Called when the lock object is deleted.""" + self.release(force=True) + + +__all__ = [ + "AcquireReturnProxy", + "BaseFileLock", +] diff --git a/env-llmeval/lib/python3.10/site-packages/filelock/_error.py b/env-llmeval/lib/python3.10/site-packages/filelock/_error.py new file mode 100644 index 0000000000000000000000000000000000000000..f7ff08c0f508ad7077eb6ed1990898840c952b3a --- /dev/null +++ b/env-llmeval/lib/python3.10/site-packages/filelock/_error.py @@ -0,0 +1,30 @@ +from __future__ import annotations + +from typing import Any + + +class Timeout(TimeoutError): # noqa: N818 + """Raised when the lock could not be acquired in *timeout* seconds.""" + + def __init__(self, lock_file: str) -> None: + super().__init__() + self._lock_file = lock_file + + def __reduce__(self) -> str | tuple[Any, ...]: + return self.__class__, (self._lock_file,) # Properly pickle the exception + + def __str__(self) -> str: + return f"The file lock '{self._lock_file}' could not be acquired." + + def __repr__(self) -> str: + return f"{self.__class__.__name__}({self.lock_file!r})" + + @property + def lock_file(self) -> str: + """:return: The path of the file lock.""" + return self._lock_file + + +__all__ = [ + "Timeout", +] diff --git a/env-llmeval/lib/python3.10/site-packages/filelock/_soft.py b/env-llmeval/lib/python3.10/site-packages/filelock/_soft.py new file mode 100644 index 0000000000000000000000000000000000000000..28c67f74cc82b8f55e47afd6a71972cc1fb95eb6 --- /dev/null +++ b/env-llmeval/lib/python3.10/site-packages/filelock/_soft.py @@ -0,0 +1,47 @@ +from __future__ import annotations + +import os +import sys +from contextlib import suppress +from errno import EACCES, EEXIST +from pathlib import Path + +from ._api import BaseFileLock +from ._util import ensure_directory_exists, raise_on_not_writable_file + + +class SoftFileLock(BaseFileLock): + """Simply watches the existence of the lock file.""" + + def _acquire(self) -> None: + raise_on_not_writable_file(self.lock_file) + ensure_directory_exists(self.lock_file) + # first check for exists and read-only mode as the open will mask this case as EEXIST + flags = ( + os.O_WRONLY # open for writing only + | os.O_CREAT + | os.O_EXCL # together with above raise EEXIST if the file specified by filename exists + | os.O_TRUNC # truncate the file to zero byte + ) + try: + file_handler = os.open(self.lock_file, flags, self._context.mode) + except OSError as exception: # re-raise unless expected exception + if not ( + exception.errno == EEXIST # lock already exist + or (exception.errno == EACCES and sys.platform == "win32") # has no access to this lock + ): # pragma: win32 no cover + raise + else: + self._context.lock_file_fd = file_handler + + def _release(self) -> None: + assert self._context.lock_file_fd is not None # noqa: S101 + os.close(self._context.lock_file_fd) # the lock file is definitely not None + self._context.lock_file_fd = None + with suppress(OSError): # the file is already deleted and that's what we want + Path(self.lock_file).unlink() + + +__all__ = [ + "SoftFileLock", +] diff --git a/env-llmeval/lib/python3.10/site-packages/filelock/_unix.py b/env-llmeval/lib/python3.10/site-packages/filelock/_unix.py new file mode 100644 index 0000000000000000000000000000000000000000..4ae1fbe916f95762418cd62251f91f74ba35fc8c --- /dev/null +++ b/env-llmeval/lib/python3.10/site-packages/filelock/_unix.py @@ -0,0 +1,68 @@ +from __future__ import annotations + +import os +import sys +from contextlib import suppress +from errno import ENOSYS +from pathlib import Path +from typing import cast + +from ._api import BaseFileLock +from ._util import ensure_directory_exists + +#: a flag to indicate if the fcntl API is available +has_fcntl = False +if sys.platform == "win32": # pragma: win32 cover + + class UnixFileLock(BaseFileLock): + """Uses the :func:`fcntl.flock` to hard lock the lock file on unix systems.""" + + def _acquire(self) -> None: + raise NotImplementedError + + def _release(self) -> None: + raise NotImplementedError + +else: # pragma: win32 no cover + try: + import fcntl + except ImportError: + pass + else: + has_fcntl = True + + class UnixFileLock(BaseFileLock): + """Uses the :func:`fcntl.flock` to hard lock the lock file on unix systems.""" + + def _acquire(self) -> None: + ensure_directory_exists(self.lock_file) + open_flags = os.O_RDWR | os.O_TRUNC + if not Path(self.lock_file).exists(): + open_flags |= os.O_CREAT + fd = os.open(self.lock_file, open_flags, self._context.mode) + with suppress(PermissionError): # This locked is not owned by this UID + os.fchmod(fd, self._context.mode) + try: + fcntl.flock(fd, fcntl.LOCK_EX | fcntl.LOCK_NB) + except OSError as exception: + os.close(fd) + if exception.errno == ENOSYS: # NotImplemented error + msg = "FileSystem does not appear to support flock; use SoftFileLock instead" + raise NotImplementedError(msg) from exception + else: + self._context.lock_file_fd = fd + + def _release(self) -> None: + # Do not remove the lockfile: + # https://github.com/tox-dev/py-filelock/issues/31 + # https://stackoverflow.com/questions/17708885/flock-removing-locked-file-without-race-condition + fd = cast(int, self._context.lock_file_fd) + self._context.lock_file_fd = None + fcntl.flock(fd, fcntl.LOCK_UN) + os.close(fd) + + +__all__ = [ + "UnixFileLock", + "has_fcntl", +] diff --git a/env-llmeval/lib/python3.10/site-packages/filelock/_util.py b/env-llmeval/lib/python3.10/site-packages/filelock/_util.py new file mode 100644 index 0000000000000000000000000000000000000000..c671e8533873948f0e1b5575ff952c722019f067 --- /dev/null +++ b/env-llmeval/lib/python3.10/site-packages/filelock/_util.py @@ -0,0 +1,52 @@ +from __future__ import annotations + +import os +import stat +import sys +from errno import EACCES, EISDIR +from pathlib import Path + + +def raise_on_not_writable_file(filename: str) -> None: + """ + Raise an exception if attempting to open the file for writing would fail. + + This is done so files that will never be writable can be separated from files that are writable but currently + locked. + + :param filename: file to check + :raises OSError: as if the file was opened for writing. + + """ + try: # use stat to do exists + can write to check without race condition + file_stat = os.stat(filename) # noqa: PTH116 + except OSError: + return # swallow does not exist or other errors + + if file_stat.st_mtime != 0: # if os.stat returns but modification is zero that's an invalid os.stat - ignore it + if not (file_stat.st_mode & stat.S_IWUSR): + raise PermissionError(EACCES, "Permission denied", filename) + + if stat.S_ISDIR(file_stat.st_mode): + if sys.platform == "win32": # pragma: win32 cover + # On Windows, this is PermissionError + raise PermissionError(EACCES, "Permission denied", filename) + else: # pragma: win32 no cover # noqa: RET506 + # On linux / macOS, this is IsADirectoryError + raise IsADirectoryError(EISDIR, "Is a directory", filename) + + +def ensure_directory_exists(filename: Path | str) -> None: + """ + Ensure the directory containing the file exists (create it if necessary). + + :param filename: file. + + """ + Path(filename).parent.mkdir(parents=True, exist_ok=True) + + +__all__ = [ + "ensure_directory_exists", + "raise_on_not_writable_file", +] diff --git a/env-llmeval/lib/python3.10/site-packages/filelock/_windows.py b/env-llmeval/lib/python3.10/site-packages/filelock/_windows.py new file mode 100644 index 0000000000000000000000000000000000000000..8db55dcbaa3e7bab091781b17ce22fde1fc239f2 --- /dev/null +++ b/env-llmeval/lib/python3.10/site-packages/filelock/_windows.py @@ -0,0 +1,65 @@ +from __future__ import annotations + +import os +import sys +from contextlib import suppress +from errno import EACCES +from pathlib import Path +from typing import cast + +from ._api import BaseFileLock +from ._util import ensure_directory_exists, raise_on_not_writable_file + +if sys.platform == "win32": # pragma: win32 cover + import msvcrt + + class WindowsFileLock(BaseFileLock): + """Uses the :func:`msvcrt.locking` function to hard lock the lock file on Windows systems.""" + + def _acquire(self) -> None: + raise_on_not_writable_file(self.lock_file) + ensure_directory_exists(self.lock_file) + flags = ( + os.O_RDWR # open for read and write + | os.O_CREAT # create file if not exists + | os.O_TRUNC # truncate file if not empty + ) + try: + fd = os.open(self.lock_file, flags, self._context.mode) + except OSError as exception: + if exception.errno != EACCES: # has no access to this lock + raise + else: + try: + msvcrt.locking(fd, msvcrt.LK_NBLCK, 1) + except OSError as exception: + os.close(fd) # close file first + if exception.errno != EACCES: # file is already locked + raise + else: + self._context.lock_file_fd = fd + + def _release(self) -> None: + fd = cast(int, self._context.lock_file_fd) + self._context.lock_file_fd = None + msvcrt.locking(fd, msvcrt.LK_UNLCK, 1) + os.close(fd) + + with suppress(OSError): # Probably another instance of the application hat acquired the file lock. + Path(self.lock_file).unlink() + +else: # pragma: win32 no cover + + class WindowsFileLock(BaseFileLock): + """Uses the :func:`msvcrt.locking` function to hard lock the lock file on Windows systems.""" + + def _acquire(self) -> None: + raise NotImplementedError + + def _release(self) -> None: + raise NotImplementedError + + +__all__ = [ + "WindowsFileLock", +] diff --git a/env-llmeval/lib/python3.10/site-packages/filelock/py.typed b/env-llmeval/lib/python3.10/site-packages/filelock/py.typed new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/env-llmeval/lib/python3.10/site-packages/filelock/version.py b/env-llmeval/lib/python3.10/site-packages/filelock/version.py new file mode 100644 index 0000000000000000000000000000000000000000..cf2a247c31b187acc0502a58ec7062029e31f0fe --- /dev/null +++ b/env-llmeval/lib/python3.10/site-packages/filelock/version.py @@ -0,0 +1,16 @@ +# file generated by setuptools_scm +# don't change, don't track in version control +TYPE_CHECKING = False +if TYPE_CHECKING: + from typing import Tuple, Union + VERSION_TUPLE = Tuple[Union[int, str], ...] +else: + VERSION_TUPLE = object + +version: str +__version__: str +__version_tuple__: VERSION_TUPLE +version_tuple: VERSION_TUPLE + +__version__ = version = '3.13.4' +__version_tuple__ = version_tuple = (3, 13, 4) diff --git a/env-llmeval/lib/python3.10/site-packages/pip/_vendor/cachecontrol/__pycache__/__init__.cpython-310.pyc b/env-llmeval/lib/python3.10/site-packages/pip/_vendor/cachecontrol/__pycache__/__init__.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..ceb8ea99ccca82b83ad9dbc031ba7b01278126c4 Binary files /dev/null and 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0000000000000000000000000000000000000000..f1d780768a720ec0e496ca6017cdd10ba688d5cf Binary files /dev/null and b/env-llmeval/lib/python3.10/site-packages/pip/_vendor/cachecontrol/caches/__pycache__/redis_cache.cpython-310.pyc differ diff --git a/env-llmeval/lib/python3.10/site-packages/pip/_vendor/cachecontrol/caches/file_cache.py b/env-llmeval/lib/python3.10/site-packages/pip/_vendor/cachecontrol/caches/file_cache.py new file mode 100644 index 0000000000000000000000000000000000000000..6cd1106f880092f55569ff46c6dc8100e7b5ffb9 --- /dev/null +++ b/env-llmeval/lib/python3.10/site-packages/pip/_vendor/cachecontrol/caches/file_cache.py @@ -0,0 +1,150 @@ +# SPDX-FileCopyrightText: 2015 Eric Larson +# +# SPDX-License-Identifier: Apache-2.0 + +import hashlib +import os +from textwrap import dedent + +from ..cache import BaseCache +from ..controller import CacheController + +try: + FileNotFoundError +except NameError: + # py2.X + FileNotFoundError = (IOError, OSError) + + +def _secure_open_write(filename, fmode): + # We only want to write to this file, so open it in write only mode + flags = os.O_WRONLY + + # os.O_CREAT | os.O_EXCL will fail if the file already exists, so we only + # will open *new* files. + # We specify this because we want to ensure that the mode we pass is the + # mode of the file. + flags |= os.O_CREAT | os.O_EXCL + + # Do not follow symlinks to prevent someone from making a symlink that + # we follow and insecurely open a cache file. + if hasattr(os, "O_NOFOLLOW"): + flags |= os.O_NOFOLLOW + + # On Windows we'll mark this file as binary + if hasattr(os, "O_BINARY"): + flags |= os.O_BINARY + + # Before we open our file, we want to delete any existing file that is + # there + try: + os.remove(filename) + except (IOError, OSError): + # The file must not exist already, so we can just skip ahead to opening + pass + + # Open our file, the use of os.O_CREAT | os.O_EXCL will ensure that if a + # race condition happens between the os.remove and this line, that an + # error will be raised. Because we utilize a lockfile this should only + # happen if someone is attempting to attack us. + fd = os.open(filename, flags, fmode) + try: + return os.fdopen(fd, "wb") + + except: + # An error occurred wrapping our FD in a file object + os.close(fd) + raise + + +class FileCache(BaseCache): + + def __init__( + self, + directory, + forever=False, + filemode=0o0600, + dirmode=0o0700, + use_dir_lock=None, + lock_class=None, + ): + + if use_dir_lock is not None and lock_class is not None: + raise ValueError("Cannot use use_dir_lock and lock_class together") + + try: + from lockfile import LockFile + from lockfile.mkdirlockfile import MkdirLockFile + except ImportError: + notice = dedent( + """ + NOTE: In order to use the FileCache you must have + lockfile installed. You can install it via pip: + pip install lockfile + """ + ) + raise ImportError(notice) + + else: + if use_dir_lock: + lock_class = MkdirLockFile + + elif lock_class is None: + lock_class = LockFile + + self.directory = directory + self.forever = forever + self.filemode = filemode + self.dirmode = dirmode + self.lock_class = lock_class + + @staticmethod + def encode(x): + return hashlib.sha224(x.encode()).hexdigest() + + def _fn(self, name): + # NOTE: This method should not change as some may depend on it. + # See: https://github.com/ionrock/cachecontrol/issues/63 + hashed = self.encode(name) + parts = list(hashed[:5]) + [hashed] + return os.path.join(self.directory, *parts) + + def get(self, key): + name = self._fn(key) + try: + with open(name, "rb") as fh: + return fh.read() + + except FileNotFoundError: + return None + + def set(self, key, value, expires=None): + name = self._fn(key) + + # Make sure the directory exists + try: + os.makedirs(os.path.dirname(name), self.dirmode) + except (IOError, OSError): + pass + + with self.lock_class(name) as lock: + # Write our actual file + with _secure_open_write(lock.path, self.filemode) as fh: + fh.write(value) + + def delete(self, key): + name = self._fn(key) + if not self.forever: + try: + os.remove(name) + except FileNotFoundError: + pass + + +def url_to_file_path(url, filecache): + """Return the file cache path based on the URL. + + This does not ensure the file exists! + """ + key = CacheController.cache_url(url) + return filecache._fn(key) diff --git a/env-llmeval/lib/python3.10/site-packages/pip/_vendor/cachecontrol/caches/redis_cache.py b/env-llmeval/lib/python3.10/site-packages/pip/_vendor/cachecontrol/caches/redis_cache.py new file mode 100644 index 0000000000000000000000000000000000000000..720b507c523ddb54fef42245f1ace7610f2cf8b5 --- /dev/null +++ b/env-llmeval/lib/python3.10/site-packages/pip/_vendor/cachecontrol/caches/redis_cache.py @@ -0,0 +1,37 @@ +# SPDX-FileCopyrightText: 2015 Eric Larson +# +# SPDX-License-Identifier: Apache-2.0 + +from __future__ import division + +from datetime import datetime +from pip._vendor.cachecontrol.cache import BaseCache + + +class RedisCache(BaseCache): + + def __init__(self, conn): + self.conn = conn + + def get(self, key): + return self.conn.get(key) + + def set(self, key, value, expires=None): + if not expires: + self.conn.set(key, value) + else: + expires = expires - datetime.utcnow() + self.conn.setex(key, int(expires.total_seconds()), value) + + def delete(self, key): + self.conn.delete(key) + + def clear(self): + """Helper for clearing all the keys in a database. Use with + caution!""" + for key in self.conn.keys(): + self.conn.delete(key) + + def close(self): + """Redis uses connection pooling, no need to close the connection.""" + pass diff --git a/env-llmeval/lib/python3.10/site-packages/pip/_vendor/cachecontrol/compat.py b/env-llmeval/lib/python3.10/site-packages/pip/_vendor/cachecontrol/compat.py new file mode 100644 index 0000000000000000000000000000000000000000..ccec9379dba2b03015ce123dd04a042f32431235 --- /dev/null +++ b/env-llmeval/lib/python3.10/site-packages/pip/_vendor/cachecontrol/compat.py @@ -0,0 +1,32 @@ +# SPDX-FileCopyrightText: 2015 Eric Larson +# +# SPDX-License-Identifier: Apache-2.0 + +try: + from urllib.parse import urljoin +except ImportError: + from urlparse import urljoin + + +try: + import cPickle as pickle +except ImportError: + import pickle + +# Handle the case where the requests module has been patched to not have +# urllib3 bundled as part of its source. +try: + from pip._vendor.requests.packages.urllib3.response import HTTPResponse +except ImportError: + from pip._vendor.urllib3.response import HTTPResponse + +try: + from pip._vendor.requests.packages.urllib3.util import is_fp_closed +except ImportError: + from pip._vendor.urllib3.util import is_fp_closed + +# Replicate some six behaviour +try: + text_type = unicode +except NameError: + text_type = str diff --git a/env-llmeval/lib/python3.10/site-packages/pip/_vendor/cachecontrol/controller.py b/env-llmeval/lib/python3.10/site-packages/pip/_vendor/cachecontrol/controller.py new file mode 100644 index 0000000000000000000000000000000000000000..d7e73802818c336c4316ee1fc596efa54763bb4b --- /dev/null +++ b/env-llmeval/lib/python3.10/site-packages/pip/_vendor/cachecontrol/controller.py @@ -0,0 +1,415 @@ +# SPDX-FileCopyrightText: 2015 Eric Larson +# +# SPDX-License-Identifier: Apache-2.0 + +""" +The httplib2 algorithms ported for use with requests. +""" +import logging +import re +import calendar +import time +from email.utils import parsedate_tz + +from pip._vendor.requests.structures import CaseInsensitiveDict + +from .cache import DictCache +from .serialize import Serializer + + +logger = logging.getLogger(__name__) + +URI = re.compile(r"^(([^:/?#]+):)?(//([^/?#]*))?([^?#]*)(\?([^#]*))?(#(.*))?") + +PERMANENT_REDIRECT_STATUSES = (301, 308) + + +def parse_uri(uri): + """Parses a URI using the regex given in Appendix B of RFC 3986. + + (scheme, authority, path, query, fragment) = parse_uri(uri) + """ + groups = URI.match(uri).groups() + return (groups[1], groups[3], groups[4], groups[6], groups[8]) + + +class CacheController(object): + """An interface to see if request should cached or not. + """ + + def __init__( + self, cache=None, cache_etags=True, serializer=None, status_codes=None + ): + self.cache = DictCache() if cache is None else cache + self.cache_etags = cache_etags + self.serializer = serializer or Serializer() + self.cacheable_status_codes = status_codes or (200, 203, 300, 301, 308) + + @classmethod + def _urlnorm(cls, uri): + """Normalize the URL to create a safe key for the cache""" + (scheme, authority, path, query, fragment) = parse_uri(uri) + if not scheme or not authority: + raise Exception("Only absolute URIs are allowed. uri = %s" % uri) + + scheme = scheme.lower() + authority = authority.lower() + + if not path: + path = "/" + + # Could do syntax based normalization of the URI before + # computing the digest. See Section 6.2.2 of Std 66. + request_uri = query and "?".join([path, query]) or path + defrag_uri = scheme + "://" + authority + request_uri + + return defrag_uri + + @classmethod + def cache_url(cls, uri): + return cls._urlnorm(uri) + + def parse_cache_control(self, headers): + known_directives = { + # https://tools.ietf.org/html/rfc7234#section-5.2 + "max-age": (int, True), + "max-stale": (int, False), + "min-fresh": (int, True), + "no-cache": (None, False), + "no-store": (None, False), + "no-transform": (None, False), + "only-if-cached": (None, False), + "must-revalidate": (None, False), + "public": (None, False), + "private": (None, False), + "proxy-revalidate": (None, False), + "s-maxage": (int, True), + } + + cc_headers = headers.get("cache-control", headers.get("Cache-Control", "")) + + retval = {} + + for cc_directive in cc_headers.split(","): + if not cc_directive.strip(): + continue + + parts = cc_directive.split("=", 1) + directive = parts[0].strip() + + try: + typ, required = known_directives[directive] + except KeyError: + logger.debug("Ignoring unknown cache-control directive: %s", directive) + continue + + if not typ or not required: + retval[directive] = None + if typ: + try: + retval[directive] = typ(parts[1].strip()) + except IndexError: + if required: + logger.debug( + "Missing value for cache-control " "directive: %s", + directive, + ) + except ValueError: + logger.debug( + "Invalid value for cache-control directive " "%s, must be %s", + directive, + typ.__name__, + ) + + return retval + + def cached_request(self, request): + """ + Return a cached response if it exists in the cache, otherwise + return False. + """ + cache_url = self.cache_url(request.url) + logger.debug('Looking up "%s" in the cache', cache_url) + cc = self.parse_cache_control(request.headers) + + # Bail out if the request insists on fresh data + if "no-cache" in cc: + logger.debug('Request header has "no-cache", cache bypassed') + return False + + if "max-age" in cc and cc["max-age"] == 0: + logger.debug('Request header has "max_age" as 0, cache bypassed') + return False + + # Request allows serving from the cache, let's see if we find something + cache_data = self.cache.get(cache_url) + if cache_data is None: + logger.debug("No cache entry available") + return False + + # Check whether it can be deserialized + resp = self.serializer.loads(request, cache_data) + if not resp: + logger.warning("Cache entry deserialization failed, entry ignored") + return False + + # If we have a cached permanent redirect, return it immediately. We + # don't need to test our response for other headers b/c it is + # intrinsically "cacheable" as it is Permanent. + # + # See: + # https://tools.ietf.org/html/rfc7231#section-6.4.2 + # + # Client can try to refresh the value by repeating the request + # with cache busting headers as usual (ie no-cache). + if int(resp.status) in PERMANENT_REDIRECT_STATUSES: + msg = ( + "Returning cached permanent redirect response " + "(ignoring date and etag information)" + ) + logger.debug(msg) + return resp + + headers = CaseInsensitiveDict(resp.headers) + if not headers or "date" not in headers: + if "etag" not in headers: + # Without date or etag, the cached response can never be used + # and should be deleted. + logger.debug("Purging cached response: no date or etag") + self.cache.delete(cache_url) + logger.debug("Ignoring cached response: no date") + return False + + now = time.time() + date = calendar.timegm(parsedate_tz(headers["date"])) + current_age = max(0, now - date) + logger.debug("Current age based on date: %i", current_age) + + # TODO: There is an assumption that the result will be a + # urllib3 response object. This may not be best since we + # could probably avoid instantiating or constructing the + # response until we know we need it. + resp_cc = self.parse_cache_control(headers) + + # determine freshness + freshness_lifetime = 0 + + # Check the max-age pragma in the cache control header + if "max-age" in resp_cc: + freshness_lifetime = resp_cc["max-age"] + logger.debug("Freshness lifetime from max-age: %i", freshness_lifetime) + + # If there isn't a max-age, check for an expires header + elif "expires" in headers: + expires = parsedate_tz(headers["expires"]) + if expires is not None: + expire_time = calendar.timegm(expires) - date + freshness_lifetime = max(0, expire_time) + logger.debug("Freshness lifetime from expires: %i", freshness_lifetime) + + # Determine if we are setting freshness limit in the + # request. Note, this overrides what was in the response. + if "max-age" in cc: + freshness_lifetime = cc["max-age"] + logger.debug( + "Freshness lifetime from request max-age: %i", freshness_lifetime + ) + + if "min-fresh" in cc: + min_fresh = cc["min-fresh"] + # adjust our current age by our min fresh + current_age += min_fresh + logger.debug("Adjusted current age from min-fresh: %i", current_age) + + # Return entry if it is fresh enough + if freshness_lifetime > current_age: + logger.debug('The response is "fresh", returning cached response') + logger.debug("%i > %i", freshness_lifetime, current_age) + return resp + + # we're not fresh. If we don't have an Etag, clear it out + if "etag" not in headers: + logger.debug('The cached response is "stale" with no etag, purging') + self.cache.delete(cache_url) + + # return the original handler + return False + + def conditional_headers(self, request): + cache_url = self.cache_url(request.url) + resp = self.serializer.loads(request, self.cache.get(cache_url)) + new_headers = {} + + if resp: + headers = CaseInsensitiveDict(resp.headers) + + if "etag" in headers: + new_headers["If-None-Match"] = headers["ETag"] + + if "last-modified" in headers: + new_headers["If-Modified-Since"] = headers["Last-Modified"] + + return new_headers + + def cache_response(self, request, response, body=None, status_codes=None): + """ + Algorithm for caching requests. + + This assumes a requests Response object. + """ + # From httplib2: Don't cache 206's since we aren't going to + # handle byte range requests + cacheable_status_codes = status_codes or self.cacheable_status_codes + if response.status not in cacheable_status_codes: + logger.debug( + "Status code %s not in %s", response.status, cacheable_status_codes + ) + return + + response_headers = CaseInsensitiveDict(response.headers) + + if "date" in response_headers: + date = calendar.timegm(parsedate_tz(response_headers["date"])) + else: + date = 0 + + # If we've been given a body, our response has a Content-Length, that + # Content-Length is valid then we can check to see if the body we've + # been given matches the expected size, and if it doesn't we'll just + # skip trying to cache it. + if ( + body is not None + and "content-length" in response_headers + and response_headers["content-length"].isdigit() + and int(response_headers["content-length"]) != len(body) + ): + return + + cc_req = self.parse_cache_control(request.headers) + cc = self.parse_cache_control(response_headers) + + cache_url = self.cache_url(request.url) + logger.debug('Updating cache with response from "%s"', cache_url) + + # Delete it from the cache if we happen to have it stored there + no_store = False + if "no-store" in cc: + no_store = True + logger.debug('Response header has "no-store"') + if "no-store" in cc_req: + no_store = True + logger.debug('Request header has "no-store"') + if no_store and self.cache.get(cache_url): + logger.debug('Purging existing cache entry to honor "no-store"') + self.cache.delete(cache_url) + if no_store: + return + + # https://tools.ietf.org/html/rfc7234#section-4.1: + # A Vary header field-value of "*" always fails to match. + # Storing such a response leads to a deserialization warning + # during cache lookup and is not allowed to ever be served, + # so storing it can be avoided. + if "*" in response_headers.get("vary", ""): + logger.debug('Response header has "Vary: *"') + return + + # If we've been given an etag, then keep the response + if self.cache_etags and "etag" in response_headers: + expires_time = 0 + if response_headers.get("expires"): + expires = parsedate_tz(response_headers["expires"]) + if expires is not None: + expires_time = calendar.timegm(expires) - date + + expires_time = max(expires_time, 14 * 86400) + + logger.debug("etag object cached for {0} seconds".format(expires_time)) + logger.debug("Caching due to etag") + self.cache.set( + cache_url, + self.serializer.dumps(request, response, body), + expires=expires_time, + ) + + # Add to the cache any permanent redirects. We do this before looking + # that the Date headers. + elif int(response.status) in PERMANENT_REDIRECT_STATUSES: + logger.debug("Caching permanent redirect") + self.cache.set(cache_url, self.serializer.dumps(request, response, b"")) + + # Add to the cache if the response headers demand it. If there + # is no date header then we can't do anything about expiring + # the cache. + elif "date" in response_headers: + date = calendar.timegm(parsedate_tz(response_headers["date"])) + # cache when there is a max-age > 0 + if "max-age" in cc and cc["max-age"] > 0: + logger.debug("Caching b/c date exists and max-age > 0") + expires_time = cc["max-age"] + self.cache.set( + cache_url, + self.serializer.dumps(request, response, body), + expires=expires_time, + ) + + # If the request can expire, it means we should cache it + # in the meantime. + elif "expires" in response_headers: + if response_headers["expires"]: + expires = parsedate_tz(response_headers["expires"]) + if expires is not None: + expires_time = calendar.timegm(expires) - date + else: + expires_time = None + + logger.debug( + "Caching b/c of expires header. expires in {0} seconds".format( + expires_time + ) + ) + self.cache.set( + cache_url, + self.serializer.dumps(request, response, body=body), + expires=expires_time, + ) + + def update_cached_response(self, request, response): + """On a 304 we will get a new set of headers that we want to + update our cached value with, assuming we have one. + + This should only ever be called when we've sent an ETag and + gotten a 304 as the response. + """ + cache_url = self.cache_url(request.url) + + cached_response = self.serializer.loads(request, self.cache.get(cache_url)) + + if not cached_response: + # we didn't have a cached response + return response + + # Lets update our headers with the headers from the new request: + # http://tools.ietf.org/html/draft-ietf-httpbis-p4-conditional-26#section-4.1 + # + # The server isn't supposed to send headers that would make + # the cached body invalid. But... just in case, we'll be sure + # to strip out ones we know that might be problmatic due to + # typical assumptions. + excluded_headers = ["content-length"] + + cached_response.headers.update( + dict( + (k, v) + for k, v in response.headers.items() + if k.lower() not in excluded_headers + ) + ) + + # we want a 200 b/c we have content via the cache + cached_response.status = 200 + + # update our cache + self.cache.set(cache_url, self.serializer.dumps(request, cached_response)) + + return cached_response diff --git a/env-llmeval/lib/python3.10/site-packages/pip/_vendor/cachecontrol/filewrapper.py b/env-llmeval/lib/python3.10/site-packages/pip/_vendor/cachecontrol/filewrapper.py new file mode 100644 index 0000000000000000000000000000000000000000..f5ed5f6f6ec0eae90a9f48753622b2b5ee5d4a4f --- /dev/null +++ b/env-llmeval/lib/python3.10/site-packages/pip/_vendor/cachecontrol/filewrapper.py @@ -0,0 +1,111 @@ +# SPDX-FileCopyrightText: 2015 Eric Larson +# +# SPDX-License-Identifier: Apache-2.0 + +from tempfile import NamedTemporaryFile +import mmap + + +class CallbackFileWrapper(object): + """ + Small wrapper around a fp object which will tee everything read into a + buffer, and when that file is closed it will execute a callback with the + contents of that buffer. + + All attributes are proxied to the underlying file object. + + This class uses members with a double underscore (__) leading prefix so as + not to accidentally shadow an attribute. + + The data is stored in a temporary file until it is all available. As long + as the temporary files directory is disk-based (sometimes it's a + memory-backed-``tmpfs`` on Linux), data will be unloaded to disk if memory + pressure is high. For small files the disk usually won't be used at all, + it'll all be in the filesystem memory cache, so there should be no + performance impact. + """ + + def __init__(self, fp, callback): + self.__buf = NamedTemporaryFile("rb+", delete=True) + self.__fp = fp + self.__callback = callback + + def __getattr__(self, name): + # The vaguaries of garbage collection means that self.__fp is + # not always set. By using __getattribute__ and the private + # name[0] allows looking up the attribute value and raising an + # AttributeError when it doesn't exist. This stop thigns from + # infinitely recursing calls to getattr in the case where + # self.__fp hasn't been set. + # + # [0] https://docs.python.org/2/reference/expressions.html#atom-identifiers + fp = self.__getattribute__("_CallbackFileWrapper__fp") + return getattr(fp, name) + + def __is_fp_closed(self): + try: + return self.__fp.fp is None + + except AttributeError: + pass + + try: + return self.__fp.closed + + except AttributeError: + pass + + # We just don't cache it then. + # TODO: Add some logging here... + return False + + def _close(self): + if self.__callback: + if self.__buf.tell() == 0: + # Empty file: + result = b"" + else: + # Return the data without actually loading it into memory, + # relying on Python's buffer API and mmap(). mmap() just gives + # a view directly into the filesystem's memory cache, so it + # doesn't result in duplicate memory use. + self.__buf.seek(0, 0) + result = memoryview( + mmap.mmap(self.__buf.fileno(), 0, access=mmap.ACCESS_READ) + ) + self.__callback(result) + + # We assign this to None here, because otherwise we can get into + # really tricky problems where the CPython interpreter dead locks + # because the callback is holding a reference to something which + # has a __del__ method. Setting this to None breaks the cycle + # and allows the garbage collector to do it's thing normally. + self.__callback = None + + # Closing the temporary file releases memory and frees disk space. + # Important when caching big files. + self.__buf.close() + + def read(self, amt=None): + data = self.__fp.read(amt) + if data: + # We may be dealing with b'', a sign that things are over: + # it's passed e.g. after we've already closed self.__buf. + self.__buf.write(data) + if self.__is_fp_closed(): + self._close() + + return data + + def _safe_read(self, amt): + data = self.__fp._safe_read(amt) + if amt == 2 and data == b"\r\n": + # urllib executes this read to toss the CRLF at the end + # of the chunk. + return data + + self.__buf.write(data) + if self.__is_fp_closed(): + self._close() + + return data diff --git a/env-llmeval/lib/python3.10/site-packages/pip/_vendor/cachecontrol/heuristics.py b/env-llmeval/lib/python3.10/site-packages/pip/_vendor/cachecontrol/heuristics.py new file mode 100644 index 0000000000000000000000000000000000000000..ebe4a96f589474f6f441858de2bb961c5e473c6d --- /dev/null +++ b/env-llmeval/lib/python3.10/site-packages/pip/_vendor/cachecontrol/heuristics.py @@ -0,0 +1,139 @@ +# SPDX-FileCopyrightText: 2015 Eric Larson +# +# SPDX-License-Identifier: Apache-2.0 + +import calendar +import time + +from email.utils import formatdate, parsedate, parsedate_tz + +from datetime import datetime, timedelta + +TIME_FMT = "%a, %d %b %Y %H:%M:%S GMT" + + +def expire_after(delta, date=None): + date = date or datetime.utcnow() + return date + delta + + +def datetime_to_header(dt): + return formatdate(calendar.timegm(dt.timetuple())) + + +class BaseHeuristic(object): + + def warning(self, response): + """ + Return a valid 1xx warning header value describing the cache + adjustments. + + The response is provided too allow warnings like 113 + http://tools.ietf.org/html/rfc7234#section-5.5.4 where we need + to explicitly say response is over 24 hours old. + """ + return '110 - "Response is Stale"' + + def update_headers(self, response): + """Update the response headers with any new headers. + + NOTE: This SHOULD always include some Warning header to + signify that the response was cached by the client, not + by way of the provided headers. + """ + return {} + + def apply(self, response): + updated_headers = self.update_headers(response) + + if updated_headers: + response.headers.update(updated_headers) + warning_header_value = self.warning(response) + if warning_header_value is not None: + response.headers.update({"Warning": warning_header_value}) + + return response + + +class OneDayCache(BaseHeuristic): + """ + Cache the response by providing an expires 1 day in the + future. + """ + + def update_headers(self, response): + headers = {} + + if "expires" not in response.headers: + date = parsedate(response.headers["date"]) + expires = expire_after(timedelta(days=1), date=datetime(*date[:6])) + headers["expires"] = datetime_to_header(expires) + headers["cache-control"] = "public" + return headers + + +class ExpiresAfter(BaseHeuristic): + """ + Cache **all** requests for a defined time period. + """ + + def __init__(self, **kw): + self.delta = timedelta(**kw) + + def update_headers(self, response): + expires = expire_after(self.delta) + return {"expires": datetime_to_header(expires), "cache-control": "public"} + + def warning(self, response): + tmpl = "110 - Automatically cached for %s. Response might be stale" + return tmpl % self.delta + + +class LastModified(BaseHeuristic): + """ + If there is no Expires header already, fall back on Last-Modified + using the heuristic from + http://tools.ietf.org/html/rfc7234#section-4.2.2 + to calculate a reasonable value. + + Firefox also does something like this per + https://developer.mozilla.org/en-US/docs/Web/HTTP/Caching_FAQ + http://lxr.mozilla.org/mozilla-release/source/netwerk/protocol/http/nsHttpResponseHead.cpp#397 + Unlike mozilla we limit this to 24-hr. + """ + cacheable_by_default_statuses = { + 200, 203, 204, 206, 300, 301, 404, 405, 410, 414, 501 + } + + def update_headers(self, resp): + headers = resp.headers + + if "expires" in headers: + return {} + + if "cache-control" in headers and headers["cache-control"] != "public": + return {} + + if resp.status not in self.cacheable_by_default_statuses: + return {} + + if "date" not in headers or "last-modified" not in headers: + return {} + + date = calendar.timegm(parsedate_tz(headers["date"])) + last_modified = parsedate(headers["last-modified"]) + if date is None or last_modified is None: + return {} + + now = time.time() + current_age = max(0, now - date) + delta = date - calendar.timegm(last_modified) + freshness_lifetime = max(0, min(delta / 10, 24 * 3600)) + if freshness_lifetime <= current_age: + return {} + + expires = date + freshness_lifetime + return {"expires": time.strftime(TIME_FMT, time.gmtime(expires))} + + def warning(self, resp): + return None diff --git a/env-llmeval/lib/python3.10/site-packages/pip/_vendor/colorama/__pycache__/initialise.cpython-310.pyc b/env-llmeval/lib/python3.10/site-packages/pip/_vendor/colorama/__pycache__/initialise.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..5a369a6331d317781d37fdf3b44555a82fdd741e Binary files /dev/null and b/env-llmeval/lib/python3.10/site-packages/pip/_vendor/colorama/__pycache__/initialise.cpython-310.pyc differ diff --git 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b/env-llmeval/lib/python3.10/site-packages/pip/_vendor/msgpack/_version.py new file mode 100644 index 0000000000000000000000000000000000000000..fb878b353dea2fcf3fd8aecd045cd25cd0536c83 --- /dev/null +++ b/env-llmeval/lib/python3.10/site-packages/pip/_vendor/msgpack/_version.py @@ -0,0 +1 @@ +version = (1, 0, 3) diff --git a/env-llmeval/lib/python3.10/site-packages/pip/_vendor/resolvelib/__init__.py b/env-llmeval/lib/python3.10/site-packages/pip/_vendor/resolvelib/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..ce05fd3027447fdc64986ecdee950643822e45e6 --- /dev/null +++ b/env-llmeval/lib/python3.10/site-packages/pip/_vendor/resolvelib/__init__.py @@ -0,0 +1,26 @@ +__all__ = [ + "__version__", + "AbstractProvider", + "AbstractResolver", + "BaseReporter", + "InconsistentCandidate", + "Resolver", + "RequirementsConflicted", + "ResolutionError", + "ResolutionImpossible", + "ResolutionTooDeep", +] + +__version__ = "0.8.1" + + +from .providers import AbstractProvider, AbstractResolver +from .reporters import BaseReporter +from .resolvers import ( + InconsistentCandidate, + RequirementsConflicted, + ResolutionError, + ResolutionImpossible, + ResolutionTooDeep, + Resolver, +) diff --git a/env-llmeval/lib/python3.10/site-packages/pip/_vendor/resolvelib/__pycache__/__init__.cpython-310.pyc b/env-llmeval/lib/python3.10/site-packages/pip/_vendor/resolvelib/__pycache__/__init__.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..3ba1d65646e00e5b2d51f453d466845188c6f43f Binary files /dev/null and b/env-llmeval/lib/python3.10/site-packages/pip/_vendor/resolvelib/__pycache__/__init__.cpython-310.pyc differ diff --git a/env-llmeval/lib/python3.10/site-packages/pip/_vendor/resolvelib/__pycache__/providers.cpython-310.pyc b/env-llmeval/lib/python3.10/site-packages/pip/_vendor/resolvelib/__pycache__/providers.cpython-310.pyc new file mode 100644 index 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a/env-llmeval/lib/python3.10/site-packages/pip/_vendor/resolvelib/compat/collections_abc.py b/env-llmeval/lib/python3.10/site-packages/pip/_vendor/resolvelib/compat/collections_abc.py new file mode 100644 index 0000000000000000000000000000000000000000..1becc5093c5ab8e196bb9fee415e2381e7158fc3 --- /dev/null +++ b/env-llmeval/lib/python3.10/site-packages/pip/_vendor/resolvelib/compat/collections_abc.py @@ -0,0 +1,6 @@ +__all__ = ["Mapping", "Sequence"] + +try: + from collections.abc import Mapping, Sequence +except ImportError: + from collections import Mapping, Sequence diff --git a/env-llmeval/lib/python3.10/site-packages/pip/_vendor/resolvelib/providers.py b/env-llmeval/lib/python3.10/site-packages/pip/_vendor/resolvelib/providers.py new file mode 100644 index 0000000000000000000000000000000000000000..7d0a9c22a4656951910a9fbb70af59a0706cadde --- /dev/null +++ b/env-llmeval/lib/python3.10/site-packages/pip/_vendor/resolvelib/providers.py @@ -0,0 +1,133 @@ +class AbstractProvider(object): + """Delegate class to provide requirement interface for the resolver.""" + + def identify(self, requirement_or_candidate): + """Given a requirement, return an identifier for it. + + This is used to identify a requirement, e.g. whether two requirements + should have their specifier parts merged. + """ + raise NotImplementedError + + def get_preference( + self, + identifier, + resolutions, + candidates, + information, + backtrack_causes, + ): + """Produce a sort key for given requirement based on preference. + + The preference is defined as "I think this requirement should be + resolved first". The lower the return value is, the more preferred + this group of arguments is. + + :param identifier: An identifier as returned by ``identify()``. This + identifies the dependency matches of which should be returned. + :param resolutions: Mapping of candidates currently pinned by the + resolver. Each key is an identifier, and the value a candidate. + The candidate may conflict with requirements from ``information``. + :param candidates: Mapping of each dependency's possible candidates. + Each value is an iterator of candidates. + :param information: Mapping of requirement information of each package. + Each value is an iterator of *requirement information*. + :param backtrack_causes: Sequence of requirement information that were + the requirements that caused the resolver to most recently backtrack. + + A *requirement information* instance is a named tuple with two members: + + * ``requirement`` specifies a requirement contributing to the current + list of candidates. + * ``parent`` specifies the candidate that provides (dependend on) the + requirement, or ``None`` to indicate a root requirement. + + The preference could depend on a various of issues, including (not + necessarily in this order): + + * Is this package pinned in the current resolution result? + * How relaxed is the requirement? Stricter ones should probably be + worked on first? (I don't know, actually.) + * How many possibilities are there to satisfy this requirement? Those + with few left should likely be worked on first, I guess? + * Are there any known conflicts for this requirement? We should + probably work on those with the most known conflicts. + + A sortable value should be returned (this will be used as the ``key`` + parameter of the built-in sorting function). The smaller the value is, + the more preferred this requirement is (i.e. the sorting function + is called with ``reverse=False``). + """ + raise NotImplementedError + + def find_matches(self, identifier, requirements, incompatibilities): + """Find all possible candidates that satisfy given constraints. + + :param identifier: An identifier as returned by ``identify()``. This + identifies the dependency matches of which should be returned. + :param requirements: A mapping of requirements that all returned + candidates must satisfy. Each key is an identifier, and the value + an iterator of requirements for that dependency. + :param incompatibilities: A mapping of known incompatibilities of + each dependency. Each key is an identifier, and the value an + iterator of incompatibilities known to the resolver. All + incompatibilities *must* be excluded from the return value. + + This should try to get candidates based on the requirements' types. + For VCS, local, and archive requirements, the one-and-only match is + returned, and for a "named" requirement, the index(es) should be + consulted to find concrete candidates for this requirement. + + The return value should produce candidates ordered by preference; the + most preferred candidate should come first. The return type may be one + of the following: + + * A callable that returns an iterator that yields candidates. + * An collection of candidates. + * An iterable of candidates. This will be consumed immediately into a + list of candidates. + """ + raise NotImplementedError + + def is_satisfied_by(self, requirement, candidate): + """Whether the given requirement can be satisfied by a candidate. + + The candidate is guarenteed to have been generated from the + requirement. + + A boolean should be returned to indicate whether ``candidate`` is a + viable solution to the requirement. + """ + raise NotImplementedError + + def get_dependencies(self, candidate): + """Get dependencies of a candidate. + + This should return a collection of requirements that `candidate` + specifies as its dependencies. + """ + raise NotImplementedError + + +class AbstractResolver(object): + """The thing that performs the actual resolution work.""" + + base_exception = Exception + + def __init__(self, provider, reporter): + self.provider = provider + self.reporter = reporter + + def resolve(self, requirements, **kwargs): + """Take a collection of constraints, spit out the resolution result. + + This returns a representation of the final resolution state, with one + guarenteed attribute ``mapping`` that contains resolved candidates as + values. The keys are their respective identifiers. + + :param requirements: A collection of constraints. + :param kwargs: Additional keyword arguments that subclasses may accept. + + :raises: ``self.base_exception`` or its subclass. + """ + raise NotImplementedError diff --git a/env-llmeval/lib/python3.10/site-packages/pip/_vendor/resolvelib/reporters.py b/env-llmeval/lib/python3.10/site-packages/pip/_vendor/resolvelib/reporters.py new file mode 100644 index 0000000000000000000000000000000000000000..6695480fff4c87608ac2002dfb341f90ed1a5ce4 --- /dev/null +++ b/env-llmeval/lib/python3.10/site-packages/pip/_vendor/resolvelib/reporters.py @@ -0,0 +1,43 @@ +class BaseReporter(object): + """Delegate class to provider progress reporting for the resolver.""" + + def starting(self): + """Called before the resolution actually starts.""" + + def starting_round(self, index): + """Called before each round of resolution starts. + + The index is zero-based. + """ + + def ending_round(self, index, state): + """Called before each round of resolution ends. + + This is NOT called if the resolution ends at this round. Use `ending` + if you want to report finalization. The index is zero-based. + """ + + def ending(self, state): + """Called before the resolution ends successfully.""" + + def adding_requirement(self, requirement, parent): + """Called when adding a new requirement into the resolve criteria. + + :param requirement: The additional requirement to be applied to filter + the available candidaites. + :param parent: The candidate that requires ``requirement`` as a + dependency, or None if ``requirement`` is one of the root + requirements passed in from ``Resolver.resolve()``. + """ + + def resolving_conflicts(self, causes): + """Called when starting to attempt requirement conflict resolution. + + :param causes: The information on the collision that caused the backtracking. + """ + + def backtracking(self, candidate): + """Called when rejecting a candidate during backtracking.""" + + def pinning(self, candidate): + """Called when adding a candidate to the potential solution.""" diff --git a/env-llmeval/lib/python3.10/site-packages/pip/_vendor/resolvelib/resolvers.py b/env-llmeval/lib/python3.10/site-packages/pip/_vendor/resolvelib/resolvers.py new file mode 100644 index 0000000000000000000000000000000000000000..787681b03e9ec2fd4490de10cdc95e58c893c8b5 --- /dev/null +++ b/env-llmeval/lib/python3.10/site-packages/pip/_vendor/resolvelib/resolvers.py @@ -0,0 +1,482 @@ +import collections +import operator + +from .providers import AbstractResolver +from .structs import DirectedGraph, IteratorMapping, build_iter_view + +RequirementInformation = collections.namedtuple( + "RequirementInformation", ["requirement", "parent"] +) + + +class ResolverException(Exception): + """A base class for all exceptions raised by this module. + + Exceptions derived by this class should all be handled in this module. Any + bubbling pass the resolver should be treated as a bug. + """ + + +class RequirementsConflicted(ResolverException): + def __init__(self, criterion): + super(RequirementsConflicted, self).__init__(criterion) + self.criterion = criterion + + def __str__(self): + return "Requirements conflict: {}".format( + ", ".join(repr(r) for r in self.criterion.iter_requirement()), + ) + + +class InconsistentCandidate(ResolverException): + def __init__(self, candidate, criterion): + super(InconsistentCandidate, self).__init__(candidate, criterion) + self.candidate = candidate + self.criterion = criterion + + def __str__(self): + return "Provided candidate {!r} does not satisfy {}".format( + self.candidate, + ", ".join(repr(r) for r in self.criterion.iter_requirement()), + ) + + +class Criterion(object): + """Representation of possible resolution results of a package. + + This holds three attributes: + + * `information` is a collection of `RequirementInformation` pairs. + Each pair is a requirement contributing to this criterion, and the + candidate that provides the requirement. + * `incompatibilities` is a collection of all known not-to-work candidates + to exclude from consideration. + * `candidates` is a collection containing all possible candidates deducted + from the union of contributing requirements and known incompatibilities. + It should never be empty, except when the criterion is an attribute of a + raised `RequirementsConflicted` (in which case it is always empty). + + .. note:: + This class is intended to be externally immutable. **Do not** mutate + any of its attribute containers. + """ + + def __init__(self, candidates, information, incompatibilities): + self.candidates = candidates + self.information = information + self.incompatibilities = incompatibilities + + def __repr__(self): + requirements = ", ".join( + "({!r}, via={!r})".format(req, parent) + for req, parent in self.information + ) + return "Criterion({})".format(requirements) + + def iter_requirement(self): + return (i.requirement for i in self.information) + + def iter_parent(self): + return (i.parent for i in self.information) + + +class ResolutionError(ResolverException): + pass + + +class ResolutionImpossible(ResolutionError): + def __init__(self, causes): + super(ResolutionImpossible, self).__init__(causes) + # causes is a list of RequirementInformation objects + self.causes = causes + + +class ResolutionTooDeep(ResolutionError): + def __init__(self, round_count): + super(ResolutionTooDeep, self).__init__(round_count) + self.round_count = round_count + + +# Resolution state in a round. +State = collections.namedtuple("State", "mapping criteria backtrack_causes") + + +class Resolution(object): + """Stateful resolution object. + + This is designed as a one-off object that holds information to kick start + the resolution process, and holds the results afterwards. + """ + + def __init__(self, provider, reporter): + self._p = provider + self._r = reporter + self._states = [] + + @property + def state(self): + try: + return self._states[-1] + except IndexError: + raise AttributeError("state") + + def _push_new_state(self): + """Push a new state into history. + + This new state will be used to hold resolution results of the next + coming round. + """ + base = self._states[-1] + state = State( + mapping=base.mapping.copy(), + criteria=base.criteria.copy(), + backtrack_causes=base.backtrack_causes[:], + ) + self._states.append(state) + + def _add_to_criteria(self, criteria, requirement, parent): + self._r.adding_requirement(requirement=requirement, parent=parent) + + identifier = self._p.identify(requirement_or_candidate=requirement) + criterion = criteria.get(identifier) + if criterion: + incompatibilities = list(criterion.incompatibilities) + else: + incompatibilities = [] + + matches = self._p.find_matches( + identifier=identifier, + requirements=IteratorMapping( + criteria, + operator.methodcaller("iter_requirement"), + {identifier: [requirement]}, + ), + incompatibilities=IteratorMapping( + criteria, + operator.attrgetter("incompatibilities"), + {identifier: incompatibilities}, + ), + ) + + if criterion: + information = list(criterion.information) + information.append(RequirementInformation(requirement, parent)) + else: + information = [RequirementInformation(requirement, parent)] + + criterion = Criterion( + candidates=build_iter_view(matches), + information=information, + incompatibilities=incompatibilities, + ) + if not criterion.candidates: + raise RequirementsConflicted(criterion) + criteria[identifier] = criterion + + def _get_preference(self, name): + return self._p.get_preference( + identifier=name, + resolutions=self.state.mapping, + candidates=IteratorMapping( + self.state.criteria, + operator.attrgetter("candidates"), + ), + information=IteratorMapping( + self.state.criteria, + operator.attrgetter("information"), + ), + backtrack_causes=self.state.backtrack_causes, + ) + + def _is_current_pin_satisfying(self, name, criterion): + try: + current_pin = self.state.mapping[name] + except KeyError: + return False + return all( + self._p.is_satisfied_by(requirement=r, candidate=current_pin) + for r in criterion.iter_requirement() + ) + + def _get_updated_criteria(self, candidate): + criteria = self.state.criteria.copy() + for requirement in self._p.get_dependencies(candidate=candidate): + self._add_to_criteria(criteria, requirement, parent=candidate) + return criteria + + def _attempt_to_pin_criterion(self, name): + criterion = self.state.criteria[name] + + causes = [] + for candidate in criterion.candidates: + try: + criteria = self._get_updated_criteria(candidate) + except RequirementsConflicted as e: + causes.append(e.criterion) + continue + + # Check the newly-pinned candidate actually works. This should + # always pass under normal circumstances, but in the case of a + # faulty provider, we will raise an error to notify the implementer + # to fix find_matches() and/or is_satisfied_by(). + satisfied = all( + self._p.is_satisfied_by(requirement=r, candidate=candidate) + for r in criterion.iter_requirement() + ) + if not satisfied: + raise InconsistentCandidate(candidate, criterion) + + self._r.pinning(candidate=candidate) + self.state.criteria.update(criteria) + + # Put newly-pinned candidate at the end. This is essential because + # backtracking looks at this mapping to get the last pin. + self.state.mapping.pop(name, None) + self.state.mapping[name] = candidate + + return [] + + # All candidates tried, nothing works. This criterion is a dead + # end, signal for backtracking. + return causes + + def _backtrack(self): + """Perform backtracking. + + When we enter here, the stack is like this:: + + [ state Z ] + [ state Y ] + [ state X ] + .... earlier states are irrelevant. + + 1. No pins worked for Z, so it does not have a pin. + 2. We want to reset state Y to unpinned, and pin another candidate. + 3. State X holds what state Y was before the pin, but does not + have the incompatibility information gathered in state Y. + + Each iteration of the loop will: + + 1. Discard Z. + 2. Discard Y but remember its incompatibility information gathered + previously, and the failure we're dealing with right now. + 3. Push a new state Y' based on X, and apply the incompatibility + information from Y to Y'. + 4a. If this causes Y' to conflict, we need to backtrack again. Make Y' + the new Z and go back to step 2. + 4b. If the incompatibilities apply cleanly, end backtracking. + """ + while len(self._states) >= 3: + # Remove the state that triggered backtracking. + del self._states[-1] + + # Retrieve the last candidate pin and known incompatibilities. + broken_state = self._states.pop() + name, candidate = broken_state.mapping.popitem() + incompatibilities_from_broken = [ + (k, list(v.incompatibilities)) + for k, v in broken_state.criteria.items() + ] + + # Also mark the newly known incompatibility. + incompatibilities_from_broken.append((name, [candidate])) + + self._r.backtracking(candidate=candidate) + + # Create a new state from the last known-to-work one, and apply + # the previously gathered incompatibility information. + def _patch_criteria(): + for k, incompatibilities in incompatibilities_from_broken: + if not incompatibilities: + continue + try: + criterion = self.state.criteria[k] + except KeyError: + continue + matches = self._p.find_matches( + identifier=k, + requirements=IteratorMapping( + self.state.criteria, + operator.methodcaller("iter_requirement"), + ), + incompatibilities=IteratorMapping( + self.state.criteria, + operator.attrgetter("incompatibilities"), + {k: incompatibilities}, + ), + ) + candidates = build_iter_view(matches) + if not candidates: + return False + incompatibilities.extend(criterion.incompatibilities) + self.state.criteria[k] = Criterion( + candidates=candidates, + information=list(criterion.information), + incompatibilities=incompatibilities, + ) + return True + + self._push_new_state() + success = _patch_criteria() + + # It works! Let's work on this new state. + if success: + return True + + # State does not work after applying known incompatibilities. + # Try the still previous state. + + # No way to backtrack anymore. + return False + + def resolve(self, requirements, max_rounds): + if self._states: + raise RuntimeError("already resolved") + + self._r.starting() + + # Initialize the root state. + self._states = [ + State( + mapping=collections.OrderedDict(), + criteria={}, + backtrack_causes=[], + ) + ] + for r in requirements: + try: + self._add_to_criteria(self.state.criteria, r, parent=None) + except RequirementsConflicted as e: + raise ResolutionImpossible(e.criterion.information) + + # The root state is saved as a sentinel so the first ever pin can have + # something to backtrack to if it fails. The root state is basically + # pinning the virtual "root" package in the graph. + self._push_new_state() + + for round_index in range(max_rounds): + self._r.starting_round(index=round_index) + + unsatisfied_names = [ + key + for key, criterion in self.state.criteria.items() + if not self._is_current_pin_satisfying(key, criterion) + ] + + # All criteria are accounted for. Nothing more to pin, we are done! + if not unsatisfied_names: + self._r.ending(state=self.state) + return self.state + + # Choose the most preferred unpinned criterion to try. + name = min(unsatisfied_names, key=self._get_preference) + failure_causes = self._attempt_to_pin_criterion(name) + + if failure_causes: + causes = [i for c in failure_causes for i in c.information] + # Backtrack if pinning fails. The backtrack process puts us in + # an unpinned state, so we can work on it in the next round. + self._r.resolving_conflicts(causes=causes) + success = self._backtrack() + self.state.backtrack_causes[:] = causes + + # Dead ends everywhere. Give up. + if not success: + raise ResolutionImpossible(self.state.backtrack_causes) + else: + # Pinning was successful. Push a new state to do another pin. + self._push_new_state() + + self._r.ending_round(index=round_index, state=self.state) + + raise ResolutionTooDeep(max_rounds) + + +def _has_route_to_root(criteria, key, all_keys, connected): + if key in connected: + return True + if key not in criteria: + return False + for p in criteria[key].iter_parent(): + try: + pkey = all_keys[id(p)] + except KeyError: + continue + if pkey in connected: + connected.add(key) + return True + if _has_route_to_root(criteria, pkey, all_keys, connected): + connected.add(key) + return True + return False + + +Result = collections.namedtuple("Result", "mapping graph criteria") + + +def _build_result(state): + mapping = state.mapping + all_keys = {id(v): k for k, v in mapping.items()} + all_keys[id(None)] = None + + graph = DirectedGraph() + graph.add(None) # Sentinel as root dependencies' parent. + + connected = {None} + for key, criterion in state.criteria.items(): + if not _has_route_to_root(state.criteria, key, all_keys, connected): + continue + if key not in graph: + graph.add(key) + for p in criterion.iter_parent(): + try: + pkey = all_keys[id(p)] + except KeyError: + continue + if pkey not in graph: + graph.add(pkey) + graph.connect(pkey, key) + + return Result( + mapping={k: v for k, v in mapping.items() if k in connected}, + graph=graph, + criteria=state.criteria, + ) + + +class Resolver(AbstractResolver): + """The thing that performs the actual resolution work.""" + + base_exception = ResolverException + + def resolve(self, requirements, max_rounds=100): + """Take a collection of constraints, spit out the resolution result. + + The return value is a representation to the final resolution result. It + is a tuple subclass with three public members: + + * `mapping`: A dict of resolved candidates. Each key is an identifier + of a requirement (as returned by the provider's `identify` method), + and the value is the resolved candidate. + * `graph`: A `DirectedGraph` instance representing the dependency tree. + The vertices are keys of `mapping`, and each edge represents *why* + a particular package is included. A special vertex `None` is + included to represent parents of user-supplied requirements. + * `criteria`: A dict of "criteria" that hold detailed information on + how edges in the graph are derived. Each key is an identifier of a + requirement, and the value is a `Criterion` instance. + + The following exceptions may be raised if a resolution cannot be found: + + * `ResolutionImpossible`: A resolution cannot be found for the given + combination of requirements. The `causes` attribute of the + exception is a list of (requirement, parent), giving the + requirements that could not be satisfied. + * `ResolutionTooDeep`: The dependency tree is too deeply nested and + the resolver gave up. This is usually caused by a circular + dependency, but you can try to resolve this by increasing the + `max_rounds` argument. + """ + resolution = Resolution(self.provider, self.reporter) + state = resolution.resolve(requirements, max_rounds=max_rounds) + return _build_result(state) diff --git a/env-llmeval/lib/python3.10/site-packages/pip/_vendor/resolvelib/structs.py b/env-llmeval/lib/python3.10/site-packages/pip/_vendor/resolvelib/structs.py new file mode 100644 index 0000000000000000000000000000000000000000..93d1568bd4d5d63281b798b09cedbafdfaa158ee --- /dev/null +++ b/env-llmeval/lib/python3.10/site-packages/pip/_vendor/resolvelib/structs.py @@ -0,0 +1,165 @@ +import itertools + +from .compat import collections_abc + + +class DirectedGraph(object): + """A graph structure with directed edges.""" + + def __init__(self): + self._vertices = set() + self._forwards = {} # -> Set[] + self._backwards = {} # -> Set[] + + def __iter__(self): + return iter(self._vertices) + + def __len__(self): + return len(self._vertices) + + def __contains__(self, key): + return key in self._vertices + + def copy(self): + """Return a shallow copy of this graph.""" + other = DirectedGraph() + other._vertices = set(self._vertices) + other._forwards = {k: set(v) for k, v in self._forwards.items()} + other._backwards = {k: set(v) for k, v in self._backwards.items()} + return other + + def add(self, key): + """Add a new vertex to the graph.""" + if key in self._vertices: + raise ValueError("vertex exists") + self._vertices.add(key) + self._forwards[key] = set() + self._backwards[key] = set() + + def remove(self, key): + """Remove a vertex from the graph, disconnecting all edges from/to it.""" + self._vertices.remove(key) + for f in self._forwards.pop(key): + self._backwards[f].remove(key) + for t in self._backwards.pop(key): + self._forwards[t].remove(key) + + def connected(self, f, t): + return f in self._backwards[t] and t in self._forwards[f] + + def connect(self, f, t): + """Connect two existing vertices. + + Nothing happens if the vertices are already connected. + """ + if t not in self._vertices: + raise KeyError(t) + self._forwards[f].add(t) + self._backwards[t].add(f) + + def iter_edges(self): + for f, children in self._forwards.items(): + for t in children: + yield f, t + + def iter_children(self, key): + return iter(self._forwards[key]) + + def iter_parents(self, key): + return iter(self._backwards[key]) + + +class IteratorMapping(collections_abc.Mapping): + def __init__(self, mapping, accessor, appends=None): + self._mapping = mapping + self._accessor = accessor + self._appends = appends or {} + + def __repr__(self): + return "IteratorMapping({!r}, {!r}, {!r})".format( + self._mapping, + self._accessor, + self._appends, + ) + + def __bool__(self): + return bool(self._mapping or self._appends) + + __nonzero__ = __bool__ # XXX: Python 2. + + def __contains__(self, key): + return key in self._mapping or key in self._appends + + def __getitem__(self, k): + try: + v = self._mapping[k] + except KeyError: + return iter(self._appends[k]) + return itertools.chain(self._accessor(v), self._appends.get(k, ())) + + def __iter__(self): + more = (k for k in self._appends if k not in self._mapping) + return itertools.chain(self._mapping, more) + + def __len__(self): + more = sum(1 for k in self._appends if k not in self._mapping) + return len(self._mapping) + more + + +class _FactoryIterableView(object): + """Wrap an iterator factory returned by `find_matches()`. + + Calling `iter()` on this class would invoke the underlying iterator + factory, making it a "collection with ordering" that can be iterated + through multiple times, but lacks random access methods presented in + built-in Python sequence types. + """ + + def __init__(self, factory): + self._factory = factory + + def __repr__(self): + return "{}({})".format(type(self).__name__, list(self._factory())) + + def __bool__(self): + try: + next(self._factory()) + except StopIteration: + return False + return True + + __nonzero__ = __bool__ # XXX: Python 2. + + def __iter__(self): + return self._factory() + + +class _SequenceIterableView(object): + """Wrap an iterable returned by find_matches(). + + This is essentially just a proxy to the underlying sequence that provides + the same interface as `_FactoryIterableView`. + """ + + def __init__(self, sequence): + self._sequence = sequence + + def __repr__(self): + return "{}({})".format(type(self).__name__, self._sequence) + + def __bool__(self): + return bool(self._sequence) + + __nonzero__ = __bool__ # XXX: Python 2. + + def __iter__(self): + return iter(self._sequence) + + +def build_iter_view(matches): + """Build an iterable view from the value returned by `find_matches()`.""" + if callable(matches): + return _FactoryIterableView(matches) + if not isinstance(matches, collections_abc.Sequence): + matches = list(matches) + return _SequenceIterableView(matches) diff --git a/env-llmeval/lib/python3.10/site-packages/pip/_vendor/webencodings/__init__.py b/env-llmeval/lib/python3.10/site-packages/pip/_vendor/webencodings/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..d21d697c887bed1f8ab7f36d10185e986d9f1e54 --- /dev/null +++ b/env-llmeval/lib/python3.10/site-packages/pip/_vendor/webencodings/__init__.py @@ -0,0 +1,342 @@ +# coding: utf-8 +""" + + webencodings + ~~~~~~~~~~~~ + + This is a Python implementation of the `WHATWG Encoding standard + `. See README for details. + + :copyright: Copyright 2012 by Simon Sapin + :license: BSD, see LICENSE for details. + +""" + +from __future__ import unicode_literals + +import codecs + +from .labels import LABELS + + +VERSION = '0.5.1' + + +# Some names in Encoding are not valid Python aliases. Remap these. +PYTHON_NAMES = { + 'iso-8859-8-i': 'iso-8859-8', + 'x-mac-cyrillic': 'mac-cyrillic', + 'macintosh': 'mac-roman', + 'windows-874': 'cp874'} + +CACHE = {} + + +def ascii_lower(string): + r"""Transform (only) ASCII letters to lower case: A-Z is mapped to a-z. + + :param string: An Unicode string. + :returns: A new Unicode string. + + This is used for `ASCII case-insensitive + `_ + matching of encoding labels. + The same matching is also used, among other things, + for `CSS keywords `_. + + This is different from the :meth:`~py:str.lower` method of Unicode strings + which also affect non-ASCII characters, + sometimes mapping them into the ASCII range: + + >>> keyword = u'Bac\N{KELVIN SIGN}ground' + >>> assert keyword.lower() == u'background' + >>> assert ascii_lower(keyword) != keyword.lower() + >>> assert ascii_lower(keyword) == u'bac\N{KELVIN SIGN}ground' + + """ + # This turns out to be faster than unicode.translate() + return string.encode('utf8').lower().decode('utf8') + + +def lookup(label): + """ + Look for an encoding by its label. + This is the spec’s `get an encoding + `_ algorithm. + Supported labels are listed there. + + :param label: A string. + :returns: + An :class:`Encoding` object, or :obj:`None` for an unknown label. + + """ + # Only strip ASCII whitespace: U+0009, U+000A, U+000C, U+000D, and U+0020. + label = ascii_lower(label.strip('\t\n\f\r ')) + name = LABELS.get(label) + if name is None: + return None + encoding = CACHE.get(name) + if encoding is None: + if name == 'x-user-defined': + from .x_user_defined import codec_info + else: + python_name = PYTHON_NAMES.get(name, name) + # Any python_name value that gets to here should be valid. + codec_info = codecs.lookup(python_name) + encoding = Encoding(name, codec_info) + CACHE[name] = encoding + return encoding + + +def _get_encoding(encoding_or_label): + """ + Accept either an encoding object or label. + + :param encoding: An :class:`Encoding` object or a label string. + :returns: An :class:`Encoding` object. + :raises: :exc:`~exceptions.LookupError` for an unknown label. + + """ + if hasattr(encoding_or_label, 'codec_info'): + return encoding_or_label + + encoding = lookup(encoding_or_label) + if encoding is None: + raise LookupError('Unknown encoding label: %r' % encoding_or_label) + return encoding + + +class Encoding(object): + """Reresents a character encoding such as UTF-8, + that can be used for decoding or encoding. + + .. attribute:: name + + Canonical name of the encoding + + .. attribute:: codec_info + + The actual implementation of the encoding, + a stdlib :class:`~codecs.CodecInfo` object. + See :func:`codecs.register`. + + """ + def __init__(self, name, codec_info): + self.name = name + self.codec_info = codec_info + + def __repr__(self): + return '' % self.name + + +#: The UTF-8 encoding. Should be used for new content and formats. +UTF8 = lookup('utf-8') + +_UTF16LE = lookup('utf-16le') +_UTF16BE = lookup('utf-16be') + + +def decode(input, fallback_encoding, errors='replace'): + """ + Decode a single string. + + :param input: A byte string + :param fallback_encoding: + An :class:`Encoding` object or a label string. + The encoding to use if :obj:`input` does note have a BOM. + :param errors: Type of error handling. See :func:`codecs.register`. + :raises: :exc:`~exceptions.LookupError` for an unknown encoding label. + :return: + A ``(output, encoding)`` tuple of an Unicode string + and an :obj:`Encoding`. + + """ + # Fail early if `encoding` is an invalid label. + fallback_encoding = _get_encoding(fallback_encoding) + bom_encoding, input = _detect_bom(input) + encoding = bom_encoding or fallback_encoding + return encoding.codec_info.decode(input, errors)[0], encoding + + +def _detect_bom(input): + """Return (bom_encoding, input), with any BOM removed from the input.""" + if input.startswith(b'\xFF\xFE'): + return _UTF16LE, input[2:] + if input.startswith(b'\xFE\xFF'): + return _UTF16BE, input[2:] + if input.startswith(b'\xEF\xBB\xBF'): + return UTF8, input[3:] + return None, input + + +def encode(input, encoding=UTF8, errors='strict'): + """ + Encode a single string. + + :param input: An Unicode string. + :param encoding: An :class:`Encoding` object or a label string. + :param errors: Type of error handling. See :func:`codecs.register`. + :raises: :exc:`~exceptions.LookupError` for an unknown encoding label. + :return: A byte string. + + """ + return _get_encoding(encoding).codec_info.encode(input, errors)[0] + + +def iter_decode(input, fallback_encoding, errors='replace'): + """ + "Pull"-based decoder. + + :param input: + An iterable of byte strings. + + The input is first consumed just enough to determine the encoding + based on the precense of a BOM, + then consumed on demand when the return value is. + :param fallback_encoding: + An :class:`Encoding` object or a label string. + The encoding to use if :obj:`input` does note have a BOM. + :param errors: Type of error handling. See :func:`codecs.register`. + :raises: :exc:`~exceptions.LookupError` for an unknown encoding label. + :returns: + An ``(output, encoding)`` tuple. + :obj:`output` is an iterable of Unicode strings, + :obj:`encoding` is the :obj:`Encoding` that is being used. + + """ + + decoder = IncrementalDecoder(fallback_encoding, errors) + generator = _iter_decode_generator(input, decoder) + encoding = next(generator) + return generator, encoding + + +def _iter_decode_generator(input, decoder): + """Return a generator that first yields the :obj:`Encoding`, + then yields output chukns as Unicode strings. + + """ + decode = decoder.decode + input = iter(input) + for chunck in input: + output = decode(chunck) + if output: + assert decoder.encoding is not None + yield decoder.encoding + yield output + break + else: + # Input exhausted without determining the encoding + output = decode(b'', final=True) + assert decoder.encoding is not None + yield decoder.encoding + if output: + yield output + return + + for chunck in input: + output = decode(chunck) + if output: + yield output + output = decode(b'', final=True) + if output: + yield output + + +def iter_encode(input, encoding=UTF8, errors='strict'): + """ + “Pull”-based encoder. + + :param input: An iterable of Unicode strings. + :param encoding: An :class:`Encoding` object or a label string. + :param errors: Type of error handling. See :func:`codecs.register`. + :raises: :exc:`~exceptions.LookupError` for an unknown encoding label. + :returns: An iterable of byte strings. + + """ + # Fail early if `encoding` is an invalid label. + encode = IncrementalEncoder(encoding, errors).encode + return _iter_encode_generator(input, encode) + + +def _iter_encode_generator(input, encode): + for chunck in input: + output = encode(chunck) + if output: + yield output + output = encode('', final=True) + if output: + yield output + + +class IncrementalDecoder(object): + """ + “Push”-based decoder. + + :param fallback_encoding: + An :class:`Encoding` object or a label string. + The encoding to use if :obj:`input` does note have a BOM. + :param errors: Type of error handling. See :func:`codecs.register`. + :raises: :exc:`~exceptions.LookupError` for an unknown encoding label. + + """ + def __init__(self, fallback_encoding, errors='replace'): + # Fail early if `encoding` is an invalid label. + self._fallback_encoding = _get_encoding(fallback_encoding) + self._errors = errors + self._buffer = b'' + self._decoder = None + #: The actual :class:`Encoding` that is being used, + #: or :obj:`None` if that is not determined yet. + #: (Ie. if there is not enough input yet to determine + #: if there is a BOM.) + self.encoding = None # Not known yet. + + def decode(self, input, final=False): + """Decode one chunk of the input. + + :param input: A byte string. + :param final: + Indicate that no more input is available. + Must be :obj:`True` if this is the last call. + :returns: An Unicode string. + + """ + decoder = self._decoder + if decoder is not None: + return decoder(input, final) + + input = self._buffer + input + encoding, input = _detect_bom(input) + if encoding is None: + if len(input) < 3 and not final: # Not enough data yet. + self._buffer = input + return '' + else: # No BOM + encoding = self._fallback_encoding + decoder = encoding.codec_info.incrementaldecoder(self._errors).decode + self._decoder = decoder + self.encoding = encoding + return decoder(input, final) + + +class IncrementalEncoder(object): + """ + “Push”-based encoder. + + :param encoding: An :class:`Encoding` object or a label string. + :param errors: Type of error handling. See :func:`codecs.register`. + :raises: :exc:`~exceptions.LookupError` for an unknown encoding label. + + .. method:: encode(input, final=False) + + :param input: An Unicode string. + :param final: + Indicate that no more input is available. + Must be :obj:`True` if this is the last call. + :returns: A byte string. + + """ + def __init__(self, encoding=UTF8, errors='strict'): + encoding = _get_encoding(encoding) + self.encode = encoding.codec_info.incrementalencoder(errors).encode diff --git a/env-llmeval/lib/python3.10/site-packages/pip/_vendor/webencodings/__pycache__/mklabels.cpython-310.pyc b/env-llmeval/lib/python3.10/site-packages/pip/_vendor/webencodings/__pycache__/mklabels.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..7a5a4aa3927d09a2be21e4a0e0acfab2b9dcebf2 Binary files /dev/null and b/env-llmeval/lib/python3.10/site-packages/pip/_vendor/webencodings/__pycache__/mklabels.cpython-310.pyc differ diff --git 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a/env-llmeval/lib/python3.10/site-packages/pip/_vendor/webencodings/labels.py b/env-llmeval/lib/python3.10/site-packages/pip/_vendor/webencodings/labels.py new file mode 100644 index 0000000000000000000000000000000000000000..29cbf91ef79b89971e51db9ddfc3720d8b4db82a --- /dev/null +++ b/env-llmeval/lib/python3.10/site-packages/pip/_vendor/webencodings/labels.py @@ -0,0 +1,231 @@ +""" + + webencodings.labels + ~~~~~~~~~~~~~~~~~~~ + + Map encoding labels to their name. + + :copyright: Copyright 2012 by Simon Sapin + :license: BSD, see LICENSE for details. + +""" + +# XXX Do not edit! +# This file is automatically generated by mklabels.py + +LABELS = { + 'unicode-1-1-utf-8': 'utf-8', + 'utf-8': 'utf-8', + 'utf8': 'utf-8', + '866': 'ibm866', + 'cp866': 'ibm866', + 'csibm866': 'ibm866', + 'ibm866': 'ibm866', + 'csisolatin2': 'iso-8859-2', + 'iso-8859-2': 'iso-8859-2', + 'iso-ir-101': 'iso-8859-2', + 'iso8859-2': 'iso-8859-2', + 'iso88592': 'iso-8859-2', + 'iso_8859-2': 'iso-8859-2', + 'iso_8859-2:1987': 'iso-8859-2', + 'l2': 'iso-8859-2', + 'latin2': 'iso-8859-2', + 'csisolatin3': 'iso-8859-3', + 'iso-8859-3': 'iso-8859-3', + 'iso-ir-109': 'iso-8859-3', + 'iso8859-3': 'iso-8859-3', + 'iso88593': 'iso-8859-3', + 'iso_8859-3': 'iso-8859-3', + 'iso_8859-3:1988': 'iso-8859-3', + 'l3': 'iso-8859-3', + 'latin3': 'iso-8859-3', + 'csisolatin4': 'iso-8859-4', + 'iso-8859-4': 'iso-8859-4', + 'iso-ir-110': 'iso-8859-4', + 'iso8859-4': 'iso-8859-4', + 'iso88594': 'iso-8859-4', + 'iso_8859-4': 'iso-8859-4', + 'iso_8859-4:1988': 'iso-8859-4', + 'l4': 'iso-8859-4', + 'latin4': 'iso-8859-4', + 'csisolatincyrillic': 'iso-8859-5', + 'cyrillic': 'iso-8859-5', + 'iso-8859-5': 'iso-8859-5', + 'iso-ir-144': 'iso-8859-5', + 'iso8859-5': 'iso-8859-5', + 'iso88595': 'iso-8859-5', + 'iso_8859-5': 'iso-8859-5', + 'iso_8859-5:1988': 'iso-8859-5', + 'arabic': 'iso-8859-6', + 'asmo-708': 'iso-8859-6', + 'csiso88596e': 'iso-8859-6', + 'csiso88596i': 'iso-8859-6', + 'csisolatinarabic': 'iso-8859-6', + 'ecma-114': 'iso-8859-6', + 'iso-8859-6': 'iso-8859-6', + 'iso-8859-6-e': 'iso-8859-6', + 'iso-8859-6-i': 'iso-8859-6', + 'iso-ir-127': 'iso-8859-6', + 'iso8859-6': 'iso-8859-6', + 'iso88596': 'iso-8859-6', + 'iso_8859-6': 'iso-8859-6', + 'iso_8859-6:1987': 'iso-8859-6', + 'csisolatingreek': 'iso-8859-7', + 'ecma-118': 'iso-8859-7', + 'elot_928': 'iso-8859-7', + 'greek': 'iso-8859-7', + 'greek8': 'iso-8859-7', + 'iso-8859-7': 'iso-8859-7', + 'iso-ir-126': 'iso-8859-7', + 'iso8859-7': 'iso-8859-7', + 'iso88597': 'iso-8859-7', + 'iso_8859-7': 'iso-8859-7', + 'iso_8859-7:1987': 'iso-8859-7', + 'sun_eu_greek': 'iso-8859-7', + 'csiso88598e': 'iso-8859-8', + 'csisolatinhebrew': 'iso-8859-8', + 'hebrew': 'iso-8859-8', + 'iso-8859-8': 'iso-8859-8', + 'iso-8859-8-e': 'iso-8859-8', + 'iso-ir-138': 'iso-8859-8', + 'iso8859-8': 'iso-8859-8', + 'iso88598': 'iso-8859-8', + 'iso_8859-8': 'iso-8859-8', + 'iso_8859-8:1988': 'iso-8859-8', + 'visual': 'iso-8859-8', + 'csiso88598i': 'iso-8859-8-i', + 'iso-8859-8-i': 'iso-8859-8-i', + 'logical': 'iso-8859-8-i', + 'csisolatin6': 'iso-8859-10', + 'iso-8859-10': 'iso-8859-10', + 'iso-ir-157': 'iso-8859-10', + 'iso8859-10': 'iso-8859-10', + 'iso885910': 'iso-8859-10', + 'l6': 'iso-8859-10', + 'latin6': 'iso-8859-10', + 'iso-8859-13': 'iso-8859-13', + 'iso8859-13': 'iso-8859-13', + 'iso885913': 'iso-8859-13', + 'iso-8859-14': 'iso-8859-14', + 'iso8859-14': 'iso-8859-14', + 'iso885914': 'iso-8859-14', + 'csisolatin9': 'iso-8859-15', + 'iso-8859-15': 'iso-8859-15', + 'iso8859-15': 'iso-8859-15', + 'iso885915': 'iso-8859-15', + 'iso_8859-15': 'iso-8859-15', + 'l9': 'iso-8859-15', + 'iso-8859-16': 'iso-8859-16', + 'cskoi8r': 'koi8-r', + 'koi': 'koi8-r', + 'koi8': 'koi8-r', + 'koi8-r': 'koi8-r', + 'koi8_r': 'koi8-r', + 'koi8-u': 'koi8-u', + 'csmacintosh': 'macintosh', + 'mac': 'macintosh', + 'macintosh': 'macintosh', + 'x-mac-roman': 'macintosh', + 'dos-874': 'windows-874', + 'iso-8859-11': 'windows-874', + 'iso8859-11': 'windows-874', + 'iso885911': 'windows-874', + 'tis-620': 'windows-874', + 'windows-874': 'windows-874', + 'cp1250': 'windows-1250', + 'windows-1250': 'windows-1250', + 'x-cp1250': 'windows-1250', + 'cp1251': 'windows-1251', + 'windows-1251': 'windows-1251', + 'x-cp1251': 'windows-1251', + 'ansi_x3.4-1968': 'windows-1252', + 'ascii': 'windows-1252', + 'cp1252': 'windows-1252', + 'cp819': 'windows-1252', + 'csisolatin1': 'windows-1252', + 'ibm819': 'windows-1252', + 'iso-8859-1': 'windows-1252', + 'iso-ir-100': 'windows-1252', + 'iso8859-1': 'windows-1252', + 'iso88591': 'windows-1252', + 'iso_8859-1': 'windows-1252', + 'iso_8859-1:1987': 'windows-1252', + 'l1': 'windows-1252', + 'latin1': 'windows-1252', + 'us-ascii': 'windows-1252', + 'windows-1252': 'windows-1252', + 'x-cp1252': 'windows-1252', + 'cp1253': 'windows-1253', + 'windows-1253': 'windows-1253', + 'x-cp1253': 'windows-1253', + 'cp1254': 'windows-1254', + 'csisolatin5': 'windows-1254', + 'iso-8859-9': 'windows-1254', + 'iso-ir-148': 'windows-1254', + 'iso8859-9': 'windows-1254', + 'iso88599': 'windows-1254', + 'iso_8859-9': 'windows-1254', + 'iso_8859-9:1989': 'windows-1254', + 'l5': 'windows-1254', + 'latin5': 'windows-1254', + 'windows-1254': 'windows-1254', + 'x-cp1254': 'windows-1254', + 'cp1255': 'windows-1255', + 'windows-1255': 'windows-1255', + 'x-cp1255': 'windows-1255', + 'cp1256': 'windows-1256', + 'windows-1256': 'windows-1256', + 'x-cp1256': 'windows-1256', + 'cp1257': 'windows-1257', + 'windows-1257': 'windows-1257', + 'x-cp1257': 'windows-1257', + 'cp1258': 'windows-1258', + 'windows-1258': 'windows-1258', + 'x-cp1258': 'windows-1258', + 'x-mac-cyrillic': 'x-mac-cyrillic', + 'x-mac-ukrainian': 'x-mac-cyrillic', + 'chinese': 'gbk', + 'csgb2312': 'gbk', + 'csiso58gb231280': 'gbk', + 'gb2312': 'gbk', + 'gb_2312': 'gbk', + 'gb_2312-80': 'gbk', + 'gbk': 'gbk', + 'iso-ir-58': 'gbk', + 'x-gbk': 'gbk', + 'gb18030': 'gb18030', + 'hz-gb-2312': 'hz-gb-2312', + 'big5': 'big5', + 'big5-hkscs': 'big5', + 'cn-big5': 'big5', + 'csbig5': 'big5', + 'x-x-big5': 'big5', + 'cseucpkdfmtjapanese': 'euc-jp', + 'euc-jp': 'euc-jp', + 'x-euc-jp': 'euc-jp', + 'csiso2022jp': 'iso-2022-jp', + 'iso-2022-jp': 'iso-2022-jp', + 'csshiftjis': 'shift_jis', + 'ms_kanji': 'shift_jis', + 'shift-jis': 'shift_jis', + 'shift_jis': 'shift_jis', + 'sjis': 'shift_jis', + 'windows-31j': 'shift_jis', + 'x-sjis': 'shift_jis', + 'cseuckr': 'euc-kr', + 'csksc56011987': 'euc-kr', + 'euc-kr': 'euc-kr', + 'iso-ir-149': 'euc-kr', + 'korean': 'euc-kr', + 'ks_c_5601-1987': 'euc-kr', + 'ks_c_5601-1989': 'euc-kr', + 'ksc5601': 'euc-kr', + 'ksc_5601': 'euc-kr', + 'windows-949': 'euc-kr', + 'csiso2022kr': 'iso-2022-kr', + 'iso-2022-kr': 'iso-2022-kr', + 'utf-16be': 'utf-16be', + 'utf-16': 'utf-16le', + 'utf-16le': 'utf-16le', + 'x-user-defined': 'x-user-defined', +} diff --git a/env-llmeval/lib/python3.10/site-packages/pip/_vendor/webencodings/mklabels.py b/env-llmeval/lib/python3.10/site-packages/pip/_vendor/webencodings/mklabels.py new file mode 100644 index 0000000000000000000000000000000000000000..295dc928ba71fc00caa52708ac70097abe6dc3e4 --- /dev/null +++ b/env-llmeval/lib/python3.10/site-packages/pip/_vendor/webencodings/mklabels.py @@ -0,0 +1,59 @@ +""" + + webencodings.mklabels + ~~~~~~~~~~~~~~~~~~~~~ + + Regenarate the webencodings.labels module. + + :copyright: Copyright 2012 by Simon Sapin + :license: BSD, see LICENSE for details. + +""" + +import json +try: + from urllib import urlopen +except ImportError: + from urllib.request import urlopen + + +def assert_lower(string): + assert string == string.lower() + return string + + +def generate(url): + parts = ['''\ +""" + + webencodings.labels + ~~~~~~~~~~~~~~~~~~~ + + Map encoding labels to their name. + + :copyright: Copyright 2012 by Simon Sapin + :license: BSD, see LICENSE for details. + +""" + +# XXX Do not edit! +# This file is automatically generated by mklabels.py + +LABELS = { +'''] + labels = [ + (repr(assert_lower(label)).lstrip('u'), + repr(encoding['name']).lstrip('u')) + for category in json.loads(urlopen(url).read().decode('ascii')) + for encoding in category['encodings'] + for label in encoding['labels']] + max_len = max(len(label) for label, name in labels) + parts.extend( + ' %s:%s %s,\n' % (label, ' ' * (max_len - len(label)), name) + for label, name in labels) + parts.append('}') + return ''.join(parts) + + +if __name__ == '__main__': + print(generate('http://encoding.spec.whatwg.org/encodings.json')) diff --git a/env-llmeval/lib/python3.10/site-packages/pip/_vendor/webencodings/tests.py b/env-llmeval/lib/python3.10/site-packages/pip/_vendor/webencodings/tests.py new file mode 100644 index 0000000000000000000000000000000000000000..e12c10d033026f09cf97b81d29555e12aae8c762 --- /dev/null +++ b/env-llmeval/lib/python3.10/site-packages/pip/_vendor/webencodings/tests.py @@ -0,0 +1,153 @@ +# coding: utf-8 +""" + + webencodings.tests + ~~~~~~~~~~~~~~~~~~ + + A basic test suite for Encoding. + + :copyright: Copyright 2012 by Simon Sapin + :license: BSD, see LICENSE for details. + +""" + +from __future__ import unicode_literals + +from . import (lookup, LABELS, decode, encode, iter_decode, iter_encode, + IncrementalDecoder, IncrementalEncoder, UTF8) + + +def assert_raises(exception, function, *args, **kwargs): + try: + function(*args, **kwargs) + except exception: + return + else: # pragma: no cover + raise AssertionError('Did not raise %s.' % exception) + + +def test_labels(): + assert lookup('utf-8').name == 'utf-8' + assert lookup('Utf-8').name == 'utf-8' + assert lookup('UTF-8').name == 'utf-8' + assert lookup('utf8').name == 'utf-8' + assert lookup('utf8').name == 'utf-8' + assert lookup('utf8 ').name == 'utf-8' + assert lookup(' \r\nutf8\t').name == 'utf-8' + assert lookup('u8') is None # Python label. + assert lookup('utf-8 ') is None # Non-ASCII white space. + + assert lookup('US-ASCII').name == 'windows-1252' + assert lookup('iso-8859-1').name == 'windows-1252' + assert lookup('latin1').name == 'windows-1252' + assert lookup('LATIN1').name == 'windows-1252' + assert lookup('latin-1') is None + assert lookup('LATİN1') is None # ASCII-only case insensitivity. + + +def test_all_labels(): + for label in LABELS: + assert decode(b'', label) == ('', lookup(label)) + assert encode('', label) == b'' + for repeat in [0, 1, 12]: + output, _ = iter_decode([b''] * repeat, label) + assert list(output) == [] + assert list(iter_encode([''] * repeat, label)) == [] + decoder = IncrementalDecoder(label) + assert decoder.decode(b'') == '' + assert decoder.decode(b'', final=True) == '' + encoder = IncrementalEncoder(label) + assert encoder.encode('') == b'' + assert encoder.encode('', final=True) == b'' + # All encoding names are valid labels too: + for name in set(LABELS.values()): + assert lookup(name).name == name + + +def test_invalid_label(): + assert_raises(LookupError, decode, b'\xEF\xBB\xBF\xc3\xa9', 'invalid') + assert_raises(LookupError, encode, 'é', 'invalid') + assert_raises(LookupError, iter_decode, [], 'invalid') + assert_raises(LookupError, iter_encode, [], 'invalid') + assert_raises(LookupError, IncrementalDecoder, 'invalid') + assert_raises(LookupError, IncrementalEncoder, 'invalid') + + +def test_decode(): + assert decode(b'\x80', 'latin1') == ('€', lookup('latin1')) + assert decode(b'\x80', lookup('latin1')) == ('€', lookup('latin1')) + assert decode(b'\xc3\xa9', 'utf8') == ('é', lookup('utf8')) + assert decode(b'\xc3\xa9', UTF8) == ('é', lookup('utf8')) + assert decode(b'\xc3\xa9', 'ascii') == ('é', lookup('ascii')) + assert decode(b'\xEF\xBB\xBF\xc3\xa9', 'ascii') == ('é', lookup('utf8')) # UTF-8 with BOM + + assert decode(b'\xFE\xFF\x00\xe9', 'ascii') == ('é', lookup('utf-16be')) # UTF-16-BE with BOM + assert decode(b'\xFF\xFE\xe9\x00', 'ascii') == ('é', lookup('utf-16le')) # UTF-16-LE with BOM + assert decode(b'\xFE\xFF\xe9\x00', 'ascii') == ('\ue900', lookup('utf-16be')) + assert decode(b'\xFF\xFE\x00\xe9', 'ascii') == ('\ue900', lookup('utf-16le')) + + assert decode(b'\x00\xe9', 'UTF-16BE') == ('é', lookup('utf-16be')) + assert decode(b'\xe9\x00', 'UTF-16LE') == ('é', lookup('utf-16le')) + assert decode(b'\xe9\x00', 'UTF-16') == ('é', lookup('utf-16le')) + + assert decode(b'\xe9\x00', 'UTF-16BE') == ('\ue900', lookup('utf-16be')) + assert decode(b'\x00\xe9', 'UTF-16LE') == ('\ue900', lookup('utf-16le')) + assert decode(b'\x00\xe9', 'UTF-16') == ('\ue900', lookup('utf-16le')) + + +def test_encode(): + assert encode('é', 'latin1') == b'\xe9' + assert encode('é', 'utf8') == b'\xc3\xa9' + assert encode('é', 'utf8') == b'\xc3\xa9' + assert encode('é', 'utf-16') == b'\xe9\x00' + assert encode('é', 'utf-16le') == b'\xe9\x00' + assert encode('é', 'utf-16be') == b'\x00\xe9' + + +def test_iter_decode(): + def iter_decode_to_string(input, fallback_encoding): + output, _encoding = iter_decode(input, fallback_encoding) + return ''.join(output) + assert iter_decode_to_string([], 'latin1') == '' + assert iter_decode_to_string([b''], 'latin1') == '' + assert iter_decode_to_string([b'\xe9'], 'latin1') == 'é' + assert iter_decode_to_string([b'hello'], 'latin1') == 'hello' + assert iter_decode_to_string([b'he', b'llo'], 'latin1') == 'hello' + assert iter_decode_to_string([b'hell', b'o'], 'latin1') == 'hello' + assert iter_decode_to_string([b'\xc3\xa9'], 'latin1') == 'é' + assert iter_decode_to_string([b'\xEF\xBB\xBF\xc3\xa9'], 'latin1') == 'é' + assert iter_decode_to_string([ + b'\xEF\xBB\xBF', b'\xc3', b'\xa9'], 'latin1') == 'é' + assert iter_decode_to_string([ + b'\xEF\xBB\xBF', b'a', b'\xc3'], 'latin1') == 'a\uFFFD' + assert iter_decode_to_string([ + b'', b'\xEF', b'', b'', b'\xBB\xBF\xc3', b'\xa9'], 'latin1') == 'é' + assert iter_decode_to_string([b'\xEF\xBB\xBF'], 'latin1') == '' + assert iter_decode_to_string([b'\xEF\xBB'], 'latin1') == 'ï»' + assert iter_decode_to_string([b'\xFE\xFF\x00\xe9'], 'latin1') == 'é' + assert iter_decode_to_string([b'\xFF\xFE\xe9\x00'], 'latin1') == 'é' + assert iter_decode_to_string([ + b'', b'\xFF', b'', b'', b'\xFE\xe9', b'\x00'], 'latin1') == 'é' + assert iter_decode_to_string([ + b'', b'h\xe9', b'llo'], 'x-user-defined') == 'h\uF7E9llo' + + +def test_iter_encode(): + assert b''.join(iter_encode([], 'latin1')) == b'' + assert b''.join(iter_encode([''], 'latin1')) == b'' + assert b''.join(iter_encode(['é'], 'latin1')) == b'\xe9' + assert b''.join(iter_encode(['', 'é', '', ''], 'latin1')) == b'\xe9' + assert b''.join(iter_encode(['', 'é', '', ''], 'utf-16')) == b'\xe9\x00' + assert b''.join(iter_encode(['', 'é', '', ''], 'utf-16le')) == b'\xe9\x00' + assert b''.join(iter_encode(['', 'é', '', ''], 'utf-16be')) == b'\x00\xe9' + assert b''.join(iter_encode([ + '', 'h\uF7E9', '', 'llo'], 'x-user-defined')) == b'h\xe9llo' + + +def test_x_user_defined(): + encoded = b'2,\x0c\x0b\x1aO\xd9#\xcb\x0f\xc9\xbbt\xcf\xa8\xca' + decoded = '2,\x0c\x0b\x1aO\uf7d9#\uf7cb\x0f\uf7c9\uf7bbt\uf7cf\uf7a8\uf7ca' + encoded = b'aa' + decoded = 'aa' + assert decode(encoded, 'x-user-defined') == (decoded, lookup('x-user-defined')) + assert encode(decoded, 'x-user-defined') == encoded diff --git a/env-llmeval/lib/python3.10/site-packages/pip/_vendor/webencodings/x_user_defined.py b/env-llmeval/lib/python3.10/site-packages/pip/_vendor/webencodings/x_user_defined.py new file mode 100644 index 0000000000000000000000000000000000000000..d16e326024c05a59548619e13258acad781e0a6d --- /dev/null +++ b/env-llmeval/lib/python3.10/site-packages/pip/_vendor/webencodings/x_user_defined.py @@ -0,0 +1,325 @@ +# coding: utf-8 +""" + + webencodings.x_user_defined + ~~~~~~~~~~~~~~~~~~~~~~~~~~~ + + An implementation of the x-user-defined encoding. + + :copyright: Copyright 2012 by Simon Sapin + :license: BSD, see LICENSE for details. + +""" + +from __future__ import unicode_literals + +import codecs + + +### Codec APIs + +class Codec(codecs.Codec): + + def encode(self, input, errors='strict'): + return codecs.charmap_encode(input, errors, encoding_table) + + def decode(self, input, errors='strict'): + return codecs.charmap_decode(input, errors, decoding_table) + + +class IncrementalEncoder(codecs.IncrementalEncoder): + def encode(self, input, final=False): + return codecs.charmap_encode(input, self.errors, encoding_table)[0] + + +class IncrementalDecoder(codecs.IncrementalDecoder): + def decode(self, input, final=False): + return codecs.charmap_decode(input, self.errors, decoding_table)[0] + + +class StreamWriter(Codec, codecs.StreamWriter): + pass + + +class StreamReader(Codec, codecs.StreamReader): + pass + + +### encodings module API + +codec_info = codecs.CodecInfo( + name='x-user-defined', + encode=Codec().encode, + decode=Codec().decode, + incrementalencoder=IncrementalEncoder, + incrementaldecoder=IncrementalDecoder, + streamreader=StreamReader, + streamwriter=StreamWriter, +) + + +### Decoding Table + +# Python 3: +# for c in range(256): print(' %r' % chr(c if c < 128 else c + 0xF700)) +decoding_table = ( + '\x00' + '\x01' + '\x02' + '\x03' + '\x04' + '\x05' + '\x06' + '\x07' + '\x08' + '\t' + '\n' + '\x0b' + '\x0c' + '\r' + '\x0e' + '\x0f' + '\x10' + '\x11' + '\x12' + '\x13' + '\x14' + '\x15' + '\x16' + '\x17' + '\x18' + '\x19' + '\x1a' + '\x1b' + '\x1c' + '\x1d' + '\x1e' + '\x1f' + ' ' + '!' + '"' + '#' + '$' + '%' + '&' + "'" + '(' + ')' + '*' + '+' + ',' + '-' + '.' + '/' + '0' + '1' + '2' + '3' + '4' + '5' + '6' + '7' + '8' + '9' + ':' + ';' + '<' + '=' + '>' + '?' + '@' + 'A' + 'B' + 'C' + 'D' + 'E' + 'F' + 'G' + 'H' + 'I' + 'J' + 'K' + 'L' + 'M' + 'N' + 'O' + 'P' + 'Q' + 'R' + 'S' + 'T' + 'U' + 'V' + 'W' + 'X' + 'Y' + 'Z' + '[' + '\\' + ']' + '^' + '_' + '`' + 'a' + 'b' + 'c' + 'd' + 'e' + 'f' + 'g' + 'h' + 'i' + 'j' + 'k' + 'l' + 'm' + 'n' + 'o' + 'p' + 'q' + 'r' + 's' + 't' + 'u' + 'v' + 'w' + 'x' + 'y' + 'z' + '{' + '|' + '}' + '~' + '\x7f' + '\uf780' + '\uf781' + '\uf782' + '\uf783' + '\uf784' + '\uf785' + '\uf786' + '\uf787' + '\uf788' + '\uf789' + '\uf78a' + '\uf78b' + '\uf78c' + '\uf78d' + '\uf78e' + '\uf78f' + '\uf790' + '\uf791' + '\uf792' + '\uf793' + '\uf794' + '\uf795' + '\uf796' + '\uf797' + '\uf798' + '\uf799' + '\uf79a' + '\uf79b' + '\uf79c' + '\uf79d' + '\uf79e' + '\uf79f' + '\uf7a0' + '\uf7a1' + '\uf7a2' + '\uf7a3' + '\uf7a4' + '\uf7a5' + '\uf7a6' + '\uf7a7' + '\uf7a8' + '\uf7a9' + '\uf7aa' + '\uf7ab' + '\uf7ac' + '\uf7ad' + '\uf7ae' + '\uf7af' + '\uf7b0' + '\uf7b1' + '\uf7b2' + '\uf7b3' + '\uf7b4' + '\uf7b5' + '\uf7b6' + '\uf7b7' + '\uf7b8' + '\uf7b9' + '\uf7ba' + '\uf7bb' + '\uf7bc' + '\uf7bd' + '\uf7be' + '\uf7bf' + '\uf7c0' + '\uf7c1' + '\uf7c2' + '\uf7c3' + '\uf7c4' + '\uf7c5' + '\uf7c6' + '\uf7c7' + '\uf7c8' + '\uf7c9' + '\uf7ca' + '\uf7cb' + '\uf7cc' + '\uf7cd' + '\uf7ce' + '\uf7cf' + '\uf7d0' + '\uf7d1' + '\uf7d2' + '\uf7d3' + '\uf7d4' + '\uf7d5' + '\uf7d6' + '\uf7d7' + '\uf7d8' + '\uf7d9' + '\uf7da' + '\uf7db' + '\uf7dc' + '\uf7dd' + '\uf7de' + '\uf7df' + '\uf7e0' + '\uf7e1' + '\uf7e2' + '\uf7e3' + '\uf7e4' + '\uf7e5' + '\uf7e6' + '\uf7e7' + '\uf7e8' + '\uf7e9' + '\uf7ea' + '\uf7eb' + '\uf7ec' + '\uf7ed' + '\uf7ee' + '\uf7ef' + '\uf7f0' + '\uf7f1' + '\uf7f2' + '\uf7f3' + '\uf7f4' + '\uf7f5' + '\uf7f6' + '\uf7f7' + '\uf7f8' + '\uf7f9' + '\uf7fa' + '\uf7fb' + '\uf7fc' + '\uf7fd' + '\uf7fe' + '\uf7ff' +) + +### Encoding table +encoding_table = codecs.charmap_build(decoding_table) diff --git a/env-llmeval/lib/python3.10/site-packages/pyarrow-15.0.2.dist-info/INSTALLER b/env-llmeval/lib/python3.10/site-packages/pyarrow-15.0.2.dist-info/INSTALLER new file mode 100644 index 0000000000000000000000000000000000000000..a1b589e38a32041e49332e5e81c2d363dc418d68 --- /dev/null +++ b/env-llmeval/lib/python3.10/site-packages/pyarrow-15.0.2.dist-info/INSTALLER @@ -0,0 +1 @@ +pip diff --git a/env-llmeval/lib/python3.10/site-packages/pyarrow-15.0.2.dist-info/LICENSE.txt b/env-llmeval/lib/python3.10/site-packages/pyarrow-15.0.2.dist-info/LICENSE.txt new file mode 100644 index 0000000000000000000000000000000000000000..0423854567b2671d144ba83a0b1764a9395b8f07 --- /dev/null +++ b/env-llmeval/lib/python3.10/site-packages/pyarrow-15.0.2.dist-info/LICENSE.txt @@ -0,0 +1,2254 @@ + + Apache License + Version 2.0, January 2004 + http://www.apache.org/licenses/ + + TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION + + 1. 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IN NO EVENT SHALL THE +AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +SOFTWARE. diff --git a/env-llmeval/lib/python3.10/site-packages/pyarrow-15.0.2.dist-info/METADATA b/env-llmeval/lib/python3.10/site-packages/pyarrow-15.0.2.dist-info/METADATA new file mode 100644 index 0000000000000000000000000000000000000000..91864c4d0c529f382fcbcff99324384d5090b6cf --- /dev/null +++ b/env-llmeval/lib/python3.10/site-packages/pyarrow-15.0.2.dist-info/METADATA @@ -0,0 +1,81 @@ +Metadata-Version: 2.1 +Name: pyarrow +Version: 15.0.2 +Summary: Python library for Apache Arrow +Home-page: https://arrow.apache.org/ +Maintainer: Apache Arrow Developers +Maintainer-email: dev@arrow.apache.org +License: Apache License, Version 2.0 +Project-URL: Documentation, https://arrow.apache.org/docs/python +Project-URL: Source, https://github.com/apache/arrow +Classifier: License :: OSI Approved :: Apache Software License +Classifier: Programming Language :: Python :: 3.8 +Classifier: Programming Language :: Python :: 3.9 +Classifier: Programming Language :: Python :: 3.10 +Classifier: Programming Language :: Python :: 3.11 +Classifier: Programming Language :: Python :: 3.12 +Requires-Python: >=3.8 +Description-Content-Type: text/markdown +License-File: ../LICENSE.txt +License-File: ../NOTICE.txt +Requires-Dist: numpy <2,>=1.16.6 + + + +## Python library for Apache Arrow + +[![pypi](https://img.shields.io/pypi/v/pyarrow.svg)](https://pypi.org/project/pyarrow/) [![conda-forge](https://img.shields.io/conda/vn/conda-forge/pyarrow.svg)](https://anaconda.org/conda-forge/pyarrow) + +This library provides a Python API for functionality provided by the Arrow C++ +libraries, along with tools for Arrow integration and interoperability with +pandas, NumPy, and other software in the Python ecosystem. + +## Installing + +Across platforms, you can install a recent version of pyarrow with the conda +package manager: + +```shell +conda install pyarrow -c conda-forge +``` + +On Linux, macOS, and Windows, you can also install binary wheels from PyPI with +pip: + +```shell +pip install pyarrow +``` + +If you encounter any issues importing the pip wheels on Windows, you may need +to install the [Visual C++ Redistributable for Visual Studio 2015][6]. + +## Development + +See [Python Development][2] in the documentation subproject. + +### Building the documentation + +See [documentation build instructions][1] in the documentation subproject. + +[1]: https://github.com/apache/arrow/blob/main/docs/source/developers/documentation.rst +[2]: https://github.com/apache/arrow/blob/main/docs/source/developers/python.rst +[3]: https://github.com/pandas-dev/pandas +[5]: https://arrow.apache.org/docs/latest/python/benchmarks.html +[6]: https://www.microsoft.com/en-us/download/details.aspx?id=48145 diff --git a/env-llmeval/lib/python3.10/site-packages/pyarrow-15.0.2.dist-info/NOTICE.txt b/env-llmeval/lib/python3.10/site-packages/pyarrow-15.0.2.dist-info/NOTICE.txt new file mode 100644 index 0000000000000000000000000000000000000000..2089c6fb20358ccf5e3a58ea27a234555b923f6b --- /dev/null +++ b/env-llmeval/lib/python3.10/site-packages/pyarrow-15.0.2.dist-info/NOTICE.txt @@ -0,0 +1,84 @@ +Apache Arrow +Copyright 2016-2024 The Apache Software Foundation + +This product includes software developed at +The Apache Software Foundation (http://www.apache.org/). + +This product includes software from the SFrame project (BSD, 3-clause). +* Copyright (C) 2015 Dato, Inc. +* Copyright (c) 2009 Carnegie Mellon University. + +This product includes software from the Feather project (Apache 2.0) +https://github.com/wesm/feather + +This product includes software from the DyND project (BSD 2-clause) +https://github.com/libdynd + +This product includes software from the LLVM project + * distributed under the University of Illinois Open Source + +This product includes software from the google-lint project + * Copyright (c) 2009 Google Inc. All rights reserved. + +This product includes software from the mman-win32 project + * Copyright https://code.google.com/p/mman-win32/ + * Licensed under the MIT License; + +This product includes software from the LevelDB project + * Copyright (c) 2011 The LevelDB Authors. All rights reserved. + * Use of this source code is governed by a BSD-style license that can be + * Moved from Kudu http://github.com/cloudera/kudu + +This product includes software from the CMake project + * Copyright 2001-2009 Kitware, Inc. + * Copyright 2012-2014 Continuum Analytics, Inc. + * All rights reserved. + +This product includes software from https://github.com/matthew-brett/multibuild (BSD 2-clause) + * Copyright (c) 2013-2016, Matt Terry and Matthew Brett; all rights reserved. + +This product includes software from the Ibis project (Apache 2.0) + * Copyright (c) 2015 Cloudera, Inc. + * https://github.com/cloudera/ibis + +This product includes software from Dremio (Apache 2.0) + * Copyright (C) 2017-2018 Dremio Corporation + * https://github.com/dremio/dremio-oss + +This product includes software from Google Guava (Apache 2.0) + * Copyright (C) 2007 The Guava Authors + * https://github.com/google/guava + +This product include software from CMake (BSD 3-Clause) + * CMake - Cross Platform Makefile Generator + * Copyright 2000-2019 Kitware, Inc. and Contributors + +The web site includes files generated by Jekyll. + +-------------------------------------------------------------------------------- + +This product includes code from Apache Kudu, which includes the following in +its NOTICE file: + + Apache Kudu + Copyright 2016 The Apache Software Foundation + + This product includes software developed at + The Apache Software Foundation (http://www.apache.org/). + + Portions of this software were developed at + Cloudera, Inc (http://www.cloudera.com/). + +-------------------------------------------------------------------------------- + +This product includes code from Apache ORC, which includes the following in +its NOTICE file: + + Apache ORC + Copyright 2013-2019 The Apache Software Foundation + + This product includes software developed by The Apache Software + Foundation (http://www.apache.org/). + + This product includes software developed by 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b/env-llmeval/lib/python3.10/site-packages/transformers-4.39.3.dist-info/INSTALLER new file mode 100644 index 0000000000000000000000000000000000000000..a1b589e38a32041e49332e5e81c2d363dc418d68 --- /dev/null +++ b/env-llmeval/lib/python3.10/site-packages/transformers-4.39.3.dist-info/INSTALLER @@ -0,0 +1 @@ +pip diff --git a/env-llmeval/lib/python3.10/site-packages/transformers-4.39.3.dist-info/LICENSE b/env-llmeval/lib/python3.10/site-packages/transformers-4.39.3.dist-info/LICENSE new file mode 100644 index 0000000000000000000000000000000000000000..68b7d66c97d66c58de883ed0c451af2b3183e6f3 --- /dev/null +++ b/env-llmeval/lib/python3.10/site-packages/transformers-4.39.3.dist-info/LICENSE @@ -0,0 +1,203 @@ +Copyright 2018- The Hugging Face team. 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+ + + + Hugging Face Transformers Library + +
+
+

+ +

+ + Build + + + GitHub + + + Documentation + + + GitHub release + + + Contributor Covenant + + DOI +

+ +

+

+ English | + 简体中文 | + 繁體中文 | + 한국어 | + Español | + 日本語 | + हिन्दी | + Русский | + Рortuguês | + తెలుగు | + Français | + Deutsch | + Tiếng Việt | +

+

+ +

+

State-of-the-art Machine Learning for JAX, PyTorch and TensorFlow

+

+ +

+ +

+ +🤗 Transformers provides thousands of pretrained models to perform tasks on different modalities such as text, vision, and audio. + +These models can be applied on: + +* 📝 Text, for tasks like text classification, information extraction, question answering, summarization, translation, and text generation, in over 100 languages. +* 🖼️ Images, for tasks like image classification, object detection, and segmentation. +* 🗣️ Audio, for tasks like speech recognition and audio classification. + +Transformer models can also perform tasks on **several modalities combined**, such as table question answering, optical character recognition, information extraction from scanned documents, video classification, and visual question answering. + +🤗 Transformers provides APIs to quickly download and use those pretrained models on a given text, fine-tune them on your own datasets and then share them with the community on our [model hub](https://huggingface.co/models). At the same time, each python module defining an architecture is fully standalone and can be modified to enable quick research experiments. + +🤗 Transformers is backed by the three most popular deep learning libraries — [Jax](https://jax.readthedocs.io/en/latest/), [PyTorch](https://pytorch.org/) and [TensorFlow](https://www.tensorflow.org/) — with a seamless integration between them. It's straightforward to train your models with one before loading them for inference with the other. + +## Online demos + +You can test most of our models directly on their pages from the [model hub](https://huggingface.co/models). We also offer [private model hosting, versioning, & an inference API](https://huggingface.co/pricing) for public and private models. + +Here are a few examples: + +In Natural Language Processing: +- [Masked word completion with BERT](https://huggingface.co/google-bert/bert-base-uncased?text=Paris+is+the+%5BMASK%5D+of+France) +- [Named Entity Recognition with Electra](https://huggingface.co/dbmdz/electra-large-discriminator-finetuned-conll03-english?text=My+name+is+Sarah+and+I+live+in+London+city) +- [Text generation with Mistral](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2) +- [Natural Language Inference with RoBERTa](https://huggingface.co/FacebookAI/roberta-large-mnli?text=The+dog+was+lost.+Nobody+lost+any+animal) +- [Summarization with BART](https://huggingface.co/facebook/bart-large-cnn?text=The+tower+is+324+metres+%281%2C063+ft%29+tall%2C+about+the+same+height+as+an+81-storey+building%2C+and+the+tallest+structure+in+Paris.+Its+base+is+square%2C+measuring+125+metres+%28410+ft%29+on+each+side.+During+its+construction%2C+the+Eiffel+Tower+surpassed+the+Washington+Monument+to+become+the+tallest+man-made+structure+in+the+world%2C+a+title+it+held+for+41+years+until+the+Chrysler+Building+in+New+York+City+was+finished+in+1930.+It+was+the+first+structure+to+reach+a+height+of+300+metres.+Due+to+the+addition+of+a+broadcasting+aerial+at+the+top+of+the+tower+in+1957%2C+it+is+now+taller+than+the+Chrysler+Building+by+5.2+metres+%2817+ft%29.+Excluding+transmitters%2C+the+Eiffel+Tower+is+the+second+tallest+free-standing+structure+in+France+after+the+Millau+Viaduct) +- [Question answering with DistilBERT](https://huggingface.co/distilbert/distilbert-base-uncased-distilled-squad?text=Which+name+is+also+used+to+describe+the+Amazon+rainforest+in+English%3F&context=The+Amazon+rainforest+%28Portuguese%3A+Floresta+Amaz%C3%B4nica+or+Amaz%C3%B4nia%3B+Spanish%3A+Selva+Amaz%C3%B3nica%2C+Amazon%C3%ADa+or+usually+Amazonia%3B+French%3A+For%C3%AAt+amazonienne%3B+Dutch%3A+Amazoneregenwoud%29%2C+also+known+in+English+as+Amazonia+or+the+Amazon+Jungle%2C+is+a+moist+broadleaf+forest+that+covers+most+of+the+Amazon+basin+of+South+America.+This+basin+encompasses+7%2C000%2C000+square+kilometres+%282%2C700%2C000+sq+mi%29%2C+of+which+5%2C500%2C000+square+kilometres+%282%2C100%2C000+sq+mi%29+are+covered+by+the+rainforest.+This+region+includes+territory+belonging+to+nine+nations.+The+majority+of+the+forest+is+contained+within+Brazil%2C+with+60%25+of+the+rainforest%2C+followed+by+Peru+with+13%25%2C+Colombia+with+10%25%2C+and+with+minor+amounts+in+Venezuela%2C+Ecuador%2C+Bolivia%2C+Guyana%2C+Suriname+and+French+Guiana.+States+or+departments+in+four+nations+contain+%22Amazonas%22+in+their+names.+The+Amazon+represents+over+half+of+the+planet%27s+remaining+rainforests%2C+and+comprises+the+largest+and+most+biodiverse+tract+of+tropical+rainforest+in+the+world%2C+with+an+estimated+390+billion+individual+trees+divided+into+16%2C000+species) +- [Translation with T5](https://huggingface.co/google-t5/t5-base?text=My+name+is+Wolfgang+and+I+live+in+Berlin) + +In Computer Vision: +- [Image classification with ViT](https://huggingface.co/google/vit-base-patch16-224) +- [Object Detection with DETR](https://huggingface.co/facebook/detr-resnet-50) +- [Semantic Segmentation with SegFormer](https://huggingface.co/nvidia/segformer-b0-finetuned-ade-512-512) +- [Panoptic Segmentation with Mask2Former](https://huggingface.co/facebook/mask2former-swin-large-coco-panoptic) +- [Depth Estimation with Depth Anything](https://huggingface.co/docs/transformers/main/model_doc/depth_anything) +- [Video Classification with VideoMAE](https://huggingface.co/docs/transformers/model_doc/videomae) +- [Universal Segmentation with OneFormer](https://huggingface.co/shi-labs/oneformer_ade20k_dinat_large) + +In Audio: +- [Automatic Speech Recognition with Whisper](https://huggingface.co/openai/whisper-large-v3) +- [Keyword Spotting with Wav2Vec2](https://huggingface.co/superb/wav2vec2-base-superb-ks) +- [Audio Classification with Audio Spectrogram Transformer](https://huggingface.co/MIT/ast-finetuned-audioset-10-10-0.4593) + +In Multimodal tasks: +- [Table Question Answering with TAPAS](https://huggingface.co/google/tapas-base-finetuned-wtq) +- [Visual Question Answering with ViLT](https://huggingface.co/dandelin/vilt-b32-finetuned-vqa) +- [Image captioning with LLaVa](https://huggingface.co/llava-hf/llava-1.5-7b-hf) +- [Zero-shot Image Classification with SigLIP](https://huggingface.co/google/siglip-so400m-patch14-384) +- [Document Question Answering with LayoutLM](https://huggingface.co/impira/layoutlm-document-qa) +- [Zero-shot Video Classification with X-CLIP](https://huggingface.co/docs/transformers/model_doc/xclip) +- [Zero-shot Object Detection with OWLv2](https://huggingface.co/docs/transformers/en/model_doc/owlv2) +- [Zero-shot Image Segmentation with CLIPSeg](https://huggingface.co/docs/transformers/model_doc/clipseg) +- [Automatic Mask Generation with SAM](https://huggingface.co/docs/transformers/model_doc/sam) + + +## 100 projects using Transformers + +Transformers is more than a toolkit to use pretrained models: it's a community of projects built around it and the +Hugging Face Hub. We want Transformers to enable developers, researchers, students, professors, engineers, and anyone +else to build their dream projects. + +In order to celebrate the 100,000 stars of transformers, we have decided to put the spotlight on the +community, and we have created the [awesome-transformers](./awesome-transformers.md) page which lists 100 +incredible projects built in the vicinity of transformers. + +If you own or use a project that you believe should be part of the list, please open a PR to add it! + +## If you are looking for custom support from the Hugging Face team + + + HuggingFace Expert Acceleration Program +
+ +## Quick tour + +To immediately use a model on a given input (text, image, audio, ...), we provide the `pipeline` API. Pipelines group together a pretrained model with the preprocessing that was used during that model's training. Here is how to quickly use a pipeline to classify positive versus negative texts: + +```python +>>> from transformers import pipeline + +# Allocate a pipeline for sentiment-analysis +>>> classifier = pipeline('sentiment-analysis') +>>> classifier('We are very happy to introduce pipeline to the transformers repository.') +[{'label': 'POSITIVE', 'score': 0.9996980428695679}] +``` + +The second line of code downloads and caches the pretrained model used by the pipeline, while the third evaluates it on the given text. Here, the answer is "positive" with a confidence of 99.97%. + +Many tasks have a pre-trained `pipeline` ready to go, in NLP but also in computer vision and speech. For example, we can easily extract detected objects in an image: + +``` python +>>> import requests +>>> from PIL import Image +>>> from transformers import pipeline + +# Download an image with cute cats +>>> url = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/coco_sample.png" +>>> image_data = requests.get(url, stream=True).raw +>>> image = Image.open(image_data) + +# Allocate a pipeline for object detection +>>> object_detector = pipeline('object-detection') +>>> object_detector(image) +[{'score': 0.9982201457023621, + 'label': 'remote', + 'box': {'xmin': 40, 'ymin': 70, 'xmax': 175, 'ymax': 117}}, + {'score': 0.9960021376609802, + 'label': 'remote', + 'box': {'xmin': 333, 'ymin': 72, 'xmax': 368, 'ymax': 187}}, + {'score': 0.9954745173454285, + 'label': 'couch', + 'box': {'xmin': 0, 'ymin': 1, 'xmax': 639, 'ymax': 473}}, + {'score': 0.9988006353378296, + 'label': 'cat', + 'box': {'xmin': 13, 'ymin': 52, 'xmax': 314, 'ymax': 470}}, + {'score': 0.9986783862113953, + 'label': 'cat', + 'box': {'xmin': 345, 'ymin': 23, 'xmax': 640, 'ymax': 368}}] +``` + +Here, we get a list of objects detected in the image, with a box surrounding the object and a confidence score. Here is the original image on the left, with the predictions displayed on the right: + +

+ + +

+ +You can learn more about the tasks supported by the `pipeline` API in [this tutorial](https://huggingface.co/docs/transformers/task_summary). + +In addition to `pipeline`, to download and use any of the pretrained models on your given task, all it takes is three lines of code. Here is the PyTorch version: +```python +>>> from transformers import AutoTokenizer, AutoModel + +>>> tokenizer = AutoTokenizer.from_pretrained("google-bert/bert-base-uncased") +>>> model = AutoModel.from_pretrained("google-bert/bert-base-uncased") + +>>> inputs = tokenizer("Hello world!", return_tensors="pt") +>>> outputs = model(**inputs) +``` + +And here is the equivalent code for TensorFlow: +```python +>>> from transformers import AutoTokenizer, TFAutoModel + +>>> tokenizer = AutoTokenizer.from_pretrained("google-bert/bert-base-uncased") +>>> model = TFAutoModel.from_pretrained("google-bert/bert-base-uncased") + +>>> inputs = tokenizer("Hello world!", return_tensors="tf") +>>> outputs = model(**inputs) +``` + +The tokenizer is responsible for all the preprocessing the pretrained model expects and can be called directly on a single string (as in the above examples) or a list. It will output a dictionary that you can use in downstream code or simply directly pass to your model using the ** argument unpacking operator. + +The model itself is a regular [Pytorch `nn.Module`](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) or a [TensorFlow `tf.keras.Model`](https://www.tensorflow.org/api_docs/python/tf/keras/Model) (depending on your backend) which you can use as usual. [This tutorial](https://huggingface.co/docs/transformers/training) explains how to integrate such a model into a classic PyTorch or TensorFlow training loop, or how to use our `Trainer` API to quickly fine-tune on a new dataset. + +## Why should I use transformers? + +1. Easy-to-use state-of-the-art models: + - High performance on natural language understanding & generation, computer vision, and audio tasks. + - Low barrier to entry for educators and practitioners. + - Few user-facing abstractions with just three classes to learn. + - A unified API for using all our pretrained models. + +1. Lower compute costs, smaller carbon footprint: + - Researchers can share trained models instead of always retraining. + - Practitioners can reduce compute time and production costs. + - Dozens of architectures with over 400,000 pretrained models across all modalities. + +1. Choose the right framework for every part of a model's lifetime: + - Train state-of-the-art models in 3 lines of code. + - Move a single model between TF2.0/PyTorch/JAX frameworks at will. + - Seamlessly pick the right framework for training, evaluation, and production. + +1. Easily customize a model or an example to your needs: + - We provide examples for each architecture to reproduce the results published by its original authors. + - Model internals are exposed as consistently as possible. + - Model files can be used independently of the library for quick experiments. + +## Why shouldn't I use transformers? + +- This library is not a modular toolbox of building blocks for neural nets. The code in the model files is not refactored with additional abstractions on purpose, so that researchers can quickly iterate on each of the models without diving into additional abstractions/files. +- The training API is not intended to work on any model but is optimized to work with the models provided by the library. For generic machine learning loops, you should use another library (possibly, [Accelerate](https://huggingface.co/docs/accelerate)). +- While we strive to present as many use cases as possible, the scripts in our [examples folder](https://github.com/huggingface/transformers/tree/main/examples) are just that: examples. It is expected that they won't work out-of-the-box on your specific problem and that you will be required to change a few lines of code to adapt them to your needs. + +## Installation + +### With pip + +This repository is tested on Python 3.8+, Flax 0.4.1+, PyTorch 1.11+, and TensorFlow 2.6+. + +You should install 🤗 Transformers in a [virtual environment](https://docs.python.org/3/library/venv.html). If you're unfamiliar with Python virtual environments, check out the [user guide](https://packaging.python.org/guides/installing-using-pip-and-virtual-environments/). + +First, create a virtual environment with the version of Python you're going to use and activate it. + +Then, you will need to install at least one of Flax, PyTorch, or TensorFlow. +Please refer to [TensorFlow installation page](https://www.tensorflow.org/install/), [PyTorch installation page](https://pytorch.org/get-started/locally/#start-locally) and/or [Flax](https://github.com/google/flax#quick-install) and [Jax](https://github.com/google/jax#installation) installation pages regarding the specific installation command for your platform. + +When one of those backends has been installed, 🤗 Transformers can be installed using pip as follows: + +```bash +pip install transformers +``` + +If you'd like to play with the examples or need the bleeding edge of the code and can't wait for a new release, you must [install the library from source](https://huggingface.co/docs/transformers/installation#installing-from-source). + +### With conda + +🤗 Transformers can be installed using conda as follows: + +```shell script +conda install conda-forge::transformers +``` + +> **_NOTE:_** Installing `transformers` from the `huggingface` channel is deprecated. + +Follow the installation pages of Flax, PyTorch or TensorFlow to see how to install them with conda. + +> **_NOTE:_** On Windows, you may be prompted to activate Developer Mode in order to benefit from caching. If this is not an option for you, please let us know in [this issue](https://github.com/huggingface/huggingface_hub/issues/1062). + +## Model architectures + +**[All the model checkpoints](https://huggingface.co/models)** provided by 🤗 Transformers are seamlessly integrated from the huggingface.co [model hub](https://huggingface.co/models), where they are uploaded directly by [users](https://huggingface.co/users) and [organizations](https://huggingface.co/organizations). + +Current number of checkpoints: ![](https://img.shields.io/endpoint?url=https://huggingface.co/api/shields/models&color=brightgreen) + +🤗 Transformers currently provides the following architectures (see [here](https://huggingface.co/docs/transformers/model_summary) for a high-level summary of each them): + +1. **[ALBERT](https://huggingface.co/docs/transformers/model_doc/albert)** (from Google Research and the Toyota Technological Institute at Chicago) released with the paper [ALBERT: A Lite BERT for Self-supervised Learning of Language Representations](https://arxiv.org/abs/1909.11942), by Zhenzhong Lan, Mingda Chen, Sebastian Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut. +1. **[ALIGN](https://huggingface.co/docs/transformers/model_doc/align)** (from Google Research) released with the paper [Scaling Up Visual and Vision-Language Representation Learning With Noisy Text Supervision](https://arxiv.org/abs/2102.05918) by Chao Jia, Yinfei Yang, Ye Xia, Yi-Ting Chen, Zarana Parekh, Hieu Pham, Quoc V. Le, Yunhsuan Sung, Zhen Li, Tom Duerig. +1. **[AltCLIP](https://huggingface.co/docs/transformers/model_doc/altclip)** (from BAAI) released with the paper [AltCLIP: Altering the Language Encoder in CLIP for Extended Language Capabilities](https://arxiv.org/abs/2211.06679) by Chen, Zhongzhi and Liu, Guang and Zhang, Bo-Wen and Ye, Fulong and Yang, Qinghong and Wu, Ledell. +1. **[Audio Spectrogram Transformer](https://huggingface.co/docs/transformers/model_doc/audio-spectrogram-transformer)** (from MIT) released with the paper [AST: Audio Spectrogram Transformer](https://arxiv.org/abs/2104.01778) by Yuan Gong, Yu-An Chung, James Glass. +1. **[Autoformer](https://huggingface.co/docs/transformers/model_doc/autoformer)** (from Tsinghua University) released with the paper [Autoformer: Decomposition Transformers with Auto-Correlation for Long-Term Series Forecasting](https://arxiv.org/abs/2106.13008) by Haixu Wu, Jiehui Xu, Jianmin Wang, Mingsheng Long. +1. **[Bark](https://huggingface.co/docs/transformers/model_doc/bark)** (from Suno) released in the repository [suno-ai/bark](https://github.com/suno-ai/bark) by Suno AI team. +1. **[BART](https://huggingface.co/docs/transformers/model_doc/bart)** (from Facebook) released with the paper [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://arxiv.org/abs/1910.13461) by Mike Lewis, Yinhan Liu, Naman Goyal, Marjan Ghazvininejad, Abdelrahman Mohamed, Omer Levy, Ves Stoyanov, and Luke Zettlemoyer. +1. **[BARThez](https://huggingface.co/docs/transformers/model_doc/barthez)** (from École polytechnique) released with the paper [BARThez: a Skilled Pretrained French Sequence-to-Sequence Model](https://arxiv.org/abs/2010.12321) by Moussa Kamal Eddine, Antoine J.-P. Tixier, Michalis Vazirgiannis. +1. **[BARTpho](https://huggingface.co/docs/transformers/model_doc/bartpho)** (from VinAI Research) released with the paper [BARTpho: Pre-trained Sequence-to-Sequence Models for Vietnamese](https://arxiv.org/abs/2109.09701) by Nguyen Luong Tran, Duong Minh Le and Dat Quoc Nguyen. +1. **[BEiT](https://huggingface.co/docs/transformers/model_doc/beit)** (from Microsoft) released with the paper [BEiT: BERT Pre-Training of Image Transformers](https://arxiv.org/abs/2106.08254) by Hangbo Bao, Li Dong, Furu Wei. +1. **[BERT](https://huggingface.co/docs/transformers/model_doc/bert)** (from Google) released with the paper [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://arxiv.org/abs/1810.04805) by Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. +1. **[BERT For Sequence Generation](https://huggingface.co/docs/transformers/model_doc/bert-generation)** (from Google) released with the paper [Leveraging Pre-trained Checkpoints for Sequence Generation Tasks](https://arxiv.org/abs/1907.12461) by Sascha Rothe, Shashi Narayan, Aliaksei Severyn. +1. **[BERTweet](https://huggingface.co/docs/transformers/model_doc/bertweet)** (from VinAI Research) released with the paper [BERTweet: A pre-trained language model for English Tweets](https://aclanthology.org/2020.emnlp-demos.2/) by Dat Quoc Nguyen, Thanh Vu and Anh Tuan Nguyen. +1. **[BigBird-Pegasus](https://huggingface.co/docs/transformers/model_doc/bigbird_pegasus)** (from Google Research) released with the paper [Big Bird: Transformers for Longer Sequences](https://arxiv.org/abs/2007.14062) by Manzil Zaheer, Guru Guruganesh, Avinava Dubey, Joshua Ainslie, Chris Alberti, Santiago Ontanon, Philip Pham, Anirudh Ravula, Qifan Wang, Li Yang, Amr Ahmed. +1. **[BigBird-RoBERTa](https://huggingface.co/docs/transformers/model_doc/big_bird)** (from Google Research) released with the paper [Big Bird: Transformers for Longer Sequences](https://arxiv.org/abs/2007.14062) by Manzil Zaheer, Guru Guruganesh, Avinava Dubey, Joshua Ainslie, Chris Alberti, Santiago Ontanon, Philip Pham, Anirudh Ravula, Qifan Wang, Li Yang, Amr Ahmed. +1. **[BioGpt](https://huggingface.co/docs/transformers/model_doc/biogpt)** (from Microsoft Research AI4Science) released with the paper [BioGPT: generative pre-trained transformer for biomedical text generation and mining](https://academic.oup.com/bib/advance-article/doi/10.1093/bib/bbac409/6713511?guestAccessKey=a66d9b5d-4f83-4017-bb52-405815c907b9) by Renqian Luo, Liai Sun, Yingce Xia, Tao Qin, Sheng Zhang, Hoifung Poon and Tie-Yan Liu. +1. **[BiT](https://huggingface.co/docs/transformers/model_doc/bit)** (from Google AI) released with the paper [Big Transfer (BiT): General Visual Representation Learning](https://arxiv.org/abs/1912.11370) by Alexander Kolesnikov, Lucas Beyer, Xiaohua Zhai, Joan Puigcerver, Jessica Yung, Sylvain Gelly, Neil Houlsby. +1. **[Blenderbot](https://huggingface.co/docs/transformers/model_doc/blenderbot)** (from Facebook) released with the paper [Recipes for building an open-domain chatbot](https://arxiv.org/abs/2004.13637) by Stephen Roller, Emily Dinan, Naman Goyal, Da Ju, Mary Williamson, Yinhan Liu, Jing Xu, Myle Ott, Kurt Shuster, Eric M. Smith, Y-Lan Boureau, Jason Weston. +1. **[BlenderbotSmall](https://huggingface.co/docs/transformers/model_doc/blenderbot-small)** (from Facebook) released with the paper [Recipes for building an open-domain chatbot](https://arxiv.org/abs/2004.13637) by Stephen Roller, Emily Dinan, Naman Goyal, Da Ju, Mary Williamson, Yinhan Liu, Jing Xu, Myle Ott, Kurt Shuster, Eric M. Smith, Y-Lan Boureau, Jason Weston. +1. **[BLIP](https://huggingface.co/docs/transformers/model_doc/blip)** (from Salesforce) released with the paper [BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generation](https://arxiv.org/abs/2201.12086) by Junnan Li, Dongxu Li, Caiming Xiong, Steven Hoi. +1. **[BLIP-2](https://huggingface.co/docs/transformers/model_doc/blip-2)** (from Salesforce) released with the paper [BLIP-2: Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language Models](https://arxiv.org/abs/2301.12597) by Junnan Li, Dongxu Li, Silvio Savarese, Steven Hoi. +1. **[BLOOM](https://huggingface.co/docs/transformers/model_doc/bloom)** (from BigScience workshop) released by the [BigScience Workshop](https://bigscience.huggingface.co/). +1. **[BORT](https://huggingface.co/docs/transformers/model_doc/bort)** (from Alexa) released with the paper [Optimal Subarchitecture Extraction For BERT](https://arxiv.org/abs/2010.10499) by Adrian de Wynter and Daniel J. Perry. +1. **[BridgeTower](https://huggingface.co/docs/transformers/model_doc/bridgetower)** (from Harbin Institute of Technology/Microsoft Research Asia/Intel Labs) released with the paper [BridgeTower: Building Bridges Between Encoders in Vision-Language Representation Learning](https://arxiv.org/abs/2206.08657) by Xiao Xu, Chenfei Wu, Shachar Rosenman, Vasudev Lal, Wanxiang Che, Nan Duan. +1. **[BROS](https://huggingface.co/docs/transformers/model_doc/bros)** (from NAVER CLOVA) released with the paper [BROS: A Pre-trained Language Model Focusing on Text and Layout for Better Key Information Extraction from Documents](https://arxiv.org/abs/2108.04539) by Teakgyu Hong, Donghyun Kim, Mingi Ji, Wonseok Hwang, Daehyun Nam, Sungrae Park. +1. **[ByT5](https://huggingface.co/docs/transformers/model_doc/byt5)** (from Google Research) released with the paper [ByT5: Towards a token-free future with pre-trained byte-to-byte models](https://arxiv.org/abs/2105.13626) by Linting Xue, Aditya Barua, Noah Constant, Rami Al-Rfou, Sharan Narang, Mihir Kale, Adam Roberts, Colin Raffel. +1. **[CamemBERT](https://huggingface.co/docs/transformers/model_doc/camembert)** (from Inria/Facebook/Sorbonne) released with the paper [CamemBERT: a Tasty French Language Model](https://arxiv.org/abs/1911.03894) by Louis Martin*, Benjamin Muller*, Pedro Javier Ortiz Suárez*, Yoann Dupont, Laurent Romary, Éric Villemonte de la Clergerie, Djamé Seddah and Benoît Sagot. +1. **[CANINE](https://huggingface.co/docs/transformers/model_doc/canine)** (from Google Research) released with the paper [CANINE: Pre-training an Efficient Tokenization-Free Encoder for Language Representation](https://arxiv.org/abs/2103.06874) by Jonathan H. Clark, Dan Garrette, Iulia Turc, John Wieting. +1. **[Chinese-CLIP](https://huggingface.co/docs/transformers/model_doc/chinese_clip)** (from OFA-Sys) released with the paper [Chinese CLIP: Contrastive Vision-Language Pretraining in Chinese](https://arxiv.org/abs/2211.01335) by An Yang, Junshu Pan, Junyang Lin, Rui Men, Yichang Zhang, Jingren Zhou, Chang Zhou. +1. **[CLAP](https://huggingface.co/docs/transformers/model_doc/clap)** (from LAION-AI) released with the paper [Large-scale Contrastive Language-Audio Pretraining with Feature Fusion and Keyword-to-Caption Augmentation](https://arxiv.org/abs/2211.06687) by Yusong Wu, Ke Chen, Tianyu Zhang, Yuchen Hui, Taylor Berg-Kirkpatrick, Shlomo Dubnov. +1. **[CLIP](https://huggingface.co/docs/transformers/model_doc/clip)** (from OpenAI) released with the paper [Learning Transferable Visual Models From Natural Language Supervision](https://arxiv.org/abs/2103.00020) by Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, Gretchen Krueger, Ilya Sutskever. +1. **[CLIPSeg](https://huggingface.co/docs/transformers/model_doc/clipseg)** (from University of Göttingen) released with the paper [Image Segmentation Using Text and Image Prompts](https://arxiv.org/abs/2112.10003) by Timo Lüddecke and Alexander Ecker. +1. **[CLVP](https://huggingface.co/docs/transformers/model_doc/clvp)** released with the paper [Better speech synthesis through scaling](https://arxiv.org/abs/2305.07243) by James Betker. +1. **[CodeGen](https://huggingface.co/docs/transformers/model_doc/codegen)** (from Salesforce) released with the paper [A Conversational Paradigm for Program Synthesis](https://arxiv.org/abs/2203.13474) by Erik Nijkamp, Bo Pang, Hiroaki Hayashi, Lifu Tu, Huan Wang, Yingbo Zhou, Silvio Savarese, Caiming Xiong. +1. **[CodeLlama](https://huggingface.co/docs/transformers/model_doc/llama_code)** (from MetaAI) released with the paper [Code Llama: Open Foundation Models for Code](https://ai.meta.com/research/publications/code-llama-open-foundation-models-for-code/) by Baptiste Rozière, Jonas Gehring, Fabian Gloeckle, Sten Sootla, Itai Gat, Xiaoqing Ellen Tan, Yossi Adi, Jingyu Liu, Tal Remez, Jérémy Rapin, Artyom Kozhevnikov, Ivan Evtimov, Joanna Bitton, Manish Bhatt, Cristian Canton Ferrer, Aaron Grattafiori, Wenhan Xiong, Alexandre Défossez, Jade Copet, Faisal Azhar, Hugo Touvron, Louis Martin, Nicolas Usunier, Thomas Scialom, Gabriel Synnaeve. +1. **[Cohere](https://huggingface.co/docs/transformers/model_doc/cohere)** (from Cohere) released with the paper [Command-R: Retrieval Augmented Generation at Production Scale]() by Cohere. +1. **[Conditional DETR](https://huggingface.co/docs/transformers/model_doc/conditional_detr)** (from Microsoft Research Asia) released with the paper [Conditional DETR for Fast Training Convergence](https://arxiv.org/abs/2108.06152) by Depu Meng, Xiaokang Chen, Zejia Fan, Gang Zeng, Houqiang Li, Yuhui Yuan, Lei Sun, Jingdong Wang. +1. **[ConvBERT](https://huggingface.co/docs/transformers/model_doc/convbert)** (from YituTech) released with the paper [ConvBERT: Improving BERT with Span-based Dynamic Convolution](https://arxiv.org/abs/2008.02496) by Zihang Jiang, Weihao Yu, Daquan Zhou, Yunpeng Chen, Jiashi Feng, Shuicheng Yan. +1. **[ConvNeXT](https://huggingface.co/docs/transformers/model_doc/convnext)** (from Facebook AI) released with the paper [A ConvNet for the 2020s](https://arxiv.org/abs/2201.03545) by Zhuang Liu, Hanzi Mao, Chao-Yuan Wu, Christoph Feichtenhofer, Trevor Darrell, Saining Xie. +1. **[ConvNeXTV2](https://huggingface.co/docs/transformers/model_doc/convnextv2)** (from Facebook AI) released with the paper [ConvNeXt V2: Co-designing and Scaling ConvNets with Masked Autoencoders](https://arxiv.org/abs/2301.00808) by Sanghyun Woo, Shoubhik Debnath, Ronghang Hu, Xinlei Chen, Zhuang Liu, In So Kweon, Saining Xie. +1. **[CPM](https://huggingface.co/docs/transformers/model_doc/cpm)** (from Tsinghua University) released with the paper [CPM: A Large-scale Generative Chinese Pre-trained Language Model](https://arxiv.org/abs/2012.00413) by Zhengyan Zhang, Xu Han, Hao Zhou, Pei Ke, Yuxian Gu, Deming Ye, Yujia Qin, Yusheng Su, Haozhe Ji, Jian Guan, Fanchao Qi, Xiaozhi Wang, Yanan Zheng, Guoyang Zeng, Huanqi Cao, Shengqi Chen, Daixuan Li, Zhenbo Sun, Zhiyuan Liu, Minlie Huang, Wentao Han, Jie Tang, Juanzi Li, Xiaoyan Zhu, Maosong Sun. +1. **[CPM-Ant](https://huggingface.co/docs/transformers/model_doc/cpmant)** (from OpenBMB) released by the [OpenBMB](https://www.openbmb.org/). +1. **[CTRL](https://huggingface.co/docs/transformers/model_doc/ctrl)** (from Salesforce) released with the paper [CTRL: A Conditional Transformer Language Model for Controllable Generation](https://arxiv.org/abs/1909.05858) by Nitish Shirish Keskar*, Bryan McCann*, Lav R. Varshney, Caiming Xiong and Richard Socher. +1. **[CvT](https://huggingface.co/docs/transformers/model_doc/cvt)** (from Microsoft) released with the paper [CvT: Introducing Convolutions to Vision Transformers](https://arxiv.org/abs/2103.15808) by Haiping Wu, Bin Xiao, Noel Codella, Mengchen Liu, Xiyang Dai, Lu Yuan, Lei Zhang. +1. **[Data2Vec](https://huggingface.co/docs/transformers/model_doc/data2vec)** (from Facebook) released with the paper [Data2Vec: A General Framework for Self-supervised Learning in Speech, Vision and Language](https://arxiv.org/abs/2202.03555) by Alexei Baevski, Wei-Ning Hsu, Qiantong Xu, Arun Babu, Jiatao Gu, Michael Auli. +1. **[DeBERTa](https://huggingface.co/docs/transformers/model_doc/deberta)** (from Microsoft) released with the paper [DeBERTa: Decoding-enhanced BERT with Disentangled Attention](https://arxiv.org/abs/2006.03654) by Pengcheng He, Xiaodong Liu, Jianfeng Gao, Weizhu Chen. +1. **[DeBERTa-v2](https://huggingface.co/docs/transformers/model_doc/deberta-v2)** (from Microsoft) released with the paper [DeBERTa: Decoding-enhanced BERT with Disentangled Attention](https://arxiv.org/abs/2006.03654) by Pengcheng He, Xiaodong Liu, Jianfeng Gao, Weizhu Chen. +1. **[Decision Transformer](https://huggingface.co/docs/transformers/model_doc/decision_transformer)** (from Berkeley/Facebook/Google) released with the paper [Decision Transformer: Reinforcement Learning via Sequence Modeling](https://arxiv.org/abs/2106.01345) by Lili Chen, Kevin Lu, Aravind Rajeswaran, Kimin Lee, Aditya Grover, Michael Laskin, Pieter Abbeel, Aravind Srinivas, Igor Mordatch. +1. **[Deformable DETR](https://huggingface.co/docs/transformers/model_doc/deformable_detr)** (from SenseTime Research) released with the paper [Deformable DETR: Deformable Transformers for End-to-End Object Detection](https://arxiv.org/abs/2010.04159) by Xizhou Zhu, Weijie Su, Lewei Lu, Bin Li, Xiaogang Wang, Jifeng Dai. +1. **[DeiT](https://huggingface.co/docs/transformers/model_doc/deit)** (from Facebook) released with the paper [Training data-efficient image transformers & distillation through attention](https://arxiv.org/abs/2012.12877) by Hugo Touvron, Matthieu Cord, Matthijs Douze, Francisco Massa, Alexandre Sablayrolles, Hervé Jégou. +1. **[DePlot](https://huggingface.co/docs/transformers/model_doc/deplot)** (from Google AI) released with the paper [DePlot: One-shot visual language reasoning by plot-to-table translation](https://arxiv.org/abs/2212.10505) by Fangyu Liu, Julian Martin Eisenschlos, Francesco Piccinno, Syrine Krichene, Chenxi Pang, Kenton Lee, Mandar Joshi, Wenhu Chen, Nigel Collier, Yasemin Altun. +1. **[Depth Anything](https://huggingface.co/docs/transformers/model_doc/depth_anything)** (from University of Hong Kong and TikTok) released with the paper [Depth Anything: Unleashing the Power of Large-Scale Unlabeled Data](https://arxiv.org/abs/2401.10891) by Lihe Yang, Bingyi Kang, Zilong Huang, Xiaogang Xu, Jiashi Feng, Hengshuang Zhao. +1. **[DETA](https://huggingface.co/docs/transformers/model_doc/deta)** (from The University of Texas at Austin) released with the paper [NMS Strikes Back](https://arxiv.org/abs/2212.06137) by Jeffrey Ouyang-Zhang, Jang Hyun Cho, Xingyi Zhou, Philipp Krähenbühl. +1. **[DETR](https://huggingface.co/docs/transformers/model_doc/detr)** (from Facebook) released with the paper [End-to-End Object Detection with Transformers](https://arxiv.org/abs/2005.12872) by Nicolas Carion, Francisco Massa, Gabriel Synnaeve, Nicolas Usunier, Alexander Kirillov, Sergey Zagoruyko. +1. **[DialoGPT](https://huggingface.co/docs/transformers/model_doc/dialogpt)** (from Microsoft Research) released with the paper [DialoGPT: Large-Scale Generative Pre-training for Conversational Response Generation](https://arxiv.org/abs/1911.00536) by Yizhe Zhang, Siqi Sun, Michel Galley, Yen-Chun Chen, Chris Brockett, Xiang Gao, Jianfeng Gao, Jingjing Liu, Bill Dolan. +1. **[DiNAT](https://huggingface.co/docs/transformers/model_doc/dinat)** (from SHI Labs) released with the paper [Dilated Neighborhood Attention Transformer](https://arxiv.org/abs/2209.15001) by Ali Hassani and Humphrey Shi. +1. **[DINOv2](https://huggingface.co/docs/transformers/model_doc/dinov2)** (from Meta AI) released with the paper [DINOv2: Learning Robust Visual Features without Supervision](https://arxiv.org/abs/2304.07193) by Maxime Oquab, Timothée Darcet, Théo Moutakanni, Huy Vo, Marc Szafraniec, Vasil Khalidov, Pierre Fernandez, Daniel Haziza, Francisco Massa, Alaaeldin El-Nouby, Mahmoud Assran, Nicolas Ballas, Wojciech Galuba, Russell Howes, Po-Yao Huang, Shang-Wen Li, Ishan Misra, Michael Rabbat, Vasu Sharma, Gabriel Synnaeve, Hu Xu, Hervé Jegou, Julien Mairal, Patrick Labatut, Armand Joulin, Piotr Bojanowski. +1. **[DistilBERT](https://huggingface.co/docs/transformers/model_doc/distilbert)** (from HuggingFace), released together with the paper [DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter](https://arxiv.org/abs/1910.01108) by Victor Sanh, Lysandre Debut and Thomas Wolf. The same method has been applied to compress GPT2 into [DistilGPT2](https://github.com/huggingface/transformers/tree/main/examples/research_projects/distillation), RoBERTa into [DistilRoBERTa](https://github.com/huggingface/transformers/tree/main/examples/research_projects/distillation), Multilingual BERT into [DistilmBERT](https://github.com/huggingface/transformers/tree/main/examples/research_projects/distillation) and a German version of DistilBERT. +1. **[DiT](https://huggingface.co/docs/transformers/model_doc/dit)** (from Microsoft Research) released with the paper [DiT: Self-supervised Pre-training for Document Image Transformer](https://arxiv.org/abs/2203.02378) by Junlong Li, Yiheng Xu, Tengchao Lv, Lei Cui, Cha Zhang, Furu Wei. +1. **[Donut](https://huggingface.co/docs/transformers/model_doc/donut)** (from NAVER), released together with the paper [OCR-free Document Understanding Transformer](https://arxiv.org/abs/2111.15664) by Geewook Kim, Teakgyu Hong, Moonbin Yim, Jeongyeon Nam, Jinyoung Park, Jinyeong Yim, Wonseok Hwang, Sangdoo Yun, Dongyoon Han, Seunghyun Park. +1. **[DPR](https://huggingface.co/docs/transformers/model_doc/dpr)** (from Facebook) released with the paper [Dense Passage Retrieval for Open-Domain Question Answering](https://arxiv.org/abs/2004.04906) by Vladimir Karpukhin, Barlas Oğuz, Sewon Min, Patrick Lewis, Ledell Wu, Sergey Edunov, Danqi Chen, and Wen-tau Yih. +1. **[DPT](https://huggingface.co/docs/transformers/master/model_doc/dpt)** (from Intel Labs) released with the paper [Vision Transformers for Dense Prediction](https://arxiv.org/abs/2103.13413) by René Ranftl, Alexey Bochkovskiy, Vladlen Koltun. +1. **[EfficientFormer](https://huggingface.co/docs/transformers/model_doc/efficientformer)** (from Snap Research) released with the paper [EfficientFormer: Vision Transformers at MobileNetSpeed](https://arxiv.org/abs/2206.01191) by Yanyu Li, Geng Yuan, Yang Wen, Ju Hu, Georgios Evangelidis, Sergey Tulyakov, Yanzhi Wang, Jian Ren. +1. **[EfficientNet](https://huggingface.co/docs/transformers/model_doc/efficientnet)** (from Google Brain) released with the paper [EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks](https://arxiv.org/abs/1905.11946) by Mingxing Tan, Quoc V. Le. +1. **[ELECTRA](https://huggingface.co/docs/transformers/model_doc/electra)** (from Google Research/Stanford University) released with the paper [ELECTRA: Pre-training text encoders as discriminators rather than generators](https://arxiv.org/abs/2003.10555) by Kevin Clark, Minh-Thang Luong, Quoc V. Le, Christopher D. Manning. +1. **[EnCodec](https://huggingface.co/docs/transformers/model_doc/encodec)** (from Meta AI) released with the paper [High Fidelity Neural Audio Compression](https://arxiv.org/abs/2210.13438) by Alexandre Défossez, Jade Copet, Gabriel Synnaeve, Yossi Adi. +1. **[EncoderDecoder](https://huggingface.co/docs/transformers/model_doc/encoder-decoder)** (from Google Research) released with the paper [Leveraging Pre-trained Checkpoints for Sequence Generation Tasks](https://arxiv.org/abs/1907.12461) by Sascha Rothe, Shashi Narayan, Aliaksei Severyn. +1. **[ERNIE](https://huggingface.co/docs/transformers/model_doc/ernie)** (from Baidu) released with the paper [ERNIE: Enhanced Representation through Knowledge Integration](https://arxiv.org/abs/1904.09223) by Yu Sun, Shuohuan Wang, Yukun Li, Shikun Feng, Xuyi Chen, Han Zhang, Xin Tian, Danxiang Zhu, Hao Tian, Hua Wu. +1. **[ErnieM](https://huggingface.co/docs/transformers/model_doc/ernie_m)** (from Baidu) released with the paper [ERNIE-M: Enhanced Multilingual Representation by Aligning Cross-lingual Semantics with Monolingual Corpora](https://arxiv.org/abs/2012.15674) by Xuan Ouyang, Shuohuan Wang, Chao Pang, Yu Sun, Hao Tian, Hua Wu, Haifeng Wang. +1. **[ESM](https://huggingface.co/docs/transformers/model_doc/esm)** (from Meta AI) are transformer protein language models. **ESM-1b** was released with the paper [Biological structure and function emerge from scaling unsupervised learning to 250 million protein sequences](https://www.pnas.org/content/118/15/e2016239118) by Alexander Rives, Joshua Meier, Tom Sercu, Siddharth Goyal, Zeming Lin, Jason Liu, Demi Guo, Myle Ott, C. Lawrence Zitnick, Jerry Ma, and Rob Fergus. **ESM-1v** was released with the paper [Language models enable zero-shot prediction of the effects of mutations on protein function](https://doi.org/10.1101/2021.07.09.450648) by Joshua Meier, Roshan Rao, Robert Verkuil, Jason Liu, Tom Sercu and Alexander Rives. **ESM-2 and ESMFold** were released with the paper [Language models of protein sequences at the scale of evolution enable accurate structure prediction](https://doi.org/10.1101/2022.07.20.500902) by Zeming Lin, Halil Akin, Roshan Rao, Brian Hie, Zhongkai Zhu, Wenting Lu, Allan dos Santos Costa, Maryam Fazel-Zarandi, Tom Sercu, Sal Candido, Alexander Rives. +1. **[Falcon](https://huggingface.co/docs/transformers/model_doc/falcon)** (from Technology Innovation Institute) by Almazrouei, Ebtesam and Alobeidli, Hamza and Alshamsi, Abdulaziz and Cappelli, Alessandro and Cojocaru, Ruxandra and Debbah, Merouane and Goffinet, Etienne and Heslow, Daniel and Launay, Julien and Malartic, Quentin and Noune, Badreddine and Pannier, Baptiste and Penedo, Guilherme. +1. **[FastSpeech2Conformer](https://huggingface.co/docs/transformers/model_doc/fastspeech2_conformer)** (from ESPnet) released with the paper [Recent Developments On Espnet Toolkit Boosted By Conformer](https://arxiv.org/abs/2010.13956) by Pengcheng Guo, Florian Boyer, Xuankai Chang, Tomoki Hayashi, Yosuke Higuchi, Hirofumi Inaguma, Naoyuki Kamo, Chenda Li, Daniel Garcia-Romero, Jiatong Shi, Jing Shi, Shinji Watanabe, Kun Wei, Wangyou Zhang, and Yuekai Zhang. +1. **[FLAN-T5](https://huggingface.co/docs/transformers/model_doc/flan-t5)** (from Google AI) released in the repository [google-research/t5x](https://github.com/google-research/t5x/blob/main/docs/models.md#flan-t5-checkpoints) by Hyung Won Chung, Le Hou, Shayne Longpre, Barret Zoph, Yi Tay, William Fedus, Eric Li, Xuezhi Wang, Mostafa Dehghani, Siddhartha Brahma, Albert Webson, Shixiang Shane Gu, Zhuyun Dai, Mirac Suzgun, Xinyun Chen, Aakanksha Chowdhery, Sharan Narang, Gaurav Mishra, Adams Yu, Vincent Zhao, Yanping Huang, Andrew Dai, Hongkun Yu, Slav Petrov, Ed H. Chi, Jeff Dean, Jacob Devlin, Adam Roberts, Denny Zhou, Quoc V. Le, and Jason Wei +1. **[FLAN-UL2](https://huggingface.co/docs/transformers/model_doc/flan-ul2)** (from Google AI) released in the repository [google-research/t5x](https://github.com/google-research/t5x/blob/main/docs/models.md#flan-ul2-checkpoints) by Hyung Won Chung, Le Hou, Shayne Longpre, Barret Zoph, Yi Tay, William Fedus, Eric Li, Xuezhi Wang, Mostafa Dehghani, Siddhartha Brahma, Albert Webson, Shixiang Shane Gu, Zhuyun Dai, Mirac Suzgun, Xinyun Chen, Aakanksha Chowdhery, Sharan Narang, Gaurav Mishra, Adams Yu, Vincent Zhao, Yanping Huang, Andrew Dai, Hongkun Yu, Slav Petrov, Ed H. Chi, Jeff Dean, Jacob Devlin, Adam Roberts, Denny Zhou, Quoc V. Le, and Jason Wei +1. **[FlauBERT](https://huggingface.co/docs/transformers/model_doc/flaubert)** (from CNRS) released with the paper [FlauBERT: Unsupervised Language Model Pre-training for French](https://arxiv.org/abs/1912.05372) by Hang Le, Loïc Vial, Jibril Frej, Vincent Segonne, Maximin Coavoux, Benjamin Lecouteux, Alexandre Allauzen, Benoît Crabbé, Laurent Besacier, Didier Schwab. +1. **[FLAVA](https://huggingface.co/docs/transformers/model_doc/flava)** (from Facebook AI) released with the paper [FLAVA: A Foundational Language And Vision Alignment Model](https://arxiv.org/abs/2112.04482) by Amanpreet Singh, Ronghang Hu, Vedanuj Goswami, Guillaume Couairon, Wojciech Galuba, Marcus Rohrbach, and Douwe Kiela. +1. **[FNet](https://huggingface.co/docs/transformers/model_doc/fnet)** (from Google Research) released with the paper [FNet: Mixing Tokens with Fourier Transforms](https://arxiv.org/abs/2105.03824) by James Lee-Thorp, Joshua Ainslie, Ilya Eckstein, Santiago Ontanon. +1. **[FocalNet](https://huggingface.co/docs/transformers/model_doc/focalnet)** (from Microsoft Research) released with the paper [Focal Modulation Networks](https://arxiv.org/abs/2203.11926) by Jianwei Yang, Chunyuan Li, Xiyang Dai, Lu Yuan, Jianfeng Gao. +1. **[Funnel Transformer](https://huggingface.co/docs/transformers/model_doc/funnel)** (from CMU/Google Brain) released with the paper [Funnel-Transformer: Filtering out Sequential Redundancy for Efficient Language Processing](https://arxiv.org/abs/2006.03236) by Zihang Dai, Guokun Lai, Yiming Yang, Quoc V. Le. +1. **[Fuyu](https://huggingface.co/docs/transformers/model_doc/fuyu)** (from ADEPT) Rohan Bavishi, Erich Elsen, Curtis Hawthorne, Maxwell Nye, Augustus Odena, Arushi Somani, Sağnak Taşırlar. Released with the paper [blog post](https://www.adept.ai/blog/fuyu-8b) +1. **[Gemma](https://huggingface.co/docs/transformers/model_doc/gemma)** (from Google) released with the paper [Gemma: Open Models Based on Gemini Technology and Research](https://blog.google/technology/developers/gemma-open-models/) by the Gemma Google team. +1. **[GIT](https://huggingface.co/docs/transformers/model_doc/git)** (from Microsoft Research) released with the paper [GIT: A Generative Image-to-text Transformer for Vision and Language](https://arxiv.org/abs/2205.14100) by Jianfeng Wang, Zhengyuan Yang, Xiaowei Hu, Linjie Li, Kevin Lin, Zhe Gan, Zicheng Liu, Ce Liu, Lijuan Wang. +1. **[GLPN](https://huggingface.co/docs/transformers/model_doc/glpn)** (from KAIST) released with the paper [Global-Local Path Networks for Monocular Depth Estimation with Vertical CutDepth](https://arxiv.org/abs/2201.07436) by Doyeon Kim, Woonghyun Ga, Pyungwhan Ahn, Donggyu Joo, Sehwan Chun, Junmo Kim. +1. **[GPT](https://huggingface.co/docs/transformers/model_doc/openai-gpt)** (from OpenAI) released with the paper [Improving Language Understanding by Generative Pre-Training](https://openai.com/research/language-unsupervised/) by Alec Radford, Karthik Narasimhan, Tim Salimans and Ilya Sutskever. +1. **[GPT Neo](https://huggingface.co/docs/transformers/model_doc/gpt_neo)** (from EleutherAI) released in the repository [EleutherAI/gpt-neo](https://github.com/EleutherAI/gpt-neo) by Sid Black, Stella Biderman, Leo Gao, Phil Wang and Connor Leahy. +1. **[GPT NeoX](https://huggingface.co/docs/transformers/model_doc/gpt_neox)** (from EleutherAI) released with the paper [GPT-NeoX-20B: An Open-Source Autoregressive Language Model](https://arxiv.org/abs/2204.06745) by Sid Black, Stella Biderman, Eric Hallahan, Quentin Anthony, Leo Gao, Laurence Golding, Horace He, Connor Leahy, Kyle McDonell, Jason Phang, Michael Pieler, USVSN Sai Prashanth, Shivanshu Purohit, Laria Reynolds, Jonathan Tow, Ben Wang, Samuel Weinbach +1. **[GPT NeoX Japanese](https://huggingface.co/docs/transformers/model_doc/gpt_neox_japanese)** (from ABEJA) released by Shinya Otani, Takayoshi Makabe, Anuj Arora, and Kyo Hattori. +1. **[GPT-2](https://huggingface.co/docs/transformers/model_doc/gpt2)** (from OpenAI) released with the paper [Language Models are Unsupervised Multitask Learners](https://openai.com/research/better-language-models/) by Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei and Ilya Sutskever. +1. **[GPT-J](https://huggingface.co/docs/transformers/model_doc/gptj)** (from EleutherAI) released in the repository [kingoflolz/mesh-transformer-jax](https://github.com/kingoflolz/mesh-transformer-jax/) by Ben Wang and Aran Komatsuzaki. +1. **[GPT-Sw3](https://huggingface.co/docs/transformers/model_doc/gpt-sw3)** (from AI-Sweden) released with the paper [Lessons Learned from GPT-SW3: Building the First Large-Scale Generative Language Model for Swedish](http://www.lrec-conf.org/proceedings/lrec2022/pdf/2022.lrec-1.376.pdf) by Ariel Ekgren, Amaru Cuba Gyllensten, Evangelia Gogoulou, Alice Heiman, Severine Verlinden, Joey Öhman, Fredrik Carlsson, Magnus Sahlgren. +1. **[GPTBigCode](https://huggingface.co/docs/transformers/model_doc/gpt_bigcode)** (from BigCode) released with the paper [SantaCoder: don't reach for the stars!](https://arxiv.org/abs/2301.03988) by Loubna Ben Allal, Raymond Li, Denis Kocetkov, Chenghao Mou, Christopher Akiki, Carlos Munoz Ferrandis, Niklas Muennighoff, Mayank Mishra, Alex Gu, Manan Dey, Logesh Kumar Umapathi, Carolyn Jane Anderson, Yangtian Zi, Joel Lamy Poirier, Hailey Schoelkopf, Sergey Troshin, Dmitry Abulkhanov, Manuel Romero, Michael Lappert, Francesco De Toni, Bernardo García del Río, Qian Liu, Shamik Bose, Urvashi Bhattacharyya, Terry Yue Zhuo, Ian Yu, Paulo Villegas, Marco Zocca, Sourab Mangrulkar, David Lansky, Huu Nguyen, Danish Contractor, Luis Villa, Jia Li, Dzmitry Bahdanau, Yacine Jernite, Sean Hughes, Daniel Fried, Arjun Guha, Harm de Vries, Leandro von Werra. +1. **[GPTSAN-japanese](https://huggingface.co/docs/transformers/model_doc/gptsan-japanese)** released in the repository [tanreinama/GPTSAN](https://github.com/tanreinama/GPTSAN/blob/main/report/model.md) by Toshiyuki Sakamoto(tanreinama). +1. **[Graphormer](https://huggingface.co/docs/transformers/model_doc/graphormer)** (from Microsoft) released with the paper [Do Transformers Really Perform Bad for Graph Representation?](https://arxiv.org/abs/2106.05234) by Chengxuan Ying, Tianle Cai, Shengjie Luo, Shuxin Zheng, Guolin Ke, Di He, Yanming Shen, Tie-Yan Liu. +1. **[GroupViT](https://huggingface.co/docs/transformers/model_doc/groupvit)** (from UCSD, NVIDIA) released with the paper [GroupViT: Semantic Segmentation Emerges from Text Supervision](https://arxiv.org/abs/2202.11094) by Jiarui Xu, Shalini De Mello, Sifei Liu, Wonmin Byeon, Thomas Breuel, Jan Kautz, Xiaolong Wang. +1. **[HerBERT](https://huggingface.co/docs/transformers/model_doc/herbert)** (from Allegro.pl, AGH University of Science and Technology) released with the paper [KLEJ: Comprehensive Benchmark for Polish Language Understanding](https://www.aclweb.org/anthology/2020.acl-main.111.pdf) by Piotr Rybak, Robert Mroczkowski, Janusz Tracz, Ireneusz Gawlik. +1. **[Hubert](https://huggingface.co/docs/transformers/model_doc/hubert)** (from Facebook) released with the paper [HuBERT: Self-Supervised Speech Representation Learning by Masked Prediction of Hidden Units](https://arxiv.org/abs/2106.07447) by Wei-Ning Hsu, Benjamin Bolte, Yao-Hung Hubert Tsai, Kushal Lakhotia, Ruslan Salakhutdinov, Abdelrahman Mohamed. +1. **[I-BERT](https://huggingface.co/docs/transformers/model_doc/ibert)** (from Berkeley) released with the paper [I-BERT: Integer-only BERT Quantization](https://arxiv.org/abs/2101.01321) by Sehoon Kim, Amir Gholami, Zhewei Yao, Michael W. Mahoney, Kurt Keutzer. +1. **[IDEFICS](https://huggingface.co/docs/transformers/model_doc/idefics)** (from HuggingFace) released with the paper [OBELICS: An Open Web-Scale Filtered Dataset of Interleaved Image-Text Documents](https://huggingface.co/papers/2306.16527) by Hugo Laurençon, Lucile Saulnier, Léo Tronchon, Stas Bekman, Amanpreet Singh, Anton Lozhkov, Thomas Wang, Siddharth Karamcheti, Alexander M. Rush, Douwe Kiela, Matthieu Cord, Victor Sanh. +1. **[ImageGPT](https://huggingface.co/docs/transformers/model_doc/imagegpt)** (from OpenAI) released with the paper [Generative Pretraining from Pixels](https://openai.com/blog/image-gpt/) by Mark Chen, Alec Radford, Rewon Child, Jeffrey Wu, Heewoo Jun, David Luan, Ilya Sutskever. +1. **[Informer](https://huggingface.co/docs/transformers/model_doc/informer)** (from Beihang University, UC Berkeley, Rutgers University, SEDD Company) released with the paper [Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecasting](https://arxiv.org/abs/2012.07436) by Haoyi Zhou, Shanghang Zhang, Jieqi Peng, Shuai Zhang, Jianxin Li, Hui Xiong, and Wancai Zhang. +1. **[InstructBLIP](https://huggingface.co/docs/transformers/model_doc/instructblip)** (from Salesforce) released with the paper [InstructBLIP: Towards General-purpose Vision-Language Models with Instruction Tuning](https://arxiv.org/abs/2305.06500) by Wenliang Dai, Junnan Li, Dongxu Li, Anthony Meng Huat Tiong, Junqi Zhao, Weisheng Wang, Boyang Li, Pascale Fung, Steven Hoi. +1. **[Jukebox](https://huggingface.co/docs/transformers/model_doc/jukebox)** (from OpenAI) released with the paper [Jukebox: A Generative Model for Music](https://arxiv.org/pdf/2005.00341.pdf) by Prafulla Dhariwal, Heewoo Jun, Christine Payne, Jong Wook Kim, Alec Radford, Ilya Sutskever. +1. **[KOSMOS-2](https://huggingface.co/docs/transformers/model_doc/kosmos-2)** (from Microsoft Research Asia) released with the paper [Kosmos-2: Grounding Multimodal Large Language Models to the World](https://arxiv.org/abs/2306.14824) by Zhiliang Peng, Wenhui Wang, Li Dong, Yaru Hao, Shaohan Huang, Shuming Ma, Furu Wei. +1. **[LayoutLM](https://huggingface.co/docs/transformers/model_doc/layoutlm)** (from Microsoft Research Asia) released with the paper [LayoutLM: Pre-training of Text and Layout for Document Image Understanding](https://arxiv.org/abs/1912.13318) by Yiheng Xu, Minghao Li, Lei Cui, Shaohan Huang, Furu Wei, Ming Zhou. +1. **[LayoutLMv2](https://huggingface.co/docs/transformers/model_doc/layoutlmv2)** (from Microsoft Research Asia) released with the paper [LayoutLMv2: Multi-modal Pre-training for Visually-Rich Document Understanding](https://arxiv.org/abs/2012.14740) by Yang Xu, Yiheng Xu, Tengchao Lv, Lei Cui, Furu Wei, Guoxin Wang, Yijuan Lu, Dinei Florencio, Cha Zhang, Wanxiang Che, Min Zhang, Lidong Zhou. +1. **[LayoutLMv3](https://huggingface.co/docs/transformers/model_doc/layoutlmv3)** (from Microsoft Research Asia) released with the paper [LayoutLMv3: Pre-training for Document AI with Unified Text and Image Masking](https://arxiv.org/abs/2204.08387) by Yupan Huang, Tengchao Lv, Lei Cui, Yutong Lu, Furu Wei. +1. **[LayoutXLM](https://huggingface.co/docs/transformers/model_doc/layoutxlm)** (from Microsoft Research Asia) released with the paper [LayoutXLM: Multimodal Pre-training for Multilingual Visually-rich Document Understanding](https://arxiv.org/abs/2104.08836) by Yiheng Xu, Tengchao Lv, Lei Cui, Guoxin Wang, Yijuan Lu, Dinei Florencio, Cha Zhang, Furu Wei. +1. **[LED](https://huggingface.co/docs/transformers/model_doc/led)** (from AllenAI) released with the paper [Longformer: The Long-Document Transformer](https://arxiv.org/abs/2004.05150) by Iz Beltagy, Matthew E. Peters, Arman Cohan. +1. **[LeViT](https://huggingface.co/docs/transformers/model_doc/levit)** (from Meta AI) released with the paper [LeViT: A Vision Transformer in ConvNet's Clothing for Faster Inference](https://arxiv.org/abs/2104.01136) by Ben Graham, Alaaeldin El-Nouby, Hugo Touvron, Pierre Stock, Armand Joulin, Hervé Jégou, Matthijs Douze. +1. **[LiLT](https://huggingface.co/docs/transformers/model_doc/lilt)** (from South China University of Technology) released with the paper [LiLT: A Simple yet Effective Language-Independent Layout Transformer for Structured Document Understanding](https://arxiv.org/abs/2202.13669) by Jiapeng Wang, Lianwen Jin, Kai Ding. +1. **[LLaMA](https://huggingface.co/docs/transformers/model_doc/llama)** (from The FAIR team of Meta AI) released with the paper [LLaMA: Open and Efficient Foundation Language Models](https://arxiv.org/abs/2302.13971) by Hugo Touvron, Thibaut Lavril, Gautier Izacard, Xavier Martinet, Marie-Anne Lachaux, Timothée Lacroix, Baptiste Rozière, Naman Goyal, Eric Hambro, Faisal Azhar, Aurelien Rodriguez, Armand Joulin, Edouard Grave, Guillaume Lample. +1. **[Llama2](https://huggingface.co/docs/transformers/model_doc/llama2)** (from The FAIR team of Meta AI) released with the paper [Llama2: Open Foundation and Fine-Tuned Chat Models](https://ai.meta.com/research/publications/llama-2-open-foundation-and-fine-tuned-chat-models/) by Hugo Touvron, Louis Martin, Kevin Stone, Peter Albert, Amjad Almahairi, Yasmine Babaei, Nikolay Bashlykov, Soumya Batra, Prajjwal Bhargava, Shruti Bhosale, Dan Bikel, Lukas Blecher, Cristian Canton Ferrer, Moya Chen, Guillem Cucurull, David Esiobu, Jude Fernandes, Jeremy Fu, Wenyin Fu, Brian Fuller, Cynthia Gao, Vedanuj Goswami, Naman Goyal, Anthony Hartshorn, Saghar Hosseini, Rui Hou, Hakan Inan, Marcin Kardas, Viktor Kerkez Madian Khabsa, Isabel Kloumann, Artem Korenev, Punit Singh Koura, Marie-Anne Lachaux, Thibaut Lavril, Jenya Lee, Diana Liskovich, Yinghai Lu, Yuning Mao, Xavier Martinet, Todor Mihaylov, Pushka rMishra, Igor Molybog, Yixin Nie, Andrew Poulton, Jeremy Reizenstein, Rashi Rungta, Kalyan Saladi, Alan Schelten, Ruan Silva, Eric Michael Smith, Ranjan Subramanian, Xiaoqing EllenTan, Binh Tang, Ross Taylor, Adina Williams, Jian Xiang Kuan, Puxin Xu, Zheng Yan, Iliyan Zarov, Yuchen Zhang, Angela Fan, Melanie Kambadur, Sharan Narang, Aurelien Rodriguez, Robert Stojnic, Sergey Edunov, Thomas Scialom. +1. **[LLaVa](https://huggingface.co/docs/transformers/model_doc/llava)** (from Microsoft Research & University of Wisconsin-Madison) released with the paper [Visual Instruction Tuning](https://arxiv.org/abs/2304.08485) by Haotian Liu, Chunyuan Li, Yuheng Li and Yong Jae Lee. +1. **[LLaVA-NeXT](https://huggingface.co/docs/transformers/model_doc/llava_next)** (from Microsoft Research & University of Wisconsin-Madison) released with the paper [Improved Baselines with Visual Instruction Tuning](https://arxiv.org/abs/2310.03744) by Haotian Liu, Chunyuan Li, Yuheng Li and Yong Jae Lee. +1. **[Longformer](https://huggingface.co/docs/transformers/model_doc/longformer)** (from AllenAI) released with the paper [Longformer: The Long-Document Transformer](https://arxiv.org/abs/2004.05150) by Iz Beltagy, Matthew E. Peters, Arman Cohan. +1. **[LongT5](https://huggingface.co/docs/transformers/model_doc/longt5)** (from Google AI) released with the paper [LongT5: Efficient Text-To-Text Transformer for Long Sequences](https://arxiv.org/abs/2112.07916) by Mandy Guo, Joshua Ainslie, David Uthus, Santiago Ontanon, Jianmo Ni, Yun-Hsuan Sung, Yinfei Yang. +1. **[LUKE](https://huggingface.co/docs/transformers/model_doc/luke)** (from Studio Ousia) released with the paper [LUKE: Deep Contextualized Entity Representations with Entity-aware Self-attention](https://arxiv.org/abs/2010.01057) by Ikuya Yamada, Akari Asai, Hiroyuki Shindo, Hideaki Takeda, Yuji Matsumoto. +1. **[LXMERT](https://huggingface.co/docs/transformers/model_doc/lxmert)** (from UNC Chapel Hill) released with the paper [LXMERT: Learning Cross-Modality Encoder Representations from Transformers for Open-Domain Question Answering](https://arxiv.org/abs/1908.07490) by Hao Tan and Mohit Bansal. +1. **[M-CTC-T](https://huggingface.co/docs/transformers/model_doc/mctct)** (from Facebook) released with the paper [Pseudo-Labeling For Massively Multilingual Speech Recognition](https://arxiv.org/abs/2111.00161) by Loren Lugosch, Tatiana Likhomanenko, Gabriel Synnaeve, and Ronan Collobert. +1. **[M2M100](https://huggingface.co/docs/transformers/model_doc/m2m_100)** (from Facebook) released with the paper [Beyond English-Centric Multilingual Machine Translation](https://arxiv.org/abs/2010.11125) by Angela Fan, Shruti Bhosale, Holger Schwenk, Zhiyi Ma, Ahmed El-Kishky, Siddharth Goyal, Mandeep Baines, Onur Celebi, Guillaume Wenzek, Vishrav Chaudhary, Naman Goyal, Tom Birch, Vitaliy Liptchinsky, Sergey Edunov, Edouard Grave, Michael Auli, Armand Joulin. +1. **[MADLAD-400](https://huggingface.co/docs/transformers/model_doc/madlad-400)** (from Google) released with the paper [MADLAD-400: A Multilingual And Document-Level Large Audited Dataset](https://arxiv.org/abs/2309.04662) by Sneha Kudugunta, Isaac Caswell, Biao Zhang, Xavier Garcia, Christopher A. Choquette-Choo, Katherine Lee, Derrick Xin, Aditya Kusupati, Romi Stella, Ankur Bapna, Orhan Firat. +1. **[Mamba](https://huggingface.co/docs/transformers/model_doc/mamba)** (from Albert Gu and Tri Dao) released with the paper [Mamba: Linear-Time Sequence Modeling with Selective State Spaces](https://arxiv.org/abs/2312.00752) by Albert Gu and Tri Dao. +1. **[MarianMT](https://huggingface.co/docs/transformers/model_doc/marian)** Machine translation models trained using [OPUS](http://opus.nlpl.eu/) data by Jörg Tiedemann. The [Marian Framework](https://marian-nmt.github.io/) is being developed by the Microsoft Translator Team. +1. **[MarkupLM](https://huggingface.co/docs/transformers/model_doc/markuplm)** (from Microsoft Research Asia) released with the paper [MarkupLM: Pre-training of Text and Markup Language for Visually-rich Document Understanding](https://arxiv.org/abs/2110.08518) by Junlong Li, Yiheng Xu, Lei Cui, Furu Wei. +1. **[Mask2Former](https://huggingface.co/docs/transformers/model_doc/mask2former)** (from FAIR and UIUC) released with the paper [Masked-attention Mask Transformer for Universal Image Segmentation](https://arxiv.org/abs/2112.01527) by Bowen Cheng, Ishan Misra, Alexander G. Schwing, Alexander Kirillov, Rohit Girdhar. +1. **[MaskFormer](https://huggingface.co/docs/transformers/model_doc/maskformer)** (from Meta and UIUC) released with the paper [Per-Pixel Classification is Not All You Need for Semantic Segmentation](https://arxiv.org/abs/2107.06278) by Bowen Cheng, Alexander G. Schwing, Alexander Kirillov. +1. **[MatCha](https://huggingface.co/docs/transformers/model_doc/matcha)** (from Google AI) released with the paper [MatCha: Enhancing Visual Language Pretraining with Math Reasoning and Chart Derendering](https://arxiv.org/abs/2212.09662) by Fangyu Liu, Francesco Piccinno, Syrine Krichene, Chenxi Pang, Kenton Lee, Mandar Joshi, Yasemin Altun, Nigel Collier, Julian Martin Eisenschlos. +1. **[mBART](https://huggingface.co/docs/transformers/model_doc/mbart)** (from Facebook) released with the paper [Multilingual Denoising Pre-training for Neural Machine Translation](https://arxiv.org/abs/2001.08210) by Yinhan Liu, Jiatao Gu, Naman Goyal, Xian Li, Sergey Edunov, Marjan Ghazvininejad, Mike Lewis, Luke Zettlemoyer. +1. **[mBART-50](https://huggingface.co/docs/transformers/model_doc/mbart)** (from Facebook) released with the paper [Multilingual Translation with Extensible Multilingual Pretraining and Finetuning](https://arxiv.org/abs/2008.00401) by Yuqing Tang, Chau Tran, Xian Li, Peng-Jen Chen, Naman Goyal, Vishrav Chaudhary, Jiatao Gu, Angela Fan. +1. **[MEGA](https://huggingface.co/docs/transformers/model_doc/mega)** (from Meta/USC/CMU/SJTU) released with the paper [Mega: Moving Average Equipped Gated Attention](https://arxiv.org/abs/2209.10655) by Xuezhe Ma, Chunting Zhou, Xiang Kong, Junxian He, Liangke Gui, Graham Neubig, Jonathan May, and Luke Zettlemoyer. +1. **[Megatron-BERT](https://huggingface.co/docs/transformers/model_doc/megatron-bert)** (from NVIDIA) released with the paper [Megatron-LM: Training Multi-Billion Parameter Language Models Using Model Parallelism](https://arxiv.org/abs/1909.08053) by Mohammad Shoeybi, Mostofa Patwary, Raul Puri, Patrick LeGresley, Jared Casper and Bryan Catanzaro. +1. **[Megatron-GPT2](https://huggingface.co/docs/transformers/model_doc/megatron_gpt2)** (from NVIDIA) released with the paper [Megatron-LM: Training Multi-Billion Parameter Language Models Using Model Parallelism](https://arxiv.org/abs/1909.08053) by Mohammad Shoeybi, Mostofa Patwary, Raul Puri, Patrick LeGresley, Jared Casper and Bryan Catanzaro. +1. **[MGP-STR](https://huggingface.co/docs/transformers/model_doc/mgp-str)** (from Alibaba Research) released with the paper [Multi-Granularity Prediction for Scene Text Recognition](https://arxiv.org/abs/2209.03592) by Peng Wang, Cheng Da, and Cong Yao. +1. **[Mistral](https://huggingface.co/docs/transformers/model_doc/mistral)** (from Mistral AI) by The [Mistral AI](https://mistral.ai) team: Albert Jiang, Alexandre Sablayrolles, Arthur Mensch, Chris Bamford, Devendra Singh Chaplot, Diego de las Casas, Florian Bressand, Gianna Lengyel, Guillaume Lample, Lélio Renard Lavaud, Lucile Saulnier, Marie-Anne Lachaux, Pierre Stock, Teven Le Scao, Thibaut Lavril, Thomas Wang, Timothée Lacroix, William El Sayed. +1. **[Mixtral](https://huggingface.co/docs/transformers/model_doc/mixtral)** (from Mistral AI) by The [Mistral AI](https://mistral.ai) team: Albert Jiang, Alexandre Sablayrolles, Arthur Mensch, Chris Bamford, Devendra Singh Chaplot, Diego de las Casas, Florian Bressand, Gianna Lengyel, Guillaume Lample, Lélio Renard Lavaud, Lucile Saulnier, Marie-Anne Lachaux, Pierre Stock, Teven Le Scao, Thibaut Lavril, Thomas Wang, Timothée Lacroix, William El Sayed. +1. **[mLUKE](https://huggingface.co/docs/transformers/model_doc/mluke)** (from Studio Ousia) released with the paper [mLUKE: The Power of Entity Representations in Multilingual Pretrained Language Models](https://arxiv.org/abs/2110.08151) by Ryokan Ri, Ikuya Yamada, and Yoshimasa Tsuruoka. +1. **[MMS](https://huggingface.co/docs/transformers/model_doc/mms)** (from Facebook) released with the paper [Scaling Speech Technology to 1,000+ Languages](https://arxiv.org/abs/2305.13516) by Vineel Pratap, Andros Tjandra, Bowen Shi, Paden Tomasello, Arun Babu, Sayani Kundu, Ali Elkahky, Zhaoheng Ni, Apoorv Vyas, Maryam Fazel-Zarandi, Alexei Baevski, Yossi Adi, Xiaohui Zhang, Wei-Ning Hsu, Alexis Conneau, Michael Auli. +1. **[MobileBERT](https://huggingface.co/docs/transformers/model_doc/mobilebert)** (from CMU/Google Brain) released with the paper [MobileBERT: a Compact Task-Agnostic BERT for Resource-Limited Devices](https://arxiv.org/abs/2004.02984) by Zhiqing Sun, Hongkun Yu, Xiaodan Song, Renjie Liu, Yiming Yang, and Denny Zhou. +1. **[MobileNetV1](https://huggingface.co/docs/transformers/model_doc/mobilenet_v1)** (from Google Inc.) released with the paper [MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications](https://arxiv.org/abs/1704.04861) by Andrew G. Howard, Menglong Zhu, Bo Chen, Dmitry Kalenichenko, Weijun Wang, Tobias Weyand, Marco Andreetto, Hartwig Adam. +1. **[MobileNetV2](https://huggingface.co/docs/transformers/model_doc/mobilenet_v2)** (from Google Inc.) released with the paper [MobileNetV2: Inverted Residuals and Linear Bottlenecks](https://arxiv.org/abs/1801.04381) by Mark Sandler, Andrew Howard, Menglong Zhu, Andrey Zhmoginov, Liang-Chieh Chen. +1. **[MobileViT](https://huggingface.co/docs/transformers/model_doc/mobilevit)** (from Apple) released with the paper [MobileViT: Light-weight, General-purpose, and Mobile-friendly Vision Transformer](https://arxiv.org/abs/2110.02178) by Sachin Mehta and Mohammad Rastegari. +1. **[MobileViTV2](https://huggingface.co/docs/transformers/model_doc/mobilevitv2)** (from Apple) released with the paper [Separable Self-attention for Mobile Vision Transformers](https://arxiv.org/abs/2206.02680) by Sachin Mehta and Mohammad Rastegari. +1. **[MPNet](https://huggingface.co/docs/transformers/model_doc/mpnet)** (from Microsoft Research) released with the paper [MPNet: Masked and Permuted Pre-training for Language Understanding](https://arxiv.org/abs/2004.09297) by Kaitao Song, Xu Tan, Tao Qin, Jianfeng Lu, Tie-Yan Liu. +1. **[MPT](https://huggingface.co/docs/transformers/model_doc/mpt)** (from MosaiML) released with the repository [llm-foundry](https://github.com/mosaicml/llm-foundry/) by the MosaicML NLP Team. +1. **[MRA](https://huggingface.co/docs/transformers/model_doc/mra)** (from the University of Wisconsin - Madison) released with the paper [Multi Resolution Analysis (MRA) for Approximate Self-Attention](https://arxiv.org/abs/2207.10284) by Zhanpeng Zeng, Sourav Pal, Jeffery Kline, Glenn M Fung, Vikas Singh. +1. **[MT5](https://huggingface.co/docs/transformers/model_doc/mt5)** (from Google AI) released with the paper [mT5: A massively multilingual pre-trained text-to-text transformer](https://arxiv.org/abs/2010.11934) by Linting Xue, Noah Constant, Adam Roberts, Mihir Kale, Rami Al-Rfou, Aditya Siddhant, Aditya Barua, Colin Raffel. +1. **[MusicGen](https://huggingface.co/docs/transformers/model_doc/musicgen)** (from Meta) released with the paper [Simple and Controllable Music Generation](https://arxiv.org/abs/2306.05284) by Jade Copet, Felix Kreuk, Itai Gat, Tal Remez, David Kant, Gabriel Synnaeve, Yossi Adi and Alexandre Défossez. +1. **[MusicGen Melody](https://huggingface.co/docs/transformers/model_doc/musicgen_melody)** (from Meta) released with the paper [Simple and Controllable Music Generation](https://arxiv.org/abs/2306.05284) by Jade Copet, Felix Kreuk, Itai Gat, Tal Remez, David Kant, Gabriel Synnaeve, Yossi Adi and Alexandre Défossez. +1. **[MVP](https://huggingface.co/docs/transformers/model_doc/mvp)** (from RUC AI Box) released with the paper [MVP: Multi-task Supervised Pre-training for Natural Language Generation](https://arxiv.org/abs/2206.12131) by Tianyi Tang, Junyi Li, Wayne Xin Zhao and Ji-Rong Wen. +1. **[NAT](https://huggingface.co/docs/transformers/model_doc/nat)** (from SHI Labs) released with the paper [Neighborhood Attention Transformer](https://arxiv.org/abs/2204.07143) by Ali Hassani, Steven Walton, Jiachen Li, Shen Li, and Humphrey Shi. +1. **[Nezha](https://huggingface.co/docs/transformers/model_doc/nezha)** (from Huawei Noah’s Ark Lab) released with the paper [NEZHA: Neural Contextualized Representation for Chinese Language Understanding](https://arxiv.org/abs/1909.00204) by Junqiu Wei, Xiaozhe Ren, Xiaoguang Li, Wenyong Huang, Yi Liao, Yasheng Wang, Jiashu Lin, Xin Jiang, Xiao Chen and Qun Liu. +1. **[NLLB](https://huggingface.co/docs/transformers/model_doc/nllb)** (from Meta) released with the paper [No Language Left Behind: Scaling Human-Centered Machine Translation](https://arxiv.org/abs/2207.04672) by the NLLB team. +1. **[NLLB-MOE](https://huggingface.co/docs/transformers/model_doc/nllb-moe)** (from Meta) released with the paper [No Language Left Behind: Scaling Human-Centered Machine Translation](https://arxiv.org/abs/2207.04672) by the NLLB team. +1. **[Nougat](https://huggingface.co/docs/transformers/model_doc/nougat)** (from Meta AI) released with the paper [Nougat: Neural Optical Understanding for Academic Documents](https://arxiv.org/abs/2308.13418) by Lukas Blecher, Guillem Cucurull, Thomas Scialom, Robert Stojnic. +1. **[Nyströmformer](https://huggingface.co/docs/transformers/model_doc/nystromformer)** (from the University of Wisconsin - Madison) released with the paper [Nyströmformer: A Nyström-Based Algorithm for Approximating Self-Attention](https://arxiv.org/abs/2102.03902) by Yunyang Xiong, Zhanpeng Zeng, Rudrasis Chakraborty, Mingxing Tan, Glenn Fung, Yin Li, Vikas Singh. +1. **[OneFormer](https://huggingface.co/docs/transformers/model_doc/oneformer)** (from SHI Labs) released with the paper [OneFormer: One Transformer to Rule Universal Image Segmentation](https://arxiv.org/abs/2211.06220) by Jitesh Jain, Jiachen Li, MangTik Chiu, Ali Hassani, Nikita Orlov, Humphrey Shi. +1. **[OpenLlama](https://huggingface.co/docs/transformers/model_doc/open-llama)** (from [s-JoL](https://huggingface.co/s-JoL)) released on GitHub (now removed). +1. **[OPT](https://huggingface.co/docs/transformers/master/model_doc/opt)** (from Meta AI) released with the paper [OPT: Open Pre-trained Transformer Language Models](https://arxiv.org/abs/2205.01068) by Susan Zhang, Stephen Roller, Naman Goyal, Mikel Artetxe, Moya Chen, Shuohui Chen et al. +1. **[OWL-ViT](https://huggingface.co/docs/transformers/model_doc/owlvit)** (from Google AI) released with the paper [Simple Open-Vocabulary Object Detection with Vision Transformers](https://arxiv.org/abs/2205.06230) by Matthias Minderer, Alexey Gritsenko, Austin Stone, Maxim Neumann, Dirk Weissenborn, Alexey Dosovitskiy, Aravindh Mahendran, Anurag Arnab, Mostafa Dehghani, Zhuoran Shen, Xiao Wang, Xiaohua Zhai, Thomas Kipf, and Neil Houlsby. +1. **[OWLv2](https://huggingface.co/docs/transformers/model_doc/owlv2)** (from Google AI) released with the paper [Scaling Open-Vocabulary Object Detection](https://arxiv.org/abs/2306.09683) by Matthias Minderer, Alexey Gritsenko, Neil Houlsby. +1. **[PatchTSMixer](https://huggingface.co/docs/transformers/model_doc/patchtsmixer)** (from IBM Research) released with the paper [TSMixer: Lightweight MLP-Mixer Model for Multivariate Time Series Forecasting](https://arxiv.org/pdf/2306.09364.pdf) by Vijay Ekambaram, Arindam Jati, Nam Nguyen, Phanwadee Sinthong, Jayant Kalagnanam. +1. **[PatchTST](https://huggingface.co/docs/transformers/model_doc/patchtst)** (from IBM) released with the paper [A Time Series is Worth 64 Words: Long-term Forecasting with Transformers](https://arxiv.org/abs/2211.14730) by Yuqi Nie, Nam H. Nguyen, Phanwadee Sinthong, Jayant Kalagnanam. +1. **[Pegasus](https://huggingface.co/docs/transformers/model_doc/pegasus)** (from Google) released with the paper [PEGASUS: Pre-training with Extracted Gap-sentences for Abstractive Summarization](https://arxiv.org/abs/1912.08777) by Jingqing Zhang, Yao Zhao, Mohammad Saleh and Peter J. Liu. +1. **[PEGASUS-X](https://huggingface.co/docs/transformers/model_doc/pegasus_x)** (from Google) released with the paper [Investigating Efficiently Extending Transformers for Long Input Summarization](https://arxiv.org/abs/2208.04347) by Jason Phang, Yao Zhao, and Peter J. Liu. +1. **[Perceiver IO](https://huggingface.co/docs/transformers/model_doc/perceiver)** (from Deepmind) released with the paper [Perceiver IO: A General Architecture for Structured Inputs & Outputs](https://arxiv.org/abs/2107.14795) by Andrew Jaegle, Sebastian Borgeaud, Jean-Baptiste Alayrac, Carl Doersch, Catalin Ionescu, David Ding, Skanda Koppula, Daniel Zoran, Andrew Brock, Evan Shelhamer, Olivier Hénaff, Matthew M. Botvinick, Andrew Zisserman, Oriol Vinyals, João Carreira. +1. **[Persimmon](https://huggingface.co/docs/transformers/model_doc/persimmon)** (from ADEPT) released in a [blog post](https://www.adept.ai/blog/persimmon-8b) by Erich Elsen, Augustus Odena, Maxwell Nye, Sağnak Taşırlar, Tri Dao, Curtis Hawthorne, Deepak Moparthi, Arushi Somani. +1. **[Phi](https://huggingface.co/docs/transformers/model_doc/phi)** (from Microsoft) released with the papers - [Textbooks Are All You Need](https://arxiv.org/abs/2306.11644) by Suriya Gunasekar, Yi Zhang, Jyoti Aneja, Caio César Teodoro Mendes, Allie Del Giorno, Sivakanth Gopi, Mojan Javaheripi, Piero Kauffmann, Gustavo de Rosa, Olli Saarikivi, Adil Salim, Shital Shah, Harkirat Singh Behl, Xin Wang, Sébastien Bubeck, Ronen Eldan, Adam Tauman Kalai, Yin Tat Lee and Yuanzhi Li, [Textbooks Are All You Need II: phi-1.5 technical report](https://arxiv.org/abs/2309.05463) by Yuanzhi Li, Sébastien Bubeck, Ronen Eldan, Allie Del Giorno, Suriya Gunasekar and Yin Tat Lee. +1. **[PhoBERT](https://huggingface.co/docs/transformers/model_doc/phobert)** (from VinAI Research) released with the paper [PhoBERT: Pre-trained language models for Vietnamese](https://www.aclweb.org/anthology/2020.findings-emnlp.92/) by Dat Quoc Nguyen and Anh Tuan Nguyen. +1. **[Pix2Struct](https://huggingface.co/docs/transformers/model_doc/pix2struct)** (from Google) released with the paper [Pix2Struct: Screenshot Parsing as Pretraining for Visual Language Understanding](https://arxiv.org/abs/2210.03347) by Kenton Lee, Mandar Joshi, Iulia Turc, Hexiang Hu, Fangyu Liu, Julian Eisenschlos, Urvashi Khandelwal, Peter Shaw, Ming-Wei Chang, Kristina Toutanova. +1. **[PLBart](https://huggingface.co/docs/transformers/model_doc/plbart)** (from UCLA NLP) released with the paper [Unified Pre-training for Program Understanding and Generation](https://arxiv.org/abs/2103.06333) by Wasi Uddin Ahmad, Saikat Chakraborty, Baishakhi Ray, Kai-Wei Chang. +1. **[PoolFormer](https://huggingface.co/docs/transformers/model_doc/poolformer)** (from Sea AI Labs) released with the paper [MetaFormer is Actually What You Need for Vision](https://arxiv.org/abs/2111.11418) by Yu, Weihao and Luo, Mi and Zhou, Pan and Si, Chenyang and Zhou, Yichen and Wang, Xinchao and Feng, Jiashi and Yan, Shuicheng. +1. **[Pop2Piano](https://huggingface.co/docs/transformers/model_doc/pop2piano)** released with the paper [Pop2Piano : Pop Audio-based Piano Cover Generation](https://arxiv.org/abs/2211.00895) by Jongho Choi and Kyogu Lee. +1. **[ProphetNet](https://huggingface.co/docs/transformers/model_doc/prophetnet)** (from Microsoft Research) released with the paper [ProphetNet: Predicting Future N-gram for Sequence-to-Sequence Pre-training](https://arxiv.org/abs/2001.04063) by Yu Yan, Weizhen Qi, Yeyun Gong, Dayiheng Liu, Nan Duan, Jiusheng Chen, Ruofei Zhang and Ming Zhou. +1. **[PVT](https://huggingface.co/docs/transformers/model_doc/pvt)** (from Nanjing University, The University of Hong Kong etc.) released with the paper [Pyramid Vision Transformer: A Versatile Backbone for Dense Prediction without Convolutions](https://arxiv.org/pdf/2102.12122.pdf) by Wenhai Wang, Enze Xie, Xiang Li, Deng-Ping Fan, Kaitao Song, Ding Liang, Tong Lu, Ping Luo, Ling Shao. +1. **[PVTv2](https://huggingface.co/docs/transformers/model_doc/pvt_v2)** (from Shanghai AI Laboratory, Nanjing University, The University of Hong Kong etc.) released with the paper [PVT v2: Improved Baselines with Pyramid Vision Transformer](https://arxiv.org/abs/2106.13797) by Wenhai Wang, Enze Xie, Xiang Li, Deng-Ping Fan, Kaitao Song, Ding Liang, Tong Lu, Ping Luo, Ling Shao. +1. **[QDQBert](https://huggingface.co/docs/transformers/model_doc/qdqbert)** (from NVIDIA) released with the paper [Integer Quantization for Deep Learning Inference: Principles and Empirical Evaluation](https://arxiv.org/abs/2004.09602) by Hao Wu, Patrick Judd, Xiaojie Zhang, Mikhail Isaev and Paulius Micikevicius. +1. **[Qwen2](https://huggingface.co/docs/transformers/model_doc/qwen2)** (from the Qwen team, Alibaba Group) released with the paper [Qwen Technical Report](https://arxiv.org/abs/2309.16609) by Jinze Bai, Shuai Bai, Yunfei Chu, Zeyu Cui, Kai Dang, Xiaodong Deng, Yang Fan, Wenbin Ge, Yu Han, Fei Huang, Binyuan Hui, Luo Ji, Mei Li, Junyang Lin, Runji Lin, Dayiheng Liu, Gao Liu, Chengqiang Lu, Keming Lu, Jianxin Ma, Rui Men, Xingzhang Ren, Xuancheng Ren, Chuanqi Tan, Sinan Tan, Jianhong Tu, Peng Wang, Shijie Wang, Wei Wang, Shengguang Wu, Benfeng Xu, Jin Xu, An Yang, Hao Yang, Jian Yang, Shusheng Yang, Yang Yao, Bowen Yu, Hongyi Yuan, Zheng Yuan, Jianwei Zhang, Xingxuan Zhang, Yichang Zhang, Zhenru Zhang, Chang Zhou, Jingren Zhou, Xiaohuan Zhou and Tianhang Zhu. +1. **[RAG](https://huggingface.co/docs/transformers/model_doc/rag)** (from Facebook) released with the paper [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://arxiv.org/abs/2005.11401) by Patrick Lewis, Ethan Perez, Aleksandara Piktus, Fabio Petroni, Vladimir Karpukhin, Naman Goyal, Heinrich Küttler, Mike Lewis, Wen-tau Yih, Tim Rocktäschel, Sebastian Riedel, Douwe Kiela. +1. **[REALM](https://huggingface.co/docs/transformers/model_doc/realm.html)** (from Google Research) released with the paper [REALM: Retrieval-Augmented Language Model Pre-Training](https://arxiv.org/abs/2002.08909) by Kelvin Guu, Kenton Lee, Zora Tung, Panupong Pasupat and Ming-Wei Chang. +1. **[Reformer](https://huggingface.co/docs/transformers/model_doc/reformer)** (from Google Research) released with the paper [Reformer: The Efficient Transformer](https://arxiv.org/abs/2001.04451) by Nikita Kitaev, Łukasz Kaiser, Anselm Levskaya. +1. **[RegNet](https://huggingface.co/docs/transformers/model_doc/regnet)** (from META Platforms) released with the paper [Designing Network Design Space](https://arxiv.org/abs/2003.13678) by Ilija Radosavovic, Raj Prateek Kosaraju, Ross Girshick, Kaiming He, Piotr Dollár. +1. **[RemBERT](https://huggingface.co/docs/transformers/model_doc/rembert)** (from Google Research) released with the paper [Rethinking embedding coupling in pre-trained language models](https://arxiv.org/abs/2010.12821) by Hyung Won Chung, Thibault Févry, Henry Tsai, M. Johnson, Sebastian Ruder. +1. **[ResNet](https://huggingface.co/docs/transformers/model_doc/resnet)** (from Microsoft Research) released with the paper [Deep Residual Learning for Image Recognition](https://arxiv.org/abs/1512.03385) by Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun. +1. **[RoBERTa](https://huggingface.co/docs/transformers/model_doc/roberta)** (from Facebook), released together with the paper [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://arxiv.org/abs/1907.11692) by Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer, Veselin Stoyanov. +1. **[RoBERTa-PreLayerNorm](https://huggingface.co/docs/transformers/model_doc/roberta-prelayernorm)** (from Facebook) released with the paper [fairseq: A Fast, Extensible Toolkit for Sequence Modeling](https://arxiv.org/abs/1904.01038) by Myle Ott, Sergey Edunov, Alexei Baevski, Angela Fan, Sam Gross, Nathan Ng, David Grangier, Michael Auli. +1. **[RoCBert](https://huggingface.co/docs/transformers/model_doc/roc_bert)** (from WeChatAI) released with the paper [RoCBert: Robust Chinese Bert with Multimodal Contrastive Pretraining](https://aclanthology.org/2022.acl-long.65.pdf) by HuiSu, WeiweiShi, XiaoyuShen, XiaoZhou, TuoJi, JiaruiFang, JieZhou. +1. **[RoFormer](https://huggingface.co/docs/transformers/model_doc/roformer)** (from ZhuiyiTechnology), released together with the paper [RoFormer: Enhanced Transformer with Rotary Position Embedding](https://arxiv.org/abs/2104.09864) by Jianlin Su and Yu Lu and Shengfeng Pan and Bo Wen and Yunfeng Liu. +1. **[RWKV](https://huggingface.co/docs/transformers/model_doc/rwkv)** (from Bo Peng), released on [this repo](https://github.com/BlinkDL/RWKV-LM) by Bo Peng. +1. **[SeamlessM4T](https://huggingface.co/docs/transformers/model_doc/seamless_m4t)** (from Meta AI) released with the paper [SeamlessM4T — Massively Multilingual & Multimodal Machine Translation](https://dl.fbaipublicfiles.com/seamless/seamless_m4t_paper.pdf) by the Seamless Communication team. +1. **[SeamlessM4Tv2](https://huggingface.co/docs/transformers/model_doc/seamless_m4t_v2)** (from Meta AI) released with the paper [Seamless: Multilingual Expressive and Streaming Speech Translation](https://ai.meta.com/research/publications/seamless-multilingual-expressive-and-streaming-speech-translation/) by the Seamless Communication team. +1. **[SegFormer](https://huggingface.co/docs/transformers/model_doc/segformer)** (from NVIDIA) released with the paper [SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers](https://arxiv.org/abs/2105.15203) by Enze Xie, Wenhai Wang, Zhiding Yu, Anima Anandkumar, Jose M. Alvarez, Ping Luo. +1. **[SegGPT](https://huggingface.co/docs/transformers/model_doc/seggpt)** (from Beijing Academy of Artificial Intelligence (BAAI)) released with the paper [SegGPT: Segmenting Everything In Context](https://arxiv.org/abs/2304.03284) by Xinlong Wang, Xiaosong Zhang, Yue Cao, Wen Wang, Chunhua Shen, Tiejun Huang. +1. **[Segment Anything](https://huggingface.co/docs/transformers/model_doc/sam)** (from Meta AI) released with the paper [Segment Anything](https://arxiv.org/pdf/2304.02643v1.pdf) by Alexander Kirillov, Eric Mintun, Nikhila Ravi, Hanzi Mao, Chloe Rolland, Laura Gustafson, Tete Xiao, Spencer Whitehead, Alex Berg, Wan-Yen Lo, Piotr Dollar, Ross Girshick. +1. **[SEW](https://huggingface.co/docs/transformers/model_doc/sew)** (from ASAPP) released with the paper [Performance-Efficiency Trade-offs in Unsupervised Pre-training for Speech Recognition](https://arxiv.org/abs/2109.06870) by Felix Wu, Kwangyoun Kim, Jing Pan, Kyu Han, Kilian Q. Weinberger, Yoav Artzi. +1. **[SEW-D](https://huggingface.co/docs/transformers/model_doc/sew_d)** (from ASAPP) released with the paper [Performance-Efficiency Trade-offs in Unsupervised Pre-training for Speech Recognition](https://arxiv.org/abs/2109.06870) by Felix Wu, Kwangyoun Kim, Jing Pan, Kyu Han, Kilian Q. Weinberger, Yoav Artzi. +1. **[SigLIP](https://huggingface.co/docs/transformers/model_doc/siglip)** (from Google AI) released with the paper [Sigmoid Loss for Language Image Pre-Training](https://arxiv.org/abs/2303.15343) by Xiaohua Zhai, Basil Mustafa, Alexander Kolesnikov, Lucas Beyer. +1. **[SpeechT5](https://huggingface.co/docs/transformers/model_doc/speecht5)** (from Microsoft Research) released with the paper [SpeechT5: Unified-Modal Encoder-Decoder Pre-Training for Spoken Language Processing](https://arxiv.org/abs/2110.07205) by Junyi Ao, Rui Wang, Long Zhou, Chengyi Wang, Shuo Ren, Yu Wu, Shujie Liu, Tom Ko, Qing Li, Yu Zhang, Zhihua Wei, Yao Qian, Jinyu Li, Furu Wei. +1. **[SpeechToTextTransformer](https://huggingface.co/docs/transformers/model_doc/speech_to_text)** (from Facebook), released together with the paper [fairseq S2T: Fast Speech-to-Text Modeling with fairseq](https://arxiv.org/abs/2010.05171) by Changhan Wang, Yun Tang, Xutai Ma, Anne Wu, Dmytro Okhonko, Juan Pino. +1. **[SpeechToTextTransformer2](https://huggingface.co/docs/transformers/model_doc/speech_to_text_2)** (from Facebook), released together with the paper [Large-Scale Self- and Semi-Supervised Learning for Speech Translation](https://arxiv.org/abs/2104.06678) by Changhan Wang, Anne Wu, Juan Pino, Alexei Baevski, Michael Auli, Alexis Conneau. +1. **[Splinter](https://huggingface.co/docs/transformers/model_doc/splinter)** (from Tel Aviv University), released together with the paper [Few-Shot Question Answering by Pretraining Span Selection](https://arxiv.org/abs/2101.00438) by Ori Ram, Yuval Kirstain, Jonathan Berant, Amir Globerson, Omer Levy. +1. **[SqueezeBERT](https://huggingface.co/docs/transformers/model_doc/squeezebert)** (from Berkeley) released with the paper [SqueezeBERT: What can computer vision teach NLP about efficient neural networks?](https://arxiv.org/abs/2006.11316) by Forrest N. Iandola, Albert E. Shaw, Ravi Krishna, and Kurt W. Keutzer. +1. **[StableLm](https://huggingface.co/docs/transformers/model_doc/stablelm)** (from Stability AI) released with the paper [StableLM 3B 4E1T (Technical Report)](https://stability.wandb.io/stability-llm/stable-lm/reports/StableLM-3B-4E1T--VmlldzoyMjU4?accessToken=u3zujipenkx5g7rtcj9qojjgxpconyjktjkli2po09nffrffdhhchq045vp0wyfo) by Jonathan Tow, Marco Bellagente, Dakota Mahan, Carlos Riquelme Ruiz, Duy Phung, Maksym Zhuravinskyi, Nathan Cooper, Nikhil Pinnaparaju, Reshinth Adithyan, and James Baicoianu. +1. **[Starcoder2](https://huggingface.co/docs/transformers/model_doc/starcoder2)** (from BigCode team) released with the paper [StarCoder 2 and The Stack v2: The Next Generation](https://arxiv.org/abs/2402.19173) by Anton Lozhkov, Raymond Li, Loubna Ben Allal, Federico Cassano, Joel Lamy-Poirier, Nouamane Tazi, Ao Tang, Dmytro Pykhtar, Jiawei Liu, Yuxiang Wei, Tianyang Liu, Max Tian, Denis Kocetkov, Arthur Zucker, Younes Belkada, Zijian Wang, Qian Liu, Dmitry Abulkhanov, Indraneil Paul, Zhuang Li, Wen-Ding Li, Megan Risdal, Jia Li, Jian Zhu, Terry Yue Zhuo, Evgenii Zheltonozhskii, Nii Osae Osae Dade, Wenhao Yu, Lucas Krauß, Naman Jain, Yixuan Su, Xuanli He, Manan Dey, Edoardo Abati, Yekun Chai, Niklas Muennighoff, Xiangru Tang, Muhtasham Oblokulov, Christopher Akiki, Marc Marone, Chenghao Mou, Mayank Mishra, Alex Gu, Binyuan Hui, Tri Dao, Armel Zebaze, Olivier Dehaene, Nicolas Patry, Canwen Xu, Julian McAuley, Han Hu, Torsten Scholak, Sebastien Paquet, Jennifer Robinson, Carolyn Jane Anderson, Nicolas Chapados, Mostofa Patwary, Nima Tajbakhsh, Yacine Jernite, Carlos Muñoz Ferrandis, Lingming Zhang, Sean Hughes, Thomas Wolf, Arjun Guha, Leandro von Werra, and Harm de Vries. +1. **[SuperPoint](https://huggingface.co/docs/transformers/model_doc/superpoint)** (from MagicLeap) released with the paper [SuperPoint: Self-Supervised Interest Point Detection and Description](https://arxiv.org/abs/1712.07629) by Daniel DeTone, Tomasz Malisiewicz and Andrew Rabinovich. +1. **[SwiftFormer](https://huggingface.co/docs/transformers/model_doc/swiftformer)** (from MBZUAI) released with the paper [SwiftFormer: Efficient Additive Attention for Transformer-based Real-time Mobile Vision Applications](https://arxiv.org/abs/2303.15446) by Abdelrahman Shaker, Muhammad Maaz, Hanoona Rasheed, Salman Khan, Ming-Hsuan Yang, Fahad Shahbaz Khan. +1. **[Swin Transformer](https://huggingface.co/docs/transformers/model_doc/swin)** (from Microsoft) released with the paper [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://arxiv.org/abs/2103.14030) by Ze Liu, Yutong Lin, Yue Cao, Han Hu, Yixuan Wei, Zheng Zhang, Stephen Lin, Baining Guo. +1. **[Swin Transformer V2](https://huggingface.co/docs/transformers/model_doc/swinv2)** (from Microsoft) released with the paper [Swin Transformer V2: Scaling Up Capacity and Resolution](https://arxiv.org/abs/2111.09883) by Ze Liu, Han Hu, Yutong Lin, Zhuliang Yao, Zhenda Xie, Yixuan Wei, Jia Ning, Yue Cao, Zheng Zhang, Li Dong, Furu Wei, Baining Guo. +1. **[Swin2SR](https://huggingface.co/docs/transformers/model_doc/swin2sr)** (from University of Würzburg) released with the paper [Swin2SR: SwinV2 Transformer for Compressed Image Super-Resolution and Restoration](https://arxiv.org/abs/2209.11345) by Marcos V. Conde, Ui-Jin Choi, Maxime Burchi, Radu Timofte. +1. **[SwitchTransformers](https://huggingface.co/docs/transformers/model_doc/switch_transformers)** (from Google) released with the paper [Switch Transformers: Scaling to Trillion Parameter Models with Simple and Efficient Sparsity](https://arxiv.org/abs/2101.03961) by William Fedus, Barret Zoph, Noam Shazeer. +1. **[T5](https://huggingface.co/docs/transformers/model_doc/t5)** (from Google AI) released with the paper [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://arxiv.org/abs/1910.10683) by Colin Raffel and Noam Shazeer and Adam Roberts and Katherine Lee and Sharan Narang and Michael Matena and Yanqi Zhou and Wei Li and Peter J. Liu. +1. **[T5v1.1](https://huggingface.co/docs/transformers/model_doc/t5v1.1)** (from Google AI) released in the repository [google-research/text-to-text-transfer-transformer](https://github.com/google-research/text-to-text-transfer-transformer/blob/main/released_checkpoints.md#t511) by Colin Raffel and Noam Shazeer and Adam Roberts and Katherine Lee and Sharan Narang and Michael Matena and Yanqi Zhou and Wei Li and Peter J. Liu. +1. **[Table Transformer](https://huggingface.co/docs/transformers/model_doc/table-transformer)** (from Microsoft Research) released with the paper [PubTables-1M: Towards Comprehensive Table Extraction From Unstructured Documents](https://arxiv.org/abs/2110.00061) by Brandon Smock, Rohith Pesala, Robin Abraham. +1. **[TAPAS](https://huggingface.co/docs/transformers/model_doc/tapas)** (from Google AI) released with the paper [TAPAS: Weakly Supervised Table Parsing via Pre-training](https://arxiv.org/abs/2004.02349) by Jonathan Herzig, Paweł Krzysztof Nowak, Thomas Müller, Francesco Piccinno and Julian Martin Eisenschlos. +1. **[TAPEX](https://huggingface.co/docs/transformers/model_doc/tapex)** (from Microsoft Research) released with the paper [TAPEX: Table Pre-training via Learning a Neural SQL Executor](https://arxiv.org/abs/2107.07653) by Qian Liu, Bei Chen, Jiaqi Guo, Morteza Ziyadi, Zeqi Lin, Weizhu Chen, Jian-Guang Lou. +1. **[Time Series Transformer](https://huggingface.co/docs/transformers/model_doc/time_series_transformer)** (from HuggingFace). +1. **[TimeSformer](https://huggingface.co/docs/transformers/model_doc/timesformer)** (from Facebook) released with the paper [Is Space-Time Attention All You Need for Video Understanding?](https://arxiv.org/abs/2102.05095) by Gedas Bertasius, Heng Wang, Lorenzo Torresani. +1. **[Trajectory Transformer](https://huggingface.co/docs/transformers/model_doc/trajectory_transformers)** (from the University of California at Berkeley) released with the paper [Offline Reinforcement Learning as One Big Sequence Modeling Problem](https://arxiv.org/abs/2106.02039) by Michael Janner, Qiyang Li, Sergey Levine +1. **[Transformer-XL](https://huggingface.co/docs/transformers/model_doc/transfo-xl)** (from Google/CMU) released with the paper [Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context](https://arxiv.org/abs/1901.02860) by Zihang Dai*, Zhilin Yang*, Yiming Yang, Jaime Carbonell, Quoc V. Le, Ruslan Salakhutdinov. +1. **[TrOCR](https://huggingface.co/docs/transformers/model_doc/trocr)** (from Microsoft), released together with the paper [TrOCR: Transformer-based Optical Character Recognition with Pre-trained Models](https://arxiv.org/abs/2109.10282) by Minghao Li, Tengchao Lv, Lei Cui, Yijuan Lu, Dinei Florencio, Cha Zhang, Zhoujun Li, Furu Wei. +1. **[TVLT](https://huggingface.co/docs/transformers/model_doc/tvlt)** (from UNC Chapel Hill) released with the paper [TVLT: Textless Vision-Language Transformer](https://arxiv.org/abs/2209.14156) by Zineng Tang, Jaemin Cho, Yixin Nie, Mohit Bansal. +1. **[TVP](https://huggingface.co/docs/transformers/model_doc/tvp)** (from Intel) released with the paper [Text-Visual Prompting for Efficient 2D Temporal Video Grounding](https://arxiv.org/abs/2303.04995) by Yimeng Zhang, Xin Chen, Jinghan Jia, Sijia Liu, Ke Ding. +1. **[UDOP](https://huggingface.co/docs/transformers/model_doc/udop)** (from Microsoft Research) released with the paper [Unifying Vision, Text, and Layout for Universal Document Processing](https://arxiv.org/abs/2212.02623) by Zineng Tang, Ziyi Yang, Guoxin Wang, Yuwei Fang, Yang Liu, Chenguang Zhu, Michael Zeng, Cha Zhang, Mohit Bansal. +1. **[UL2](https://huggingface.co/docs/transformers/model_doc/ul2)** (from Google Research) released with the paper [Unifying Language Learning Paradigms](https://arxiv.org/abs/2205.05131v1) by Yi Tay, Mostafa Dehghani, Vinh Q. Tran, Xavier Garcia, Dara Bahri, Tal Schuster, Huaixiu Steven Zheng, Neil Houlsby, Donald Metzler +1. **[UMT5](https://huggingface.co/docs/transformers/model_doc/umt5)** (from Google Research) released with the paper [UniMax: Fairer and More Effective Language Sampling for Large-Scale Multilingual Pretraining](https://openreview.net/forum?id=kXwdL1cWOAi) by Hyung Won Chung, Xavier Garcia, Adam Roberts, Yi Tay, Orhan Firat, Sharan Narang, Noah Constant. +1. **[UniSpeech](https://huggingface.co/docs/transformers/model_doc/unispeech)** (from Microsoft Research) released with the paper [UniSpeech: Unified Speech Representation Learning with Labeled and Unlabeled Data](https://arxiv.org/abs/2101.07597) by Chengyi Wang, Yu Wu, Yao Qian, Kenichi Kumatani, Shujie Liu, Furu Wei, Michael Zeng, Xuedong Huang. +1. **[UniSpeechSat](https://huggingface.co/docs/transformers/model_doc/unispeech-sat)** (from Microsoft Research) released with the paper [UNISPEECH-SAT: UNIVERSAL SPEECH REPRESENTATION LEARNING WITH SPEAKER AWARE PRE-TRAINING](https://arxiv.org/abs/2110.05752) by Sanyuan Chen, Yu Wu, Chengyi Wang, Zhengyang Chen, Zhuo Chen, Shujie Liu, Jian Wu, Yao Qian, Furu Wei, Jinyu Li, Xiangzhan Yu. +1. **[UnivNet](https://huggingface.co/docs/transformers/model_doc/univnet)** (from Kakao Corporation) released with the paper [UnivNet: A Neural Vocoder with Multi-Resolution Spectrogram Discriminators for High-Fidelity Waveform Generation](https://arxiv.org/abs/2106.07889) by Won Jang, Dan Lim, Jaesam Yoon, Bongwan Kim, and Juntae Kim. +1. **[UPerNet](https://huggingface.co/docs/transformers/model_doc/upernet)** (from Peking University) released with the paper [Unified Perceptual Parsing for Scene Understanding](https://arxiv.org/abs/1807.10221) by Tete Xiao, Yingcheng Liu, Bolei Zhou, Yuning Jiang, Jian Sun. +1. **[VAN](https://huggingface.co/docs/transformers/model_doc/van)** (from Tsinghua University and Nankai University) released with the paper [Visual Attention Network](https://arxiv.org/abs/2202.09741) by Meng-Hao Guo, Cheng-Ze Lu, Zheng-Ning Liu, Ming-Ming Cheng, Shi-Min Hu. +1. **[VideoMAE](https://huggingface.co/docs/transformers/model_doc/videomae)** (from Multimedia Computing Group, Nanjing University) released with the paper [VideoMAE: Masked Autoencoders are Data-Efficient Learners for Self-Supervised Video Pre-Training](https://arxiv.org/abs/2203.12602) by Zhan Tong, Yibing Song, Jue Wang, Limin Wang. +1. **[ViLT](https://huggingface.co/docs/transformers/model_doc/vilt)** (from NAVER AI Lab/Kakao Enterprise/Kakao Brain) released with the paper [ViLT: Vision-and-Language Transformer Without Convolution or Region Supervision](https://arxiv.org/abs/2102.03334) by Wonjae Kim, Bokyung Son, Ildoo Kim. +1. **[VipLlava](https://huggingface.co/docs/transformers/model_doc/vipllava)** (from University of Wisconsin–Madison) released with the paper [Making Large Multimodal Models Understand Arbitrary Visual Prompts](https://arxiv.org/abs/2312.00784) by Mu Cai, Haotian Liu, Siva Karthik Mustikovela, Gregory P. Meyer, Yuning Chai, Dennis Park, Yong Jae Lee. +1. **[Vision Transformer (ViT)](https://huggingface.co/docs/transformers/model_doc/vit)** (from Google AI) released with the paper [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://arxiv.org/abs/2010.11929) by Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, Jakob Uszkoreit, Neil Houlsby. +1. **[VisualBERT](https://huggingface.co/docs/transformers/model_doc/visual_bert)** (from UCLA NLP) released with the paper [VisualBERT: A Simple and Performant Baseline for Vision and Language](https://arxiv.org/pdf/1908.03557) by Liunian Harold Li, Mark Yatskar, Da Yin, Cho-Jui Hsieh, Kai-Wei Chang. +1. **[ViT Hybrid](https://huggingface.co/docs/transformers/model_doc/vit_hybrid)** (from Google AI) released with the paper [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://arxiv.org/abs/2010.11929) by Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, Jakob Uszkoreit, Neil Houlsby. +1. **[VitDet](https://huggingface.co/docs/transformers/model_doc/vitdet)** (from Meta AI) released with the paper [Exploring Plain Vision Transformer Backbones for Object Detection](https://arxiv.org/abs/2203.16527) by Yanghao Li, Hanzi Mao, Ross Girshick, Kaiming He. +1. **[ViTMAE](https://huggingface.co/docs/transformers/model_doc/vit_mae)** (from Meta AI) released with the paper [Masked Autoencoders Are Scalable Vision Learners](https://arxiv.org/abs/2111.06377) by Kaiming He, Xinlei Chen, Saining Xie, Yanghao Li, Piotr Dollár, Ross Girshick. +1. **[ViTMatte](https://huggingface.co/docs/transformers/model_doc/vitmatte)** (from HUST-VL) released with the paper [ViTMatte: Boosting Image Matting with Pretrained Plain Vision Transformers](https://arxiv.org/abs/2305.15272) by Jingfeng Yao, Xinggang Wang, Shusheng Yang, Baoyuan Wang. +1. **[ViTMSN](https://huggingface.co/docs/transformers/model_doc/vit_msn)** (from Meta AI) released with the paper [Masked Siamese Networks for Label-Efficient Learning](https://arxiv.org/abs/2204.07141) by Mahmoud Assran, Mathilde Caron, Ishan Misra, Piotr Bojanowski, Florian Bordes, Pascal Vincent, Armand Joulin, Michael Rabbat, Nicolas Ballas. +1. **[VITS](https://huggingface.co/docs/transformers/model_doc/vits)** (from Kakao Enterprise) released with the paper [Conditional Variational Autoencoder with Adversarial Learning for End-to-End Text-to-Speech](https://arxiv.org/abs/2106.06103) by Jaehyeon Kim, Jungil Kong, Juhee Son. +1. **[ViViT](https://huggingface.co/docs/transformers/model_doc/vivit)** (from Google Research) released with the paper [ViViT: A Video Vision Transformer](https://arxiv.org/abs/2103.15691) by Anurag Arnab, Mostafa Dehghani, Georg Heigold, Chen Sun, Mario Lučić, Cordelia Schmid. +1. **[Wav2Vec2](https://huggingface.co/docs/transformers/model_doc/wav2vec2)** (from Facebook AI) released with the paper [wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations](https://arxiv.org/abs/2006.11477) by Alexei Baevski, Henry Zhou, Abdelrahman Mohamed, Michael Auli. +1. **[Wav2Vec2-BERT](https://huggingface.co/docs/transformers/model_doc/wav2vec2-bert)** (from Meta AI) released with the paper [Seamless: Multilingual Expressive and Streaming Speech Translation](https://ai.meta.com/research/publications/seamless-multilingual-expressive-and-streaming-speech-translation/) by the Seamless Communication team. +1. **[Wav2Vec2-Conformer](https://huggingface.co/docs/transformers/model_doc/wav2vec2-conformer)** (from Facebook AI) released with the paper [FAIRSEQ S2T: Fast Speech-to-Text Modeling with FAIRSEQ](https://arxiv.org/abs/2010.05171) by Changhan Wang, Yun Tang, Xutai Ma, Anne Wu, Sravya Popuri, Dmytro Okhonko, Juan Pino. +1. **[Wav2Vec2Phoneme](https://huggingface.co/docs/transformers/model_doc/wav2vec2_phoneme)** (from Facebook AI) released with the paper [Simple and Effective Zero-shot Cross-lingual Phoneme Recognition](https://arxiv.org/abs/2109.11680) by Qiantong Xu, Alexei Baevski, Michael Auli. +1. **[WavLM](https://huggingface.co/docs/transformers/model_doc/wavlm)** (from Microsoft Research) released with the paper [WavLM: Large-Scale Self-Supervised Pre-Training for Full Stack Speech Processing](https://arxiv.org/abs/2110.13900) by Sanyuan Chen, Chengyi Wang, Zhengyang Chen, Yu Wu, Shujie Liu, Zhuo Chen, Jinyu Li, Naoyuki Kanda, Takuya Yoshioka, Xiong Xiao, Jian Wu, Long Zhou, Shuo Ren, Yanmin Qian, Yao Qian, Jian Wu, Michael Zeng, Furu Wei. +1. **[Whisper](https://huggingface.co/docs/transformers/model_doc/whisper)** (from OpenAI) released with the paper [Robust Speech Recognition via Large-Scale Weak Supervision](https://cdn.openai.com/papers/whisper.pdf) by Alec Radford, Jong Wook Kim, Tao Xu, Greg Brockman, Christine McLeavey, Ilya Sutskever. +1. **[X-CLIP](https://huggingface.co/docs/transformers/model_doc/xclip)** (from Microsoft Research) released with the paper [Expanding Language-Image Pretrained Models for General Video Recognition](https://arxiv.org/abs/2208.02816) by Bolin Ni, Houwen Peng, Minghao Chen, Songyang Zhang, Gaofeng Meng, Jianlong Fu, Shiming Xiang, Haibin Ling. +1. **[X-MOD](https://huggingface.co/docs/transformers/model_doc/xmod)** (from Meta AI) released with the paper [Lifting the Curse of Multilinguality by Pre-training Modular Transformers](http://dx.doi.org/10.18653/v1/2022.naacl-main.255) by Jonas Pfeiffer, Naman Goyal, Xi Lin, Xian Li, James Cross, Sebastian Riedel, Mikel Artetxe. +1. **[XGLM](https://huggingface.co/docs/transformers/model_doc/xglm)** (From Facebook AI) released with the paper [Few-shot Learning with Multilingual Language Models](https://arxiv.org/abs/2112.10668) by Xi Victoria Lin, Todor Mihaylov, Mikel Artetxe, Tianlu Wang, Shuohui Chen, Daniel Simig, Myle Ott, Naman Goyal, Shruti Bhosale, Jingfei Du, Ramakanth Pasunuru, Sam Shleifer, Punit Singh Koura, Vishrav Chaudhary, Brian O'Horo, Jeff Wang, Luke Zettlemoyer, Zornitsa Kozareva, Mona Diab, Veselin Stoyanov, Xian Li. +1. **[XLM](https://huggingface.co/docs/transformers/model_doc/xlm)** (from Facebook) released together with the paper [Cross-lingual Language Model Pretraining](https://arxiv.org/abs/1901.07291) by Guillaume Lample and Alexis Conneau. +1. **[XLM-ProphetNet](https://huggingface.co/docs/transformers/model_doc/xlm-prophetnet)** (from Microsoft Research) released with the paper [ProphetNet: Predicting Future N-gram for Sequence-to-Sequence Pre-training](https://arxiv.org/abs/2001.04063) by Yu Yan, Weizhen Qi, Yeyun Gong, Dayiheng Liu, Nan Duan, Jiusheng Chen, Ruofei Zhang and Ming Zhou. +1. **[XLM-RoBERTa](https://huggingface.co/docs/transformers/model_doc/xlm-roberta)** (from Facebook AI), released together with the paper [Unsupervised Cross-lingual Representation Learning at Scale](https://arxiv.org/abs/1911.02116) by Alexis Conneau*, Kartikay Khandelwal*, Naman Goyal, Vishrav Chaudhary, Guillaume Wenzek, Francisco Guzmán, Edouard Grave, Myle Ott, Luke Zettlemoyer and Veselin Stoyanov. +1. **[XLM-RoBERTa-XL](https://huggingface.co/docs/transformers/model_doc/xlm-roberta-xl)** (from Facebook AI), released together with the paper [Larger-Scale Transformers for Multilingual Masked Language Modeling](https://arxiv.org/abs/2105.00572) by Naman Goyal, Jingfei Du, Myle Ott, Giri Anantharaman, Alexis Conneau. +1. **[XLM-V](https://huggingface.co/docs/transformers/model_doc/xlm-v)** (from Meta AI) released with the paper [XLM-V: Overcoming the Vocabulary Bottleneck in Multilingual Masked Language Models](https://arxiv.org/abs/2301.10472) by Davis Liang, Hila Gonen, Yuning Mao, Rui Hou, Naman Goyal, Marjan Ghazvininejad, Luke Zettlemoyer, Madian Khabsa. +1. **[XLNet](https://huggingface.co/docs/transformers/model_doc/xlnet)** (from Google/CMU) released with the paper [XLNet: Generalized Autoregressive Pretraining for Language Understanding](https://arxiv.org/abs/1906.08237) by Zhilin Yang*, Zihang Dai*, Yiming Yang, Jaime Carbonell, Ruslan Salakhutdinov, Quoc V. Le. +1. **[XLS-R](https://huggingface.co/docs/transformers/model_doc/xls_r)** (from Facebook AI) released with the paper [XLS-R: Self-supervised Cross-lingual Speech Representation Learning at Scale](https://arxiv.org/abs/2111.09296) by Arun Babu, Changhan Wang, Andros Tjandra, Kushal Lakhotia, Qiantong Xu, Naman Goyal, Kritika Singh, Patrick von Platen, Yatharth Saraf, Juan Pino, Alexei Baevski, Alexis Conneau, Michael Auli. +1. **[XLSR-Wav2Vec2](https://huggingface.co/docs/transformers/model_doc/xlsr_wav2vec2)** (from Facebook AI) released with the paper [Unsupervised Cross-Lingual Representation Learning For Speech Recognition](https://arxiv.org/abs/2006.13979) by Alexis Conneau, Alexei Baevski, Ronan Collobert, Abdelrahman Mohamed, Michael Auli. +1. **[YOLOS](https://huggingface.co/docs/transformers/model_doc/yolos)** (from Huazhong University of Science & Technology) released with the paper [You Only Look at One Sequence: Rethinking Transformer in Vision through Object Detection](https://arxiv.org/abs/2106.00666) by Yuxin Fang, Bencheng Liao, Xinggang Wang, Jiemin Fang, Jiyang Qi, Rui Wu, Jianwei Niu, Wenyu Liu. +1. **[YOSO](https://huggingface.co/docs/transformers/model_doc/yoso)** (from the University of Wisconsin - Madison) released with the paper [You Only Sample (Almost) Once: Linear Cost Self-Attention Via Bernoulli Sampling](https://arxiv.org/abs/2111.09714) by Zhanpeng Zeng, Yunyang Xiong, Sathya N. Ravi, Shailesh Acharya, Glenn Fung, Vikas Singh. +1. Want to contribute a new model? We have added a **detailed guide and templates** to guide you in the process of adding a new model. You can find them in the [`templates`](./templates) folder of the repository. Be sure to check the [contributing guidelines](./CONTRIBUTING.md) and contact the maintainers or open an issue to collect feedback before starting your PR. + +To check if each model has an implementation in Flax, PyTorch or TensorFlow, or has an associated tokenizer backed by the 🤗 Tokenizers library, refer to [this table](https://huggingface.co/docs/transformers/index#supported-frameworks). + +These implementations have been tested on several datasets (see the example scripts) and should match the performance of the original implementations. You can find more details on performance in the Examples section of the [documentation](https://github.com/huggingface/transformers/tree/main/examples). + + +## Learn more + +| Section | Description | +|-|-| +| [Documentation](https://huggingface.co/docs/transformers/) | Full API documentation and tutorials | +| [Task summary](https://huggingface.co/docs/transformers/task_summary) | Tasks supported by 🤗 Transformers | +| [Preprocessing tutorial](https://huggingface.co/docs/transformers/preprocessing) | Using the `Tokenizer` class to prepare data for the models | +| [Training and fine-tuning](https://huggingface.co/docs/transformers/training) | Using the models provided by 🤗 Transformers in a PyTorch/TensorFlow training loop and the `Trainer` API | +| [Quick tour: Fine-tuning/usage scripts](https://github.com/huggingface/transformers/tree/main/examples) | Example scripts for fine-tuning models on a wide range of tasks | +| [Model sharing and uploading](https://huggingface.co/docs/transformers/model_sharing) | Upload and share your fine-tuned models with the community | + +## Citation + +We now have a [paper](https://www.aclweb.org/anthology/2020.emnlp-demos.6/) you can cite for the 🤗 Transformers library: +```bibtex +@inproceedings{wolf-etal-2020-transformers, + title = "Transformers: State-of-the-Art Natural Language Processing", + author = "Thomas Wolf and Lysandre Debut and Victor Sanh and Julien Chaumond and Clement Delangue and Anthony Moi and Pierric Cistac and Tim Rault and Rémi Louf and Morgan Funtowicz and Joe Davison and Sam Shleifer and Patrick von Platen and Clara Ma and Yacine Jernite and Julien Plu and Canwen Xu and Teven Le Scao and Sylvain Gugger and Mariama Drame and Quentin Lhoest and Alexander M. Rush", + booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations", + month = oct, + year = "2020", + address = "Online", + publisher = "Association for Computational Linguistics", + url = "https://www.aclweb.org/anthology/2020.emnlp-demos.6", + pages = "38--45" +} +``` diff --git a/env-llmeval/lib/python3.10/site-packages/transformers-4.39.3.dist-info/RECORD b/env-llmeval/lib/python3.10/site-packages/transformers-4.39.3.dist-info/RECORD new file mode 100644 index 0000000000000000000000000000000000000000..f7757c851820ce468b1505b0ba950db98ee26463 --- /dev/null +++ b/env-llmeval/lib/python3.10/site-packages/transformers-4.39.3.dist-info/RECORD @@ -0,0 +1,3253 @@ +../../../bin/transformers-cli,sha256=OXf6l6cpKYxovZ24Nw-xxylm9CBW0NCFRBXmWVjvfEA,263 +transformers-4.39.3.dist-info/INSTALLER,sha256=zuuue4knoyJ-UwPPXg8fezS7VCrXJQrAP7zeNuwvFQg,4 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b/env-llmeval/lib/python3.10/site-packages/transformers-4.39.3.dist-info/WHEEL new file mode 100644 index 0000000000000000000000000000000000000000..57e3d840d59a650ac5bccbad5baeec47d155f0ad --- /dev/null +++ b/env-llmeval/lib/python3.10/site-packages/transformers-4.39.3.dist-info/WHEEL @@ -0,0 +1,5 @@ +Wheel-Version: 1.0 +Generator: bdist_wheel (0.38.4) +Root-Is-Purelib: true +Tag: py3-none-any + diff --git a/env-llmeval/lib/python3.10/site-packages/transformers-4.39.3.dist-info/entry_points.txt b/env-llmeval/lib/python3.10/site-packages/transformers-4.39.3.dist-info/entry_points.txt new file mode 100644 index 0000000000000000000000000000000000000000..8a7995ed6f21261a78509c57d57daba51ecf1a7d --- /dev/null +++ b/env-llmeval/lib/python3.10/site-packages/transformers-4.39.3.dist-info/entry_points.txt @@ -0,0 +1,2 @@ +[console_scripts] +transformers-cli = transformers.commands.transformers_cli:main diff --git a/env-llmeval/lib/python3.10/site-packages/transformers-4.39.3.dist-info/top_level.txt b/env-llmeval/lib/python3.10/site-packages/transformers-4.39.3.dist-info/top_level.txt new file mode 100644 index 0000000000000000000000000000000000000000..976a2b1f3998279c10c413279a095be86bf69167 --- /dev/null +++ b/env-llmeval/lib/python3.10/site-packages/transformers-4.39.3.dist-info/top_level.txt @@ -0,0 +1 @@ +transformers