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llava/lib/python3.10/site-packages/setuptools/_entry_points.py

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+import functools
+import itertools
+import operator
+
+from jaraco.functools import pass_none
+from jaraco.text import yield_lines
+from more_itertools import consume
+
+from ._importlib import metadata
+from ._itertools import ensure_unique
+from .errors import OptionError
+
+
+def ensure_valid(ep):
+    """
+    Exercise one of the dynamic properties to trigger
+    the pattern match.
+    """
+    try:
+        ep.extras
+    except (AttributeError, AssertionError) as ex:
+        # Why both? See https://github.com/python/importlib_metadata/issues/488
+        msg = (
+            f"Problems to parse {ep}.\nPlease ensure entry-point follows the spec: "
+            "https://packaging.python.org/en/latest/specifications/entry-points/"
+        )
+        raise OptionError(msg) from ex
+
+
+def load_group(value, group):
+    """
+    Given a value of an entry point or series of entry points,
+    return each as an EntryPoint.
+    """
+    # normalize to a single sequence of lines
+    lines = yield_lines(value)
+    text = f'[{group}]\n' + '\n'.join(lines)
+    return metadata.EntryPoints._from_text(text)
+
+
+def by_group_and_name(ep):
+    return ep.group, ep.name
+
+
+def validate(eps: metadata.EntryPoints):
+    """
+    Ensure entry points are unique by group and name and validate each.
+    """
+    consume(map(ensure_valid, ensure_unique(eps, key=by_group_and_name)))
+    return eps
+
+
+@functools.singledispatch
+def load(eps):
+    """
+    Given a Distribution.entry_points, produce EntryPoints.
+    """
+    groups = itertools.chain.from_iterable(
+        load_group(value, group) for group, value in eps.items()
+    )
+    return validate(metadata.EntryPoints(groups))
+
+
+@load.register(str)
+def _(eps):
+    r"""
+    >>> ep, = load('[console_scripts]\nfoo=bar')
+    >>> ep.group
+    'console_scripts'
+    >>> ep.name
+    'foo'
+    >>> ep.value
+    'bar'
+    """
+    return validate(metadata.EntryPoints(metadata.EntryPoints._from_text(eps)))
+
+
+load.register(type(None), lambda x: x)
+
+
+@pass_none
+def render(eps: metadata.EntryPoints):
+    by_group = operator.attrgetter('group')
+    groups = itertools.groupby(sorted(eps, key=by_group), by_group)
+
+    return '\n'.join(f'[{group}]\n{render_items(items)}\n' for group, items in groups)
+
+
+def render_items(eps):
+    return '\n'.join(f'{ep.name} = {ep.value}' for ep in sorted(eps))

llava/lib/python3.10/site-packages/setuptools/_normalization.py

@@ -0,0 +1,144 @@
+"""
+Helpers for normalization as expected in wheel/sdist/module file names
+and core metadata
+"""
+
+import re
+
+import packaging
+
+# https://packaging.python.org/en/latest/specifications/core-metadata/#name
+_VALID_NAME = re.compile(r"^([A-Z0-9]|[A-Z0-9][A-Z0-9._-]*[A-Z0-9])$", re.I)
+_UNSAFE_NAME_CHARS = re.compile(r"[^A-Z0-9._-]+", re.I)
+_NON_ALPHANUMERIC = re.compile(r"[^A-Z0-9]+", re.I)
+_PEP440_FALLBACK = re.compile(r"^v?(?P<safe>(?:[0-9]+!)?[0-9]+(?:\.[0-9]+)*)", re.I)
+
+
+def safe_identifier(name: str) -> str:
+    """Make a string safe to be used as Python identifier.
+    >>> safe_identifier("12abc")
+    '_12abc'
+    >>> safe_identifier("__editable__.myns.pkg-78.9.3_local")
+    '__editable___myns_pkg_78_9_3_local'
+    """
+    safe = re.sub(r'\W|^(?=\d)', '_', name)
+    assert safe.isidentifier()
+    return safe
+
+
+def safe_name(component: str) -> str:
+    """Escape a component used as a project name according to Core Metadata.
+    >>> safe_name("hello world")
+    'hello-world'
+    >>> safe_name("hello?world")
+    'hello-world'
+    >>> safe_name("hello_world")
+    'hello_world'
+    """
+    # See pkg_resources.safe_name
+    return _UNSAFE_NAME_CHARS.sub("-", component)
+
+
+def safe_version(version: str) -> str:
+    """Convert an arbitrary string into a valid version string.
+    Can still raise an ``InvalidVersion`` exception.
+    To avoid exceptions use ``best_effort_version``.
+    >>> safe_version("1988 12 25")
+    '1988.12.25'
+    >>> safe_version("v0.2.1")
+    '0.2.1'
+    >>> safe_version("v0.2?beta")
+    '0.2b0'
+    >>> safe_version("v0.2 beta")
+    '0.2b0'
+    >>> safe_version("ubuntu lts")
+    Traceback (most recent call last):
+    ...
+    packaging.version.InvalidVersion: Invalid version: 'ubuntu.lts'
+    """
+    v = version.replace(' ', '.')
+    try:
+        return str(packaging.version.Version(v))
+    except packaging.version.InvalidVersion:
+        attempt = _UNSAFE_NAME_CHARS.sub("-", v)
+        return str(packaging.version.Version(attempt))
+
+
+def best_effort_version(version: str) -> str:
+    """Convert an arbitrary string into a version-like string.
+    Fallback when ``safe_version`` is not safe enough.
+    >>> best_effort_version("v0.2 beta")
+    '0.2b0'
+    >>> best_effort_version("ubuntu lts")
+    '0.dev0+sanitized.ubuntu.lts'
+    >>> best_effort_version("0.23ubuntu1")
+    '0.23.dev0+sanitized.ubuntu1'
+    >>> best_effort_version("0.23-")
+    '0.23.dev0+sanitized'
+    >>> best_effort_version("0.-_")
+    '0.dev0+sanitized'
+    >>> best_effort_version("42.+?1")
+    '42.dev0+sanitized.1'
+    """
+    # See pkg_resources._forgiving_version
+    try:
+        return safe_version(version)
+    except packaging.version.InvalidVersion:
+        v = version.replace(' ', '.')
+        match = _PEP440_FALLBACK.search(v)
+        if match:
+            safe = match["safe"]
+            rest = v[len(safe) :]
+        else:
+            safe = "0"
+            rest = version
+        safe_rest = _NON_ALPHANUMERIC.sub(".", rest).strip(".")
+        local = f"sanitized.{safe_rest}".strip(".")
+        return safe_version(f"{safe}.dev0+{local}")
+
+
+def safe_extra(extra: str) -> str:
+    """Normalize extra name according to PEP 685
+    >>> safe_extra("_FrIeNdLy-._.-bArD")
+    'friendly-bard'
+    >>> safe_extra("FrIeNdLy-._.-bArD__._-")
+    'friendly-bard'
+    """
+    return _NON_ALPHANUMERIC.sub("-", extra).strip("-").lower()
+
+
+def filename_component(value: str) -> str:
+    """Normalize each component of a filename (e.g. distribution/version part of wheel)
+    Note: ``value`` needs to be already normalized.
+    >>> filename_component("my-pkg")
+    'my_pkg'
+    """
+    return value.replace("-", "_").strip("_")
+
+
+def filename_component_broken(value: str) -> str:
+    """
+    Produce the incorrect filename component for compatibility.
+
+    See pypa/setuptools#4167 for detailed analysis.
+
+    TODO: replace this with filename_component after pip 24 is
+    nearly-ubiquitous.
+
+    >>> filename_component_broken('foo_bar-baz')
+    'foo-bar-baz'
+    """
+    return value.replace('_', '-')
+
+
+def safer_name(value: str) -> str:
+    """Like ``safe_name`` but can be used as filename component for wheel"""
+    # See bdist_wheel.safer_name
+    return filename_component(safe_name(value))
+
+
+def safer_best_effort_version(value: str) -> str:
+    """Like ``best_effort_version`` but can be used as filename component for wheel"""
+    # See bdist_wheel.safer_verion
+    # TODO: Replace with only safe_version in the future (no need for best effort)
+    return filename_component(best_effort_version(value))

llava/lib/python3.10/site-packages/setuptools/build_meta.py

@@ -0,0 +1,560 @@
+"""A PEP 517 interface to setuptools
+
+Previously, when a user or a command line tool (let's call it a "frontend")
+needed to make a request of setuptools to take a certain action, for
+example, generating a list of installation requirements, the frontend
+would call "setup.py egg_info" or "setup.py bdist_wheel" on the command line.
+
+PEP 517 defines a different method of interfacing with setuptools. Rather
+than calling "setup.py" directly, the frontend should:
+
+  1. Set the current directory to the directory with a setup.py file
+  2. Import this module into a safe python interpreter (one in which
+     setuptools can potentially set global variables or crash hard).
+  3. Call one of the functions defined in PEP 517.
+
+What each function does is defined in PEP 517. However, here is a "casual"
+definition of the functions (this definition should not be relied on for
+bug reports or API stability):
+
+  - `build_wheel`: build a wheel in the folder and return the basename
+  - `get_requires_for_build_wheel`: get the `setup_requires` to build
+  - `prepare_metadata_for_build_wheel`: get the `install_requires`
+  - `build_sdist`: build an sdist in the folder and return the basename
+  - `get_requires_for_build_sdist`: get the `setup_requires` to build
+
+Again, this is not a formal definition! Just a "taste" of the module.
+"""
+
+from __future__ import annotations
+
+import contextlib
+import io
+import os
+import shlex
+import shutil
+import sys
+import tempfile
+import tokenize
+import warnings
+from collections.abc import Iterable, Iterator, Mapping
+from pathlib import Path
+from typing import TYPE_CHECKING, Union
+
+import setuptools
+
+from . import errors
+from ._path import StrPath, same_path
+from ._reqs import parse_strings
+from .warnings import SetuptoolsDeprecationWarning
+
+import distutils
+from distutils.util import strtobool
+
+if TYPE_CHECKING:
+    from typing_extensions import TypeAlias
+
+__all__ = [
+    'get_requires_for_build_sdist',
+    'get_requires_for_build_wheel',
+    'prepare_metadata_for_build_wheel',
+    'build_wheel',
+    'build_sdist',
+    'get_requires_for_build_editable',
+    'prepare_metadata_for_build_editable',
+    'build_editable',
+    '__legacy__',
+    'SetupRequirementsError',
+]
+
+SETUPTOOLS_ENABLE_FEATURES = os.getenv("SETUPTOOLS_ENABLE_FEATURES", "").lower()
+LEGACY_EDITABLE = "legacy-editable" in SETUPTOOLS_ENABLE_FEATURES.replace("_", "-")
+
+
+class SetupRequirementsError(BaseException):
+    def __init__(self, specifiers) -> None:
+        self.specifiers = specifiers
+
+
+class Distribution(setuptools.dist.Distribution):
+    def fetch_build_eggs(self, specifiers):
+        specifier_list = list(parse_strings(specifiers))
+
+        raise SetupRequirementsError(specifier_list)
+
+    @classmethod
+    @contextlib.contextmanager
+    def patch(cls):
+        """
+        Replace
+        distutils.dist.Distribution with this class
+        for the duration of this context.
+        """
+        orig = distutils.core.Distribution
+        distutils.core.Distribution = cls  # type: ignore[misc] # monkeypatching
+        try:
+            yield
+        finally:
+            distutils.core.Distribution = orig  # type: ignore[misc] # monkeypatching
+
+
+@contextlib.contextmanager
+def no_install_setup_requires():
+    """Temporarily disable installing setup_requires
+
+    Under PEP 517, the backend reports build dependencies to the frontend,
+    and the frontend is responsible for ensuring they're installed.
+    So setuptools (acting as a backend) should not try to install them.
+    """
+    orig = setuptools._install_setup_requires
+    setuptools._install_setup_requires = lambda attrs: None
+    try:
+        yield
+    finally:
+        setuptools._install_setup_requires = orig
+
+
+def _get_immediate_subdirectories(a_dir):
+    return [
+        name for name in os.listdir(a_dir) if os.path.isdir(os.path.join(a_dir, name))
+    ]
+
+
+def _file_with_extension(directory: StrPath, extension: str | tuple[str, ...]):
+    matching = (f for f in os.listdir(directory) if f.endswith(extension))
+    try:
+        (file,) = matching
+    except ValueError:
+        raise ValueError(
+            'No distribution was found. Ensure that `setup.py` '
+            'is not empty and that it calls `setup()`.'
+        ) from None
+    return file
+
+
+def _open_setup_script(setup_script):
+    if not os.path.exists(setup_script):
+        # Supply a default setup.py
+        return io.StringIO("from setuptools import setup; setup()")
+
+    return tokenize.open(setup_script)
+
+
+@contextlib.contextmanager
+def suppress_known_deprecation():
+    with warnings.catch_warnings():
+        warnings.filterwarnings('ignore', 'setup.py install is deprecated')
+        yield
+
+
+_ConfigSettings: TypeAlias = Union[Mapping[str, Union[str, list[str], None]], None]
+"""
+Currently the user can run::
+
+    pip install -e . --config-settings key=value
+    python -m build -C--key=value -C key=value
+
+- pip will pass both key and value as strings and overwriting repeated keys
+  (pypa/pip#11059).
+- build will accumulate values associated with repeated keys in a list.
+  It will also accept keys with no associated value.
+  This means that an option passed by build can be ``str | list[str] | None``.
+- PEP 517 specifies that ``config_settings`` is an optional dict.
+"""
+
+
+class _ConfigSettingsTranslator:
+    """Translate ``config_settings`` into distutils-style command arguments.
+    Only a limited number of options is currently supported.
+    """
+
+    # See pypa/setuptools#1928 pypa/setuptools#2491
+
+    def _get_config(self, key: str, config_settings: _ConfigSettings) -> list[str]:
+        """
+        Get the value of a specific key in ``config_settings`` as a list of strings.
+
+        >>> fn = _ConfigSettingsTranslator()._get_config
+        >>> fn("--global-option", None)
+        []
+        >>> fn("--global-option", {})
+        []
+        >>> fn("--global-option", {'--global-option': 'foo'})
+        ['foo']
+        >>> fn("--global-option", {'--global-option': ['foo']})
+        ['foo']
+        >>> fn("--global-option", {'--global-option': 'foo'})
+        ['foo']
+        >>> fn("--global-option", {'--global-option': 'foo bar'})
+        ['foo', 'bar']
+        """
+        cfg = config_settings or {}
+        opts = cfg.get(key) or []
+        return shlex.split(opts) if isinstance(opts, str) else opts
+
+    def _global_args(self, config_settings: _ConfigSettings) -> Iterator[str]:
+        """
+        Let the user specify ``verbose`` or ``quiet`` + escape hatch via
+        ``--global-option``.
+        Note: ``-v``, ``-vv``, ``-vvv`` have similar effects in setuptools,
+        so we just have to cover the basic scenario ``-v``.
+
+        >>> fn = _ConfigSettingsTranslator()._global_args
+        >>> list(fn(None))
+        []
+        >>> list(fn({"verbose": "False"}))
+        ['-q']
+        >>> list(fn({"verbose": "1"}))
+        ['-v']
+        >>> list(fn({"--verbose": None}))
+        ['-v']
+        >>> list(fn({"verbose": "true", "--global-option": "-q --no-user-cfg"}))
+        ['-v', '-q', '--no-user-cfg']
+        >>> list(fn({"--quiet": None}))
+        ['-q']
+        """
+        cfg = config_settings or {}
+        falsey = {"false", "no", "0", "off"}
+        if "verbose" in cfg or "--verbose" in cfg:
+            level = str(cfg.get("verbose") or cfg.get("--verbose") or "1")
+            yield ("-q" if level.lower() in falsey else "-v")
+        if "quiet" in cfg or "--quiet" in cfg:
+            level = str(cfg.get("quiet") or cfg.get("--quiet") or "1")
+            yield ("-v" if level.lower() in falsey else "-q")
+
+        yield from self._get_config("--global-option", config_settings)
+
+    def __dist_info_args(self, config_settings: _ConfigSettings) -> Iterator[str]:
+        """
+        The ``dist_info`` command accepts ``tag-date`` and ``tag-build``.
+
+        .. warning::
+           We cannot use this yet as it requires the ``sdist`` and ``bdist_wheel``
+           commands run in ``build_sdist`` and ``build_wheel`` to reuse the egg-info
+           directory created in ``prepare_metadata_for_build_wheel``.
+
+        >>> fn = _ConfigSettingsTranslator()._ConfigSettingsTranslator__dist_info_args
+        >>> list(fn(None))
+        []
+        >>> list(fn({"tag-date": "False"}))
+        ['--no-date']
+        >>> list(fn({"tag-date": None}))
+        ['--no-date']
+        >>> list(fn({"tag-date": "true", "tag-build": ".a"}))
+        ['--tag-date', '--tag-build', '.a']
+        """
+        cfg = config_settings or {}
+        if "tag-date" in cfg:
+            val = strtobool(str(cfg["tag-date"] or "false"))
+            yield ("--tag-date" if val else "--no-date")
+        if "tag-build" in cfg:
+            yield from ["--tag-build", str(cfg["tag-build"])]
+
+    def _editable_args(self, config_settings: _ConfigSettings) -> Iterator[str]:
+        """
+        The ``editable_wheel`` command accepts ``editable-mode=strict``.
+
+        >>> fn = _ConfigSettingsTranslator()._editable_args
+        >>> list(fn(None))
+        []
+        >>> list(fn({"editable-mode": "strict"}))
+        ['--mode', 'strict']
+        """
+        cfg = config_settings or {}
+        mode = cfg.get("editable-mode") or cfg.get("editable_mode")
+        if not mode:
+            return
+        yield from ["--mode", str(mode)]
+
+    def _arbitrary_args(self, config_settings: _ConfigSettings) -> Iterator[str]:
+        """
+        Users may expect to pass arbitrary lists of arguments to a command
+        via "--global-option" (example provided in PEP 517 of a "escape hatch").
+
+        >>> fn = _ConfigSettingsTranslator()._arbitrary_args
+        >>> list(fn(None))
+        []
+        >>> list(fn({}))
+        []
+        >>> list(fn({'--build-option': 'foo'}))
+        ['foo']
+        >>> list(fn({'--build-option': ['foo']}))
+        ['foo']
+        >>> list(fn({'--build-option': 'foo'}))
+        ['foo']
+        >>> list(fn({'--build-option': 'foo bar'}))
+        ['foo', 'bar']
+        >>> list(fn({'--global-option': 'foo'}))
+        []
+        """
+        yield from self._get_config("--build-option", config_settings)
+
+
+class _BuildMetaBackend(_ConfigSettingsTranslator):
+    def _get_build_requires(
+        self, config_settings: _ConfigSettings, requirements: list[str]
+    ):
+        sys.argv = [
+            *sys.argv[:1],
+            *self._global_args(config_settings),
+            "egg_info",
+        ]
+        try:
+            with Distribution.patch():
+                self.run_setup()
+        except SetupRequirementsError as e:
+            requirements += e.specifiers
+
+        return requirements
+
+    def run_setup(self, setup_script: str = 'setup.py'):
+        # Note that we can reuse our build directory between calls
+        # Correctness comes first, then optimization later
+        __file__ = os.path.abspath(setup_script)
+        __name__ = '__main__'
+
+        with _open_setup_script(__file__) as f:
+            code = f.read().replace(r'\r\n', r'\n')
+
+        try:
+            exec(code, locals())
+        except SystemExit as e:
+            if e.code:
+                raise
+            # We ignore exit code indicating success
+            SetuptoolsDeprecationWarning.emit(
+                "Running `setup.py` directly as CLI tool is deprecated.",
+                "Please avoid using `sys.exit(0)` or similar statements "
+                "that don't fit in the paradigm of a configuration file.",
+                see_url="https://blog.ganssle.io/articles/2021/10/"
+                "setup-py-deprecated.html",
+            )
+
+    def get_requires_for_build_wheel(self, config_settings: _ConfigSettings = None):
+        return self._get_build_requires(config_settings, requirements=[])
+
+    def get_requires_for_build_sdist(self, config_settings: _ConfigSettings = None):
+        return self._get_build_requires(config_settings, requirements=[])
+
+    def _bubble_up_info_directory(
+        self, metadata_directory: StrPath, suffix: str
+    ) -> str:
+        """
+        PEP 517 requires that the .dist-info directory be placed in the
+        metadata_directory. To comply, we MUST copy the directory to the root.
+
+        Returns the basename of the info directory, e.g. `proj-0.0.0.dist-info`.
+        """
+        info_dir = self._find_info_directory(metadata_directory, suffix)
+        if not same_path(info_dir.parent, metadata_directory):
+            shutil.move(str(info_dir), metadata_directory)
+            # PEP 517 allow other files and dirs to exist in metadata_directory
+        return info_dir.name
+
+    def _find_info_directory(self, metadata_directory: StrPath, suffix: str) -> Path:
+        for parent, dirs, _ in os.walk(metadata_directory):
+            candidates = [f for f in dirs if f.endswith(suffix)]
+
+            if len(candidates) != 0 or len(dirs) != 1:
+                assert len(candidates) == 1, f"Multiple {suffix} directories found"
+                return Path(parent, candidates[0])
+
+        msg = f"No {suffix} directory found in {metadata_directory}"
+        raise errors.InternalError(msg)
+
+    def prepare_metadata_for_build_wheel(
+        self, metadata_directory: StrPath, config_settings: _ConfigSettings = None
+    ):
+        sys.argv = [
+            *sys.argv[:1],
+            *self._global_args(config_settings),
+            "dist_info",
+            "--output-dir",
+            str(metadata_directory),
+            "--keep-egg-info",
+        ]
+        with no_install_setup_requires():
+            self.run_setup()
+
+        self._bubble_up_info_directory(metadata_directory, ".egg-info")
+        return self._bubble_up_info_directory(metadata_directory, ".dist-info")
+
+    def _build_with_temp_dir(
+        self,
+        setup_command: Iterable[str],
+        result_extension: str | tuple[str, ...],
+        result_directory: StrPath,
+        config_settings: _ConfigSettings,
+        arbitrary_args: Iterable[str] = (),
+    ):
+        result_directory = os.path.abspath(result_directory)
+
+        # Build in a temporary directory, then copy to the target.
+        os.makedirs(result_directory, exist_ok=True)
+
+        with tempfile.TemporaryDirectory(
+            prefix=".tmp-", dir=result_directory
+        ) as tmp_dist_dir:
+            sys.argv = [
+                *sys.argv[:1],
+                *self._global_args(config_settings),
+                *setup_command,
+                "--dist-dir",
+                tmp_dist_dir,
+                *arbitrary_args,
+            ]
+            with no_install_setup_requires():
+                self.run_setup()
+
+            result_basename = _file_with_extension(tmp_dist_dir, result_extension)
+            result_path = os.path.join(result_directory, result_basename)
+            if os.path.exists(result_path):
+                # os.rename will fail overwriting on non-Unix.
+                os.remove(result_path)
+            os.rename(os.path.join(tmp_dist_dir, result_basename), result_path)
+
+        return result_basename
+
+    def build_wheel(
+        self,
+        wheel_directory: StrPath,
+        config_settings: _ConfigSettings = None,
+        metadata_directory: StrPath | None = None,
+    ):
+        def _build(cmd: list[str]):
+            with suppress_known_deprecation():
+                return self._build_with_temp_dir(
+                    cmd,
+                    '.whl',
+                    wheel_directory,
+                    config_settings,
+                    self._arbitrary_args(config_settings),
+                )
+
+        if metadata_directory is None:
+            return _build(['bdist_wheel'])
+
+        try:
+            return _build(['bdist_wheel', '--dist-info-dir', str(metadata_directory)])
+        except SystemExit as ex:  # pragma: nocover
+            # pypa/setuptools#4683
+            if "--dist-info-dir not recognized" not in str(ex):
+                raise
+            _IncompatibleBdistWheel.emit()
+            return _build(['bdist_wheel'])
+
+    def build_sdist(
+        self, sdist_directory: StrPath, config_settings: _ConfigSettings = None
+    ):
+        return self._build_with_temp_dir(
+            ['sdist', '--formats', 'gztar'], '.tar.gz', sdist_directory, config_settings
+        )
+
+    def _get_dist_info_dir(self, metadata_directory: StrPath | None) -> str | None:
+        if not metadata_directory:
+            return None
+        dist_info_candidates = list(Path(metadata_directory).glob("*.dist-info"))
+        assert len(dist_info_candidates) <= 1
+        return str(dist_info_candidates[0]) if dist_info_candidates else None
+
+    if not LEGACY_EDITABLE:
+        # PEP660 hooks:
+        # build_editable
+        # get_requires_for_build_editable
+        # prepare_metadata_for_build_editable
+        def build_editable(
+            self,
+            wheel_directory: StrPath,
+            config_settings: _ConfigSettings = None,
+            metadata_directory: StrPath | None = None,
+        ):
+            # XXX can or should we hide our editable_wheel command normally?
+            info_dir = self._get_dist_info_dir(metadata_directory)
+            opts = ["--dist-info-dir", info_dir] if info_dir else []
+            cmd = ["editable_wheel", *opts, *self._editable_args(config_settings)]
+            with suppress_known_deprecation():
+                return self._build_with_temp_dir(
+                    cmd, ".whl", wheel_directory, config_settings
+                )
+
+        def get_requires_for_build_editable(
+            self, config_settings: _ConfigSettings = None
+        ):
+            return self.get_requires_for_build_wheel(config_settings)
+
+        def prepare_metadata_for_build_editable(
+            self, metadata_directory: StrPath, config_settings: _ConfigSettings = None
+        ):
+            return self.prepare_metadata_for_build_wheel(
+                metadata_directory, config_settings
+            )
+
+
+class _BuildMetaLegacyBackend(_BuildMetaBackend):
+    """Compatibility backend for setuptools
+
+    This is a version of setuptools.build_meta that endeavors
+    to maintain backwards
+    compatibility with pre-PEP 517 modes of invocation. It
+    exists as a temporary
+    bridge between the old packaging mechanism and the new
+    packaging mechanism,
+    and will eventually be removed.
+    """
+
+    def run_setup(self, setup_script: str = 'setup.py'):
+        # In order to maintain compatibility with scripts assuming that
+        # the setup.py script is in a directory on the PYTHONPATH, inject
+        # '' into sys.path. (pypa/setuptools#1642)
+        sys_path = list(sys.path)  # Save the original path
+
+        script_dir = os.path.dirname(os.path.abspath(setup_script))
+        if script_dir not in sys.path:
+            sys.path.insert(0, script_dir)
+
+        # Some setup.py scripts (e.g. in pygame and numpy) use sys.argv[0] to
+        # get the directory of the source code. They expect it to refer to the
+        # setup.py script.
+        sys_argv_0 = sys.argv[0]
+        sys.argv[0] = setup_script
+
+        try:
+            super().run_setup(setup_script=setup_script)
+        finally:
+            # While PEP 517 frontends should be calling each hook in a fresh
+            # subprocess according to the standard (and thus it should not be
+            # strictly necessary to restore the old sys.path), we'll restore
+            # the original path so that the path manipulation does not persist
+            # within the hook after run_setup is called.
+            sys.path[:] = sys_path
+            sys.argv[0] = sys_argv_0
+
+
+class _IncompatibleBdistWheel(SetuptoolsDeprecationWarning):
+    _SUMMARY = "wheel.bdist_wheel is deprecated, please import it from setuptools"
+    _DETAILS = """
+    Ensure that any custom bdist_wheel implementation is a subclass of
+    setuptools.command.bdist_wheel.bdist_wheel.
+    """
+    _DUE_DATE = (2025, 10, 15)
+    # Initially introduced in 2024/10/15, but maybe too disruptive to be enforced?
+    _SEE_URL = "https://github.com/pypa/wheel/pull/631"
+
+
+# The primary backend
+_BACKEND = _BuildMetaBackend()
+
+get_requires_for_build_wheel = _BACKEND.get_requires_for_build_wheel
+get_requires_for_build_sdist = _BACKEND.get_requires_for_build_sdist
+prepare_metadata_for_build_wheel = _BACKEND.prepare_metadata_for_build_wheel
+build_wheel = _BACKEND.build_wheel
+build_sdist = _BACKEND.build_sdist
+
+if not LEGACY_EDITABLE:
+    get_requires_for_build_editable = _BACKEND.get_requires_for_build_editable
+    prepare_metadata_for_build_editable = _BACKEND.prepare_metadata_for_build_editable
+    build_editable = _BACKEND.build_editable
+
+
+# The legacy backend
+__legacy__ = _BuildMetaLegacyBackend()

llava/lib/python3.10/site-packages/setuptools/config/NOTICE

@@ -0,0 +1,10 @@
+The following files include code from opensource projects
+(either as direct copies or modified versions):
+
+- `setuptools.schema.json`, `distutils.schema.json`:
+    - project: `validate-pyproject` - licensed under MPL-2.0
+      (https://github.com/abravalheri/validate-pyproject):
+
+      This Source Code Form is subject to the terms of the Mozilla Public
+      License, v. 2.0. If a copy of the MPL was not distributed with this file,
+      You can obtain one at https://mozilla.org/MPL/2.0/.

llava/lib/python3.10/site-packages/setuptools/config/__init__.py

@@ -0,0 +1,43 @@
+"""For backward compatibility, expose main functions from
+``setuptools.config.setupcfg``
+"""
+
+from functools import wraps
+from typing import Callable, TypeVar, cast
+
+from ..warnings import SetuptoolsDeprecationWarning
+from . import setupcfg
+
+Fn = TypeVar("Fn", bound=Callable)
+
+__all__ = ('parse_configuration', 'read_configuration')
+
+
+def _deprecation_notice(fn: Fn) -> Fn:
+    @wraps(fn)
+    def _wrapper(*args, **kwargs):
+        SetuptoolsDeprecationWarning.emit(
+            "Deprecated API usage.",
+            f"""
+            As setuptools moves its configuration towards `pyproject.toml`,
+            `{__name__}.{fn.__name__}` became deprecated.
+
+            For the time being, you can use the `{setupcfg.__name__}` module
+            to access a backward compatible API, but this module is provisional
+            and might be removed in the future.
+
+            To read project metadata, consider using
+            ``build.util.project_wheel_metadata`` (https://pypi.org/project/build/).
+            For simple scenarios, you can also try parsing the file directly
+            with the help of ``configparser``.
+            """,
+            # due_date not defined yet, because the community still heavily relies on it
+            # Warning introduced in 24 Mar 2022
+        )
+        return fn(*args, **kwargs)
+
+    return cast(Fn, _wrapper)
+
+
+read_configuration = _deprecation_notice(setupcfg.read_configuration)
+parse_configuration = _deprecation_notice(setupcfg.parse_configuration)

llava/lib/python3.10/site-packages/setuptools/config/_validate_pyproject/__init__.py

@@ -0,0 +1,34 @@
+from functools import reduce
+from typing import Any, Callable, Dict
+
+from . import formats
+from .error_reporting import detailed_errors, ValidationError
+from .extra_validations import EXTRA_VALIDATIONS
+from .fastjsonschema_exceptions import JsonSchemaException, JsonSchemaValueException
+from .fastjsonschema_validations import validate as _validate
+
+__all__ = [
+    "validate",
+    "FORMAT_FUNCTIONS",
+    "EXTRA_VALIDATIONS",
+    "ValidationError",
+    "JsonSchemaException",
+    "JsonSchemaValueException",
+]
+
+
+FORMAT_FUNCTIONS: Dict[str, Callable[[str], bool]] = {
+    fn.__name__.replace("_", "-"): fn
+    for fn in formats.__dict__.values()
+    if callable(fn) and not fn.__name__.startswith("_")
+}
+
+
+def validate(data: Any) -> bool:
+    """Validate the given ``data`` object using JSON Schema
+    This function raises ``ValidationError`` if ``data`` is invalid.
+    """
+    with detailed_errors():
+        _validate(data, custom_formats=FORMAT_FUNCTIONS)
+        reduce(lambda acc, fn: fn(acc), EXTRA_VALIDATIONS, data)
+    return True

llava/lib/python3.10/site-packages/setuptools/config/distutils.schema.json

@@ -0,0 +1,26 @@
+{
+  "$schema": "http://json-schema.org/draft-07/schema#",
+
+  "$id": "https://setuptools.pypa.io/en/latest/deprecated/distutils/configfile.html",
+  "title": "``tool.distutils`` table",
+  "$$description": [
+    "**EXPERIMENTAL** (NOT OFFICIALLY SUPPORTED): Use ``tool.distutils``",
+    "subtables to configure arguments for ``distutils`` commands.",
+    "Originally, ``distutils`` allowed developers to configure arguments for",
+    "``setup.py`` commands via `distutils configuration files",
+    "<https://setuptools.pypa.io/en/latest/deprecated/distutils/configfile.html>`_.",
+    "See also `the old Python docs <https://docs.python.org/3.11/install/>_`."
+  ],
+
+  "type": "object",
+  "properties": {
+    "global": {
+      "type": "object",
+      "description": "Global options applied to all ``distutils`` commands"
+    }
+  },
+  "patternProperties": {
+    ".+": {"type": "object"}
+  },
+  "$comment": "TODO: Is there a practical way of making this schema more specific?"
+}

llava/lib/python3.10/site-packages/setuptools/config/pyprojecttoml.py

@@ -0,0 +1,468 @@
+"""
+Load setuptools configuration from ``pyproject.toml`` files.
+
+**PRIVATE MODULE**: API reserved for setuptools internal usage only.
+
+To read project metadata, consider using
+``build.util.project_wheel_metadata`` (https://pypi.org/project/build/).
+For simple scenarios, you can also try parsing the file directly
+with the help of ``tomllib`` or ``tomli``.
+"""
+
+from __future__ import annotations
+
+import logging
+import os
+from collections.abc import Mapping
+from contextlib import contextmanager
+from functools import partial
+from types import TracebackType
+from typing import TYPE_CHECKING, Any, Callable
+
+from .._path import StrPath
+from ..errors import FileError, InvalidConfigError
+from ..warnings import SetuptoolsWarning
+from . import expand as _expand
+from ._apply_pyprojecttoml import _PREVIOUSLY_DEFINED, _MissingDynamic, apply as _apply
+
+if TYPE_CHECKING:
+    from typing_extensions import Self
+
+    from setuptools.dist import Distribution
+
+_logger = logging.getLogger(__name__)
+
+
+def load_file(filepath: StrPath) -> dict:
+    from ..compat.py310 import tomllib
+
+    with open(filepath, "rb") as file:
+        return tomllib.load(file)
+
+
+def validate(config: dict, filepath: StrPath) -> bool:
+    from . import _validate_pyproject as validator
+
+    trove_classifier = validator.FORMAT_FUNCTIONS.get("trove-classifier")
+    if hasattr(trove_classifier, "_disable_download"):
+        # Improve reproducibility by default. See abravalheri/validate-pyproject#31
+        trove_classifier._disable_download()  # type: ignore[union-attr]
+
+    try:
+        return validator.validate(config)
+    except validator.ValidationError as ex:
+        summary = f"configuration error: {ex.summary}"
+        if ex.name.strip("`") != "project":
+            # Probably it is just a field missing/misnamed, not worthy the verbosity...
+            _logger.debug(summary)
+            _logger.debug(ex.details)
+
+        error = f"invalid pyproject.toml config: {ex.name}."
+        raise ValueError(f"{error}\n{summary}") from None
+
+
+def apply_configuration(
+    dist: Distribution,
+    filepath: StrPath,
+    ignore_option_errors: bool = False,
+) -> Distribution:
+    """Apply the configuration from a ``pyproject.toml`` file into an existing
+    distribution object.
+    """
+    config = read_configuration(filepath, True, ignore_option_errors, dist)
+    return _apply(dist, config, filepath)
+
+
+def read_configuration(
+    filepath: StrPath,
+    expand: bool = True,
+    ignore_option_errors: bool = False,
+    dist: Distribution | None = None,
+) -> dict[str, Any]:
+    """Read given configuration file and returns options from it as a dict.
+
+    :param str|unicode filepath: Path to configuration file in the ``pyproject.toml``
+        format.
+
+    :param bool expand: Whether to expand directives and other computed values
+        (i.e. post-process the given configuration)
+
+    :param bool ignore_option_errors: Whether to silently ignore
+        options, values of which could not be resolved (e.g. due to exceptions
+        in directives such as file:, attr:, etc.).
+        If False exceptions are propagated as expected.
+
+    :param Distribution|None: Distribution object to which the configuration refers.
+        If not given a dummy object will be created and discarded after the
+        configuration is read. This is used for auto-discovery of packages and in the
+        case a dynamic configuration (e.g. ``attr`` or ``cmdclass``) is expanded.
+        When ``expand=False`` this object is simply ignored.
+
+    :rtype: dict
+    """
+    filepath = os.path.abspath(filepath)
+
+    if not os.path.isfile(filepath):
+        raise FileError(f"Configuration file {filepath!r} does not exist.")
+
+    asdict = load_file(filepath) or {}
+    project_table = asdict.get("project", {})
+    tool_table = asdict.get("tool", {})
+    setuptools_table = tool_table.get("setuptools", {})
+    if not asdict or not (project_table or setuptools_table):
+        return {}  # User is not using pyproject to configure setuptools
+
+    if "setuptools" in asdict.get("tools", {}):
+        # let the user know they probably have a typo in their metadata
+        _ToolsTypoInMetadata.emit()
+
+    if "distutils" in tool_table:
+        _ExperimentalConfiguration.emit(subject="[tool.distutils]")
+
+    # There is an overall sense in the community that making include_package_data=True
+    # the default would be an improvement.
+    # `ini2toml` backfills include_package_data=False when nothing is explicitly given,
+    # therefore setting a default here is backwards compatible.
+    if dist and dist.include_package_data is not None:
+        setuptools_table.setdefault("include-package-data", dist.include_package_data)
+    else:
+        setuptools_table.setdefault("include-package-data", True)
+    # Persist changes:
+    asdict["tool"] = tool_table
+    tool_table["setuptools"] = setuptools_table
+
+    if "ext-modules" in setuptools_table:
+        _ExperimentalConfiguration.emit(subject="[tool.setuptools.ext-modules]")
+
+    with _ignore_errors(ignore_option_errors):
+        # Don't complain about unrelated errors (e.g. tools not using the "tool" table)
+        subset = {"project": project_table, "tool": {"setuptools": setuptools_table}}
+        validate(subset, filepath)
+
+    if expand:
+        root_dir = os.path.dirname(filepath)
+        return expand_configuration(asdict, root_dir, ignore_option_errors, dist)
+
+    return asdict
+
+
+def expand_configuration(
+    config: dict,
+    root_dir: StrPath | None = None,
+    ignore_option_errors: bool = False,
+    dist: Distribution | None = None,
+) -> dict:
+    """Given a configuration with unresolved fields (e.g. dynamic, cmdclass, ...)
+    find their final values.
+
+    :param dict config: Dict containing the configuration for the distribution
+    :param str root_dir: Top-level directory for the distribution/project
+        (the same directory where ``pyproject.toml`` is place)
+    :param bool ignore_option_errors: see :func:`read_configuration`
+    :param Distribution|None: Distribution object to which the configuration refers.
+        If not given a dummy object will be created and discarded after the
+        configuration is read. Used in the case a dynamic configuration
+        (e.g. ``attr`` or ``cmdclass``).
+
+    :rtype: dict
+    """
+    return _ConfigExpander(config, root_dir, ignore_option_errors, dist).expand()
+
+
+class _ConfigExpander:
+    def __init__(
+        self,
+        config: dict,
+        root_dir: StrPath | None = None,
+        ignore_option_errors: bool = False,
+        dist: Distribution | None = None,
+    ) -> None:
+        self.config = config
+        self.root_dir = root_dir or os.getcwd()
+        self.project_cfg = config.get("project", {})
+        self.dynamic = self.project_cfg.get("dynamic", [])
+        self.setuptools_cfg = config.get("tool", {}).get("setuptools", {})
+        self.dynamic_cfg = self.setuptools_cfg.get("dynamic", {})
+        self.ignore_option_errors = ignore_option_errors
+        self._dist = dist
+        self._referenced_files = set[str]()
+
+    def _ensure_dist(self) -> Distribution:
+        from setuptools.dist import Distribution
+
+        attrs = {"src_root": self.root_dir, "name": self.project_cfg.get("name", None)}
+        return self._dist or Distribution(attrs)
+
+    def _process_field(self, container: dict, field: str, fn: Callable):
+        if field in container:
+            with _ignore_errors(self.ignore_option_errors):
+                container[field] = fn(container[field])
+
+    def _canonic_package_data(self, field="package-data"):
+        package_data = self.setuptools_cfg.get(field, {})
+        return _expand.canonic_package_data(package_data)
+
+    def expand(self):
+        self._expand_packages()
+        self._canonic_package_data()
+        self._canonic_package_data("exclude-package-data")
+
+        # A distribution object is required for discovering the correct package_dir
+        dist = self._ensure_dist()
+        ctx = _EnsurePackagesDiscovered(dist, self.project_cfg, self.setuptools_cfg)
+        with ctx as ensure_discovered:
+            package_dir = ensure_discovered.package_dir
+            self._expand_data_files()
+            self._expand_cmdclass(package_dir)
+            self._expand_all_dynamic(dist, package_dir)
+
+        dist._referenced_files.update(self._referenced_files)
+        return self.config
+
+    def _expand_packages(self):
+        packages = self.setuptools_cfg.get("packages")
+        if packages is None or isinstance(packages, (list, tuple)):
+            return
+
+        find = packages.get("find")
+        if isinstance(find, dict):
+            find["root_dir"] = self.root_dir
+            find["fill_package_dir"] = self.setuptools_cfg.setdefault("package-dir", {})
+            with _ignore_errors(self.ignore_option_errors):
+                self.setuptools_cfg["packages"] = _expand.find_packages(**find)
+
+    def _expand_data_files(self):
+        data_files = partial(_expand.canonic_data_files, root_dir=self.root_dir)
+        self._process_field(self.setuptools_cfg, "data-files", data_files)
+
+    def _expand_cmdclass(self, package_dir: Mapping[str, str]):
+        root_dir = self.root_dir
+        cmdclass = partial(_expand.cmdclass, package_dir=package_dir, root_dir=root_dir)
+        self._process_field(self.setuptools_cfg, "cmdclass", cmdclass)
+
+    def _expand_all_dynamic(self, dist: Distribution, package_dir: Mapping[str, str]):
+        special = (  # need special handling
+            "version",
+            "readme",
+            "entry-points",
+            "scripts",
+            "gui-scripts",
+            "classifiers",
+            "dependencies",
+            "optional-dependencies",
+        )
+        # `_obtain` functions are assumed to raise appropriate exceptions/warnings.
+        obtained_dynamic = {
+            field: self._obtain(dist, field, package_dir)
+            for field in self.dynamic
+            if field not in special
+        }
+        obtained_dynamic.update(
+            self._obtain_entry_points(dist, package_dir) or {},
+            version=self._obtain_version(dist, package_dir),
+            readme=self._obtain_readme(dist),
+            classifiers=self._obtain_classifiers(dist),
+            dependencies=self._obtain_dependencies(dist),
+            optional_dependencies=self._obtain_optional_dependencies(dist),
+        )
+        # `None` indicates there is nothing in `tool.setuptools.dynamic` but the value
+        # might have already been set by setup.py/extensions, so avoid overwriting.
+        updates = {k: v for k, v in obtained_dynamic.items() if v is not None}
+        self.project_cfg.update(updates)
+
+    def _ensure_previously_set(self, dist: Distribution, field: str):
+        previous = _PREVIOUSLY_DEFINED[field](dist)
+        if previous is None and not self.ignore_option_errors:
+            msg = (
+                f"No configuration found for dynamic {field!r}.\n"
+                "Some dynamic fields need to be specified via `tool.setuptools.dynamic`"
+                "\nothers must be specified via the equivalent attribute in `setup.py`."
+            )
+            raise InvalidConfigError(msg)
+
+    def _expand_directive(
+        self, specifier: str, directive, package_dir: Mapping[str, str]
+    ):
+        from more_itertools import always_iterable
+
+        with _ignore_errors(self.ignore_option_errors):
+            root_dir = self.root_dir
+            if "file" in directive:
+                self._referenced_files.update(always_iterable(directive["file"]))
+                return _expand.read_files(directive["file"], root_dir)
+            if "attr" in directive:
+                return _expand.read_attr(directive["attr"], package_dir, root_dir)
+            raise ValueError(f"invalid `{specifier}`: {directive!r}")
+        return None
+
+    def _obtain(self, dist: Distribution, field: str, package_dir: Mapping[str, str]):
+        if field in self.dynamic_cfg:
+            return self._expand_directive(
+                f"tool.setuptools.dynamic.{field}",
+                self.dynamic_cfg[field],
+                package_dir,
+            )
+        self._ensure_previously_set(dist, field)
+        return None
+
+    def _obtain_version(self, dist: Distribution, package_dir: Mapping[str, str]):
+        # Since plugins can set version, let's silently skip if it cannot be obtained
+        if "version" in self.dynamic and "version" in self.dynamic_cfg:
+            return _expand.version(
+                # We already do an early check for the presence of "version"
+                self._obtain(dist, "version", package_dir)  # pyright: ignore[reportArgumentType]
+            )
+        return None
+
+    def _obtain_readme(self, dist: Distribution) -> dict[str, str] | None:
+        if "readme" not in self.dynamic:
+            return None
+
+        dynamic_cfg = self.dynamic_cfg
+        if "readme" in dynamic_cfg:
+            return {
+                # We already do an early check for the presence of "readme"
+                "text": self._obtain(dist, "readme", {}),
+                "content-type": dynamic_cfg["readme"].get("content-type", "text/x-rst"),
+            }  # pyright: ignore[reportReturnType]
+
+        self._ensure_previously_set(dist, "readme")
+        return None
+
+    def _obtain_entry_points(
+        self, dist: Distribution, package_dir: Mapping[str, str]
+    ) -> dict[str, dict[str, Any]] | None:
+        fields = ("entry-points", "scripts", "gui-scripts")
+        if not any(field in self.dynamic for field in fields):
+            return None
+
+        text = self._obtain(dist, "entry-points", package_dir)
+        if text is None:
+            return None
+
+        groups = _expand.entry_points(text)
+        # Any is str | dict[str, str], but causes variance issues
+        expanded: dict[str, dict[str, Any]] = {"entry-points": groups}
+
+        def _set_scripts(field: str, group: str):
+            if group in groups:
+                value = groups.pop(group)
+                if field not in self.dynamic:
+                    raise InvalidConfigError(_MissingDynamic.details(field, value))
+                expanded[field] = value
+
+        _set_scripts("scripts", "console_scripts")
+        _set_scripts("gui-scripts", "gui_scripts")
+
+        return expanded
+
+    def _obtain_classifiers(self, dist: Distribution):
+        if "classifiers" in self.dynamic:
+            value = self._obtain(dist, "classifiers", {})
+            if value:
+                return value.splitlines()
+        return None
+
+    def _obtain_dependencies(self, dist: Distribution):
+        if "dependencies" in self.dynamic:
+            value = self._obtain(dist, "dependencies", {})
+            if value:
+                return _parse_requirements_list(value)
+        return None
+
+    def _obtain_optional_dependencies(self, dist: Distribution):
+        if "optional-dependencies" not in self.dynamic:
+            return None
+        if "optional-dependencies" in self.dynamic_cfg:
+            optional_dependencies_map = self.dynamic_cfg["optional-dependencies"]
+            assert isinstance(optional_dependencies_map, dict)
+            return {
+                group: _parse_requirements_list(
+                    self._expand_directive(
+                        f"tool.setuptools.dynamic.optional-dependencies.{group}",
+                        directive,
+                        {},
+                    )
+                )
+                for group, directive in optional_dependencies_map.items()
+            }
+        self._ensure_previously_set(dist, "optional-dependencies")
+        return None
+
+
+def _parse_requirements_list(value):
+    return [
+        line
+        for line in value.splitlines()
+        if line.strip() and not line.strip().startswith("#")
+    ]
+
+
+@contextmanager
+def _ignore_errors(ignore_option_errors: bool):
+    if not ignore_option_errors:
+        yield
+        return
+
+    try:
+        yield
+    except Exception as ex:
+        _logger.debug(f"ignored error: {ex.__class__.__name__} - {ex}")
+
+
+class _EnsurePackagesDiscovered(_expand.EnsurePackagesDiscovered):
+    def __init__(
+        self, distribution: Distribution, project_cfg: dict, setuptools_cfg: dict
+    ) -> None:
+        super().__init__(distribution)
+        self._project_cfg = project_cfg
+        self._setuptools_cfg = setuptools_cfg
+
+    def __enter__(self) -> Self:
+        """When entering the context, the values of ``packages``, ``py_modules`` and
+        ``package_dir`` that are missing in ``dist`` are copied from ``setuptools_cfg``.
+        """
+        dist, cfg = self._dist, self._setuptools_cfg
+        package_dir: dict[str, str] = cfg.setdefault("package-dir", {})
+        package_dir.update(dist.package_dir or {})
+        dist.package_dir = package_dir  # needs to be the same object
+
+        dist.set_defaults._ignore_ext_modules()  # pyproject.toml-specific behaviour
+
+        # Set `name`, `py_modules` and `packages` in dist to short-circuit
+        # auto-discovery, but avoid overwriting empty lists purposefully set by users.
+        if dist.metadata.name is None:
+            dist.metadata.name = self._project_cfg.get("name")
+        if dist.py_modules is None:
+            dist.py_modules = cfg.get("py-modules")
+        if dist.packages is None:
+            dist.packages = cfg.get("packages")
+
+        return super().__enter__()
+
+    def __exit__(
+        self,
+        exc_type: type[BaseException] | None,
+        exc_value: BaseException | None,
+        traceback: TracebackType | None,
+    ) -> None:
+        """When exiting the context, if values of ``packages``, ``py_modules`` and
+        ``package_dir`` are missing in ``setuptools_cfg``, copy from ``dist``.
+        """
+        # If anything was discovered set them back, so they count in the final config.
+        self._setuptools_cfg.setdefault("packages", self._dist.packages)
+        self._setuptools_cfg.setdefault("py-modules", self._dist.py_modules)
+        return super().__exit__(exc_type, exc_value, traceback)
+
+
+class _ExperimentalConfiguration(SetuptoolsWarning):
+    _SUMMARY = (
+        "`{subject}` in `pyproject.toml` is still *experimental* "
+        "and likely to change in future releases."
+    )
+
+
+class _ToolsTypoInMetadata(SetuptoolsWarning):
+    _SUMMARY = (
+        "Ignoring [tools.setuptools] in pyproject.toml, did you mean [tool.setuptools]?"
+    )

llava/lib/python3.10/site-packages/setuptools/config/setupcfg.py

@@ -0,0 +1,780 @@
+"""
+Load setuptools configuration from ``setup.cfg`` files.
+
+**API will be made private in the future**
+
+To read project metadata, consider using
+``build.util.project_wheel_metadata`` (https://pypi.org/project/build/).
+For simple scenarios, you can also try parsing the file directly
+with the help of ``configparser``.
+"""
+
+from __future__ import annotations
+
+import contextlib
+import functools
+import os
+from collections import defaultdict
+from collections.abc import Iterable, Iterator
+from functools import partial, wraps
+from typing import TYPE_CHECKING, Any, Callable, ClassVar, Generic, TypeVar, cast
+
+from packaging.markers import default_environment as marker_env
+from packaging.requirements import InvalidRequirement, Requirement
+from packaging.version import InvalidVersion, Version
+
+from .. import _static
+from .._path import StrPath
+from ..errors import FileError, OptionError
+from ..warnings import SetuptoolsDeprecationWarning
+from . import expand
+
+if TYPE_CHECKING:
+    from typing_extensions import TypeAlias
+
+    from setuptools.dist import Distribution
+
+    from distutils.dist import DistributionMetadata
+
+SingleCommandOptions: TypeAlias = dict[str, tuple[str, Any]]
+"""Dict that associate the name of the options of a particular command to a
+tuple. The first element of the tuple indicates the origin of the option value
+(e.g. the name of the configuration file where it was read from),
+while the second element of the tuple is the option value itself
+"""
+AllCommandOptions: TypeAlias = dict[str, SingleCommandOptions]
+"""cmd name => its options"""
+Target = TypeVar("Target", "Distribution", "DistributionMetadata")
+
+
+def read_configuration(
+    filepath: StrPath, find_others: bool = False, ignore_option_errors: bool = False
+) -> dict:
+    """Read given configuration file and returns options from it as a dict.
+
+    :param str|unicode filepath: Path to configuration file
+        to get options from.
+
+    :param bool find_others: Whether to search for other configuration files
+        which could be on in various places.
+
+    :param bool ignore_option_errors: Whether to silently ignore
+        options, values of which could not be resolved (e.g. due to exceptions
+        in directives such as file:, attr:, etc.).
+        If False exceptions are propagated as expected.
+
+    :rtype: dict
+    """
+    from setuptools.dist import Distribution
+
+    dist = Distribution()
+    filenames = dist.find_config_files() if find_others else []
+    handlers = _apply(dist, filepath, filenames, ignore_option_errors)
+    return configuration_to_dict(handlers)
+
+
+def apply_configuration(dist: Distribution, filepath: StrPath) -> Distribution:
+    """Apply the configuration from a ``setup.cfg`` file into an existing
+    distribution object.
+    """
+    _apply(dist, filepath)
+    dist._finalize_requires()
+    return dist
+
+
+def _apply(
+    dist: Distribution,
+    filepath: StrPath,
+    other_files: Iterable[StrPath] = (),
+    ignore_option_errors: bool = False,
+) -> tuple[ConfigMetadataHandler, ConfigOptionsHandler]:
+    """Read configuration from ``filepath`` and applies to the ``dist`` object."""
+    from setuptools.dist import _Distribution
+
+    filepath = os.path.abspath(filepath)
+
+    if not os.path.isfile(filepath):
+        raise FileError(f'Configuration file {filepath} does not exist.')
+
+    current_directory = os.getcwd()
+    os.chdir(os.path.dirname(filepath))
+    filenames = [*other_files, filepath]
+
+    try:
+        # TODO: Temporary cast until mypy 1.12 is released with upstream fixes from typeshed
+        _Distribution.parse_config_files(dist, filenames=cast(list[str], filenames))
+        handlers = parse_configuration(
+            dist, dist.command_options, ignore_option_errors=ignore_option_errors
+        )
+        dist._finalize_license_files()
+    finally:
+        os.chdir(current_directory)
+
+    return handlers
+
+
+def _get_option(target_obj: Distribution | DistributionMetadata, key: str):
+    """
+    Given a target object and option key, get that option from
+    the target object, either through a get_{key} method or
+    from an attribute directly.
+    """
+    getter_name = f'get_{key}'
+    by_attribute = functools.partial(getattr, target_obj, key)
+    getter = getattr(target_obj, getter_name, by_attribute)
+    return getter()
+
+
+def configuration_to_dict(
+    handlers: Iterable[
+        ConfigHandler[Distribution] | ConfigHandler[DistributionMetadata]
+    ],
+) -> dict:
+    """Returns configuration data gathered by given handlers as a dict.
+
+    :param Iterable[ConfigHandler] handlers: Handlers list,
+        usually from parse_configuration()
+
+    :rtype: dict
+    """
+    config_dict: dict = defaultdict(dict)
+
+    for handler in handlers:
+        for option in handler.set_options:
+            value = _get_option(handler.target_obj, option)
+            config_dict[handler.section_prefix][option] = value
+
+    return config_dict
+
+
+def parse_configuration(
+    distribution: Distribution,
+    command_options: AllCommandOptions,
+    ignore_option_errors: bool = False,
+) -> tuple[ConfigMetadataHandler, ConfigOptionsHandler]:
+    """Performs additional parsing of configuration options
+    for a distribution.
+
+    Returns a list of used option handlers.
+
+    :param Distribution distribution:
+    :param dict command_options:
+    :param bool ignore_option_errors: Whether to silently ignore
+        options, values of which could not be resolved (e.g. due to exceptions
+        in directives such as file:, attr:, etc.).
+        If False exceptions are propagated as expected.
+    :rtype: list
+    """
+    with expand.EnsurePackagesDiscovered(distribution) as ensure_discovered:
+        options = ConfigOptionsHandler(
+            distribution,
+            command_options,
+            ignore_option_errors,
+            ensure_discovered,
+        )
+
+        options.parse()
+        if not distribution.package_dir:
+            distribution.package_dir = options.package_dir  # Filled by `find_packages`
+
+        meta = ConfigMetadataHandler(
+            distribution.metadata,
+            command_options,
+            ignore_option_errors,
+            ensure_discovered,
+            distribution.package_dir,
+            distribution.src_root,
+        )
+        meta.parse()
+        distribution._referenced_files.update(
+            options._referenced_files, meta._referenced_files
+        )
+
+    return meta, options
+
+
+def _warn_accidental_env_marker_misconfig(label: str, orig_value: str, parsed: list):
+    """Because users sometimes misinterpret this configuration:
+
+    [options.extras_require]
+    foo = bar;python_version<"4"
+
+    It looks like one requirement with an environment marker
+    but because there is no newline, it's parsed as two requirements
+    with a semicolon as separator.
+
+    Therefore, if:
+        * input string does not contain a newline AND
+        * parsed result contains two requirements AND
+        * parsing of the two parts from the result ("<first>;<second>")
+        leads in a valid Requirement with a valid marker
+    a UserWarning is shown to inform the user about the possible problem.
+    """
+    if "\n" in orig_value or len(parsed) != 2:
+        return
+
+    markers = marker_env().keys()
+
+    try:
+        req = Requirement(parsed[1])
+        if req.name in markers:
+            _AmbiguousMarker.emit(field=label, req=parsed[1])
+    except InvalidRequirement as ex:
+        if any(parsed[1].startswith(marker) for marker in markers):
+            msg = _AmbiguousMarker.message(field=label, req=parsed[1])
+            raise InvalidRequirement(msg) from ex
+
+
+class ConfigHandler(Generic[Target]):
+    """Handles metadata supplied in configuration files."""
+
+    section_prefix: str
+    """Prefix for config sections handled by this handler.
+    Must be provided by class heirs.
+
+    """
+
+    aliases: ClassVar[dict[str, str]] = {}
+    """Options aliases.
+    For compatibility with various packages. E.g.: d2to1 and pbr.
+    Note: `-` in keys is replaced with `_` by config parser.
+
+    """
+
+    def __init__(
+        self,
+        target_obj: Target,
+        options: AllCommandOptions,
+        ignore_option_errors,
+        ensure_discovered: expand.EnsurePackagesDiscovered,
+    ) -> None:
+        self.ignore_option_errors = ignore_option_errors
+        self.target_obj: Target = target_obj
+        self.sections = dict(self._section_options(options))
+        self.set_options: list[str] = []
+        self.ensure_discovered = ensure_discovered
+        self._referenced_files = set[str]()
+        """After parsing configurations, this property will enumerate
+        all files referenced by the "file:" directive. Private API for setuptools only.
+        """
+
+    @classmethod
+    def _section_options(
+        cls, options: AllCommandOptions
+    ) -> Iterator[tuple[str, SingleCommandOptions]]:
+        for full_name, value in options.items():
+            pre, _sep, name = full_name.partition(cls.section_prefix)
+            if pre:
+                continue
+            yield name.lstrip('.'), value
+
+    @property
+    def parsers(self):
+        """Metadata item name to parser function mapping."""
+        raise NotImplementedError(
+            f'{self.__class__.__name__} must provide .parsers property'
+        )
+
+    def __setitem__(self, option_name, value) -> None:
+        target_obj = self.target_obj
+
+        # Translate alias into real name.
+        option_name = self.aliases.get(option_name, option_name)
+
+        try:
+            current_value = getattr(target_obj, option_name)
+        except AttributeError as e:
+            raise KeyError(option_name) from e
+
+        if current_value:
+            # Already inhabited. Skipping.
+            return
+
+        try:
+            parsed = self.parsers.get(option_name, lambda x: x)(value)
+        except (Exception,) * self.ignore_option_errors:
+            return
+
+        simple_setter = functools.partial(target_obj.__setattr__, option_name)
+        setter = getattr(target_obj, f"set_{option_name}", simple_setter)
+        setter(parsed)
+
+        self.set_options.append(option_name)
+
+    @classmethod
+    def _parse_list(cls, value, separator=','):
+        """Represents value as a list.
+
+        Value is split either by separator (defaults to comma) or by lines.
+
+        :param value:
+        :param separator: List items separator character.
+        :rtype: list
+        """
+        if isinstance(value, list):  # _get_parser_compound case
+            return value
+
+        if '\n' in value:
+            value = value.splitlines()
+        else:
+            value = value.split(separator)
+
+        return [chunk.strip() for chunk in value if chunk.strip()]
+
+    @classmethod
+    def _parse_dict(cls, value):
+        """Represents value as a dict.
+
+        :param value:
+        :rtype: dict
+        """
+        separator = '='
+        result = {}
+        for line in cls._parse_list(value):
+            key, sep, val = line.partition(separator)
+            if sep != separator:
+                raise OptionError(f"Unable to parse option value to dict: {value}")
+            result[key.strip()] = val.strip()
+
+        return result
+
+    @classmethod
+    def _parse_bool(cls, value):
+        """Represents value as boolean.
+
+        :param value:
+        :rtype: bool
+        """
+        value = value.lower()
+        return value in ('1', 'true', 'yes')
+
+    @classmethod
+    def _exclude_files_parser(cls, key):
+        """Returns a parser function to make sure field inputs
+        are not files.
+
+        Parses a value after getting the key so error messages are
+        more informative.
+
+        :param key:
+        :rtype: callable
+        """
+
+        def parser(value):
+            exclude_directive = 'file:'
+            if value.startswith(exclude_directive):
+                raise ValueError(
+                    f'Only strings are accepted for the {key} field, '
+                    'files are not accepted'
+                )
+            return _static.Str(value)
+
+        return parser
+
+    def _parse_file(self, value, root_dir: StrPath | None):
+        """Represents value as a string, allowing including text
+        from nearest files using `file:` directive.
+
+        Directive is sandboxed and won't reach anything outside
+        directory with setup.py.
+
+        Examples:
+            file: README.rst, CHANGELOG.md, src/file.txt
+
+        :param str value:
+        :rtype: str
+        """
+        include_directive = 'file:'
+
+        if not isinstance(value, str):
+            return value
+
+        if not value.startswith(include_directive):
+            return _static.Str(value)
+
+        spec = value[len(include_directive) :]
+        filepaths = [path.strip() for path in spec.split(',')]
+        self._referenced_files.update(filepaths)
+        # XXX: Is marking as static contents coming from files too optimistic?
+        return _static.Str(expand.read_files(filepaths, root_dir))
+
+    def _parse_attr(self, value, package_dir, root_dir: StrPath):
+        """Represents value as a module attribute.
+
+        Examples:
+            attr: package.attr
+            attr: package.module.attr
+
+        :param str value:
+        :rtype: str
+        """
+        attr_directive = 'attr:'
+        if not value.startswith(attr_directive):
+            return _static.Str(value)
+
+        attr_desc = value.replace(attr_directive, '')
+
+        # Make sure package_dir is populated correctly, so `attr:` directives can work
+        package_dir.update(self.ensure_discovered.package_dir)
+        return expand.read_attr(attr_desc, package_dir, root_dir)
+
+    @classmethod
+    def _get_parser_compound(cls, *parse_methods):
+        """Returns parser function to represents value as a list.
+
+        Parses a value applying given methods one after another.
+
+        :param parse_methods:
+        :rtype: callable
+        """
+
+        def parse(value):
+            parsed = value
+
+            for method in parse_methods:
+                parsed = method(parsed)
+
+            return parsed
+
+        return parse
+
+    @classmethod
+    def _parse_section_to_dict_with_key(cls, section_options, values_parser):
+        """Parses section options into a dictionary.
+
+        Applies a given parser to each option in a section.
+
+        :param dict section_options:
+        :param callable values_parser: function with 2 args corresponding to key, value
+        :rtype: dict
+        """
+        value = {}
+        for key, (_, val) in section_options.items():
+            value[key] = values_parser(key, val)
+        return value
+
+    @classmethod
+    def _parse_section_to_dict(cls, section_options, values_parser=None):
+        """Parses section options into a dictionary.
+
+        Optionally applies a given parser to each value.
+
+        :param dict section_options:
+        :param callable values_parser: function with 1 arg corresponding to option value
+        :rtype: dict
+        """
+        parser = (lambda _, v: values_parser(v)) if values_parser else (lambda _, v: v)
+        return cls._parse_section_to_dict_with_key(section_options, parser)
+
+    def parse_section(self, section_options) -> None:
+        """Parses configuration file section.
+
+        :param dict section_options:
+        """
+        for name, (_, value) in section_options.items():
+            with contextlib.suppress(KeyError):
+                # Keep silent for a new option may appear anytime.
+                self[name] = value
+
+    def parse(self) -> None:
+        """Parses configuration file items from one
+        or more related sections.
+
+        """
+        for section_name, section_options in self.sections.items():
+            method_postfix = ''
+            if section_name:  # [section.option] variant
+                method_postfix = f"_{section_name}"
+
+            section_parser_method: Callable | None = getattr(
+                self,
+                # Dots in section names are translated into dunderscores.
+                f'parse_section{method_postfix}'.replace('.', '__'),
+                None,
+            )
+
+            if section_parser_method is None:
+                raise OptionError(
+                    "Unsupported distribution option section: "
+                    f"[{self.section_prefix}.{section_name}]"
+                )
+
+            section_parser_method(section_options)
+
+    def _deprecated_config_handler(self, func, msg, **kw):
+        """this function will wrap around parameters that are deprecated
+
+        :param msg: deprecation message
+        :param func: function to be wrapped around
+        """
+
+        @wraps(func)
+        def config_handler(*args, **kwargs):
+            kw.setdefault("stacklevel", 2)
+            _DeprecatedConfig.emit("Deprecated config in `setup.cfg`", msg, **kw)
+            return func(*args, **kwargs)
+
+        return config_handler
+
+
+class ConfigMetadataHandler(ConfigHandler["DistributionMetadata"]):
+    section_prefix = 'metadata'
+
+    aliases = {
+        'home_page': 'url',
+        'summary': 'description',
+        'classifier': 'classifiers',
+        'platform': 'platforms',
+    }
+
+    strict_mode = False
+    """We need to keep it loose, to be partially compatible with
+    `pbr` and `d2to1` packages which also uses `metadata` section.
+
+    """
+
+    def __init__(
+        self,
+        target_obj: DistributionMetadata,
+        options: AllCommandOptions,
+        ignore_option_errors: bool,
+        ensure_discovered: expand.EnsurePackagesDiscovered,
+        package_dir: dict | None = None,
+        root_dir: StrPath | None = os.curdir,
+    ) -> None:
+        super().__init__(target_obj, options, ignore_option_errors, ensure_discovered)
+        self.package_dir = package_dir
+        self.root_dir = root_dir
+
+    @property
+    def parsers(self):
+        """Metadata item name to parser function mapping."""
+        parse_list_static = self._get_parser_compound(self._parse_list, _static.List)
+        parse_dict_static = self._get_parser_compound(self._parse_dict, _static.Dict)
+        parse_file = partial(self._parse_file, root_dir=self.root_dir)
+        exclude_files_parser = self._exclude_files_parser
+
+        return {
+            'author': _static.Str,
+            'author_email': _static.Str,
+            'maintainer': _static.Str,
+            'maintainer_email': _static.Str,
+            'platforms': parse_list_static,
+            'keywords': parse_list_static,
+            'provides': parse_list_static,
+            'obsoletes': parse_list_static,
+            'classifiers': self._get_parser_compound(parse_file, parse_list_static),
+            'license': exclude_files_parser('license'),
+            'license_files': parse_list_static,
+            'description': parse_file,
+            'long_description': parse_file,
+            'long_description_content_type': _static.Str,
+            'version': self._parse_version,  # Cannot be marked as dynamic
+            'url': _static.Str,
+            'project_urls': parse_dict_static,
+        }
+
+    def _parse_version(self, value):
+        """Parses `version` option value.
+
+        :param value:
+        :rtype: str
+
+        """
+        version = self._parse_file(value, self.root_dir)
+
+        if version != value:
+            version = version.strip()
+            # Be strict about versions loaded from file because it's easy to
+            # accidentally include newlines and other unintended content
+            try:
+                Version(version)
+            except InvalidVersion as e:
+                raise OptionError(
+                    f'Version loaded from {value} does not '
+                    f'comply with PEP 440: {version}'
+                ) from e
+
+            return version
+
+        return expand.version(self._parse_attr(value, self.package_dir, self.root_dir))
+
+
+class ConfigOptionsHandler(ConfigHandler["Distribution"]):
+    section_prefix = 'options'
+
+    def __init__(
+        self,
+        target_obj: Distribution,
+        options: AllCommandOptions,
+        ignore_option_errors: bool,
+        ensure_discovered: expand.EnsurePackagesDiscovered,
+    ) -> None:
+        super().__init__(target_obj, options, ignore_option_errors, ensure_discovered)
+        self.root_dir = target_obj.src_root
+        self.package_dir: dict[str, str] = {}  # To be filled by `find_packages`
+
+    @classmethod
+    def _parse_list_semicolon(cls, value):
+        return cls._parse_list(value, separator=';')
+
+    def _parse_file_in_root(self, value):
+        return self._parse_file(value, root_dir=self.root_dir)
+
+    def _parse_requirements_list(self, label: str, value: str):
+        # Parse a requirements list, either by reading in a `file:`, or a list.
+        parsed = self._parse_list_semicolon(self._parse_file_in_root(value))
+        _warn_accidental_env_marker_misconfig(label, value, parsed)
+        # Filter it to only include lines that are not comments. `parse_list`
+        # will have stripped each line and filtered out empties.
+        return _static.List(line for line in parsed if not line.startswith("#"))
+        # ^-- Use `_static.List` to mark a non-`Dynamic` Core Metadata
+
+    @property
+    def parsers(self):
+        """Metadata item name to parser function mapping."""
+        parse_list = self._parse_list
+        parse_bool = self._parse_bool
+        parse_cmdclass = self._parse_cmdclass
+
+        return {
+            'zip_safe': parse_bool,
+            'include_package_data': parse_bool,
+            'package_dir': self._parse_dict,
+            'scripts': parse_list,
+            'eager_resources': parse_list,
+            'dependency_links': parse_list,
+            'namespace_packages': self._deprecated_config_handler(
+                parse_list,
+                "The namespace_packages parameter is deprecated, "
+                "consider using implicit namespaces instead (PEP 420).",
+                # TODO: define due date, see setuptools.dist:check_nsp.
+            ),
+            'install_requires': partial(  # Core Metadata
+                self._parse_requirements_list, "install_requires"
+            ),
+            'setup_requires': self._parse_list_semicolon,
+            'packages': self._parse_packages,
+            'entry_points': self._parse_file_in_root,
+            'py_modules': parse_list,
+            'python_requires': _static.SpecifierSet,  # Core Metadata
+            'cmdclass': parse_cmdclass,
+        }
+
+    def _parse_cmdclass(self, value):
+        package_dir = self.ensure_discovered.package_dir
+        return expand.cmdclass(self._parse_dict(value), package_dir, self.root_dir)
+
+    def _parse_packages(self, value):
+        """Parses `packages` option value.
+
+        :param value:
+        :rtype: list
+        """
+        find_directives = ['find:', 'find_namespace:']
+        trimmed_value = value.strip()
+
+        if trimmed_value not in find_directives:
+            return self._parse_list(value)
+
+        # Read function arguments from a dedicated section.
+        find_kwargs = self.parse_section_packages__find(
+            self.sections.get('packages.find', {})
+        )
+
+        find_kwargs.update(
+            namespaces=(trimmed_value == find_directives[1]),
+            root_dir=self.root_dir,
+            fill_package_dir=self.package_dir,
+        )
+
+        return expand.find_packages(**find_kwargs)
+
+    def parse_section_packages__find(self, section_options):
+        """Parses `packages.find` configuration file section.
+
+        To be used in conjunction with _parse_packages().
+
+        :param dict section_options:
+        """
+        section_data = self._parse_section_to_dict(section_options, self._parse_list)
+
+        valid_keys = ['where', 'include', 'exclude']
+        find_kwargs = {k: v for k, v in section_data.items() if k in valid_keys and v}
+
+        where = find_kwargs.get('where')
+        if where is not None:
+            find_kwargs['where'] = where[0]  # cast list to single val
+
+        return find_kwargs
+
+    def parse_section_entry_points(self, section_options) -> None:
+        """Parses `entry_points` configuration file section.
+
+        :param dict section_options:
+        """
+        parsed = self._parse_section_to_dict(section_options, self._parse_list)
+        self['entry_points'] = parsed
+
+    def _parse_package_data(self, section_options):
+        package_data = self._parse_section_to_dict(section_options, self._parse_list)
+        return expand.canonic_package_data(package_data)
+
+    def parse_section_package_data(self, section_options) -> None:
+        """Parses `package_data` configuration file section.
+
+        :param dict section_options:
+        """
+        self['package_data'] = self._parse_package_data(section_options)
+
+    def parse_section_exclude_package_data(self, section_options) -> None:
+        """Parses `exclude_package_data` configuration file section.
+
+        :param dict section_options:
+        """
+        self['exclude_package_data'] = self._parse_package_data(section_options)
+
+    def parse_section_extras_require(self, section_options) -> None:  # Core Metadata
+        """Parses `extras_require` configuration file section.
+
+        :param dict section_options:
+        """
+        parsed = self._parse_section_to_dict_with_key(
+            section_options,
+            lambda k, v: self._parse_requirements_list(f"extras_require[{k}]", v),
+        )
+
+        self['extras_require'] = _static.Dict(parsed)
+        # ^-- Use `_static.Dict` to mark a non-`Dynamic` Core Metadata
+
+    def parse_section_data_files(self, section_options) -> None:
+        """Parses `data_files` configuration file section.
+
+        :param dict section_options:
+        """
+        parsed = self._parse_section_to_dict(section_options, self._parse_list)
+        self['data_files'] = expand.canonic_data_files(parsed, self.root_dir)
+
+
+class _AmbiguousMarker(SetuptoolsDeprecationWarning):
+    _SUMMARY = "Ambiguous requirement marker."
+    _DETAILS = """
+    One of the parsed requirements in `{field}` looks like a valid environment marker:
+
+        {req!r}
+
+    Please make sure that the configuration file is correct.
+    You can use dangling lines to avoid this problem.
+    """
+    _SEE_DOCS = "userguide/declarative_config.html#opt-2"
+    # TODO: should we include due_date here? Initially introduced in 6 Aug 2022.
+    # Does this make sense with latest version of packaging?
+
+    @classmethod
+    def message(cls, **kw):
+        docs = f"https://setuptools.pypa.io/en/latest/{cls._SEE_DOCS}"
+        return cls._format(cls._SUMMARY, cls._DETAILS, see_url=docs, format_args=kw)
+
+
+class _DeprecatedConfig(SetuptoolsDeprecationWarning):
+    _SEE_DOCS = "userguide/declarative_config.html"

llava/lib/python3.10/site-packages/setuptools/config/setuptools.schema.json

@@ -0,0 +1,433 @@
+{
+  "$schema": "http://json-schema.org/draft-07/schema#",
+
+  "$id": "https://setuptools.pypa.io/en/latest/userguide/pyproject_config.html",
+  "title": "``tool.setuptools`` table",
+  "$$description": [
+    "``setuptools``-specific configurations that can be set by users that require",
+    "customization.",
+    "These configurations are completely optional and probably can be skipped when",
+    "creating simple packages. They are equivalent to some of the `Keywords",
+    "<https://setuptools.pypa.io/en/latest/references/keywords.html>`_",
+    "used by the ``setup.py`` file, and can be set via the ``tool.setuptools`` table.",
+    "It considers only ``setuptools`` `parameters",
+    "<https://setuptools.pypa.io/en/latest/userguide/pyproject_config.html#setuptools-specific-configuration>`_",
+    "that are not covered by :pep:`621`; and intentionally excludes ``dependency_links``",
+    "and ``setup_requires`` (incompatible with modern workflows/standards)."
+  ],
+
+  "type": "object",
+  "additionalProperties": false,
+  "properties": {
+    "platforms": {
+      "type": "array",
+      "items": {"type": "string"}
+    },
+    "provides": {
+      "$$description": [
+        "Package and virtual package names contained within this package",
+        "**(not supported by pip)**"
+      ],
+      "type": "array",
+      "items": {"type": "string", "format": "pep508-identifier"}
+    },
+    "obsoletes": {
+      "$$description": [
+        "Packages which this package renders obsolete",
+        "**(not supported by pip)**"
+      ],
+      "type": "array",
+      "items": {"type": "string", "format": "pep508-identifier"}
+    },
+    "zip-safe": {
+      "$$description": [
+        "Whether the project can be safely installed and run from a zip file.",
+        "**OBSOLETE**: only relevant for ``pkg_resources``, ``easy_install`` and",
+        "``setup.py install`` in the context of ``eggs`` (**DEPRECATED**)."
+      ],
+      "type": "boolean"
+    },
+    "script-files": {
+      "$$description": [
+        "Legacy way of defining scripts (entry-points are preferred).",
+        "Equivalent to the ``script`` keyword in ``setup.py``",
+        "(it was renamed to avoid confusion with entry-point based ``project.scripts``",
+        "defined in :pep:`621`).",
+        "**DISCOURAGED**: generic script wrappers are tricky and may not work properly.",
+        "Whenever possible, please use ``project.scripts`` instead."
+      ],
+      "type": "array",
+      "items": {"type": "string"},
+      "$comment": "TODO: is this field deprecated/should be removed?"
+    },
+    "eager-resources": {
+      "$$description": [
+        "Resources that should be extracted together, if any of them is needed,",
+        "or if any C extensions included in the project are imported.",
+        "**OBSOLETE**: only relevant for ``pkg_resources``, ``easy_install`` and",
+        "``setup.py install`` in the context of ``eggs`` (**DEPRECATED**)."
+      ],
+      "type": "array",
+      "items": {"type": "string"}
+    },
+    "packages": {
+      "$$description": [
+        "Packages that should be included in the distribution.",
+        "It can be given either as a list of package identifiers",
+        "or as a ``dict``-like structure with a single key ``find``",
+        "which corresponds to a dynamic call to",
+        "``setuptools.config.expand.find_packages`` function.",
+        "The ``find`` key is associated with a nested ``dict``-like structure that can",
+        "contain ``where``, ``include``, ``exclude`` and ``namespaces`` keys,",
+        "mimicking the keyword arguments of the associated function."
+      ],
+      "oneOf": [
+        {
+          "title": "Array of Python package identifiers",
+          "type": "array",
+          "items": {"$ref": "#/definitions/package-name"}
+        },
+        {"$ref": "#/definitions/find-directive"}
+      ]
+    },
+    "package-dir": {
+      "$$description": [
+        ":class:`dict`-like structure mapping from package names to directories where their",
+        "code can be found.",
+        "The empty string (as key) means that all packages are contained inside",
+        "the given directory will be included in the distribution."
+      ],
+      "type": "object",
+      "additionalProperties": false,
+      "propertyNames": {
+        "anyOf": [{"const": ""}, {"$ref": "#/definitions/package-name"}]
+      },
+      "patternProperties": {
+        "^.*$": {"type": "string" }
+      }
+    },
+    "package-data": {
+      "$$description": [
+        "Mapping from package names to lists of glob patterns.",
+        "Usually this option is not needed when using ``include-package-data = true``",
+        "For more information on how to include data files, check ``setuptools`` `docs",
+        "<https://setuptools.pypa.io/en/latest/userguide/datafiles.html>`_."
+      ],
+      "type": "object",
+      "additionalProperties": false,
+      "propertyNames": {
+        "anyOf": [{"type": "string", "format": "python-module-name"}, {"const": "*"}]
+      },
+      "patternProperties": {
+        "^.*$": {"type": "array", "items": {"type": "string"}}
+      }
+    },
+    "include-package-data": {
+      "$$description": [
+        "Automatically include any data files inside the package directories",
+        "that are specified by ``MANIFEST.in``",
+        "For more information on how to include data files, check ``setuptools`` `docs",
+        "<https://setuptools.pypa.io/en/latest/userguide/datafiles.html>`_."
+      ],
+      "type": "boolean"
+    },
+    "exclude-package-data": {
+      "$$description": [
+        "Mapping from package names to lists of glob patterns that should be excluded",
+        "For more information on how to include data files, check ``setuptools`` `docs",
+        "<https://setuptools.pypa.io/en/latest/userguide/datafiles.html>`_."
+      ],
+      "type": "object",
+      "additionalProperties": false,
+      "propertyNames": {
+        "anyOf": [{"type": "string", "format": "python-module-name"}, {"const": "*"}]
+      },
+      "patternProperties": {
+          "^.*$": {"type": "array", "items": {"type": "string"}}
+      }
+    },
+    "namespace-packages": {
+      "type": "array",
+      "items": {"type": "string", "format": "python-module-name-relaxed"},
+      "$comment": "https://setuptools.pypa.io/en/latest/userguide/package_discovery.html",
+      "description": "**DEPRECATED**: use implicit namespaces instead (:pep:`420`)."
+    },
+    "py-modules": {
+      "description": "Modules that setuptools will manipulate",
+      "type": "array",
+      "items": {"type": "string", "format": "python-module-name-relaxed"},
+      "$comment": "TODO: clarify the relationship with ``packages``"
+    },
+    "ext-modules": {
+      "description": "Extension modules to be compiled by setuptools",
+      "type": "array",
+      "items": {"$ref": "#/definitions/ext-module"}
+    },
+    "data-files": {
+      "$$description": [
+        "``dict``-like structure where each key represents a directory and",
+        "the value is a list of glob patterns that should be installed in them.",
+        "**DISCOURAGED**: please notice this might not work as expected with wheels.",
+        "Whenever possible, consider using data files inside the package directories",
+        "(or create a new namespace package that only contains data files).",
+        "See `data files support",
+        "<https://setuptools.pypa.io/en/latest/userguide/datafiles.html>`_."
+      ],
+      "type": "object",
+      "patternProperties": {
+          "^.*$": {"type": "array", "items": {"type": "string"}}
+      }
+    },
+    "cmdclass": {
+      "$$description": [
+        "Mapping of distutils-style command names to ``setuptools.Command`` subclasses",
+        "which in turn should be represented by strings with a qualified class name",
+        "(i.e., \"dotted\" form with module), e.g.::\n\n",
+        "    cmdclass = {mycmd = \"pkg.subpkg.module.CommandClass\"}\n\n",
+        "The command class should be a directly defined at the top-level of the",
+        "containing module (no class nesting)."
+      ],
+      "type": "object",
+      "patternProperties": {
+          "^.*$": {"type": "string", "format": "python-qualified-identifier"}
+      }
+    },
+    "license-files": {
+      "type": "array",
+      "items": {"type": "string"},
+      "$$description": [
+        "**PROVISIONAL**: list of glob patterns for all license files being distributed.",
+        "(likely to become standard with :pep:`639`).",
+        "By default: ``['LICEN[CS]E*', 'COPYING*', 'NOTICE*', 'AUTHORS*']``"
+      ],
+      "$comment": "TODO: revise if PEP 639 is accepted. Probably ``project.license-files``?"
+    },
+    "dynamic": {
+      "type": "object",
+      "description": "Instructions for loading :pep:`621`-related metadata dynamically",
+      "additionalProperties": false,
+      "properties": {
+        "version": {
+          "$$description": [
+            "A version dynamically loaded via either the ``attr:`` or ``file:``",
+            "directives. Please make sure the given file or attribute respects :pep:`440`.",
+            "Also ensure to set ``project.dynamic`` accordingly."
+          ],
+          "oneOf": [
+            {"$ref": "#/definitions/attr-directive"},
+            {"$ref": "#/definitions/file-directive"}
+          ]
+        },
+        "classifiers": {"$ref": "#/definitions/file-directive"},
+        "description": {"$ref": "#/definitions/file-directive"},
+        "entry-points": {"$ref": "#/definitions/file-directive"},
+        "dependencies": {"$ref": "#/definitions/file-directive-for-dependencies"},
+        "optional-dependencies": {
+          "type": "object",
+          "propertyNames": {"type": "string", "format": "pep508-identifier"},
+          "additionalProperties": false,
+          "patternProperties": {
+            ".+": {"$ref": "#/definitions/file-directive-for-dependencies"}
+          }
+        },
+        "readme": {
+          "type": "object",
+          "anyOf": [
+            {"$ref": "#/definitions/file-directive"},
+            {
+              "type": "object",
+              "properties": {
+                "content-type": {"type": "string"},
+                "file": { "$ref": "#/definitions/file-directive/properties/file" }
+              },
+              "additionalProperties": false}
+          ],
+          "required": ["file"]
+        }
+      }
+    }
+  },
+
+  "definitions": {
+    "package-name": {
+      "$id": "#/definitions/package-name",
+      "title": "Valid package name",
+      "description": "Valid package name (importable or :pep:`561`).",
+      "type": "string",
+      "anyOf": [
+        {"type": "string", "format": "python-module-name-relaxed"},
+        {"type": "string", "format": "pep561-stub-name"}
+      ]
+    },
+    "ext-module": {
+      "$id": "#/definitions/ext-module",
+      "title": "Extension module",
+      "description": "Parameters to construct a :class:`setuptools.Extension` object",
+      "type": "object",
+      "required": ["name", "sources"],
+      "additionalProperties": false,
+      "properties": {
+        "name": {
+          "type": "string",
+          "format": "python-module-name-relaxed"
+        },
+        "sources": {
+          "type": "array",
+          "items": {"type": "string"}
+        },
+        "include-dirs":{
+          "type": "array",
+          "items": {"type": "string"}
+        },
+        "define-macros": {
+          "type": "array",
+          "items": {
+            "type": "array",
+            "items": [
+              {"description": "macro name", "type": "string"},
+              {"description": "macro value", "oneOf": [{"type": "string"}, {"type": "null"}]}
+            ],
+            "additionalItems": false
+          }
+        },
+        "undef-macros": {
+          "type": "array",
+          "items": {"type": "string"}
+        },
+        "library-dirs": {
+          "type": "array",
+          "items": {"type": "string"}
+        },
+        "libraries": {
+          "type": "array",
+          "items": {"type": "string"}
+        },
+        "runtime-library-dirs": {
+          "type": "array",
+          "items": {"type": "string"}
+        },
+        "extra-objects": {
+          "type": "array",
+          "items": {"type": "string"}
+        },
+        "extra-compile-args": {
+          "type": "array",
+          "items": {"type": "string"}
+        },
+        "extra-link-args": {
+          "type": "array",
+          "items": {"type": "string"}
+        },
+        "export-symbols": {
+          "type": "array",
+          "items": {"type": "string"}
+        },
+        "swig-opts": {
+          "type": "array",
+          "items": {"type": "string"}
+        },
+        "depends": {
+          "type": "array",
+          "items": {"type": "string"}
+        },
+        "language": {"type": "string"},
+        "optional": {"type": "boolean"},
+        "py-limited-api": {"type": "boolean"}
+      }
+    },
+    "file-directive": {
+      "$id": "#/definitions/file-directive",
+      "title": "'file:' directive",
+      "description":
+        "Value is read from a file (or list of files and then concatenated)",
+      "type": "object",
+      "additionalProperties": false,
+      "properties": {
+        "file": {
+          "oneOf": [
+            {"type": "string"},
+            {"type": "array", "items": {"type": "string"}}
+          ]
+        }
+      },
+      "required": ["file"]
+    },
+    "file-directive-for-dependencies": {
+      "title": "'file:' directive for dependencies",
+      "allOf": [
+        {
+          "$$description": [
+            "**BETA**: subset of the ``requirements.txt`` format",
+            "without ``pip`` flags and options",
+            "(one :pep:`508`-compliant string per line,",
+            "lines that are blank or start with ``#`` are excluded).",
+            "See `dynamic metadata",
+            "<https://setuptools.pypa.io/en/latest/userguide/pyproject_config.html#dynamic-metadata>`_."
+          ]
+        },
+        {"$ref": "#/definitions/file-directive"}
+      ]
+    },
+    "attr-directive": {
+      "title": "'attr:' directive",
+      "$id": "#/definitions/attr-directive",
+      "$$description": [
+        "Value is read from a module attribute. Supports callables and iterables;",
+        "unsupported types are cast via ``str()``"
+      ],
+      "type": "object",
+      "additionalProperties": false,
+      "properties": {
+        "attr": {"type": "string", "format": "python-qualified-identifier"}
+      },
+      "required": ["attr"]
+    },
+    "find-directive": {
+      "$id": "#/definitions/find-directive",
+      "title": "'find:' directive",
+      "type": "object",
+      "additionalProperties": false,
+      "properties": {
+        "find": {
+          "type": "object",
+          "$$description": [
+            "Dynamic `package discovery",
+            "<https://setuptools.pypa.io/en/latest/userguide/package_discovery.html>`_."
+          ],
+          "additionalProperties": false,
+          "properties": {
+            "where": {
+              "description":
+                "Directories to be searched for packages (Unix-style relative path)",
+              "type": "array",
+              "items": {"type": "string"}
+            },
+            "exclude": {
+              "type": "array",
+              "$$description": [
+                "Exclude packages that match the values listed in this field.",
+                "Can container shell-style wildcards (e.g. ``'pkg.*'``)"
+              ],
+              "items": {"type": "string"}
+            },
+            "include": {
+              "type": "array",
+              "$$description": [
+                "Restrict the found packages to just the ones listed in this field.",
+                "Can container shell-style wildcards (e.g. ``'pkg.*'``)"
+              ],
+              "items": {"type": "string"}
+            },
+            "namespaces": {
+              "type": "boolean",
+              "$$description": [
+                "When ``True``, directories without a ``__init__.py`` file will also",
+                "be scanned for :pep:`420`-style implicit namespaces"
+              ]
+            }
+          }
+        }
+      }
+    }
+  }
+}

llava/lib/python3.10/site-packages/setuptools/depends.py

@@ -0,0 +1,185 @@
+from __future__ import annotations
+
+import contextlib
+import dis
+import marshal
+import sys
+from types import CodeType
+from typing import Any, Literal, TypeVar
+
+from packaging.version import Version
+
+from . import _imp
+from ._imp import PY_COMPILED, PY_FROZEN, PY_SOURCE, find_module
+
+_T = TypeVar("_T")
+
+__all__ = ['Require', 'find_module']
+
+
+class Require:
+    """A prerequisite to building or installing a distribution"""
+
+    def __init__(
+        self,
+        name,
+        requested_version,
+        module,
+        homepage: str = '',
+        attribute=None,
+        format=None,
+    ) -> None:
+        if format is None and requested_version is not None:
+            format = Version
+
+        if format is not None:
+            requested_version = format(requested_version)
+            if attribute is None:
+                attribute = '__version__'
+
+        self.__dict__.update(locals())
+        del self.self
+
+    def full_name(self):
+        """Return full package/distribution name, w/version"""
+        if self.requested_version is not None:
+            return f'{self.name}-{self.requested_version}'
+        return self.name
+
+    def version_ok(self, version):
+        """Is 'version' sufficiently up-to-date?"""
+        return (
+            self.attribute is None
+            or self.format is None
+            or str(version) != "unknown"
+            and self.format(version) >= self.requested_version
+        )
+
+    def get_version(
+        self, paths=None, default: _T | Literal["unknown"] = "unknown"
+    ) -> _T | Literal["unknown"] | None | Any:
+        """Get version number of installed module, 'None', or 'default'
+
+        Search 'paths' for module.  If not found, return 'None'.  If found,
+        return the extracted version attribute, or 'default' if no version
+        attribute was specified, or the value cannot be determined without
+        importing the module.  The version is formatted according to the
+        requirement's version format (if any), unless it is 'None' or the
+        supplied 'default'.
+        """
+
+        if self.attribute is None:
+            try:
+                f, _p, _i = find_module(self.module, paths)
+            except ImportError:
+                return None
+            if f:
+                f.close()
+            return default
+
+        v = get_module_constant(self.module, self.attribute, default, paths)
+
+        if v is not None and v is not default and self.format is not None:
+            return self.format(v)
+
+        return v
+
+    def is_present(self, paths=None):
+        """Return true if dependency is present on 'paths'"""
+        return self.get_version(paths) is not None
+
+    def is_current(self, paths=None):
+        """Return true if dependency is present and up-to-date on 'paths'"""
+        version = self.get_version(paths)
+        if version is None:
+            return False
+        return self.version_ok(str(version))
+
+
+def maybe_close(f):
+    @contextlib.contextmanager
+    def empty():
+        yield
+        return
+
+    if not f:
+        return empty()
+
+    return contextlib.closing(f)
+
+
+# Some objects are not available on some platforms.
+# XXX it'd be better to test assertions about bytecode instead.
+if not sys.platform.startswith('java') and sys.platform != 'cli':
+
+    def get_module_constant(
+        module, symbol, default: _T | int = -1, paths=None
+    ) -> _T | int | None | Any:
+        """Find 'module' by searching 'paths', and extract 'symbol'
+
+        Return 'None' if 'module' does not exist on 'paths', or it does not define
+        'symbol'.  If the module defines 'symbol' as a constant, return the
+        constant.  Otherwise, return 'default'."""
+
+        try:
+            f, path, (_suffix, _mode, kind) = info = find_module(module, paths)
+        except ImportError:
+            # Module doesn't exist
+            return None
+
+        with maybe_close(f):
+            if kind == PY_COMPILED:
+                f.read(8)  # skip magic & date
+                code = marshal.load(f)
+            elif kind == PY_FROZEN:
+                code = _imp.get_frozen_object(module, paths)
+            elif kind == PY_SOURCE:
+                code = compile(f.read(), path, 'exec')
+            else:
+                # Not something we can parse; we'll have to import it.  :(
+                imported = _imp.get_module(module, paths, info)
+                return getattr(imported, symbol, None)
+
+        return extract_constant(code, symbol, default)
+
+    def extract_constant(
+        code: CodeType, symbol: str, default: _T | int = -1
+    ) -> _T | int | None | Any:
+        """Extract the constant value of 'symbol' from 'code'
+
+        If the name 'symbol' is bound to a constant value by the Python code
+        object 'code', return that value.  If 'symbol' is bound to an expression,
+        return 'default'.  Otherwise, return 'None'.
+
+        Return value is based on the first assignment to 'symbol'.  'symbol' must
+        be a global, or at least a non-"fast" local in the code block.  That is,
+        only 'STORE_NAME' and 'STORE_GLOBAL' opcodes are checked, and 'symbol'
+        must be present in 'code.co_names'.
+        """
+        if symbol not in code.co_names:
+            # name's not there, can't possibly be an assignment
+            return None
+
+        name_idx = list(code.co_names).index(symbol)
+
+        STORE_NAME = dis.opmap['STORE_NAME']
+        STORE_GLOBAL = dis.opmap['STORE_GLOBAL']
+        LOAD_CONST = dis.opmap['LOAD_CONST']
+
+        const = default
+
+        for byte_code in dis.Bytecode(code):
+            op = byte_code.opcode
+            arg = byte_code.arg
+
+            if op == LOAD_CONST:
+                assert arg is not None
+                const = code.co_consts[arg]
+            elif arg == name_idx and (op == STORE_NAME or op == STORE_GLOBAL):
+                return const
+            else:
+                const = default
+
+        return None
+
+    __all__ += ['get_module_constant', 'extract_constant']

llava/lib/python3.10/site-packages/setuptools/dist.py

@@ -0,0 +1,1004 @@
+from __future__ import annotations
+
+import io
+import itertools
+import numbers
+import os
+import re
+import sys
+from collections.abc import Iterable, MutableMapping, Sequence
+from glob import iglob
+from pathlib import Path
+from typing import TYPE_CHECKING, Any, Union
+
+from more_itertools import partition, unique_everseen
+from packaging.markers import InvalidMarker, Marker
+from packaging.specifiers import InvalidSpecifier, SpecifierSet
+from packaging.version import Version
+
+from . import (
+    _entry_points,
+    _reqs,
+    _static,
+    command as _,  # noqa: F401 # imported for side-effects
+)
+from ._importlib import metadata
+from ._path import StrPath
+from ._reqs import _StrOrIter
+from .config import pyprojecttoml, setupcfg
+from .discovery import ConfigDiscovery
+from .monkey import get_unpatched
+from .warnings import InformationOnly, SetuptoolsDeprecationWarning
+
+import distutils.cmd
+import distutils.command
+import distutils.core
+import distutils.dist
+import distutils.log
+from distutils.debug import DEBUG
+from distutils.errors import DistutilsOptionError, DistutilsSetupError
+from distutils.fancy_getopt import translate_longopt
+from distutils.util import strtobool
+
+if TYPE_CHECKING:
+    from typing_extensions import TypeAlias
+
+    from pkg_resources import Distribution as _pkg_resources_Distribution
+
+
+__all__ = ['Distribution']
+
+_sequence = tuple, list
+"""
+:meta private:
+
+Supported iterable types that are known to be:
+- ordered (which `set` isn't)
+- not match a str (which `Sequence[str]` does)
+- not imply a nested type (like `dict`)
+for use with `isinstance`.
+"""
+_Sequence: TypeAlias = Union[tuple[str, ...], list[str]]
+# This is how stringifying _Sequence would look in Python 3.10
+_sequence_type_repr = "tuple[str, ...] | list[str]"
+_OrderedStrSequence: TypeAlias = Union[str, dict[str, Any], Sequence[str]]
+"""
+:meta private:
+Avoid single-use iterable. Disallow sets.
+A poor approximation of an OrderedSequence (dict doesn't match a Sequence).
+"""
+
+
+def __getattr__(name: str) -> Any:  # pragma: no cover
+    if name == "sequence":
+        SetuptoolsDeprecationWarning.emit(
+            "`setuptools.dist.sequence` is an internal implementation detail.",
+            "Please define your own `sequence = tuple, list` instead.",
+            due_date=(2025, 8, 28),  # Originally added on 2024-08-27
+        )
+        return _sequence
+    raise AttributeError(f"module {__name__!r} has no attribute {name!r}")
+
+
+def check_importable(dist, attr, value):
+    try:
+        ep = metadata.EntryPoint(value=value, name=None, group=None)
+        assert not ep.extras
+    except (TypeError, ValueError, AttributeError, AssertionError) as e:
+        raise DistutilsSetupError(
+            f"{attr!r} must be importable 'module:attrs' string (got {value!r})"
+        ) from e
+
+
+def assert_string_list(dist, attr: str, value: _Sequence) -> None:
+    """Verify that value is a string list"""
+    try:
+        # verify that value is a list or tuple to exclude unordered
+        # or single-use iterables
+        assert isinstance(value, _sequence)
+        # verify that elements of value are strings
+        assert ''.join(value) != value
+    except (TypeError, ValueError, AttributeError, AssertionError) as e:
+        raise DistutilsSetupError(
+            f"{attr!r} must be of type <{_sequence_type_repr}> (got {value!r})"
+        ) from e
+
+
+def check_nsp(dist, attr, value):
+    """Verify that namespace packages are valid"""
+    ns_packages = value
+    assert_string_list(dist, attr, ns_packages)
+    for nsp in ns_packages:
+        if not dist.has_contents_for(nsp):
+            raise DistutilsSetupError(
+                f"Distribution contains no modules or packages for namespace package {nsp!r}"
+            )
+        parent, _sep, _child = nsp.rpartition('.')
+        if parent and parent not in ns_packages:
+            distutils.log.warn(
+                "WARNING: %r is declared as a package namespace, but %r"
+                " is not: please correct this in setup.py",
+                nsp,
+                parent,
+            )
+        SetuptoolsDeprecationWarning.emit(
+            "The namespace_packages parameter is deprecated.",
+            "Please replace its usage with implicit namespaces (PEP 420).",
+            see_docs="references/keywords.html#keyword-namespace-packages",
+            # TODO: define due_date, it may break old packages that are no longer
+            # maintained (e.g. sphinxcontrib extensions) when installed from source.
+            # Warning officially introduced in May 2022, however the deprecation
+            # was mentioned much earlier in the docs (May 2020, see #2149).
+        )
+
+
+def check_extras(dist, attr, value):
+    """Verify that extras_require mapping is valid"""
+    try:
+        list(itertools.starmap(_check_extra, value.items()))
+    except (TypeError, ValueError, AttributeError) as e:
+        raise DistutilsSetupError(
+            "'extras_require' must be a dictionary whose values are "
+            "strings or lists of strings containing valid project/version "
+            "requirement specifiers."
+        ) from e
+
+
+def _check_extra(extra, reqs):
+    _name, _sep, marker = extra.partition(':')
+    try:
+        _check_marker(marker)
+    except InvalidMarker:
+        msg = f"Invalid environment marker: {marker} ({extra!r})"
+        raise DistutilsSetupError(msg) from None
+    list(_reqs.parse(reqs))
+
+
+def _check_marker(marker):
+    if not marker:
+        return
+    m = Marker(marker)
+    m.evaluate()
+
+
+def assert_bool(dist, attr, value):
+    """Verify that value is True, False, 0, or 1"""
+    if bool(value) != value:
+        raise DistutilsSetupError(f"{attr!r} must be a boolean value (got {value!r})")
+
+
+def invalid_unless_false(dist, attr, value):
+    if not value:
+        DistDeprecationWarning.emit(f"{attr} is ignored.")
+        # TODO: should there be a `due_date` here?
+        return
+    raise DistutilsSetupError(f"{attr} is invalid.")
+
+
+def check_requirements(dist, attr: str, value: _OrderedStrSequence) -> None:
+    """Verify that install_requires is a valid requirements list"""
+    try:
+        list(_reqs.parse(value))
+        if isinstance(value, set):
+            raise TypeError("Unordered types are not allowed")
+    except (TypeError, ValueError) as error:
+        msg = (
+            f"{attr!r} must be a string or iterable of strings "
+            f"containing valid project/version requirement specifiers; {error}"
+        )
+        raise DistutilsSetupError(msg) from error
+
+
+def check_specifier(dist, attr, value):
+    """Verify that value is a valid version specifier"""
+    try:
+        SpecifierSet(value)
+    except (InvalidSpecifier, AttributeError) as error:
+        msg = f"{attr!r} must be a string containing valid version specifiers; {error}"
+        raise DistutilsSetupError(msg) from error
+
+
+def check_entry_points(dist, attr, value):
+    """Verify that entry_points map is parseable"""
+    try:
+        _entry_points.load(value)
+    except Exception as e:
+        raise DistutilsSetupError(e) from e
+
+
+def check_package_data(dist, attr, value):
+    """Verify that value is a dictionary of package names to glob lists"""
+    if not isinstance(value, dict):
+        raise DistutilsSetupError(
+            f"{attr!r} must be a dictionary mapping package names to lists of "
+            "string wildcard patterns"
+        )
+    for k, v in value.items():
+        if not isinstance(k, str):
+            raise DistutilsSetupError(
+                f"keys of {attr!r} dict must be strings (got {k!r})"
+            )
+        assert_string_list(dist, f'values of {attr!r} dict', v)
+
+
+def check_packages(dist, attr, value):
+    for pkgname in value:
+        if not re.match(r'\w+(\.\w+)*', pkgname):
+            distutils.log.warn(
+                "WARNING: %r not a valid package name; please use only "
+                ".-separated package names in setup.py",
+                pkgname,
+            )
+
+
+if TYPE_CHECKING:
+    # Work around a mypy issue where type[T] can't be used as a base: https://github.com/python/mypy/issues/10962
+    from distutils.core import Distribution as _Distribution
+else:
+    _Distribution = get_unpatched(distutils.core.Distribution)
+
+
+class Distribution(_Distribution):
+    """Distribution with support for tests and package data
+
+    This is an enhanced version of 'distutils.dist.Distribution' that
+    effectively adds the following new optional keyword arguments to 'setup()':
+
+     'install_requires' -- a string or sequence of strings specifying project
+        versions that the distribution requires when installed, in the format
+        used by 'pkg_resources.require()'.  They will be installed
+        automatically when the package is installed.  If you wish to use
+        packages that are not available in PyPI, or want to give your users an
+        alternate download location, you can add a 'find_links' option to the
+        '[easy_install]' section of your project's 'setup.cfg' file, and then
+        setuptools will scan the listed web pages for links that satisfy the
+        requirements.
+
+     'extras_require' -- a dictionary mapping names of optional "extras" to the
+        additional requirement(s) that using those extras incurs. For example,
+        this::
+
+            extras_require = dict(reST = ["docutils>=0.3", "reSTedit"])
+
+        indicates that the distribution can optionally provide an extra
+        capability called "reST", but it can only be used if docutils and
+        reSTedit are installed.  If the user installs your package using
+        EasyInstall and requests one of your extras, the corresponding
+        additional requirements will be installed if needed.
+
+     'package_data' -- a dictionary mapping package names to lists of filenames
+        or globs to use to find data files contained in the named packages.
+        If the dictionary has filenames or globs listed under '""' (the empty
+        string), those names will be searched for in every package, in addition
+        to any names for the specific package.  Data files found using these
+        names/globs will be installed along with the package, in the same
+        location as the package.  Note that globs are allowed to reference
+        the contents of non-package subdirectories, as long as you use '/' as
+        a path separator.  (Globs are automatically converted to
+        platform-specific paths at runtime.)
+
+    In addition to these new keywords, this class also has several new methods
+    for manipulating the distribution's contents.  For example, the 'include()'
+    and 'exclude()' methods can be thought of as in-place add and subtract
+    commands that add or remove packages, modules, extensions, and so on from
+    the distribution.
+    """
+
+    _DISTUTILS_UNSUPPORTED_METADATA = {
+        'long_description_content_type': lambda: None,
+        'project_urls': dict,
+        'provides_extras': dict,  # behaves like an ordered set
+        'license_file': lambda: None,
+        'license_files': lambda: None,
+        'install_requires': list,
+        'extras_require': dict,
+    }
+
+    # Used by build_py, editable_wheel and install_lib commands for legacy namespaces
+    namespace_packages: list[str]  #: :meta private: DEPRECATED
+
+    # Any: Dynamic assignment results in Incompatible types in assignment
+    def __init__(self, attrs: MutableMapping[str, Any] | None = None) -> None:
+        have_package_data = hasattr(self, "package_data")
+        if not have_package_data:
+            self.package_data: dict[str, list[str]] = {}
+        attrs = attrs or {}
+        self.dist_files: list[tuple[str, str, str]] = []
+        self.include_package_data: bool | None = None
+        self.exclude_package_data: dict[str, list[str]] | None = None
+        # Filter-out setuptools' specific options.
+        self.src_root: str | None = attrs.pop("src_root", None)
+        self.dependency_links: list[str] = attrs.pop('dependency_links', [])
+        self.setup_requires: list[str] = attrs.pop('setup_requires', [])
+        for ep in metadata.entry_points(group='distutils.setup_keywords'):
+            vars(self).setdefault(ep.name, None)
+
+        metadata_only = set(self._DISTUTILS_UNSUPPORTED_METADATA)
+        metadata_only -= {"install_requires", "extras_require"}
+        dist_attrs = {k: v for k, v in attrs.items() if k not in metadata_only}
+        _Distribution.__init__(self, dist_attrs)
+
+        # Private API (setuptools-use only, not restricted to Distribution)
+        # Stores files that are referenced by the configuration and need to be in the
+        # sdist (e.g. `version = file: VERSION.txt`)
+        self._referenced_files = set[str]()
+
+        self.set_defaults = ConfigDiscovery(self)
+
+        self._set_metadata_defaults(attrs)
+
+        self.metadata.version = self._normalize_version(self.metadata.version)
+        self._finalize_requires()
+
+    def _validate_metadata(self):
+        required = {"name"}
+        provided = {
+            key
+            for key in vars(self.metadata)
+            if getattr(self.metadata, key, None) is not None
+        }
+        missing = required - provided
+
+        if missing:
+            msg = f"Required package metadata is missing: {missing}"
+            raise DistutilsSetupError(msg)
+
+    def _set_metadata_defaults(self, attrs):
+        """
+        Fill-in missing metadata fields not supported by distutils.
+        Some fields may have been set by other tools (e.g. pbr).
+        Those fields (vars(self.metadata)) take precedence to
+        supplied attrs.
+        """
+        for option, default in self._DISTUTILS_UNSUPPORTED_METADATA.items():
+            vars(self.metadata).setdefault(option, attrs.get(option, default()))
+
+    @staticmethod
+    def _normalize_version(version):
+        from . import sic
+
+        if isinstance(version, numbers.Number):
+            # Some people apparently take "version number" too literally :)
+            version = str(version)
+        elif isinstance(version, sic) or version is None:
+            return version
+
+        normalized = str(Version(version))
+        if version != normalized:
+            InformationOnly.emit(f"Normalizing '{version}' to '{normalized}'")
+            return normalized
+        return version
+
+    def _finalize_requires(self):
+        """
+        Set `metadata.python_requires` and fix environment markers
+        in `install_requires` and `extras_require`.
+        """
+        if getattr(self, 'python_requires', None):
+            self.metadata.python_requires = self.python_requires
+
+        self._normalize_requires()
+        self.metadata.install_requires = self.install_requires
+        self.metadata.extras_require = self.extras_require
+
+        if self.extras_require:
+            for extra in self.extras_require.keys():
+                # Setuptools allows a weird "<name>:<env markers> syntax for extras
+                extra = extra.split(':')[0]
+                if extra:
+                    self.metadata.provides_extras.setdefault(extra)
+
+    def _normalize_requires(self):
+        """Make sure requirement-related attributes exist and are normalized"""
+        install_requires = getattr(self, "install_requires", None) or []
+        extras_require = getattr(self, "extras_require", None) or {}
+
+        # Preserve the "static"-ness of values parsed from config files
+        list_ = _static.List if _static.is_static(install_requires) else list
+        self.install_requires = list_(map(str, _reqs.parse(install_requires)))
+
+        dict_ = _static.Dict if _static.is_static(extras_require) else dict
+        self.extras_require = dict_(
+            (k, list(map(str, _reqs.parse(v or [])))) for k, v in extras_require.items()
+        )
+
+    def _finalize_license_files(self) -> None:
+        """Compute names of all license files which should be included."""
+        license_files: list[str] | None = self.metadata.license_files
+        patterns = license_files or []
+
+        license_file: str | None = self.metadata.license_file
+        if license_file and license_file not in patterns:
+            patterns.append(license_file)
+
+        if license_files is None and license_file is None:
+            # Default patterns match the ones wheel uses
+            # See https://wheel.readthedocs.io/en/stable/user_guide.html
+            # -> 'Including license files in the generated wheel file'
+            patterns = ['LICEN[CS]E*', 'COPYING*', 'NOTICE*', 'AUTHORS*']
+
+        self.metadata.license_files = list(
+            unique_everseen(self._expand_patterns(patterns))
+        )
+
+    @staticmethod
+    def _expand_patterns(patterns):
+        """
+        >>> list(Distribution._expand_patterns(['LICENSE']))
+        ['LICENSE']
+        >>> list(Distribution._expand_patterns(['pyproject.toml', 'LIC*']))
+        ['pyproject.toml', 'LICENSE']
+        """
+        return (
+            path
+            for pattern in patterns
+            for path in sorted(iglob(pattern))
+            if not path.endswith('~') and os.path.isfile(path)
+        )
+
+    # FIXME: 'Distribution._parse_config_files' is too complex (14)
+    def _parse_config_files(self, filenames=None):  # noqa: C901
+        """
+        Adapted from distutils.dist.Distribution.parse_config_files,
+        this method provides the same functionality in subtly-improved
+        ways.
+        """
+        from configparser import ConfigParser
+
+        # Ignore install directory options if we have a venv
+        ignore_options = (
+            []
+            if sys.prefix == sys.base_prefix
+            else [
+                'install-base',
+                'install-platbase',
+                'install-lib',
+                'install-platlib',
+                'install-purelib',
+                'install-headers',
+                'install-scripts',
+                'install-data',
+                'prefix',
+                'exec-prefix',
+                'home',
+                'user',
+                'root',
+            ]
+        )
+
+        ignore_options = frozenset(ignore_options)
+
+        if filenames is None:
+            filenames = self.find_config_files()
+
+        if DEBUG:
+            self.announce("Distribution.parse_config_files():")
+
+        parser = ConfigParser()
+        parser.optionxform = str
+        for filename in filenames:
+            with open(filename, encoding='utf-8') as reader:
+                if DEBUG:
+                    self.announce("  reading {filename}".format(**locals()))
+                parser.read_file(reader)
+            for section in parser.sections():
+                options = parser.options(section)
+                opt_dict = self.get_option_dict(section)
+
+                for opt in options:
+                    if opt == '__name__' or opt in ignore_options:
+                        continue
+
+                    val = parser.get(section, opt)
+                    opt = self.warn_dash_deprecation(opt, section)
+                    opt = self.make_option_lowercase(opt, section)
+                    opt_dict[opt] = (filename, val)
+
+            # Make the ConfigParser forget everything (so we retain
+            # the original filenames that options come from)
+            parser.__init__()
+
+        if 'global' not in self.command_options:
+            return
+
+        # If there was a "global" section in the config file, use it
+        # to set Distribution options.
+
+        for opt, (src, val) in self.command_options['global'].items():
+            alias = self.negative_opt.get(opt)
+            if alias:
+                val = not strtobool(val)
+            elif opt in ('verbose', 'dry_run'):  # ugh!
+                val = strtobool(val)
+
+            try:
+                setattr(self, alias or opt, val)
+            except ValueError as e:
+                raise DistutilsOptionError(e) from e
+
+    def warn_dash_deprecation(self, opt: str, section: str) -> str:
+        if section in (
+            'options.extras_require',
+            'options.data_files',
+        ):
+            return opt
+
+        underscore_opt = opt.replace('-', '_')
+        commands = list(
+            itertools.chain(
+                distutils.command.__all__,
+                self._setuptools_commands(),
+            )
+        )
+        if (
+            not section.startswith('options')
+            and section != 'metadata'
+            and section not in commands
+        ):
+            return underscore_opt
+
+        if '-' in opt:
+            SetuptoolsDeprecationWarning.emit(
+                "Invalid dash-separated options",
+                f"""
+                Usage of dash-separated {opt!r} will not be supported in future
+                versions. Please use the underscore name {underscore_opt!r} instead.
+                """,
+                see_docs="userguide/declarative_config.html",
+                due_date=(2025, 3, 3),
+                # Warning initially introduced in 3 Mar 2021
+            )
+        return underscore_opt
+
+    def _setuptools_commands(self):
+        try:
+            entry_points = metadata.distribution('setuptools').entry_points
+            return {ep.name for ep in entry_points}  # Avoid newer API for compatibility
+        except metadata.PackageNotFoundError:
+            # during bootstrapping, distribution doesn't exist
+            return []
+
+    def make_option_lowercase(self, opt: str, section: str) -> str:
+        if section != 'metadata' or opt.islower():
+            return opt
+
+        lowercase_opt = opt.lower()
+        SetuptoolsDeprecationWarning.emit(
+            "Invalid uppercase configuration",
+            f"""
+            Usage of uppercase key {opt!r} in {section!r} will not be supported in
+            future versions. Please use lowercase {lowercase_opt!r} instead.
+            """,
+            see_docs="userguide/declarative_config.html",
+            due_date=(2025, 3, 3),
+            # Warning initially introduced in 6 Mar 2021
+        )
+        return lowercase_opt
+
+    # FIXME: 'Distribution._set_command_options' is too complex (14)
+    def _set_command_options(self, command_obj, option_dict=None):  # noqa: C901
+        """
+        Set the options for 'command_obj' from 'option_dict'.  Basically
+        this means copying elements of a dictionary ('option_dict') to
+        attributes of an instance ('command').
+
+        'command_obj' must be a Command instance.  If 'option_dict' is not
+        supplied, uses the standard option dictionary for this command
+        (from 'self.command_options').
+
+        (Adopted from distutils.dist.Distribution._set_command_options)
+        """
+        command_name = command_obj.get_command_name()
+        if option_dict is None:
+            option_dict = self.get_option_dict(command_name)
+
+        if DEBUG:
+            self.announce(f"  setting options for '{command_name}' command:")
+        for option, (source, value) in option_dict.items():
+            if DEBUG:
+                self.announce(f"    {option} = {value} (from {source})")
+            try:
+                bool_opts = [translate_longopt(o) for o in command_obj.boolean_options]
+            except AttributeError:
+                bool_opts = []
+            try:
+                neg_opt = command_obj.negative_opt
+            except AttributeError:
+                neg_opt = {}
+
+            try:
+                is_string = isinstance(value, str)
+                if option in neg_opt and is_string:
+                    setattr(command_obj, neg_opt[option], not strtobool(value))
+                elif option in bool_opts and is_string:
+                    setattr(command_obj, option, strtobool(value))
+                elif hasattr(command_obj, option):
+                    setattr(command_obj, option, value)
+                else:
+                    raise DistutilsOptionError(
+                        f"error in {source}: command '{command_name}' has no such option '{option}'"
+                    )
+            except ValueError as e:
+                raise DistutilsOptionError(e) from e
+
+    def _get_project_config_files(self, filenames: Iterable[StrPath] | None):
+        """Add default file and split between INI and TOML"""
+        tomlfiles = []
+        standard_project_metadata = Path(self.src_root or os.curdir, "pyproject.toml")
+        if filenames is not None:
+            parts = partition(lambda f: Path(f).suffix == ".toml", filenames)
+            filenames = list(parts[0])  # 1st element => predicate is False
+            tomlfiles = list(parts[1])  # 2nd element => predicate is True
+        elif standard_project_metadata.exists():
+            tomlfiles = [standard_project_metadata]
+        return filenames, tomlfiles
+
+    def parse_config_files(
+        self,
+        filenames: Iterable[StrPath] | None = None,
+        ignore_option_errors: bool = False,
+    ) -> None:
+        """Parses configuration files from various levels
+        and loads configuration.
+        """
+        inifiles, tomlfiles = self._get_project_config_files(filenames)
+
+        self._parse_config_files(filenames=inifiles)
+
+        setupcfg.parse_configuration(
+            self, self.command_options, ignore_option_errors=ignore_option_errors
+        )
+        for filename in tomlfiles:
+            pyprojecttoml.apply_configuration(self, filename, ignore_option_errors)
+
+        self._finalize_requires()
+        self._finalize_license_files()
+
+    def fetch_build_eggs(
+        self, requires: _StrOrIter
+    ) -> list[_pkg_resources_Distribution]:
+        """Resolve pre-setup requirements"""
+        from .installer import _fetch_build_eggs
+
+        return _fetch_build_eggs(self, requires)
+
+    def finalize_options(self) -> None:
+        """
+        Allow plugins to apply arbitrary operations to the
+        distribution. Each hook may optionally define a 'order'
+        to influence the order of execution. Smaller numbers
+        go first and the default is 0.
+        """
+        group = 'setuptools.finalize_distribution_options'
+
+        def by_order(hook):
+            return getattr(hook, 'order', 0)
+
+        defined = metadata.entry_points(group=group)
+        filtered = itertools.filterfalse(self._removed, defined)
+        loaded = map(lambda e: e.load(), filtered)
+        for ep in sorted(loaded, key=by_order):
+            ep(self)
+
+    @staticmethod
+    def _removed(ep):
+        """
+        When removing an entry point, if metadata is loaded
+        from an older version of Setuptools, that removed
+        entry point will attempt to be loaded and will fail.
+        See #2765 for more details.
+        """
+        removed = {
+            # removed 2021-09-05
+            '2to3_doctests',
+        }
+        return ep.name in removed
+
+    def _finalize_setup_keywords(self):
+        for ep in metadata.entry_points(group='distutils.setup_keywords'):
+            value = getattr(self, ep.name, None)
+            if value is not None:
+                ep.load()(self, ep.name, value)
+
+    def get_egg_cache_dir(self):
+        from . import windows_support
+
+        egg_cache_dir = os.path.join(os.curdir, '.eggs')
+        if not os.path.exists(egg_cache_dir):
+            os.mkdir(egg_cache_dir)
+            windows_support.hide_file(egg_cache_dir)
+            readme_txt_filename = os.path.join(egg_cache_dir, 'README.txt')
+            with open(readme_txt_filename, 'w', encoding="utf-8") as f:
+                f.write(
+                    'This directory contains eggs that were downloaded '
+                    'by setuptools to build, test, and run plug-ins.\n\n'
+                )
+                f.write(
+                    'This directory caches those eggs to prevent '
+                    'repeated downloads.\n\n'
+                )
+                f.write('However, it is safe to delete this directory.\n\n')
+
+        return egg_cache_dir
+
+    def fetch_build_egg(self, req):
+        """Fetch an egg needed for building"""
+        from .installer import fetch_build_egg
+
+        return fetch_build_egg(self, req)
+
+    def get_command_class(self, command: str) -> type[distutils.cmd.Command]:  # type: ignore[override] # Not doing complex overrides yet
+        """Pluggable version of get_command_class()"""
+        if command in self.cmdclass:
+            return self.cmdclass[command]
+
+        # Special case bdist_wheel so it's never loaded from "wheel"
+        if command == 'bdist_wheel':
+            from .command.bdist_wheel import bdist_wheel
+
+            return bdist_wheel
+
+        eps = metadata.entry_points(group='distutils.commands', name=command)
+        for ep in eps:
+            self.cmdclass[command] = cmdclass = ep.load()
+            return cmdclass
+        else:
+            return _Distribution.get_command_class(self, command)
+
+    def print_commands(self):
+        for ep in metadata.entry_points(group='distutils.commands'):
+            if ep.name not in self.cmdclass:
+                cmdclass = ep.load()
+                self.cmdclass[ep.name] = cmdclass
+        return _Distribution.print_commands(self)
+
+    def get_command_list(self):
+        for ep in metadata.entry_points(group='distutils.commands'):
+            if ep.name not in self.cmdclass:
+                cmdclass = ep.load()
+                self.cmdclass[ep.name] = cmdclass
+        return _Distribution.get_command_list(self)
+
+    def include(self, **attrs) -> None:
+        """Add items to distribution that are named in keyword arguments
+
+        For example, 'dist.include(py_modules=["x"])' would add 'x' to
+        the distribution's 'py_modules' attribute, if it was not already
+        there.
+
+        Currently, this method only supports inclusion for attributes that are
+        lists or tuples.  If you need to add support for adding to other
+        attributes in this or a subclass, you can add an '_include_X' method,
+        where 'X' is the name of the attribute.  The method will be called with
+        the value passed to 'include()'.  So, 'dist.include(foo={"bar":"baz"})'
+        will try to call 'dist._include_foo({"bar":"baz"})', which can then
+        handle whatever special inclusion logic is needed.
+        """
+        for k, v in attrs.items():
+            include = getattr(self, '_include_' + k, None)
+            if include:
+                include(v)
+            else:
+                self._include_misc(k, v)
+
+    def exclude_package(self, package: str) -> None:
+        """Remove packages, modules, and extensions in named package"""
+
+        pfx = package + '.'
+        if self.packages:
+            self.packages = [
+                p for p in self.packages if p != package and not p.startswith(pfx)
+            ]
+
+        if self.py_modules:
+            self.py_modules = [
+                p for p in self.py_modules if p != package and not p.startswith(pfx)
+            ]
+
+        if self.ext_modules:
+            self.ext_modules = [
+                p
+                for p in self.ext_modules
+                if p.name != package and not p.name.startswith(pfx)
+            ]
+
+    def has_contents_for(self, package: str) -> bool:
+        """Return true if 'exclude_package(package)' would do something"""
+
+        pfx = package + '.'
+
+        for p in self.iter_distribution_names():
+            if p == package or p.startswith(pfx):
+                return True
+
+        return False
+
+    def _exclude_misc(self, name: str, value: _Sequence) -> None:
+        """Handle 'exclude()' for list/tuple attrs without a special handler"""
+        if not isinstance(value, _sequence):
+            raise DistutilsSetupError(
+                f"{name}: setting must be of type <{_sequence_type_repr}> (got {value!r})"
+            )
+        try:
+            old = getattr(self, name)
+        except AttributeError as e:
+            raise DistutilsSetupError(f"{name}: No such distribution setting") from e
+        if old is not None and not isinstance(old, _sequence):
+            raise DistutilsSetupError(
+                name + ": this setting cannot be changed via include/exclude"
+            )
+        elif old:
+            setattr(self, name, [item for item in old if item not in value])
+
+    def _include_misc(self, name: str, value: _Sequence) -> None:
+        """Handle 'include()' for list/tuple attrs without a special handler"""
+
+        if not isinstance(value, _sequence):
+            raise DistutilsSetupError(
+                f"{name}: setting must be of type <{_sequence_type_repr}> (got {value!r})"
+            )
+        try:
+            old = getattr(self, name)
+        except AttributeError as e:
+            raise DistutilsSetupError(f"{name}: No such distribution setting") from e
+        if old is None:
+            setattr(self, name, value)
+        elif not isinstance(old, _sequence):
+            raise DistutilsSetupError(
+                name + ": this setting cannot be changed via include/exclude"
+            )
+        else:
+            new = [item for item in value if item not in old]
+            setattr(self, name, list(old) + new)
+
+    def exclude(self, **attrs) -> None:
+        """Remove items from distribution that are named in keyword arguments
+
+        For example, 'dist.exclude(py_modules=["x"])' would remove 'x' from
+        the distribution's 'py_modules' attribute.  Excluding packages uses
+        the 'exclude_package()' method, so all of the package's contained
+        packages, modules, and extensions are also excluded.
+
+        Currently, this method only supports exclusion from attributes that are
+        lists or tuples.  If you need to add support for excluding from other
+        attributes in this or a subclass, you can add an '_exclude_X' method,
+        where 'X' is the name of the attribute.  The method will be called with
+        the value passed to 'exclude()'.  So, 'dist.exclude(foo={"bar":"baz"})'
+        will try to call 'dist._exclude_foo({"bar":"baz"})', which can then
+        handle whatever special exclusion logic is needed.
+        """
+        for k, v in attrs.items():
+            exclude = getattr(self, '_exclude_' + k, None)
+            if exclude:
+                exclude(v)
+            else:
+                self._exclude_misc(k, v)
+
+    def _exclude_packages(self, packages: _Sequence) -> None:
+        if not isinstance(packages, _sequence):
+            raise DistutilsSetupError(
+                f"packages: setting must be of type <{_sequence_type_repr}> (got {packages!r})"
+            )
+        list(map(self.exclude_package, packages))
+
+    def _parse_command_opts(self, parser, args):
+        # Remove --with-X/--without-X options when processing command args
+        self.global_options = self.__class__.global_options
+        self.negative_opt = self.__class__.negative_opt
+
+        # First, expand any aliases
+        command = args[0]
+        aliases = self.get_option_dict('aliases')
+        while command in aliases:
+            _src, alias = aliases[command]
+            del aliases[command]  # ensure each alias can expand only once!
+            import shlex
+
+            args[:1] = shlex.split(alias, True)
+            command = args[0]
+
+        nargs = _Distribution._parse_command_opts(self, parser, args)
+
+        # Handle commands that want to consume all remaining arguments
+        cmd_class = self.get_command_class(command)
+        if getattr(cmd_class, 'command_consumes_arguments', None):
+            self.get_option_dict(command)['args'] = ("command line", nargs)
+            if nargs is not None:
+                return []
+
+        return nargs
+
+    def get_cmdline_options(self) -> dict[str, dict[str, str | None]]:
+        """Return a '{cmd: {opt:val}}' map of all command-line options
+
+        Option names are all long, but do not include the leading '--', and
+        contain dashes rather than underscores.  If the option doesn't take
+        an argument (e.g. '--quiet'), the 'val' is 'None'.
+
+        Note that options provided by config files are intentionally excluded.
+        """
+
+        d: dict[str, dict[str, str | None]] = {}
+
+        for cmd, opts in self.command_options.items():
+            val: str | None
+            for opt, (src, val) in opts.items():
+                if src != "command line":
+                    continue
+
+                opt = opt.replace('_', '-')
+
+                if val == 0:
+                    cmdobj = self.get_command_obj(cmd)
+                    neg_opt = self.negative_opt.copy()
+                    neg_opt.update(getattr(cmdobj, 'negative_opt', {}))
+                    for neg, pos in neg_opt.items():
+                        if pos == opt:
+                            opt = neg
+                            val = None
+                            break
+                    else:
+                        raise AssertionError("Shouldn't be able to get here")
+
+                elif val == 1:
+                    val = None
+
+                d.setdefault(cmd, {})[opt] = val
+
+        return d
+
+    def iter_distribution_names(self):
+        """Yield all packages, modules, and extension names in distribution"""
+
+        yield from self.packages or ()
+
+        yield from self.py_modules or ()
+
+        for ext in self.ext_modules or ():
+            if isinstance(ext, tuple):
+                name, _buildinfo = ext
+            else:
+                name = ext.name
+            if name.endswith('module'):
+                name = name[:-6]
+            yield name
+
+    def handle_display_options(self, option_order):
+        """If there were any non-global "display-only" options
+        (--help-commands or the metadata display options) on the command
+        line, display the requested info and return true; else return
+        false.
+        """
+        import sys
+
+        if self.help_commands:
+            return _Distribution.handle_display_options(self, option_order)
+
+        # Stdout may be StringIO (e.g. in tests)
+        if not isinstance(sys.stdout, io.TextIOWrapper):
+            return _Distribution.handle_display_options(self, option_order)
+
+        # Don't wrap stdout if utf-8 is already the encoding. Provides
+        #  workaround for #334.
+        if sys.stdout.encoding.lower() in ('utf-8', 'utf8'):
+            return _Distribution.handle_display_options(self, option_order)
+
+        # Print metadata in UTF-8 no matter the platform
+        encoding = sys.stdout.encoding
+        sys.stdout.reconfigure(encoding='utf-8')
+        try:
+            return _Distribution.handle_display_options(self, option_order)
+        finally:
+            sys.stdout.reconfigure(encoding=encoding)
+
+    def run_command(self, command) -> None:
+        self.set_defaults()
+        # Postpone defaults until all explicit configuration is considered
+        # (setup() args, config files, command line and plugins)
+
+        super().run_command(command)
+
+
+class DistDeprecationWarning(SetuptoolsDeprecationWarning):
+    """Class for warning about deprecations in dist in
+    setuptools. Not ignored by default, unlike DeprecationWarning."""

llava/lib/python3.10/site-packages/setuptools/installer.py

@@ -0,0 +1,150 @@
+from __future__ import annotations
+
+import glob
+import os
+import subprocess
+import sys
+import tempfile
+from functools import partial
+
+from pkg_resources import Distribution
+
+from . import _reqs
+from ._reqs import _StrOrIter
+from .warnings import SetuptoolsDeprecationWarning
+from .wheel import Wheel
+
+from distutils import log
+from distutils.errors import DistutilsError
+
+
+def _fixup_find_links(find_links):
+    """Ensure find-links option end-up being a list of strings."""
+    if isinstance(find_links, str):
+        return find_links.split()
+    assert isinstance(find_links, (tuple, list))
+    return find_links
+
+
+def fetch_build_egg(dist, req):
+    """Fetch an egg needed for building.
+
+    Use pip/wheel to fetch/build a wheel."""
+    _DeprecatedInstaller.emit()
+    _warn_wheel_not_available(dist)
+    return _fetch_build_egg_no_warn(dist, req)
+
+
+def _fetch_build_eggs(dist, requires: _StrOrIter) -> list[Distribution]:
+    import pkg_resources  # Delay import to avoid unnecessary side-effects
+
+    _DeprecatedInstaller.emit(stacklevel=3)
+    _warn_wheel_not_available(dist)
+
+    resolved_dists = pkg_resources.working_set.resolve(
+        _reqs.parse(requires, pkg_resources.Requirement),  # required for compatibility
+        installer=partial(_fetch_build_egg_no_warn, dist),  # avoid warning twice
+        replace_conflicting=True,
+    )
+    for dist in resolved_dists:
+        pkg_resources.working_set.add(dist, replace=True)
+    return resolved_dists
+
+
+def _fetch_build_egg_no_warn(dist, req):  # noqa: C901  # is too complex (16)  # FIXME
+    import pkg_resources  # Delay import to avoid unnecessary side-effects
+
+    # Ignore environment markers; if supplied, it is required.
+    req = strip_marker(req)
+    # Take easy_install options into account, but do not override relevant
+    # pip environment variables (like PIP_INDEX_URL or PIP_QUIET); they'll
+    # take precedence.
+    opts = dist.get_option_dict('easy_install')
+    if 'allow_hosts' in opts:
+        raise DistutilsError(
+            'the `allow-hosts` option is not supported '
+            'when using pip to install requirements.'
+        )
+    quiet = 'PIP_QUIET' not in os.environ and 'PIP_VERBOSE' not in os.environ
+    if 'PIP_INDEX_URL' in os.environ:
+        index_url = None
+    elif 'index_url' in opts:
+        index_url = opts['index_url'][1]
+    else:
+        index_url = None
+    find_links = (
+        _fixup_find_links(opts['find_links'][1])[:] if 'find_links' in opts else []
+    )
+    if dist.dependency_links:
+        find_links.extend(dist.dependency_links)
+    eggs_dir = os.path.realpath(dist.get_egg_cache_dir())
+    environment = pkg_resources.Environment()
+    for egg_dist in pkg_resources.find_distributions(eggs_dir):
+        if egg_dist in req and environment.can_add(egg_dist):
+            return egg_dist
+    with tempfile.TemporaryDirectory() as tmpdir:
+        cmd = [
+            sys.executable,
+            '-m',
+            'pip',
+            '--disable-pip-version-check',
+            'wheel',
+            '--no-deps',
+            '-w',
+            tmpdir,
+        ]
+        if quiet:
+            cmd.append('--quiet')
+        if index_url is not None:
+            cmd.extend(('--index-url', index_url))
+        for link in find_links or []:
+            cmd.extend(('--find-links', link))
+        # If requirement is a PEP 508 direct URL, directly pass
+        # the URL to pip, as `req @ url` does not work on the
+        # command line.
+        cmd.append(req.url or str(req))
+        try:
+            subprocess.check_call(cmd)
+        except subprocess.CalledProcessError as e:
+            raise DistutilsError(str(e)) from e
+        wheel = Wheel(glob.glob(os.path.join(tmpdir, '*.whl'))[0])
+        dist_location = os.path.join(eggs_dir, wheel.egg_name())
+        wheel.install_as_egg(dist_location)
+        dist_metadata = pkg_resources.PathMetadata(
+            dist_location, os.path.join(dist_location, 'EGG-INFO')
+        )
+        return pkg_resources.Distribution.from_filename(
+            dist_location, metadata=dist_metadata
+        )
+
+
+def strip_marker(req):
+    """
+    Return a new requirement without the environment marker to avoid
+    calling pip with something like `babel; extra == "i18n"`, which
+    would always be ignored.
+    """
+    import pkg_resources  # Delay import to avoid unnecessary side-effects
+
+    # create a copy to avoid mutating the input
+    req = pkg_resources.Requirement.parse(str(req))
+    req.marker = None
+    return req
+
+
+def _warn_wheel_not_available(dist):
+    import pkg_resources  # Delay import to avoid unnecessary side-effects
+
+    try:
+        pkg_resources.get_distribution('wheel')
+    except pkg_resources.DistributionNotFound:
+        dist.announce('WARNING: The wheel package is not available.', log.WARN)
+
+
+class _DeprecatedInstaller(SetuptoolsDeprecationWarning):
+    _SUMMARY = "setuptools.installer and fetch_build_eggs are deprecated."
+    _DETAILS = """
+    Requirements should be satisfied by a PEP 517 installer.
+    If you are using pip, you can try `pip install --use-pep517`.
+    """
+    # _DUE_DATE not decided yet

llava/lib/python3.10/site-packages/setuptools/logging.py

@@ -0,0 +1,40 @@
+import inspect
+import logging
+import sys
+
+from . import monkey
+
+import distutils.log
+
+
+def _not_warning(record):
+    return record.levelno < logging.WARNING
+
+
+def configure() -> None:
+    """
+    Configure logging to emit warning and above to stderr
+    and everything else to stdout. This behavior is provided
+    for compatibility with distutils.log but may change in
+    the future.
+    """
+    err_handler = logging.StreamHandler()
+    err_handler.setLevel(logging.WARNING)
+    out_handler = logging.StreamHandler(sys.stdout)
+    out_handler.addFilter(_not_warning)
+    handlers = err_handler, out_handler
+    logging.basicConfig(
+        format="{message}", style='{', handlers=handlers, level=logging.DEBUG
+    )
+    if inspect.ismodule(distutils.dist.log):
+        monkey.patch_func(set_threshold, distutils.log, 'set_threshold')
+        # For some reason `distutils.log` module is getting cached in `distutils.dist`
+        # and then loaded again when patched,
+        # implying: id(distutils.log) != id(distutils.dist.log).
+        # Make sure the same module object is used everywhere:
+        distutils.dist.log = distutils.log
+
+
+def set_threshold(level: int) -> int:
+    logging.root.setLevel(level * 10)
+    return set_threshold.unpatched(level)

llava/lib/python3.10/site-packages/setuptools/namespaces.py

@@ -0,0 +1,106 @@
+import itertools
+import os
+
+from .compat import py312
+
+from distutils import log
+
+flatten = itertools.chain.from_iterable
+
+
+class Installer:
+    nspkg_ext = '-nspkg.pth'
+
+    def install_namespaces(self) -> None:
+        nsp = self._get_all_ns_packages()
+        if not nsp:
+            return
+        filename = self._get_nspkg_file()
+        self.outputs.append(filename)
+        log.info("Installing %s", filename)
+        lines = map(self._gen_nspkg_line, nsp)
+
+        if self.dry_run:
+            # always generate the lines, even in dry run
+            list(lines)
+            return
+
+        with open(filename, 'wt', encoding=py312.PTH_ENCODING) as f:
+            # Python<3.13 requires encoding="locale" instead of "utf-8"
+            # See: python/cpython#77102
+            f.writelines(lines)
+
+    def uninstall_namespaces(self) -> None:
+        filename = self._get_nspkg_file()
+        if not os.path.exists(filename):
+            return
+        log.info("Removing %s", filename)
+        os.remove(filename)
+
+    def _get_nspkg_file(self):
+        filename, _ = os.path.splitext(self._get_target())
+        return filename + self.nspkg_ext
+
+    def _get_target(self):
+        return self.target
+
+    _nspkg_tmpl = (
+        "import sys, types, os",
+        "p = os.path.join(%(root)s, *%(pth)r)",
+        "importlib = __import__('importlib.util')",
+        "__import__('importlib.machinery')",
+        (
+            "m = "
+            "sys.modules.setdefault(%(pkg)r, "
+            "importlib.util.module_from_spec("
+            "importlib.machinery.PathFinder.find_spec(%(pkg)r, "
+            "[os.path.dirname(p)])))"
+        ),
+        ("m = m or sys.modules.setdefault(%(pkg)r, types.ModuleType(%(pkg)r))"),
+        "mp = (m or []) and m.__dict__.setdefault('__path__',[])",
+        "(p not in mp) and mp.append(p)",
+    )
+    "lines for the namespace installer"
+
+    _nspkg_tmpl_multi = ('m and setattr(sys.modules[%(parent)r], %(child)r, m)',)
+    "additional line(s) when a parent package is indicated"
+
+    def _get_root(self):
+        return "sys._getframe(1).f_locals['sitedir']"
+
+    def _gen_nspkg_line(self, pkg):
+        pth = tuple(pkg.split('.'))
+        root = self._get_root()
+        tmpl_lines = self._nspkg_tmpl
+        parent, sep, child = pkg.rpartition('.')
+        if parent:
+            tmpl_lines += self._nspkg_tmpl_multi
+        return ';'.join(tmpl_lines) % locals() + '\n'
+
+    def _get_all_ns_packages(self):
+        """Return sorted list of all package namespaces"""
+        pkgs = self.distribution.namespace_packages or []
+        return sorted(set(flatten(map(self._pkg_names, pkgs))))
+
+    @staticmethod
+    def _pkg_names(pkg):
+        """
+        Given a namespace package, yield the components of that
+        package.
+
+        >>> names = Installer._pkg_names('a.b.c')
+        >>> set(names) == set(['a', 'a.b', 'a.b.c'])
+        True
+        """
+        parts = pkg.split('.')
+        while parts:
+            yield '.'.join(parts)
+            parts.pop()
+
+
+class DevelopInstaller(Installer):
+    def _get_root(self):
+        return repr(str(self.egg_path))
+
+    def _get_target(self):
+        return self.egg_link

llava/lib/python3.10/site-packages/setuptools/sandbox.py

@@ -0,0 +1,536 @@
+from __future__ import annotations
+
+import builtins
+import contextlib
+import functools
+import itertools
+import operator
+import os
+import pickle
+import re
+import sys
+import tempfile
+import textwrap
+from types import TracebackType
+from typing import TYPE_CHECKING, Any, ClassVar
+
+import pkg_resources
+from pkg_resources import working_set
+
+from distutils.errors import DistutilsError
+
+if TYPE_CHECKING:
+    import os as _os
+elif sys.platform.startswith('java'):
+    import org.python.modules.posix.PosixModule as _os  # pyright: ignore[reportMissingImports]
+else:
+    _os = sys.modules[os.name]
+_open = open
+
+
+if TYPE_CHECKING:
+    from typing_extensions import Self
+
+__all__ = [
+    "AbstractSandbox",
+    "DirectorySandbox",
+    "SandboxViolation",
+    "run_setup",
+]
+
+
+def _execfile(filename, globals, locals=None):
+    """
+    Python 3 implementation of execfile.
+    """
+    mode = 'rb'
+    with open(filename, mode) as stream:
+        script = stream.read()
+    if locals is None:
+        locals = globals
+    code = compile(script, filename, 'exec')
+    exec(code, globals, locals)
+
+
+@contextlib.contextmanager
+def save_argv(repl=None):
+    saved = sys.argv[:]
+    if repl is not None:
+        sys.argv[:] = repl
+    try:
+        yield saved
+    finally:
+        sys.argv[:] = saved
+
+
+@contextlib.contextmanager
+def save_path():
+    saved = sys.path[:]
+    try:
+        yield saved
+    finally:
+        sys.path[:] = saved
+
+
+@contextlib.contextmanager
+def override_temp(replacement):
+    """
+    Monkey-patch tempfile.tempdir with replacement, ensuring it exists
+    """
+    os.makedirs(replacement, exist_ok=True)
+
+    saved = tempfile.tempdir
+
+    tempfile.tempdir = replacement
+
+    try:
+        yield
+    finally:
+        tempfile.tempdir = saved
+
+
+@contextlib.contextmanager
+def pushd(target):
+    saved = os.getcwd()
+    os.chdir(target)
+    try:
+        yield saved
+    finally:
+        os.chdir(saved)
+
+
+class UnpickleableException(Exception):
+    """
+    An exception representing another Exception that could not be pickled.
+    """
+
+    @staticmethod
+    def dump(type, exc):
+        """
+        Always return a dumped (pickled) type and exc. If exc can't be pickled,
+        wrap it in UnpickleableException first.
+        """
+        try:
+            return pickle.dumps(type), pickle.dumps(exc)
+        except Exception:
+            # get UnpickleableException inside the sandbox
+            from setuptools.sandbox import UnpickleableException as cls
+
+            return cls.dump(cls, cls(repr(exc)))
+
+
+class ExceptionSaver:
+    """
+    A Context Manager that will save an exception, serialize, and restore it
+    later.
+    """
+
+    def __enter__(self) -> Self:
+        return self
+
+    def __exit__(
+        self,
+        type: type[BaseException] | None,
+        exc: BaseException | None,
+        tb: TracebackType | None,
+    ) -> bool:
+        if not exc:
+            return False
+
+        # dump the exception
+        self._saved = UnpickleableException.dump(type, exc)
+        self._tb = tb
+
+        # suppress the exception
+        return True
+
+    def resume(self):
+        "restore and re-raise any exception"
+
+        if '_saved' not in vars(self):
+            return
+
+        _type, exc = map(pickle.loads, self._saved)
+        raise exc.with_traceback(self._tb)
+
+
+@contextlib.contextmanager
+def save_modules():
+    """
+    Context in which imported modules are saved.
+
+    Translates exceptions internal to the context into the equivalent exception
+    outside the context.
+    """
+    saved = sys.modules.copy()
+    with ExceptionSaver() as saved_exc:
+        yield saved
+
+    sys.modules.update(saved)
+    # remove any modules imported since
+    del_modules = (
+        mod_name
+        for mod_name in sys.modules
+        if mod_name not in saved
+        # exclude any encodings modules. See #285
+        and not mod_name.startswith('encodings.')
+    )
+    _clear_modules(del_modules)
+
+    saved_exc.resume()
+
+
+def _clear_modules(module_names):
+    for mod_name in list(module_names):
+        del sys.modules[mod_name]
+
+
+@contextlib.contextmanager
+def save_pkg_resources_state():
+    saved = pkg_resources.__getstate__()
+    try:
+        yield saved
+    finally:
+        pkg_resources.__setstate__(saved)
+
+
+@contextlib.contextmanager
+def setup_context(setup_dir):
+    temp_dir = os.path.join(setup_dir, 'temp')
+    with save_pkg_resources_state():
+        with save_modules():
+            with save_path():
+                hide_setuptools()
+                with save_argv():
+                    with override_temp(temp_dir):
+                        with pushd(setup_dir):
+                            # ensure setuptools commands are available
+                            __import__('setuptools')
+                            yield
+
+
+_MODULES_TO_HIDE = {
+    'setuptools',
+    'distutils',
+    'pkg_resources',
+    'Cython',
+    '_distutils_hack',
+}
+
+
+def _needs_hiding(mod_name):
+    """
+    >>> _needs_hiding('setuptools')
+    True
+    >>> _needs_hiding('pkg_resources')
+    True
+    >>> _needs_hiding('setuptools_plugin')
+    False
+    >>> _needs_hiding('setuptools.__init__')
+    True
+    >>> _needs_hiding('distutils')
+    True
+    >>> _needs_hiding('os')
+    False
+    >>> _needs_hiding('Cython')
+    True
+    """
+    base_module = mod_name.split('.', 1)[0]
+    return base_module in _MODULES_TO_HIDE
+
+
+def hide_setuptools():
+    """
+    Remove references to setuptools' modules from sys.modules to allow the
+    invocation to import the most appropriate setuptools. This technique is
+    necessary to avoid issues such as #315 where setuptools upgrading itself
+    would fail to find a function declared in the metadata.
+    """
+    _distutils_hack = sys.modules.get('_distutils_hack', None)
+    if _distutils_hack is not None:
+        _distutils_hack._remove_shim()
+
+    modules = filter(_needs_hiding, sys.modules)
+    _clear_modules(modules)
+
+
+def run_setup(setup_script, args):
+    """Run a distutils setup script, sandboxed in its directory"""
+    setup_dir = os.path.abspath(os.path.dirname(setup_script))
+    with setup_context(setup_dir):
+        try:
+            sys.argv[:] = [setup_script] + list(args)
+            sys.path.insert(0, setup_dir)
+            # reset to include setup dir, w/clean callback list
+            working_set.__init__()
+            working_set.callbacks.append(lambda dist: dist.activate())
+
+            with DirectorySandbox(setup_dir):
+                ns = dict(__file__=setup_script, __name__='__main__')
+                _execfile(setup_script, ns)
+        except SystemExit as v:
+            if v.args and v.args[0]:
+                raise
+            # Normal exit, just return
+
+
+class AbstractSandbox:
+    """Wrap 'os' module and 'open()' builtin for virtualizing setup scripts"""
+
+    _active = False
+
+    def __init__(self) -> None:
+        self._attrs = [
+            name
+            for name in dir(_os)
+            if not name.startswith('_') and hasattr(self, name)
+        ]
+
+    def _copy(self, source):
+        for name in self._attrs:
+            setattr(os, name, getattr(source, name))
+
+    def __enter__(self) -> None:
+        self._copy(self)
+        builtins.open = self._open
+        self._active = True
+
+    def __exit__(
+        self,
+        exc_type: type[BaseException] | None,
+        exc_value: BaseException | None,
+        traceback: TracebackType | None,
+    ):
+        self._active = False
+        builtins.open = _open
+        self._copy(_os)
+
+    def run(self, func):
+        """Run 'func' under os sandboxing"""
+        with self:
+            return func()
+
+    def _mk_dual_path_wrapper(name: str):  # type: ignore[misc] # https://github.com/pypa/setuptools/pull/4099
+        original = getattr(_os, name)
+
+        def wrap(self, src, dst, *args, **kw):
+            if self._active:
+                src, dst = self._remap_pair(name, src, dst, *args, **kw)
+            return original(src, dst, *args, **kw)
+
+        return wrap
+
+    for __name in ["rename", "link", "symlink"]:
+        if hasattr(_os, __name):
+            locals()[__name] = _mk_dual_path_wrapper(__name)
+
+    def _mk_single_path_wrapper(name: str, original=None):  # type: ignore[misc] # https://github.com/pypa/setuptools/pull/4099
+        original = original or getattr(_os, name)
+
+        def wrap(self, path, *args, **kw):
+            if self._active:
+                path = self._remap_input(name, path, *args, **kw)
+            return original(path, *args, **kw)
+
+        return wrap
+
+    _open = _mk_single_path_wrapper('open', _open)
+    for __name in [
+        "stat",
+        "listdir",
+        "chdir",
+        "open",
+        "chmod",
+        "chown",
+        "mkdir",
+        "remove",
+        "unlink",
+        "rmdir",
+        "utime",
+        "lchown",
+        "chroot",
+        "lstat",
+        "startfile",
+        "mkfifo",
+        "mknod",
+        "pathconf",
+        "access",
+    ]:
+        if hasattr(_os, __name):
+            locals()[__name] = _mk_single_path_wrapper(__name)
+
+    def _mk_single_with_return(name: str):  # type: ignore[misc] # https://github.com/pypa/setuptools/pull/4099
+        original = getattr(_os, name)
+
+        def wrap(self, path, *args, **kw):
+            if self._active:
+                path = self._remap_input(name, path, *args, **kw)
+                return self._remap_output(name, original(path, *args, **kw))
+            return original(path, *args, **kw)
+
+        return wrap
+
+    for __name in ['readlink', 'tempnam']:
+        if hasattr(_os, __name):
+            locals()[__name] = _mk_single_with_return(__name)
+
+    def _mk_query(name: str):  # type: ignore[misc] # https://github.com/pypa/setuptools/pull/4099
+        original = getattr(_os, name)
+
+        def wrap(self, *args, **kw):
+            retval = original(*args, **kw)
+            if self._active:
+                return self._remap_output(name, retval)
+            return retval
+
+        return wrap
+
+    for __name in ['getcwd', 'tmpnam']:
+        if hasattr(_os, __name):
+            locals()[__name] = _mk_query(__name)
+
+    def _validate_path(self, path):
+        """Called to remap or validate any path, whether input or output"""
+        return path
+
+    def _remap_input(self, operation, path, *args, **kw):
+        """Called for path inputs"""
+        return self._validate_path(path)
+
+    def _remap_output(self, operation, path):
+        """Called for path outputs"""
+        return self._validate_path(path)
+
+    def _remap_pair(self, operation, src, dst, *args, **kw):
+        """Called for path pairs like rename, link, and symlink operations"""
+        return (
+            self._remap_input(operation + '-from', src, *args, **kw),
+            self._remap_input(operation + '-to', dst, *args, **kw),
+        )
+
+    if TYPE_CHECKING:
+        # This is a catch-all for all the dynamically created attributes.
+        # This isn't public API anyway
+        def __getattribute__(self, name: str) -> Any: ...
+
+
+if hasattr(os, 'devnull'):
+    _EXCEPTIONS = [os.devnull]
+else:
+    _EXCEPTIONS = []
+
+
+class DirectorySandbox(AbstractSandbox):
+    """Restrict operations to a single subdirectory - pseudo-chroot"""
+
+    write_ops: ClassVar[dict[str, None]] = dict.fromkeys([
+        "open",
+        "chmod",
+        "chown",
+        "mkdir",
+        "remove",
+        "unlink",
+        "rmdir",
+        "utime",
+        "lchown",
+        "chroot",
+        "mkfifo",
+        "mknod",
+        "tempnam",
+    ])
+
+    _exception_patterns: list[str | re.Pattern] = []
+    "exempt writing to paths that match the pattern"
+
+    def __init__(self, sandbox, exceptions=_EXCEPTIONS) -> None:
+        self._sandbox = os.path.normcase(os.path.realpath(sandbox))
+        self._prefix = os.path.join(self._sandbox, '')
+        self._exceptions = [
+            os.path.normcase(os.path.realpath(path)) for path in exceptions
+        ]
+        AbstractSandbox.__init__(self)
+
+    def _violation(self, operation, *args, **kw):
+        from setuptools.sandbox import SandboxViolation
+
+        raise SandboxViolation(operation, args, kw)
+
+    def _open(self, path, mode='r', *args, **kw):
+        if mode not in ('r', 'rt', 'rb', 'rU', 'U') and not self._ok(path):
+            self._violation("open", path, mode, *args, **kw)
+        return _open(path, mode, *args, **kw)
+
+    def tmpnam(self) -> None:
+        self._violation("tmpnam")
+
+    def _ok(self, path):
+        active = self._active
+        try:
+            self._active = False
+            realpath = os.path.normcase(os.path.realpath(path))
+            return (
+                self._exempted(realpath)
+                or realpath == self._sandbox
+                or realpath.startswith(self._prefix)
+            )
+        finally:
+            self._active = active
+
+    def _exempted(self, filepath):
+        start_matches = (
+            filepath.startswith(exception) for exception in self._exceptions
+        )
+        pattern_matches = (
+            re.match(pattern, filepath) for pattern in self._exception_patterns
+        )
+        candidates = itertools.chain(start_matches, pattern_matches)
+        return any(candidates)
+
+    def _remap_input(self, operation, path, *args, **kw):
+        """Called for path inputs"""
+        if operation in self.write_ops and not self._ok(path):
+            self._violation(operation, os.path.realpath(path), *args, **kw)
+        return path
+
+    def _remap_pair(self, operation, src, dst, *args, **kw):
+        """Called for path pairs like rename, link, and symlink operations"""
+        if not self._ok(src) or not self._ok(dst):
+            self._violation(operation, src, dst, *args, **kw)
+        return (src, dst)
+
+    def open(self, file, flags, mode: int = 0o777, *args, **kw) -> int:
+        """Called for low-level os.open()"""
+        if flags & WRITE_FLAGS and not self._ok(file):
+            self._violation("os.open", file, flags, mode, *args, **kw)
+        return _os.open(file, flags, mode, *args, **kw)
+
+
+WRITE_FLAGS = functools.reduce(
+    operator.or_,
+    [
+        getattr(_os, a, 0)
+        for a in "O_WRONLY O_RDWR O_APPEND O_CREAT O_TRUNC O_TEMPORARY".split()
+    ],
+)
+
+
+class SandboxViolation(DistutilsError):
+    """A setup script attempted to modify the filesystem outside the sandbox"""
+
+    tmpl = textwrap.dedent(
+        """
+        SandboxViolation: {cmd}{args!r} {kwargs}
+
+        The package setup script has attempted to modify files on your system
+        that are not within the EasyInstall build area, and has been aborted.
+
+        This package cannot be safely installed by EasyInstall, and may not
+        support alternate installation locations even if you run its setup
+        script by hand.  Please inform the package's author and the EasyInstall
+        maintainers to find out if a fix or workaround is available.
+        """
+    ).lstrip()
+
+    def __str__(self) -> str:
+        cmd, args, kwargs = self.args
+        return self.tmpl.format(**locals())

llava/lib/python3.10/site-packages/setuptools/warnings.py

@@ -0,0 +1,110 @@
+"""Provide basic warnings used by setuptools modules.
+
+Using custom classes (other than ``UserWarning``) allow users to set
+``PYTHONWARNINGS`` filters to run tests and prepare for upcoming changes in
+setuptools.
+"""
+
+from __future__ import annotations
+
+import os
+import warnings
+from datetime import date
+from inspect import cleandoc
+from textwrap import indent
+from typing import TYPE_CHECKING
+
+if TYPE_CHECKING:
+    from typing_extensions import TypeAlias
+
+_DueDate: TypeAlias = tuple[int, int, int]  # time tuple
+_INDENT = 8 * " "
+_TEMPLATE = f"""{80 * '*'}\n{{details}}\n{80 * '*'}"""
+
+
+class SetuptoolsWarning(UserWarning):
+    """Base class in ``setuptools`` warning hierarchy."""
+
+    @classmethod
+    def emit(
+        cls,
+        summary: str | None = None,
+        details: str | None = None,
+        due_date: _DueDate | None = None,
+        see_docs: str | None = None,
+        see_url: str | None = None,
+        stacklevel: int = 2,
+        **kwargs,
+    ) -> None:
+        """Private: reserved for ``setuptools`` internal use only"""
+        # Default values:
+        summary_ = summary or getattr(cls, "_SUMMARY", None) or ""
+        details_ = details or getattr(cls, "_DETAILS", None) or ""
+        due_date = due_date or getattr(cls, "_DUE_DATE", None)
+        docs_ref = see_docs or getattr(cls, "_SEE_DOCS", None)
+        docs_url = docs_ref and f"https://setuptools.pypa.io/en/latest/{docs_ref}"
+        see_url = see_url or getattr(cls, "_SEE_URL", None)
+        due = date(*due_date) if due_date else None
+
+        text = cls._format(summary_, details_, due, see_url or docs_url, kwargs)
+        if due and due < date.today() and _should_enforce():
+            raise cls(text)
+        warnings.warn(text, cls, stacklevel=stacklevel + 1)
+
+    @classmethod
+    def _format(
+        cls,
+        summary: str,
+        details: str,
+        due_date: date | None = None,
+        see_url: str | None = None,
+        format_args: dict | None = None,
+    ) -> str:
+        """Private: reserved for ``setuptools`` internal use only"""
+        today = date.today()
+        summary = cleandoc(summary).format_map(format_args or {})
+        possible_parts = [
+            cleandoc(details).format_map(format_args or {}),
+            (
+                f"\nBy {due_date:%Y-%b-%d}, you need to update your project and remove "
+                "deprecated calls\nor your builds will no longer be supported."
+                if due_date and due_date > today
+                else None
+            ),
+            (
+                "\nThis deprecation is overdue, please update your project and remove "
+                "deprecated\ncalls to avoid build errors in the future."
+                if due_date and due_date < today
+                else None
+            ),
+            (f"\nSee {see_url} for details." if see_url else None),
+        ]
+        parts = [x for x in possible_parts if x]
+        if parts:
+            body = indent(_TEMPLATE.format(details="\n".join(parts)), _INDENT)
+            return "\n".join([summary, "!!\n", body, "\n!!"])
+        return summary
+
+
+class InformationOnly(SetuptoolsWarning):
+    """Currently there is no clear way of displaying messages to the users
+    that use the setuptools backend directly via ``pip``.
+    The only thing that might work is a warning, although it is not the
+    most appropriate tool for the job...
+
+    See pypa/packaging-problems#558.
+    """
+
+
+class SetuptoolsDeprecationWarning(SetuptoolsWarning):
+    """
+    Base class for warning deprecations in ``setuptools``
+
+    This class is not derived from ``DeprecationWarning``, and as such is
+    visible by default.
+    """
+
+
+def _should_enforce():
+    enforce = os.getenv("SETUPTOOLS_ENFORCE_DEPRECATION", "false").lower()
+    return enforce in ("true", "on", "ok", "1")

llava/lib/python3.10/site-packages/setuptools/windows_support.py

@@ -0,0 +1,30 @@
+import platform
+
+
+def windows_only(func):
+    if platform.system() != 'Windows':
+        return lambda *args, **kwargs: None
+    return func
+
+
+@windows_only
+def hide_file(path: str) -> None:
+    """
+    Set the hidden attribute on a file or directory.
+
+    From https://stackoverflow.com/questions/19622133/
+
+    `path` must be text.
+    """
+    import ctypes
+    import ctypes.wintypes
+
+    SetFileAttributes = ctypes.windll.kernel32.SetFileAttributesW
+    SetFileAttributes.argtypes = ctypes.wintypes.LPWSTR, ctypes.wintypes.DWORD
+    SetFileAttributes.restype = ctypes.wintypes.BOOL
+
+    FILE_ATTRIBUTE_HIDDEN = 0x02
+
+    ret = SetFileAttributes(path, FILE_ATTRIBUTE_HIDDEN)
+    if not ret:
+        raise ctypes.WinError()

minigpt2/lib/python3.10/site-packages/networkx/generators/atlas.dat.gz

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
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+size 8887

minigpt2/lib/python3.10/site-packages/networkx/generators/cographs.py

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+r"""Generators for cographs
+
+A cograph is a graph containing no path on four vertices.
+Cographs or $P_4$-free graphs can be obtained from a single vertex
+by disjoint union and complementation operations.
+
+References
+----------
+.. [0] D.G. Corneil, H. Lerchs, L.Stewart Burlingham,
+    "Complement reducible graphs",
+    Discrete Applied Mathematics, Volume 3, Issue 3, 1981, Pages 163-174,
+    ISSN 0166-218X.
+"""
+
+import networkx as nx
+from networkx.utils import py_random_state
+
+__all__ = ["random_cograph"]
+
+
+@py_random_state(1)
+@nx._dispatchable(graphs=None, returns_graph=True)
+def random_cograph(n, seed=None):
+    r"""Returns a random cograph with $2 ^ n$ nodes.
+
+    A cograph is a graph containing no path on four vertices.
+    Cographs or $P_4$-free graphs can be obtained from a single vertex
+    by disjoint union and complementation operations.
+
+    This generator starts off from a single vertex and performs disjoint
+    union and full join operations on itself.
+    The decision on which operation will take place is random.
+
+    Parameters
+    ----------
+    n : int
+        The order of the cograph.
+    seed : integer, random_state, or None (default)
+        Indicator of random number generation state.
+        See :ref:`Randomness<randomness>`.
+
+    Returns
+    -------
+    G : A random graph containing no path on four vertices.
+
+    See Also
+    --------
+    full_join
+    union
+
+    References
+    ----------
+    .. [1] D.G. Corneil, H. Lerchs, L.Stewart Burlingham,
+       "Complement reducible graphs",
+       Discrete Applied Mathematics, Volume 3, Issue 3, 1981, Pages 163-174,
+       ISSN 0166-218X.
+    """
+    R = nx.empty_graph(1)
+
+    for i in range(n):
+        RR = nx.relabel_nodes(R.copy(), lambda x: x + len(R))
+
+        if seed.randint(0, 1) == 0:
+            R = nx.full_join(R, RR)
+        else:
+            R = nx.disjoint_union(R, RR)
+
+    return R

minigpt2/lib/python3.10/site-packages/networkx/generators/ego.py

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+"""
+Ego graph.
+"""
+
+__all__ = ["ego_graph"]
+
+import networkx as nx
+
+
+@nx._dispatchable(preserve_all_attrs=True, returns_graph=True)
+def ego_graph(G, n, radius=1, center=True, undirected=False, distance=None):
+    """Returns induced subgraph of neighbors centered at node n within
+    a given radius.
+
+    Parameters
+    ----------
+    G : graph
+      A NetworkX Graph or DiGraph
+
+    n : node
+      A single node
+
+    radius : number, optional
+      Include all neighbors of distance<=radius from n.
+
+    center : bool, optional
+      If False, do not include center node in graph
+
+    undirected : bool, optional
+      If True use both in- and out-neighbors of directed graphs.
+
+    distance : key, optional
+      Use specified edge data key as distance.  For example, setting
+      distance='weight' will use the edge weight to measure the
+      distance from the node n.
+
+    Notes
+    -----
+    For directed graphs D this produces the "out" neighborhood
+    or successors.  If you want the neighborhood of predecessors
+    first reverse the graph with D.reverse().  If you want both
+    directions use the keyword argument undirected=True.
+
+    Node, edge, and graph attributes are copied to the returned subgraph.
+    """
+    if undirected:
+        if distance is not None:
+            sp, _ = nx.single_source_dijkstra(
+                G.to_undirected(), n, cutoff=radius, weight=distance
+            )
+        else:
+            sp = dict(
+                nx.single_source_shortest_path_length(
+                    G.to_undirected(), n, cutoff=radius
+                )
+            )
+    else:
+        if distance is not None:
+            sp, _ = nx.single_source_dijkstra(G, n, cutoff=radius, weight=distance)
+        else:
+            sp = dict(nx.single_source_shortest_path_length(G, n, cutoff=radius))
+
+    H = G.subgraph(sp).copy()
+    if not center:
+        H.remove_node(n)
+    return H

minigpt2/lib/python3.10/site-packages/networkx/generators/expanders.py

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+"""Provides explicit constructions of expander graphs."""
+
+import itertools
+
+import networkx as nx
+
+__all__ = [
+    "margulis_gabber_galil_graph",
+    "chordal_cycle_graph",
+    "paley_graph",
+    "maybe_regular_expander",
+    "is_regular_expander",
+    "random_regular_expander_graph",
+]
+
+
+# Other discrete torus expanders can be constructed by using the following edge
+# sets. For more information, see Chapter 4, "Expander Graphs", in
+# "Pseudorandomness", by Salil Vadhan.
+#
+# For a directed expander, add edges from (x, y) to:
+#
+#     (x, y),
+#     ((x + 1) % n, y),
+#     (x, (y + 1) % n),
+#     (x, (x + y) % n),
+#     (-y % n, x)
+#
+# For an undirected expander, add the reverse edges.
+#
+# Also appearing in the paper of Gabber and Galil:
+#
+#     (x, y),
+#     (x, (x + y) % n),
+#     (x, (x + y + 1) % n),
+#     ((x + y) % n, y),
+#     ((x + y + 1) % n, y)
+#
+# and:
+#
+#     (x, y),
+#     ((x + 2*y) % n, y),
+#     ((x + (2*y + 1)) % n, y),
+#     ((x + (2*y + 2)) % n, y),
+#     (x, (y + 2*x) % n),
+#     (x, (y + (2*x + 1)) % n),
+#     (x, (y + (2*x + 2)) % n),
+#
+@nx._dispatchable(graphs=None, returns_graph=True)
+def margulis_gabber_galil_graph(n, create_using=None):
+    r"""Returns the Margulis-Gabber-Galil undirected MultiGraph on `n^2` nodes.
+
+    The undirected MultiGraph is regular with degree `8`. Nodes are integer
+    pairs. The second-largest eigenvalue of the adjacency matrix of the graph
+    is at most `5 \sqrt{2}`, regardless of `n`.
+
+    Parameters
+    ----------
+    n : int
+        Determines the number of nodes in the graph: `n^2`.
+    create_using : NetworkX graph constructor, optional (default MultiGraph)
+       Graph type to create. If graph instance, then cleared before populated.
+
+    Returns
+    -------
+    G : graph
+        The constructed undirected multigraph.
+
+    Raises
+    ------
+    NetworkXError
+        If the graph is directed or not a multigraph.
+
+    """
+    G = nx.empty_graph(0, create_using, default=nx.MultiGraph)
+    if G.is_directed() or not G.is_multigraph():
+        msg = "`create_using` must be an undirected multigraph."
+        raise nx.NetworkXError(msg)
+
+    for x, y in itertools.product(range(n), repeat=2):
+        for u, v in (
+            ((x + 2 * y) % n, y),
+            ((x + (2 * y + 1)) % n, y),
+            (x, (y + 2 * x) % n),
+            (x, (y + (2 * x + 1)) % n),
+        ):
+            G.add_edge((x, y), (u, v))
+    G.graph["name"] = f"margulis_gabber_galil_graph({n})"
+    return G
+
+
+@nx._dispatchable(graphs=None, returns_graph=True)
+def chordal_cycle_graph(p, create_using=None):
+    """Returns the chordal cycle graph on `p` nodes.
+
+    The returned graph is a cycle graph on `p` nodes with chords joining each
+    vertex `x` to its inverse modulo `p`. This graph is a (mildly explicit)
+    3-regular expander [1]_.
+
+    `p` *must* be a prime number.
+
+    Parameters
+    ----------
+    p : a prime number
+
+        The number of vertices in the graph. This also indicates where the
+        chordal edges in the cycle will be created.
+
+    create_using : NetworkX graph constructor, optional (default=nx.Graph)
+       Graph type to create. If graph instance, then cleared before populated.
+
+    Returns
+    -------
+    G : graph
+        The constructed undirected multigraph.
+
+    Raises
+    ------
+    NetworkXError
+
+        If `create_using` indicates directed or not a multigraph.
+
+    References
+    ----------
+
+    .. [1] Theorem 4.4.2 in A. Lubotzky. "Discrete groups, expanding graphs and
+           invariant measures", volume 125 of Progress in Mathematics.
+           Birkhäuser Verlag, Basel, 1994.
+
+    """
+    G = nx.empty_graph(0, create_using, default=nx.MultiGraph)
+    if G.is_directed() or not G.is_multigraph():
+        msg = "`create_using` must be an undirected multigraph."
+        raise nx.NetworkXError(msg)
+
+    for x in range(p):
+        left = (x - 1) % p
+        right = (x + 1) % p
+        # Here we apply Fermat's Little Theorem to compute the multiplicative
+        # inverse of x in Z/pZ. By Fermat's Little Theorem,
+        #
+        #     x^p = x (mod p)
+        #
+        # Therefore,
+        #
+        #     x * x^(p - 2) = 1 (mod p)
+        #
+        # The number 0 is a special case: we just let its inverse be itself.
+        chord = pow(x, p - 2, p) if x > 0 else 0
+        for y in (left, right, chord):
+            G.add_edge(x, y)
+    G.graph["name"] = f"chordal_cycle_graph({p})"
+    return G
+
+
+@nx._dispatchable(graphs=None, returns_graph=True)
+def paley_graph(p, create_using=None):
+    r"""Returns the Paley $\frac{(p-1)}{2}$ -regular graph on $p$ nodes.
+
+    The returned graph is a graph on $\mathbb{Z}/p\mathbb{Z}$ with edges between $x$ and $y$
+    if and only if $x-y$ is a nonzero square in $\mathbb{Z}/p\mathbb{Z}$.
+
+    If $p \equiv 1  \pmod 4$, $-1$ is a square in $\mathbb{Z}/p\mathbb{Z}$ and therefore $x-y$ is a square if and
+    only if $y-x$ is also a square, i.e the edges in the Paley graph are symmetric.
+
+    If $p \equiv 3 \pmod 4$, $-1$ is not a square in $\mathbb{Z}/p\mathbb{Z}$ and therefore either $x-y$ or $y-x$
+    is a square in $\mathbb{Z}/p\mathbb{Z}$ but not both.
+
+    Note that a more general definition of Paley graphs extends this construction
+    to graphs over $q=p^n$ vertices, by using the finite field $F_q$ instead of $\mathbb{Z}/p\mathbb{Z}$.
+    This construction requires to compute squares in general finite fields and is
+    not what is implemented here (i.e `paley_graph(25)` does not return the true
+    Paley graph associated with $5^2$).
+
+    Parameters
+    ----------
+    p : int, an odd prime number.
+
+    create_using : NetworkX graph constructor, optional (default=nx.Graph)
+       Graph type to create. If graph instance, then cleared before populated.
+
+    Returns
+    -------
+    G : graph
+        The constructed directed graph.
+
+    Raises
+    ------
+    NetworkXError
+        If the graph is a multigraph.
+
+    References
+    ----------
+    Chapter 13 in B. Bollobas, Random Graphs. Second edition.
+    Cambridge Studies in Advanced Mathematics, 73.
+    Cambridge University Press, Cambridge (2001).
+    """
+    G = nx.empty_graph(0, create_using, default=nx.DiGraph)
+    if G.is_multigraph():
+        msg = "`create_using` cannot be a multigraph."
+        raise nx.NetworkXError(msg)
+
+    # Compute the squares in Z/pZ.
+    # Make it a set to uniquify (there are exactly (p-1)/2 squares in Z/pZ
+    # when is prime).
+    square_set = {(x**2) % p for x in range(1, p) if (x**2) % p != 0}
+
+    for x in range(p):
+        for x2 in square_set:
+            G.add_edge(x, (x + x2) % p)
+    G.graph["name"] = f"paley({p})"
+    return G
+
+
+@nx.utils.decorators.np_random_state("seed")
+@nx._dispatchable(graphs=None, returns_graph=True)
+def maybe_regular_expander(n, d, *, create_using=None, max_tries=100, seed=None):
+    r"""Utility for creating a random regular expander.
+
+    Returns a random $d$-regular graph on $n$ nodes which is an expander
+    graph with very good probability.
+
+    Parameters
+    ----------
+    n : int
+      The number of nodes.
+    d : int
+      The degree of each node.
+    create_using : Graph Instance or Constructor
+      Indicator of type of graph to return.
+      If a Graph-type instance, then clear and use it.
+      If a constructor, call it to create an empty graph.
+      Use the Graph constructor by default.
+    max_tries : int. (default: 100)
+      The number of allowed loops when generating each independent cycle
+    seed : (default: None)
+      Seed used to set random number generation state. See :ref`Randomness<randomness>`.
+
+    Notes
+    -----
+    The nodes are numbered from $0$ to $n - 1$.
+
+    The graph is generated by taking $d / 2$ random independent cycles.
+
+    Joel Friedman proved that in this model the resulting
+    graph is an expander with probability
+    $1 - O(n^{-\tau})$ where $\tau = \lceil (\sqrt{d - 1}) / 2 \rceil - 1$. [1]_
+
+    Examples
+    --------
+    >>> G = nx.maybe_regular_expander(n=200, d=6, seed=8020)
+
+    Returns
+    -------
+    G : graph
+        The constructed undirected graph.
+
+    Raises
+    ------
+    NetworkXError
+        If $d % 2 != 0$ as the degree must be even.
+        If $n - 1$ is less than $ 2d $ as the graph is complete at most.
+        If max_tries is reached
+
+    See Also
+    --------
+    is_regular_expander
+    random_regular_expander_graph
+
+    References
+    ----------
+    .. [1] Joel Friedman,
+       A Proof of Alon’s Second Eigenvalue Conjecture and Related Problems, 2004
+       https://arxiv.org/abs/cs/0405020
+
+    """
+
+    import numpy as np
+
+    if n < 1:
+        raise nx.NetworkXError("n must be a positive integer")
+
+    if not (d >= 2):
+        raise nx.NetworkXError("d must be greater than or equal to 2")
+
+    if not (d % 2 == 0):
+        raise nx.NetworkXError("d must be even")
+
+    if not (n - 1 >= d):
+        raise nx.NetworkXError(
+            f"Need n-1>= d to have room for {d//2} independent cycles with {n} nodes"
+        )
+
+    G = nx.empty_graph(n, create_using)
+
+    if n < 2:
+        return G
+
+    cycles = []
+    edges = set()
+
+    # Create d / 2 cycles
+    for i in range(d // 2):
+        iterations = max_tries
+        # Make sure the cycles are independent to have a regular graph
+        while len(edges) != (i + 1) * n:
+            iterations -= 1
+            # Faster than random.permutation(n) since there are only
+            # (n-1)! distinct cycles against n! permutations of size n
+            cycle = seed.permutation(n - 1).tolist()
+            cycle.append(n - 1)
+
+            new_edges = {
+                (u, v)
+                for u, v in nx.utils.pairwise(cycle, cyclic=True)
+                if (u, v) not in edges and (v, u) not in edges
+            }
+            # If the new cycle has no edges in common with previous cycles
+            # then add it to the list otherwise try again
+            if len(new_edges) == n:
+                cycles.append(cycle)
+                edges.update(new_edges)
+
+            if iterations == 0:
+                raise nx.NetworkXError("Too many iterations in maybe_regular_expander")
+
+    G.add_edges_from(edges)
+
+    return G
+
+
+@nx.utils.not_implemented_for("directed")
+@nx.utils.not_implemented_for("multigraph")
+@nx._dispatchable(preserve_edge_attrs={"G": {"weight": 1}})
+def is_regular_expander(G, *, epsilon=0):
+    r"""Determines whether the graph G is a regular expander. [1]_
+
+    An expander graph is a sparse graph with strong connectivity properties.
+
+    More precisely, this helper checks whether the graph is a
+    regular $(n, d, \lambda)$-expander with $\lambda$ close to
+    the Alon-Boppana bound and given by
+    $\lambda = 2 \sqrt{d - 1} + \epsilon$. [2]_
+
+    In the case where $\epsilon = 0$ then if the graph successfully passes the test
+    it is a Ramanujan graph. [3]_
+
+    A Ramanujan graph has spectral gap almost as large as possible, which makes them
+    excellent expanders.
+
+    Parameters
+    ----------
+    G : NetworkX graph
+    epsilon : int, float, default=0
+
+    Returns
+    -------
+    bool
+        Whether the given graph is a regular $(n, d, \lambda)$-expander
+        where $\lambda = 2 \sqrt{d - 1} + \epsilon$.
+
+    Examples
+    --------
+    >>> G = nx.random_regular_expander_graph(20, 4)
+    >>> nx.is_regular_expander(G)
+    True
+
+    See Also
+    --------
+    maybe_regular_expander
+    random_regular_expander_graph
+
+    References
+    ----------
+    .. [1] Expander graph, https://en.wikipedia.org/wiki/Expander_graph
+    .. [2] Alon-Boppana bound, https://en.wikipedia.org/wiki/Alon%E2%80%93Boppana_bound
+    .. [3] Ramanujan graphs, https://en.wikipedia.org/wiki/Ramanujan_graph
+
+    """
+
+    import numpy as np
+    from scipy.sparse.linalg import eigsh
+
+    if epsilon < 0:
+        raise nx.NetworkXError("epsilon must be non negative")
+
+    if not nx.is_regular(G):
+        return False
+
+    _, d = nx.utils.arbitrary_element(G.degree)
+
+    A = nx.adjacency_matrix(G, dtype=float)
+    lams = eigsh(A, which="LM", k=2, return_eigenvectors=False)
+
+    # lambda2 is the second biggest eigenvalue
+    lambda2 = min(lams)
+
+    # Use bool() to convert numpy scalar to Python Boolean
+    return bool(abs(lambda2) < 2 ** np.sqrt(d - 1) + epsilon)
+
+
+@nx.utils.decorators.np_random_state("seed")
+@nx._dispatchable(graphs=None, returns_graph=True)
+def random_regular_expander_graph(
+    n, d, *, epsilon=0, create_using=None, max_tries=100, seed=None
+):
+    r"""Returns a random regular expander graph on $n$ nodes with degree $d$.
+
+    An expander graph is a sparse graph with strong connectivity properties. [1]_
+
+    More precisely the returned graph is a $(n, d, \lambda)$-expander with
+    $\lambda = 2 \sqrt{d - 1} + \epsilon$, close to the Alon-Boppana bound. [2]_
+
+    In the case where $\epsilon = 0$ it returns a Ramanujan graph.
+    A Ramanujan graph has spectral gap almost as large as possible,
+    which makes them excellent expanders. [3]_
+
+    Parameters
+    ----------
+    n : int
+      The number of nodes.
+    d : int
+      The degree of each node.
+    epsilon : int, float, default=0
+    max_tries : int, (default: 100)
+      The number of allowed loops, also used in the maybe_regular_expander utility
+    seed : (default: None)
+      Seed used to set random number generation state. See :ref`Randomness<randomness>`.
+
+    Raises
+    ------
+    NetworkXError
+        If max_tries is reached
+
+    Examples
+    --------
+    >>> G = nx.random_regular_expander_graph(20, 4)
+    >>> nx.is_regular_expander(G)
+    True
+
+    Notes
+    -----
+    This loops over `maybe_regular_expander` and can be slow when
+    $n$ is too big or $\epsilon$ too small.
+
+    See Also
+    --------
+    maybe_regular_expander
+    is_regular_expander
+
+    References
+    ----------
+    .. [1] Expander graph, https://en.wikipedia.org/wiki/Expander_graph
+    .. [2] Alon-Boppana bound, https://en.wikipedia.org/wiki/Alon%E2%80%93Boppana_bound
+    .. [3] Ramanujan graphs, https://en.wikipedia.org/wiki/Ramanujan_graph
+
+    """
+    G = maybe_regular_expander(
+        n, d, create_using=create_using, max_tries=max_tries, seed=seed
+    )
+    iterations = max_tries
+
+    while not is_regular_expander(G, epsilon=epsilon):
+        iterations -= 1
+        G = maybe_regular_expander(
+            n=n, d=d, create_using=create_using, max_tries=max_tries, seed=seed
+        )
+
+        if iterations == 0:
+            raise nx.NetworkXError(
+                "Too many iterations in random_regular_expander_graph"
+            )
+
+    return G

minigpt2/lib/python3.10/site-packages/networkx/generators/geometric.py

@@ -0,0 +1,1048 @@
+"""Generators for geometric graphs."""
+
+import math
+from bisect import bisect_left
+from itertools import accumulate, combinations, product
+
+import networkx as nx
+from networkx.utils import py_random_state
+
+__all__ = [
+    "geometric_edges",
+    "geographical_threshold_graph",
+    "navigable_small_world_graph",
+    "random_geometric_graph",
+    "soft_random_geometric_graph",
+    "thresholded_random_geometric_graph",
+    "waxman_graph",
+    "geometric_soft_configuration_graph",
+]
+
+
+@nx._dispatchable(node_attrs="pos_name")
+def geometric_edges(G, radius, p=2, *, pos_name="pos"):
+    """Returns edge list of node pairs within `radius` of each other.
+
+    Parameters
+    ----------
+    G : networkx graph
+        The graph from which to generate the edge list. The nodes in `G` should
+        have an attribute ``pos`` corresponding to the node position, which is
+        used to compute the distance to other nodes.
+    radius : scalar
+        The distance threshold. Edges are included in the edge list if the
+        distance between the two nodes is less than `radius`.
+    pos_name : string, default="pos"
+        The name of the node attribute which represents the position of each
+        node in 2D coordinates. Every node in the Graph must have this attribute.
+    p : scalar, default=2
+        The `Minkowski distance metric
+        <https://en.wikipedia.org/wiki/Minkowski_distance>`_ used to compute
+        distances. The default value is 2, i.e. Euclidean distance.
+
+    Returns
+    -------
+    edges : list
+        List of edges whose distances are less than `radius`
+
+    Notes
+    -----
+    Radius uses Minkowski distance metric `p`.
+    If scipy is available, `scipy.spatial.cKDTree` is used to speed computation.
+
+    Examples
+    --------
+    Create a graph with nodes that have a "pos" attribute representing 2D
+    coordinates.
+
+    >>> G = nx.Graph()
+    >>> G.add_nodes_from(
+    ...     [
+    ...         (0, {"pos": (0, 0)}),
+    ...         (1, {"pos": (3, 0)}),
+    ...         (2, {"pos": (8, 0)}),
+    ...     ]
+    ... )
+    >>> nx.geometric_edges(G, radius=1)
+    []
+    >>> nx.geometric_edges(G, radius=4)
+    [(0, 1)]
+    >>> nx.geometric_edges(G, radius=6)
+    [(0, 1), (1, 2)]
+    >>> nx.geometric_edges(G, radius=9)
+    [(0, 1), (0, 2), (1, 2)]
+    """
+    # Input validation - every node must have a "pos" attribute
+    for n, pos in G.nodes(data=pos_name):
+        if pos is None:
+            raise nx.NetworkXError(
+                f"Node {n} (and all nodes) must have a '{pos_name}' attribute."
+            )
+
+    # NOTE: See _geometric_edges for the actual implementation. The reason this
+    # is split into two functions is to avoid the overhead of input validation
+    # every time the function is called internally in one of the other
+    # geometric generators
+    return _geometric_edges(G, radius, p, pos_name)
+
+
+def _geometric_edges(G, radius, p, pos_name):
+    """
+    Implements `geometric_edges` without input validation. See `geometric_edges`
+    for complete docstring.
+    """
+    nodes_pos = G.nodes(data=pos_name)
+    try:
+        import scipy as sp
+    except ImportError:
+        # no scipy KDTree so compute by for-loop
+        radius_p = radius**p
+        edges = [
+            (u, v)
+            for (u, pu), (v, pv) in combinations(nodes_pos, 2)
+            if sum(abs(a - b) ** p for a, b in zip(pu, pv)) <= radius_p
+        ]
+        return edges
+    # scipy KDTree is available
+    nodes, coords = list(zip(*nodes_pos))
+    kdtree = sp.spatial.cKDTree(coords)  # Cannot provide generator.
+    edge_indexes = kdtree.query_pairs(radius, p)
+    edges = [(nodes[u], nodes[v]) for u, v in sorted(edge_indexes)]
+    return edges
+
+
+@py_random_state(5)
+@nx._dispatchable(graphs=None, returns_graph=True)
+def random_geometric_graph(
+    n, radius, dim=2, pos=None, p=2, seed=None, *, pos_name="pos"
+):
+    """Returns a random geometric graph in the unit cube of dimensions `dim`.
+
+    The random geometric graph model places `n` nodes uniformly at
+    random in the unit cube. Two nodes are joined by an edge if the
+    distance between the nodes is at most `radius`.
+
+    Edges are determined using a KDTree when SciPy is available.
+    This reduces the time complexity from $O(n^2)$ to $O(n)$.
+
+    Parameters
+    ----------
+    n : int or iterable
+        Number of nodes or iterable of nodes
+    radius: float
+        Distance threshold value
+    dim : int, optional
+        Dimension of graph
+    pos : dict, optional
+        A dictionary keyed by node with node positions as values.
+    p : float, optional
+        Which Minkowski distance metric to use.  `p` has to meet the condition
+        ``1 <= p <= infinity``.
+
+        If this argument is not specified, the :math:`L^2` metric
+        (the Euclidean distance metric), p = 2 is used.
+        This should not be confused with the `p` of an Erdős-Rényi random
+        graph, which represents probability.
+    seed : integer, random_state, or None (default)
+        Indicator of random number generation state.
+        See :ref:`Randomness<randomness>`.
+    pos_name : string, default="pos"
+        The name of the node attribute which represents the position
+        in 2D coordinates of the node in the returned graph.
+
+    Returns
+    -------
+    Graph
+        A random geometric graph, undirected and without self-loops.
+        Each node has a node attribute ``'pos'`` that stores the
+        position of that node in Euclidean space as provided by the
+        ``pos`` keyword argument or, if ``pos`` was not provided, as
+        generated by this function.
+
+    Examples
+    --------
+    Create a random geometric graph on twenty nodes where nodes are joined by
+    an edge if their distance is at most 0.1::
+
+    >>> G = nx.random_geometric_graph(20, 0.1)
+
+    Notes
+    -----
+    This uses a *k*-d tree to build the graph.
+
+    The `pos` keyword argument can be used to specify node positions so you
+    can create an arbitrary distribution and domain for positions.
+
+    For example, to use a 2D Gaussian distribution of node positions with mean
+    (0, 0) and standard deviation 2::
+
+    >>> import random
+    >>> n = 20
+    >>> pos = {i: (random.gauss(0, 2), random.gauss(0, 2)) for i in range(n)}
+    >>> G = nx.random_geometric_graph(n, 0.2, pos=pos)
+
+    References
+    ----------
+    .. [1] Penrose, Mathew, *Random Geometric Graphs*,
+           Oxford Studies in Probability, 5, 2003.
+
+    """
+    # TODO Is this function just a special case of the geographical
+    # threshold graph?
+    #
+    #     half_radius = {v: radius / 2 for v in n}
+    #     return geographical_threshold_graph(nodes, theta=1, alpha=1,
+    #                                         weight=half_radius)
+    #
+    G = nx.empty_graph(n)
+    # If no positions are provided, choose uniformly random vectors in
+    # Euclidean space of the specified dimension.
+    if pos is None:
+        pos = {v: [seed.random() for i in range(dim)] for v in G}
+    nx.set_node_attributes(G, pos, pos_name)
+
+    G.add_edges_from(_geometric_edges(G, radius, p, pos_name))
+    return G
+
+
+@py_random_state(6)
+@nx._dispatchable(graphs=None, returns_graph=True)
+def soft_random_geometric_graph(
+    n, radius, dim=2, pos=None, p=2, p_dist=None, seed=None, *, pos_name="pos"
+):
+    r"""Returns a soft random geometric graph in the unit cube.
+
+    The soft random geometric graph [1] model places `n` nodes uniformly at
+    random in the unit cube in dimension `dim`. Two nodes of distance, `dist`,
+    computed by the `p`-Minkowski distance metric are joined by an edge with
+    probability `p_dist` if the computed distance metric value of the nodes
+    is at most `radius`, otherwise they are not joined.
+
+    Edges within `radius` of each other are determined using a KDTree when
+    SciPy is available. This reduces the time complexity from :math:`O(n^2)`
+    to :math:`O(n)`.
+
+    Parameters
+    ----------
+    n : int or iterable
+        Number of nodes or iterable of nodes
+    radius: float
+        Distance threshold value
+    dim : int, optional
+        Dimension of graph
+    pos : dict, optional
+        A dictionary keyed by node with node positions as values.
+    p : float, optional
+        Which Minkowski distance metric to use.
+        `p` has to meet the condition ``1 <= p <= infinity``.
+
+        If this argument is not specified, the :math:`L^2` metric
+        (the Euclidean distance metric), p = 2 is used.
+
+        This should not be confused with the `p` of an Erdős-Rényi random
+        graph, which represents probability.
+    p_dist : function, optional
+        A probability density function computing the probability of
+        connecting two nodes that are of distance, dist, computed by the
+        Minkowski distance metric. The probability density function, `p_dist`,
+        must be any function that takes the metric value as input
+        and outputs a single probability value between 0-1. The scipy.stats
+        package has many probability distribution functions implemented and
+        tools for custom probability distribution definitions [2], and passing
+        the .pdf method of scipy.stats distributions can be used here.  If the
+        probability function, `p_dist`, is not supplied, the default function
+        is an exponential distribution with rate parameter :math:`\lambda=1`.
+    seed : integer, random_state, or None (default)
+        Indicator of random number generation state.
+        See :ref:`Randomness<randomness>`.
+    pos_name : string, default="pos"
+        The name of the node attribute which represents the position
+        in 2D coordinates of the node in the returned graph.
+
+    Returns
+    -------
+    Graph
+        A soft random geometric graph, undirected and without self-loops.
+        Each node has a node attribute ``'pos'`` that stores the
+        position of that node in Euclidean space as provided by the
+        ``pos`` keyword argument or, if ``pos`` was not provided, as
+        generated by this function.
+
+    Examples
+    --------
+    Default Graph:
+
+    G = nx.soft_random_geometric_graph(50, 0.2)
+
+    Custom Graph:
+
+    Create a soft random geometric graph on 100 uniformly distributed nodes
+    where nodes are joined by an edge with probability computed from an
+    exponential distribution with rate parameter :math:`\lambda=1` if their
+    Euclidean distance is at most 0.2.
+
+    Notes
+    -----
+    This uses a *k*-d tree to build the graph.
+
+    The `pos` keyword argument can be used to specify node positions so you
+    can create an arbitrary distribution and domain for positions.
+
+    For example, to use a 2D Gaussian distribution of node positions with mean
+    (0, 0) and standard deviation 2
+
+    The scipy.stats package can be used to define the probability distribution
+    with the .pdf method used as `p_dist`.
+
+    ::
+
+    >>> import random
+    >>> import math
+    >>> n = 100
+    >>> pos = {i: (random.gauss(0, 2), random.gauss(0, 2)) for i in range(n)}
+    >>> p_dist = lambda dist: math.exp(-dist)
+    >>> G = nx.soft_random_geometric_graph(n, 0.2, pos=pos, p_dist=p_dist)
+
+    References
+    ----------
+    .. [1] Penrose, Mathew D. "Connectivity of soft random geometric graphs."
+           The Annals of Applied Probability 26.2 (2016): 986-1028.
+    .. [2] scipy.stats -
+           https://docs.scipy.org/doc/scipy/reference/tutorial/stats.html
+
+    """
+    G = nx.empty_graph(n)
+    G.name = f"soft_random_geometric_graph({n}, {radius}, {dim})"
+    # If no positions are provided, choose uniformly random vectors in
+    # Euclidean space of the specified dimension.
+    if pos is None:
+        pos = {v: [seed.random() for i in range(dim)] for v in G}
+    nx.set_node_attributes(G, pos, pos_name)
+
+    # if p_dist function not supplied the default function is an exponential
+    # distribution with rate parameter :math:`\lambda=1`.
+    if p_dist is None:
+
+        def p_dist(dist):
+            return math.exp(-dist)
+
+    def should_join(edge):
+        u, v = edge
+        dist = (sum(abs(a - b) ** p for a, b in zip(pos[u], pos[v]))) ** (1 / p)
+        return seed.random() < p_dist(dist)
+
+    G.add_edges_from(filter(should_join, _geometric_edges(G, radius, p, pos_name)))
+    return G
+
+
+@py_random_state(7)
+@nx._dispatchable(graphs=None, returns_graph=True)
+def geographical_threshold_graph(
+    n,
+    theta,
+    dim=2,
+    pos=None,
+    weight=None,
+    metric=None,
+    p_dist=None,
+    seed=None,
+    *,
+    pos_name="pos",
+    weight_name="weight",
+):
+    r"""Returns a geographical threshold graph.
+
+    The geographical threshold graph model places $n$ nodes uniformly at
+    random in a rectangular domain.  Each node $u$ is assigned a weight
+    $w_u$. Two nodes $u$ and $v$ are joined by an edge if
+
+    .. math::
+
+       (w_u + w_v)p_{dist}(r) \ge \theta
+
+    where `r` is the distance between `u` and `v`, `p_dist` is any function of
+    `r`, and :math:`\theta` as the threshold parameter. `p_dist` is used to
+    give weight to the distance between nodes when deciding whether or not
+    they should be connected. The larger `p_dist` is, the more prone nodes
+    separated by `r` are to be connected, and vice versa.
+
+    Parameters
+    ----------
+    n : int or iterable
+        Number of nodes or iterable of nodes
+    theta: float
+        Threshold value
+    dim : int, optional
+        Dimension of graph
+    pos : dict
+        Node positions as a dictionary of tuples keyed by node.
+    weight : dict
+        Node weights as a dictionary of numbers keyed by node.
+    metric : function
+        A metric on vectors of numbers (represented as lists or
+        tuples). This must be a function that accepts two lists (or
+        tuples) as input and yields a number as output. The function
+        must also satisfy the four requirements of a `metric`_.
+        Specifically, if $d$ is the function and $x$, $y$,
+        and $z$ are vectors in the graph, then $d$ must satisfy
+
+        1. $d(x, y) \ge 0$,
+        2. $d(x, y) = 0$ if and only if $x = y$,
+        3. $d(x, y) = d(y, x)$,
+        4. $d(x, z) \le d(x, y) + d(y, z)$.
+
+        If this argument is not specified, the Euclidean distance metric is
+        used.
+
+        .. _metric: https://en.wikipedia.org/wiki/Metric_%28mathematics%29
+    p_dist : function, optional
+        Any function used to give weight to the distance between nodes when
+        deciding whether or not they should be connected. `p_dist` was
+        originally conceived as a probability density function giving the
+        probability of connecting two nodes that are of metric distance `r`
+        apart. The implementation here allows for more arbitrary definitions
+        of `p_dist` that do not need to correspond to valid probability
+        density functions. The :mod:`scipy.stats` package has many
+        probability density functions implemented and tools for custom
+        probability density definitions, and passing the ``.pdf`` method of
+        scipy.stats distributions can be used here. If ``p_dist=None``
+        (the default), the exponential function :math:`r^{-2}` is used.
+    seed : integer, random_state, or None (default)
+        Indicator of random number generation state.
+        See :ref:`Randomness<randomness>`.
+    pos_name : string, default="pos"
+        The name of the node attribute which represents the position
+        in 2D coordinates of the node in the returned graph.
+    weight_name : string, default="weight"
+        The name of the node attribute which represents the weight
+        of the node in the returned graph.
+
+    Returns
+    -------
+    Graph
+        A random geographic threshold graph, undirected and without
+        self-loops.
+
+        Each node has a node attribute ``pos`` that stores the
+        position of that node in Euclidean space as provided by the
+        ``pos`` keyword argument or, if ``pos`` was not provided, as
+        generated by this function. Similarly, each node has a node
+        attribute ``weight`` that stores the weight of that node as
+        provided or as generated.
+
+    Examples
+    --------
+    Specify an alternate distance metric using the ``metric`` keyword
+    argument. For example, to use the `taxicab metric`_ instead of the
+    default `Euclidean metric`_::
+
+        >>> dist = lambda x, y: sum(abs(a - b) for a, b in zip(x, y))
+        >>> G = nx.geographical_threshold_graph(10, 0.1, metric=dist)
+
+    .. _taxicab metric: https://en.wikipedia.org/wiki/Taxicab_geometry
+    .. _Euclidean metric: https://en.wikipedia.org/wiki/Euclidean_distance
+
+    Notes
+    -----
+    If weights are not specified they are assigned to nodes by drawing randomly
+    from the exponential distribution with rate parameter $\lambda=1$.
+    To specify weights from a different distribution, use the `weight` keyword
+    argument::
+
+    >>> import random
+    >>> n = 20
+    >>> w = {i: random.expovariate(5.0) for i in range(n)}
+    >>> G = nx.geographical_threshold_graph(20, 50, weight=w)
+
+    If node positions are not specified they are randomly assigned from the
+    uniform distribution.
+
+    References
+    ----------
+    .. [1] Masuda, N., Miwa, H., Konno, N.:
+       Geographical threshold graphs with small-world and scale-free
+       properties.
+       Physical Review E 71, 036108 (2005)
+    .. [2]  Milan Bradonjić, Aric Hagberg and Allon G. Percus,
+       Giant component and connectivity in geographical threshold graphs,
+       in Algorithms and Models for the Web-Graph (WAW 2007),
+       Antony Bonato and Fan Chung (Eds), pp. 209--216, 2007
+    """
+    G = nx.empty_graph(n)
+    # If no weights are provided, choose them from an exponential
+    # distribution.
+    if weight is None:
+        weight = {v: seed.expovariate(1) for v in G}
+    # If no positions are provided, choose uniformly random vectors in
+    # Euclidean space of the specified dimension.
+    if pos is None:
+        pos = {v: [seed.random() for i in range(dim)] for v in G}
+    # If no distance metric is provided, use Euclidean distance.
+    if metric is None:
+        metric = math.dist
+    nx.set_node_attributes(G, weight, weight_name)
+    nx.set_node_attributes(G, pos, pos_name)
+
+    # if p_dist is not supplied, use default r^-2
+    if p_dist is None:
+
+        def p_dist(r):
+            return r**-2
+
+    # Returns ``True`` if and only if the nodes whose attributes are
+    # ``du`` and ``dv`` should be joined, according to the threshold
+    # condition.
+    def should_join(pair):
+        u, v = pair
+        u_pos, v_pos = pos[u], pos[v]
+        u_weight, v_weight = weight[u], weight[v]
+        return (u_weight + v_weight) * p_dist(metric(u_pos, v_pos)) >= theta
+
+    G.add_edges_from(filter(should_join, combinations(G, 2)))
+    return G
+
+
+@py_random_state(6)
+@nx._dispatchable(graphs=None, returns_graph=True)
+def waxman_graph(
+    n,
+    beta=0.4,
+    alpha=0.1,
+    L=None,
+    domain=(0, 0, 1, 1),
+    metric=None,
+    seed=None,
+    *,
+    pos_name="pos",
+):
+    r"""Returns a Waxman random graph.
+
+    The Waxman random graph model places `n` nodes uniformly at random
+    in a rectangular domain. Each pair of nodes at distance `d` is
+    joined by an edge with probability
+
+    .. math::
+            p = \beta \exp(-d / \alpha L).
+
+    This function implements both Waxman models, using the `L` keyword
+    argument.
+
+    * Waxman-1: if `L` is not specified, it is set to be the maximum distance
+      between any pair of nodes.
+    * Waxman-2: if `L` is specified, the distance between a pair of nodes is
+      chosen uniformly at random from the interval `[0, L]`.
+
+    Parameters
+    ----------
+    n : int or iterable
+        Number of nodes or iterable of nodes
+    beta: float
+        Model parameter
+    alpha: float
+        Model parameter
+    L : float, optional
+        Maximum distance between nodes.  If not specified, the actual distance
+        is calculated.
+    domain : four-tuple of numbers, optional
+        Domain size, given as a tuple of the form `(x_min, y_min, x_max,
+        y_max)`.
+    metric : function
+        A metric on vectors of numbers (represented as lists or
+        tuples). This must be a function that accepts two lists (or
+        tuples) as input and yields a number as output. The function
+        must also satisfy the four requirements of a `metric`_.
+        Specifically, if $d$ is the function and $x$, $y$,
+        and $z$ are vectors in the graph, then $d$ must satisfy
+
+        1. $d(x, y) \ge 0$,
+        2. $d(x, y) = 0$ if and only if $x = y$,
+        3. $d(x, y) = d(y, x)$,
+        4. $d(x, z) \le d(x, y) + d(y, z)$.
+
+        If this argument is not specified, the Euclidean distance metric is
+        used.
+
+        .. _metric: https://en.wikipedia.org/wiki/Metric_%28mathematics%29
+
+    seed : integer, random_state, or None (default)
+        Indicator of random number generation state.
+        See :ref:`Randomness<randomness>`.
+    pos_name : string, default="pos"
+        The name of the node attribute which represents the position
+        in 2D coordinates of the node in the returned graph.
+
+    Returns
+    -------
+    Graph
+        A random Waxman graph, undirected and without self-loops. Each
+        node has a node attribute ``'pos'`` that stores the position of
+        that node in Euclidean space as generated by this function.
+
+    Examples
+    --------
+    Specify an alternate distance metric using the ``metric`` keyword
+    argument. For example, to use the "`taxicab metric`_" instead of the
+    default `Euclidean metric`_::
+
+        >>> dist = lambda x, y: sum(abs(a - b) for a, b in zip(x, y))
+        >>> G = nx.waxman_graph(10, 0.5, 0.1, metric=dist)
+
+    .. _taxicab metric: https://en.wikipedia.org/wiki/Taxicab_geometry
+    .. _Euclidean metric: https://en.wikipedia.org/wiki/Euclidean_distance
+
+    Notes
+    -----
+    Starting in NetworkX 2.0 the parameters alpha and beta align with their
+    usual roles in the probability distribution. In earlier versions their
+    positions in the expression were reversed. Their position in the calling
+    sequence reversed as well to minimize backward incompatibility.
+
+    References
+    ----------
+    .. [1]  B. M. Waxman, *Routing of multipoint connections*.
+       IEEE J. Select. Areas Commun. 6(9),(1988) 1617--1622.
+    """
+    G = nx.empty_graph(n)
+    (xmin, ymin, xmax, ymax) = domain
+    # Each node gets a uniformly random position in the given rectangle.
+    pos = {v: (seed.uniform(xmin, xmax), seed.uniform(ymin, ymax)) for v in G}
+    nx.set_node_attributes(G, pos, pos_name)
+    # If no distance metric is provided, use Euclidean distance.
+    if metric is None:
+        metric = math.dist
+    # If the maximum distance L is not specified (that is, we are in the
+    # Waxman-1 model), then find the maximum distance between any pair
+    # of nodes.
+    #
+    # In the Waxman-1 model, join nodes randomly based on distance. In
+    # the Waxman-2 model, join randomly based on random l.
+    if L is None:
+        L = max(metric(x, y) for x, y in combinations(pos.values(), 2))
+
+        def dist(u, v):
+            return metric(pos[u], pos[v])
+
+    else:
+
+        def dist(u, v):
+            return seed.random() * L
+
+    # `pair` is the pair of nodes to decide whether to join.
+    def should_join(pair):
+        return seed.random() < beta * math.exp(-dist(*pair) / (alpha * L))
+
+    G.add_edges_from(filter(should_join, combinations(G, 2)))
+    return G
+
+
+@py_random_state(5)
+@nx._dispatchable(graphs=None, returns_graph=True)
+def navigable_small_world_graph(n, p=1, q=1, r=2, dim=2, seed=None):
+    r"""Returns a navigable small-world graph.
+
+    A navigable small-world graph is a directed grid with additional long-range
+    connections that are chosen randomly.
+
+      [...] we begin with a set of nodes [...] that are identified with the set
+      of lattice points in an $n \times n$ square,
+      $\{(i, j): i \in \{1, 2, \ldots, n\}, j \in \{1, 2, \ldots, n\}\}$,
+      and we define the *lattice distance* between two nodes $(i, j)$ and
+      $(k, l)$ to be the number of "lattice steps" separating them:
+      $d((i, j), (k, l)) = |k - i| + |l - j|$.
+
+      For a universal constant $p >= 1$, the node $u$ has a directed edge to
+      every other node within lattice distance $p$---these are its *local
+      contacts*. For universal constants $q >= 0$ and $r >= 0$ we also
+      construct directed edges from $u$ to $q$ other nodes (the *long-range
+      contacts*) using independent random trials; the $i$th directed edge from
+      $u$ has endpoint $v$ with probability proportional to $[d(u,v)]^{-r}$.
+
+      -- [1]_
+
+    Parameters
+    ----------
+    n : int
+        The length of one side of the lattice; the number of nodes in
+        the graph is therefore $n^2$.
+    p : int
+        The diameter of short range connections. Each node is joined with every
+        other node within this lattice distance.
+    q : int
+        The number of long-range connections for each node.
+    r : float
+        Exponent for decaying probability of connections.  The probability of
+        connecting to a node at lattice distance $d$ is $1/d^r$.
+    dim : int
+        Dimension of grid
+    seed : integer, random_state, or None (default)
+        Indicator of random number generation state.
+        See :ref:`Randomness<randomness>`.
+
+    References
+    ----------
+    .. [1] J. Kleinberg. The small-world phenomenon: An algorithmic
+       perspective. Proc. 32nd ACM Symposium on Theory of Computing, 2000.
+    """
+    if p < 1:
+        raise nx.NetworkXException("p must be >= 1")
+    if q < 0:
+        raise nx.NetworkXException("q must be >= 0")
+    if r < 0:
+        raise nx.NetworkXException("r must be >= 0")
+
+    G = nx.DiGraph()
+    nodes = list(product(range(n), repeat=dim))
+    for p1 in nodes:
+        probs = [0]
+        for p2 in nodes:
+            if p1 == p2:
+                continue
+            d = sum((abs(b - a) for a, b in zip(p1, p2)))
+            if d <= p:
+                G.add_edge(p1, p2)
+            probs.append(d**-r)
+        cdf = list(accumulate(probs))
+        for _ in range(q):
+            target = nodes[bisect_left(cdf, seed.uniform(0, cdf[-1]))]
+            G.add_edge(p1, target)
+    return G
+
+
+@py_random_state(7)
+@nx._dispatchable(graphs=None, returns_graph=True)
+def thresholded_random_geometric_graph(
+    n,
+    radius,
+    theta,
+    dim=2,
+    pos=None,
+    weight=None,
+    p=2,
+    seed=None,
+    *,
+    pos_name="pos",
+    weight_name="weight",
+):
+    r"""Returns a thresholded random geometric graph in the unit cube.
+
+    The thresholded random geometric graph [1] model places `n` nodes
+    uniformly at random in the unit cube of dimensions `dim`. Each node
+    `u` is assigned a weight :math:`w_u`. Two nodes `u` and `v` are
+    joined by an edge if they are within the maximum connection distance,
+    `radius` computed by the `p`-Minkowski distance and the summation of
+    weights :math:`w_u` + :math:`w_v` is greater than or equal
+    to the threshold parameter `theta`.
+
+    Edges within `radius` of each other are determined using a KDTree when
+    SciPy is available. This reduces the time complexity from :math:`O(n^2)`
+    to :math:`O(n)`.
+
+    Parameters
+    ----------
+    n : int or iterable
+        Number of nodes or iterable of nodes
+    radius: float
+        Distance threshold value
+    theta: float
+        Threshold value
+    dim : int, optional
+        Dimension of graph
+    pos : dict, optional
+        A dictionary keyed by node with node positions as values.
+    weight : dict, optional
+        Node weights as a dictionary of numbers keyed by node.
+    p : float, optional (default 2)
+        Which Minkowski distance metric to use.  `p` has to meet the condition
+        ``1 <= p <= infinity``.
+
+        If this argument is not specified, the :math:`L^2` metric
+        (the Euclidean distance metric), p = 2 is used.
+
+        This should not be confused with the `p` of an Erdős-Rényi random
+        graph, which represents probability.
+    seed : integer, random_state, or None (default)
+        Indicator of random number generation state.
+        See :ref:`Randomness<randomness>`.
+    pos_name : string, default="pos"
+        The name of the node attribute which represents the position
+        in 2D coordinates of the node in the returned graph.
+    weight_name : string, default="weight"
+        The name of the node attribute which represents the weight
+        of the node in the returned graph.
+
+    Returns
+    -------
+    Graph
+        A thresholded random geographic graph, undirected and without
+        self-loops.
+
+        Each node has a node attribute ``'pos'`` that stores the
+        position of that node in Euclidean space as provided by the
+        ``pos`` keyword argument or, if ``pos`` was not provided, as
+        generated by this function. Similarly, each node has a nodethre
+        attribute ``'weight'`` that stores the weight of that node as
+        provided or as generated.
+
+    Examples
+    --------
+    Default Graph:
+
+    G = nx.thresholded_random_geometric_graph(50, 0.2, 0.1)
+
+    Custom Graph:
+
+    Create a thresholded random geometric graph on 50 uniformly distributed
+    nodes where nodes are joined by an edge if their sum weights drawn from
+    a exponential distribution with rate = 5 are >= theta = 0.1 and their
+    Euclidean distance is at most 0.2.
+
+    Notes
+    -----
+    This uses a *k*-d tree to build the graph.
+
+    The `pos` keyword argument can be used to specify node positions so you
+    can create an arbitrary distribution and domain for positions.
+
+    For example, to use a 2D Gaussian distribution of node positions with mean
+    (0, 0) and standard deviation 2
+
+    If weights are not specified they are assigned to nodes by drawing randomly
+    from the exponential distribution with rate parameter :math:`\lambda=1`.
+    To specify weights from a different distribution, use the `weight` keyword
+    argument::
+
+    ::
+
+    >>> import random
+    >>> import math
+    >>> n = 50
+    >>> pos = {i: (random.gauss(0, 2), random.gauss(0, 2)) for i in range(n)}
+    >>> w = {i: random.expovariate(5.0) for i in range(n)}
+    >>> G = nx.thresholded_random_geometric_graph(n, 0.2, 0.1, 2, pos, w)
+
+    References
+    ----------
+    .. [1] http://cole-maclean.github.io/blog/files/thesis.pdf
+
+    """
+    G = nx.empty_graph(n)
+    G.name = f"thresholded_random_geometric_graph({n}, {radius}, {theta}, {dim})"
+    # If no weights are provided, choose them from an exponential
+    # distribution.
+    if weight is None:
+        weight = {v: seed.expovariate(1) for v in G}
+    # If no positions are provided, choose uniformly random vectors in
+    # Euclidean space of the specified dimension.
+    if pos is None:
+        pos = {v: [seed.random() for i in range(dim)] for v in G}
+    # If no distance metric is provided, use Euclidean distance.
+    nx.set_node_attributes(G, weight, weight_name)
+    nx.set_node_attributes(G, pos, pos_name)
+
+    edges = (
+        (u, v)
+        for u, v in _geometric_edges(G, radius, p, pos_name)
+        if weight[u] + weight[v] >= theta
+    )
+    G.add_edges_from(edges)
+    return G
+
+
+@py_random_state(5)
+@nx._dispatchable(graphs=None, returns_graph=True)
+def geometric_soft_configuration_graph(
+    *, beta, n=None, gamma=None, mean_degree=None, kappas=None, seed=None
+):
+    r"""Returns a random graph from the geometric soft configuration model.
+
+    The $\mathbb{S}^1$ model [1]_ is the geometric soft configuration model
+    which is able to explain many fundamental features of real networks such as
+    small-world property, heteregenous degree distributions, high level of
+    clustering, and self-similarity.
+
+    In the geometric soft configuration model, a node $i$ is assigned two hidden
+    variables: a hidden degree $\kappa_i$, quantifying its popularity, influence,
+    or importance, and an angular position $\theta_i$ in a circle abstracting the
+    similarity space, where angular distances between nodes are a proxy for their
+    similarity. Focusing on the angular position, this model is often called
+    the $\mathbb{S}^1$ model (a one-dimensional sphere). The circle's radius is
+    adjusted to $R = N/2\pi$, where $N$ is the number of nodes, so that the density
+    is set to 1 without loss of generality.
+
+    The connection probability between any pair of nodes increases with
+    the product of their hidden degrees (i.e., their combined popularities),
+    and decreases with the angular distance between the two nodes.
+    Specifically, nodes $i$ and $j$ are connected with the probability
+
+    $p_{ij} = \frac{1}{1 + \frac{d_{ij}^\beta}{\left(\mu \kappa_i \kappa_j\right)^{\max(1, \beta)}}}$
+
+    where $d_{ij} = R\Delta\theta_{ij}$ is the arc length of the circle between
+    nodes $i$ and $j$ separated by an angular distance $\Delta\theta_{ij}$.
+    Parameters $\mu$ and $\beta$ (also called inverse temperature) control the
+    average degree and the clustering coefficient, respectively.
+
+    It can be shown [2]_ that the model undergoes a structural phase transition
+    at $\beta=1$ so that for $\beta<1$ networks are unclustered in the thermodynamic
+    limit (when $N\to \infty$) whereas for $\beta>1$ the ensemble generates
+    networks with finite clustering coefficient.
+
+    The $\mathbb{S}^1$ model can be expressed as a purely geometric model
+    $\mathbb{H}^2$ in the hyperbolic plane [3]_ by mapping the hidden degree of
+    each node into a radial coordinate as
+
+    $r_i = \hat{R} - \frac{2 \max(1, \beta)}{\beta \zeta} \ln \left(\frac{\kappa_i}{\kappa_0}\right)$
+
+    where $\hat{R}$ is the radius of the hyperbolic disk and $\zeta$ is the curvature,
+
+    $\hat{R} = \frac{2}{\zeta} \ln \left(\frac{N}{\pi}\right)
+    - \frac{2\max(1, \beta)}{\beta \zeta} \ln (\mu \kappa_0^2)$
+
+    The connection probability then reads
+
+    $p_{ij} = \frac{1}{1 + \exp\left({\frac{\beta\zeta}{2} (x_{ij} - \hat{R})}\right)}$
+
+    where
+
+    $x_{ij} = r_i + r_j + \frac{2}{\zeta} \ln \frac{\Delta\theta_{ij}}{2}$
+
+    is a good approximation of the hyperbolic distance between two nodes separated
+    by an angular distance $\Delta\theta_{ij}$ with radial coordinates $r_i$ and $r_j$.
+    For $\beta > 1$, the curvature $\zeta = 1$, for $\beta < 1$, $\zeta = \beta^{-1}$.
+
+
+    Parameters
+    ----------
+    Either `n`, `gamma`, `mean_degree` are provided or `kappas`. The values of
+    `n`, `gamma`, `mean_degree` (if provided) are used to construct a random
+    kappa-dict keyed by node with values sampled from a power-law distribution.
+
+    beta : positive number
+        Inverse temperature, controlling the clustering coefficient.
+    n : int (default: None)
+        Size of the network (number of nodes).
+        If not provided, `kappas` must be provided and holds the nodes.
+    gamma : float (default: None)
+        Exponent of the power-law distribution for hidden degrees `kappas`.
+        If not provided, `kappas` must be provided directly.
+    mean_degree : float (default: None)
+        The mean degree in the network.
+        If not provided, `kappas` must be provided directly.
+    kappas : dict (default: None)
+        A dict keyed by node to its hidden degree value.
+        If not provided, random values are computed based on a power-law
+        distribution using `n`, `gamma` and `mean_degree`.
+    seed : int, random_state, or None (default)
+        Indicator of random number generation state.
+        See :ref:`Randomness<randomness>`.
+
+    Returns
+    -------
+    Graph
+        A random geometric soft configuration graph (undirected with no self-loops).
+        Each node has three node-attributes:
+
+        - ``kappa`` that represents the hidden degree.
+
+        - ``theta`` the position in the similarity space ($\mathbb{S}^1$) which is
+          also the angular position in the hyperbolic plane.
+
+        - ``radius`` the radial position in the hyperbolic plane
+          (based on the hidden degree).
+
+
+    Examples
+    --------
+    Generate a network with specified parameters:
+
+    >>> G = nx.geometric_soft_configuration_graph(
+    ...     beta=1.5, n=100, gamma=2.7, mean_degree=5
+    ... )
+
+    Create a geometric soft configuration graph with 100 nodes. The $\beta$ parameter
+    is set to 1.5 and the exponent of the powerlaw distribution of the hidden
+    degrees is 2.7 with mean value of 5.
+
+    Generate a network with predefined hidden degrees:
+
+    >>> kappas = {i: 10 for i in range(100)}
+    >>> G = nx.geometric_soft_configuration_graph(beta=2.5, kappas=kappas)
+
+    Create a geometric soft configuration graph with 100 nodes. The $\beta$ parameter
+    is set to 2.5 and all nodes with hidden degree $\kappa=10$.
+
+
+    References
+    ----------
+    .. [1] Serrano, M. Á., Krioukov, D., & Boguñá, M. (2008). Self-similarity
+       of complex networks and hidden metric spaces. Physical review letters, 100(7), 078701.
+
+    .. [2] van der Kolk, J., Serrano, M. Á., & Boguñá, M. (2022). An anomalous
+       topological phase transition in spatial random graphs. Communications Physics, 5(1), 245.
+
+    .. [3] Krioukov, D., Papadopoulos, F., Kitsak, M., Vahdat, A., & Boguná, M. (2010).
+       Hyperbolic geometry of complex networks. Physical Review E, 82(3), 036106.
+
+    """
+    if beta <= 0:
+        raise nx.NetworkXError("The parameter beta cannot be smaller or equal to 0.")
+
+    if kappas is not None:
+        if not all((n is None, gamma is None, mean_degree is None)):
+            raise nx.NetworkXError(
+                "When kappas is input, n, gamma and mean_degree must not be."
+            )
+
+        n = len(kappas)
+        mean_degree = sum(kappas) / len(kappas)
+    else:
+        if any((n is None, gamma is None, mean_degree is None)):
+            raise nx.NetworkXError(
+                "Please provide either kappas, or all 3 of: n, gamma and mean_degree."
+            )
+
+        # Generate `n` hidden degrees from a powerlaw distribution
+        # with given exponent `gamma` and mean value `mean_degree`
+        gam_ratio = (gamma - 2) / (gamma - 1)
+        kappa_0 = mean_degree * gam_ratio * (1 - 1 / n) / (1 - 1 / n**gam_ratio)
+        base = 1 - 1 / n
+        power = 1 / (1 - gamma)
+        kappas = {i: kappa_0 * (1 - seed.random() * base) ** power for i in range(n)}
+
+    G = nx.Graph()
+    R = n / (2 * math.pi)
+
+    # Approximate values for mu in the thermodynamic limit (when n -> infinity)
+    if beta > 1:
+        mu = beta * math.sin(math.pi / beta) / (2 * math.pi * mean_degree)
+    elif beta == 1:
+        mu = 1 / (2 * mean_degree * math.log(n))
+    else:
+        mu = (1 - beta) / (2**beta * mean_degree * n ** (1 - beta))
+
+    # Generate random positions on a circle
+    thetas = {k: seed.uniform(0, 2 * math.pi) for k in kappas}
+
+    for u in kappas:
+        for v in list(G):
+            angle = math.pi - math.fabs(math.pi - math.fabs(thetas[u] - thetas[v]))
+            dij = math.pow(R * angle, beta)
+            mu_kappas = math.pow(mu * kappas[u] * kappas[v], max(1, beta))
+            p_ij = 1 / (1 + dij / mu_kappas)
+
+            # Create an edge with a certain connection probability
+            if seed.random() < p_ij:
+                G.add_edge(u, v)
+        G.add_node(u)
+
+    nx.set_node_attributes(G, thetas, "theta")
+    nx.set_node_attributes(G, kappas, "kappa")
+
+    # Map hidden degrees into the radial coordinates
+    zeta = 1 if beta > 1 else 1 / beta
+    kappa_min = min(kappas.values())
+    R_c = 2 * max(1, beta) / (beta * zeta)
+    R_hat = (2 / zeta) * math.log(n / math.pi) - R_c * math.log(mu * kappa_min)
+    radii = {node: R_hat - R_c * math.log(kappa) for node, kappa in kappas.items()}
+    nx.set_node_attributes(G, radii, "radius")
+
+    return G

minigpt2/lib/python3.10/site-packages/networkx/generators/joint_degree_seq.py

@@ -0,0 +1,664 @@
+"""Generate graphs with a given joint degree and directed joint degree"""
+
+import networkx as nx
+from networkx.utils import py_random_state
+
+__all__ = [
+    "is_valid_joint_degree",
+    "is_valid_directed_joint_degree",
+    "joint_degree_graph",
+    "directed_joint_degree_graph",
+]
+
+
+@nx._dispatchable(graphs=None)
+def is_valid_joint_degree(joint_degrees):
+    """Checks whether the given joint degree dictionary is realizable.
+
+    A *joint degree dictionary* is a dictionary of dictionaries, in
+    which entry ``joint_degrees[k][l]`` is an integer representing the
+    number of edges joining nodes of degree *k* with nodes of degree
+    *l*. Such a dictionary is realizable as a simple graph if and only
+    if the following conditions are satisfied.
+
+    - each entry must be an integer,
+    - the total number of nodes of degree *k*, computed by
+      ``sum(joint_degrees[k].values()) / k``, must be an integer,
+    - the total number of edges joining nodes of degree *k* with
+      nodes of degree *l* cannot exceed the total number of possible edges,
+    - each diagonal entry ``joint_degrees[k][k]`` must be even (this is
+      a convention assumed by the :func:`joint_degree_graph` function).
+
+
+    Parameters
+    ----------
+    joint_degrees :  dictionary of dictionary of integers
+        A joint degree dictionary in which entry ``joint_degrees[k][l]``
+        is the number of edges joining nodes of degree *k* with nodes of
+        degree *l*.
+
+    Returns
+    -------
+    bool
+        Whether the given joint degree dictionary is realizable as a
+        simple graph.
+
+    References
+    ----------
+    .. [1] M. Gjoka, M. Kurant, A. Markopoulou, "2.5K Graphs: from Sampling
+       to Generation", IEEE Infocom, 2013.
+    .. [2] I. Stanton, A. Pinar, "Constructing and sampling graphs with a
+       prescribed joint degree distribution", Journal of Experimental
+       Algorithmics, 2012.
+    """
+
+    degree_count = {}
+    for k in joint_degrees:
+        if k > 0:
+            k_size = sum(joint_degrees[k].values()) / k
+            if not k_size.is_integer():
+                return False
+            degree_count[k] = k_size
+
+    for k in joint_degrees:
+        for l in joint_degrees[k]:
+            if not float(joint_degrees[k][l]).is_integer():
+                return False
+
+            if (k != l) and (joint_degrees[k][l] > degree_count[k] * degree_count[l]):
+                return False
+            elif k == l:
+                if joint_degrees[k][k] > degree_count[k] * (degree_count[k] - 1):
+                    return False
+                if joint_degrees[k][k] % 2 != 0:
+                    return False
+
+    # if all above conditions have been satisfied then the input
+    # joint degree is realizable as a simple graph.
+    return True
+
+
+def _neighbor_switch(G, w, unsat, h_node_residual, avoid_node_id=None):
+    """Releases one free stub for ``w``, while preserving joint degree in G.
+
+    Parameters
+    ----------
+    G : NetworkX graph
+        Graph in which the neighbor switch will take place.
+    w : integer
+        Node id for which we will execute this neighbor switch.
+    unsat : set of integers
+        Set of unsaturated node ids that have the same degree as w.
+    h_node_residual: dictionary of integers
+        Keeps track of the remaining stubs  for a given node.
+    avoid_node_id: integer
+        Node id to avoid when selecting w_prime.
+
+    Notes
+    -----
+    First, it selects *w_prime*, an  unsaturated node that has the same degree
+    as ``w``. Second, it selects *switch_node*, a neighbor node of ``w`` that
+    is not  connected to *w_prime*. Then it executes an edge swap i.e. removes
+    (``w``,*switch_node*) and adds (*w_prime*,*switch_node*). Gjoka et. al. [1]
+    prove that such an edge swap is always possible.
+
+    References
+    ----------
+    .. [1] M. Gjoka, B. Tillman, A. Markopoulou, "Construction of Simple
+       Graphs with a Target Joint Degree Matrix and Beyond", IEEE Infocom, '15
+    """
+
+    if (avoid_node_id is None) or (h_node_residual[avoid_node_id] > 1):
+        # select unsaturated node w_prime that has the same degree as w
+        w_prime = next(iter(unsat))
+    else:
+        # assume that the node pair (v,w) has been selected for connection. if
+        # - neighbor_switch is called for node w,
+        # - nodes v and w have the same degree,
+        # - node v=avoid_node_id has only one stub left,
+        # then prevent v=avoid_node_id from being selected as w_prime.
+
+        iter_var = iter(unsat)
+        while True:
+            w_prime = next(iter_var)
+            if w_prime != avoid_node_id:
+                break
+
+    # select switch_node, a neighbor of w, that is not connected to w_prime
+    w_prime_neighbs = G[w_prime]  # slightly faster declaring this variable
+    for v in G[w]:
+        if (v not in w_prime_neighbs) and (v != w_prime):
+            switch_node = v
+            break
+
+    # remove edge (w,switch_node), add edge (w_prime,switch_node) and update
+    # data structures
+    G.remove_edge(w, switch_node)
+    G.add_edge(w_prime, switch_node)
+    h_node_residual[w] += 1
+    h_node_residual[w_prime] -= 1
+    if h_node_residual[w_prime] == 0:
+        unsat.remove(w_prime)
+
+
+@py_random_state(1)
+@nx._dispatchable(graphs=None, returns_graph=True)
+def joint_degree_graph(joint_degrees, seed=None):
+    """Generates a random simple graph with the given joint degree dictionary.
+
+    Parameters
+    ----------
+    joint_degrees :  dictionary of dictionary of integers
+        A joint degree dictionary in which entry ``joint_degrees[k][l]`` is the
+        number of edges joining nodes of degree *k* with nodes of degree *l*.
+    seed : integer, random_state, or None (default)
+        Indicator of random number generation state.
+        See :ref:`Randomness<randomness>`.
+
+    Returns
+    -------
+    G : Graph
+        A graph with the specified joint degree dictionary.
+
+    Raises
+    ------
+    NetworkXError
+        If *joint_degrees* dictionary is not realizable.
+
+    Notes
+    -----
+    In each iteration of the "while loop" the algorithm picks two disconnected
+    nodes *v* and *w*, of degree *k* and *l* correspondingly,  for which
+    ``joint_degrees[k][l]`` has not reached its target yet. It then adds
+    edge (*v*, *w*) and increases the number of edges in graph G by one.
+
+    The intelligence of the algorithm lies in the fact that  it is always
+    possible to add an edge between such disconnected nodes *v* and *w*,
+    even if one or both nodes do not have free stubs. That is made possible by
+    executing a "neighbor switch", an edge rewiring move that releases
+    a free stub while keeping the joint degree of G the same.
+
+    The algorithm continues for E (number of edges) iterations of
+    the "while loop", at the which point all entries of the given
+    ``joint_degrees[k][l]`` have reached their target values and the
+    construction is complete.
+
+    References
+    ----------
+    ..  [1] M. Gjoka, B. Tillman, A. Markopoulou, "Construction of Simple
+        Graphs with a Target Joint Degree Matrix and Beyond", IEEE Infocom, '15
+
+    Examples
+    --------
+    >>> joint_degrees = {
+    ...     1: {4: 1},
+    ...     2: {2: 2, 3: 2, 4: 2},
+    ...     3: {2: 2, 4: 1},
+    ...     4: {1: 1, 2: 2, 3: 1},
+    ... }
+    >>> G = nx.joint_degree_graph(joint_degrees)
+    >>>
+    """
+
+    if not is_valid_joint_degree(joint_degrees):
+        msg = "Input joint degree dict not realizable as a simple graph"
+        raise nx.NetworkXError(msg)
+
+    # compute degree count from joint_degrees
+    degree_count = {k: sum(l.values()) // k for k, l in joint_degrees.items() if k > 0}
+
+    # start with empty N-node graph
+    N = sum(degree_count.values())
+    G = nx.empty_graph(N)
+
+    # for a given degree group, keep the list of all node ids
+    h_degree_nodelist = {}
+
+    # for a given node, keep track of the remaining stubs
+    h_node_residual = {}
+
+    # populate h_degree_nodelist and h_node_residual
+    nodeid = 0
+    for degree, num_nodes in degree_count.items():
+        h_degree_nodelist[degree] = range(nodeid, nodeid + num_nodes)
+        for v in h_degree_nodelist[degree]:
+            h_node_residual[v] = degree
+        nodeid += int(num_nodes)
+
+    # iterate over every degree pair (k,l) and add the number of edges given
+    # for each pair
+    for k in joint_degrees:
+        for l in joint_degrees[k]:
+            # n_edges_add is the number of edges to add for the
+            # degree pair (k,l)
+            n_edges_add = joint_degrees[k][l]
+
+            if (n_edges_add > 0) and (k >= l):
+                # number of nodes with degree k and l
+                k_size = degree_count[k]
+                l_size = degree_count[l]
+
+                # k_nodes and l_nodes consist of all nodes of degree k and l
+                k_nodes = h_degree_nodelist[k]
+                l_nodes = h_degree_nodelist[l]
+
+                # k_unsat and l_unsat consist of nodes of degree k and l that
+                # are unsaturated (nodes that have at least 1 available stub)
+                k_unsat = {v for v in k_nodes if h_node_residual[v] > 0}
+
+                if k != l:
+                    l_unsat = {w for w in l_nodes if h_node_residual[w] > 0}
+                else:
+                    l_unsat = k_unsat
+                    n_edges_add = joint_degrees[k][l] // 2
+
+                while n_edges_add > 0:
+                    # randomly pick nodes v and w that have degrees k and l
+                    v = k_nodes[seed.randrange(k_size)]
+                    w = l_nodes[seed.randrange(l_size)]
+
+                    # if nodes v and w are disconnected then attempt to connect
+                    if not G.has_edge(v, w) and (v != w):
+                        # if node v has no free stubs then do neighbor switch
+                        if h_node_residual[v] == 0:
+                            _neighbor_switch(G, v, k_unsat, h_node_residual)
+
+                        # if node w has no free stubs then do neighbor switch
+                        if h_node_residual[w] == 0:
+                            if k != l:
+                                _neighbor_switch(G, w, l_unsat, h_node_residual)
+                            else:
+                                _neighbor_switch(
+                                    G, w, l_unsat, h_node_residual, avoid_node_id=v
+                                )
+
+                        # add edge (v, w) and update data structures
+                        G.add_edge(v, w)
+                        h_node_residual[v] -= 1
+                        h_node_residual[w] -= 1
+                        n_edges_add -= 1
+
+                        if h_node_residual[v] == 0:
+                            k_unsat.discard(v)
+                        if h_node_residual[w] == 0:
+                            l_unsat.discard(w)
+    return G
+
+
+@nx._dispatchable(graphs=None)
+def is_valid_directed_joint_degree(in_degrees, out_degrees, nkk):
+    """Checks whether the given directed joint degree input is realizable
+
+    Parameters
+    ----------
+    in_degrees :  list of integers
+        in degree sequence contains the in degrees of nodes.
+    out_degrees : list of integers
+        out degree sequence contains the out degrees of nodes.
+    nkk  :  dictionary of dictionary of integers
+        directed joint degree dictionary. for nodes of out degree k (first
+        level of dict) and nodes of in degree l (second level of dict)
+        describes the number of edges.
+
+    Returns
+    -------
+    boolean
+        returns true if given input is realizable, else returns false.
+
+    Notes
+    -----
+    Here is the list of conditions that the inputs (in/out degree sequences,
+    nkk) need to satisfy for simple directed graph realizability:
+
+    - Condition 0: in_degrees and out_degrees have the same length
+    - Condition 1: nkk[k][l]  is integer for all k,l
+    - Condition 2: sum(nkk[k])/k = number of nodes with partition id k, is an
+                   integer and matching degree sequence
+    - Condition 3: number of edges and non-chords between k and l cannot exceed
+                   maximum possible number of edges
+
+
+    References
+    ----------
+    [1] B. Tillman, A. Markopoulou, C. T. Butts & M. Gjoka,
+        "Construction of Directed 2K Graphs". In Proc. of KDD 2017.
+    """
+    V = {}  # number of nodes with in/out degree.
+    forbidden = {}
+    if len(in_degrees) != len(out_degrees):
+        return False
+
+    for idx in range(len(in_degrees)):
+        i = in_degrees[idx]
+        o = out_degrees[idx]
+        V[(i, 0)] = V.get((i, 0), 0) + 1
+        V[(o, 1)] = V.get((o, 1), 0) + 1
+
+        forbidden[(o, i)] = forbidden.get((o, i), 0) + 1
+
+    S = {}  # number of edges going from in/out degree nodes.
+    for k in nkk:
+        for l in nkk[k]:
+            val = nkk[k][l]
+            if not float(val).is_integer():  # condition 1
+                return False
+
+            if val > 0:
+                S[(k, 1)] = S.get((k, 1), 0) + val
+                S[(l, 0)] = S.get((l, 0), 0) + val
+                # condition 3
+                if val + forbidden.get((k, l), 0) > V[(k, 1)] * V[(l, 0)]:
+                    return False
+
+    return all(S[s] / s[0] == V[s] for s in S)
+
+
+def _directed_neighbor_switch(
+    G, w, unsat, h_node_residual_out, chords, h_partition_in, partition
+):
+    """Releases one free stub for node w, while preserving joint degree in G.
+
+    Parameters
+    ----------
+    G : networkx directed graph
+        graph within which the edge swap will take place.
+    w : integer
+        node id for which we need to perform a neighbor switch.
+    unsat: set of integers
+        set of node ids that have the same degree as w and are unsaturated.
+    h_node_residual_out: dict of integers
+        for a given node, keeps track of the remaining stubs to be added.
+    chords: set of tuples
+        keeps track of available positions to add edges.
+    h_partition_in: dict of integers
+        for a given node, keeps track of its partition id (in degree).
+    partition: integer
+        partition id to check if chords have to be updated.
+
+    Notes
+    -----
+    First, it selects node w_prime that (1) has the same degree as w and
+    (2) is unsaturated. Then, it selects node v, a neighbor of w, that is
+    not connected to w_prime and does an edge swap i.e. removes (w,v) and
+    adds (w_prime,v). If neighbor switch is not possible for w using
+    w_prime and v, then return w_prime; in [1] it's proven that
+    such unsaturated nodes can be used.
+
+    References
+    ----------
+    [1] B. Tillman, A. Markopoulou, C. T. Butts & M. Gjoka,
+        "Construction of Directed 2K Graphs". In Proc. of KDD 2017.
+    """
+    w_prime = unsat.pop()
+    unsat.add(w_prime)
+    # select node t, a neighbor of w, that is not connected to w_prime
+    w_neighbs = list(G.successors(w))
+    # slightly faster declaring this variable
+    w_prime_neighbs = list(G.successors(w_prime))
+
+    for v in w_neighbs:
+        if (v not in w_prime_neighbs) and w_prime != v:
+            # removes (w,v), add (w_prime,v)  and update data structures
+            G.remove_edge(w, v)
+            G.add_edge(w_prime, v)
+
+            if h_partition_in[v] == partition:
+                chords.add((w, v))
+                chords.discard((w_prime, v))
+
+            h_node_residual_out[w] += 1
+            h_node_residual_out[w_prime] -= 1
+            if h_node_residual_out[w_prime] == 0:
+                unsat.remove(w_prime)
+            return None
+
+    # If neighbor switch didn't work, use unsaturated node
+    return w_prime
+
+
+def _directed_neighbor_switch_rev(
+    G, w, unsat, h_node_residual_in, chords, h_partition_out, partition
+):
+    """The reverse of directed_neighbor_switch.
+
+    Parameters
+    ----------
+    G : networkx directed graph
+        graph within which the edge swap will take place.
+    w : integer
+        node id for which we need to perform a neighbor switch.
+    unsat: set of integers
+        set of node ids that have the same degree as w and are unsaturated.
+    h_node_residual_in: dict of integers
+        for a given node, keeps track of the remaining stubs to be added.
+    chords: set of tuples
+        keeps track of available positions to add edges.
+    h_partition_out: dict of integers
+        for a given node, keeps track of its partition id (out degree).
+    partition: integer
+        partition id to check if chords have to be updated.
+
+    Notes
+    -----
+    Same operation as directed_neighbor_switch except it handles this operation
+    for incoming edges instead of outgoing.
+    """
+    w_prime = unsat.pop()
+    unsat.add(w_prime)
+    # slightly faster declaring these as variables.
+    w_neighbs = list(G.predecessors(w))
+    w_prime_neighbs = list(G.predecessors(w_prime))
+    # select node v, a neighbor of w, that is not connected to w_prime.
+    for v in w_neighbs:
+        if (v not in w_prime_neighbs) and w_prime != v:
+            # removes (v,w), add (v,w_prime) and update data structures.
+            G.remove_edge(v, w)
+            G.add_edge(v, w_prime)
+            if h_partition_out[v] == partition:
+                chords.add((v, w))
+                chords.discard((v, w_prime))
+
+            h_node_residual_in[w] += 1
+            h_node_residual_in[w_prime] -= 1
+            if h_node_residual_in[w_prime] == 0:
+                unsat.remove(w_prime)
+            return None
+
+    # If neighbor switch didn't work, use the unsaturated node.
+    return w_prime
+
+
+@py_random_state(3)
+@nx._dispatchable(graphs=None, returns_graph=True)
+def directed_joint_degree_graph(in_degrees, out_degrees, nkk, seed=None):
+    """Generates a random simple directed graph with the joint degree.
+
+    Parameters
+    ----------
+    degree_seq :  list of tuples (of size 3)
+        degree sequence contains tuples of nodes with node id, in degree and
+        out degree.
+    nkk  :  dictionary of dictionary of integers
+        directed joint degree dictionary, for nodes of out degree k (first
+        level of dict) and nodes of in degree l (second level of dict)
+        describes the number of edges.
+    seed : hashable object, optional
+        Seed for random number generator.
+
+    Returns
+    -------
+    G : Graph
+        A directed graph with the specified inputs.
+
+    Raises
+    ------
+    NetworkXError
+        If degree_seq and nkk are not realizable as a simple directed graph.
+
+
+    Notes
+    -----
+    Similarly to the undirected version:
+    In each iteration of the "while loop" the algorithm picks two disconnected
+    nodes v and w, of degree k and l correspondingly,  for which nkk[k][l] has
+    not reached its target yet i.e. (for given k,l): n_edges_add < nkk[k][l].
+    It then adds edge (v,w) and always increases the number of edges in graph G
+    by one.
+
+    The intelligence of the algorithm lies in the fact that  it is always
+    possible to add an edge between disconnected nodes v and w, for which
+    nkk[degree(v)][degree(w)] has not reached its target, even if one or both
+    nodes do not have free stubs. If either node v or w does not have a free
+    stub, we perform a "neighbor switch", an edge rewiring move that releases a
+    free stub while keeping nkk the same.
+
+    The difference for the directed version lies in the fact that neighbor
+    switches might not be able to rewire, but in these cases unsaturated nodes
+    can be reassigned to use instead, see [1] for detailed description and
+    proofs.
+
+    The algorithm continues for E (number of edges in the graph) iterations of
+    the "while loop", at which point all entries of the given nkk[k][l] have
+    reached their target values and the construction is complete.
+
+    References
+    ----------
+    [1] B. Tillman, A. Markopoulou, C. T. Butts & M. Gjoka,
+        "Construction of Directed 2K Graphs". In Proc. of KDD 2017.
+
+    Examples
+    --------
+    >>> in_degrees = [0, 1, 1, 2]
+    >>> out_degrees = [1, 1, 1, 1]
+    >>> nkk = {1: {1: 2, 2: 2}}
+    >>> G = nx.directed_joint_degree_graph(in_degrees, out_degrees, nkk)
+    >>>
+    """
+    if not is_valid_directed_joint_degree(in_degrees, out_degrees, nkk):
+        msg = "Input is not realizable as a simple graph"
+        raise nx.NetworkXError(msg)
+
+    # start with an empty directed graph.
+    G = nx.DiGraph()
+
+    # for a given group, keep the list of all node ids.
+    h_degree_nodelist_in = {}
+    h_degree_nodelist_out = {}
+    # for a given group, keep the list of all unsaturated node ids.
+    h_degree_nodelist_in_unsat = {}
+    h_degree_nodelist_out_unsat = {}
+    # for a given node, keep track of the remaining stubs to be added.
+    h_node_residual_out = {}
+    h_node_residual_in = {}
+    # for a given node, keep track of the partition id.
+    h_partition_out = {}
+    h_partition_in = {}
+    # keep track of non-chords between pairs of partition ids.
+    non_chords = {}
+
+    # populate data structures
+    for idx, i in enumerate(in_degrees):
+        idx = int(idx)
+        if i > 0:
+            h_degree_nodelist_in.setdefault(i, [])
+            h_degree_nodelist_in_unsat.setdefault(i, set())
+            h_degree_nodelist_in[i].append(idx)
+            h_degree_nodelist_in_unsat[i].add(idx)
+            h_node_residual_in[idx] = i
+            h_partition_in[idx] = i
+
+    for idx, o in enumerate(out_degrees):
+        o = out_degrees[idx]
+        non_chords[(o, in_degrees[idx])] = non_chords.get((o, in_degrees[idx]), 0) + 1
+        idx = int(idx)
+        if o > 0:
+            h_degree_nodelist_out.setdefault(o, [])
+            h_degree_nodelist_out_unsat.setdefault(o, set())
+            h_degree_nodelist_out[o].append(idx)
+            h_degree_nodelist_out_unsat[o].add(idx)
+            h_node_residual_out[idx] = o
+            h_partition_out[idx] = o
+
+        G.add_node(idx)
+
+    nk_in = {}
+    nk_out = {}
+    for p in h_degree_nodelist_in:
+        nk_in[p] = len(h_degree_nodelist_in[p])
+    for p in h_degree_nodelist_out:
+        nk_out[p] = len(h_degree_nodelist_out[p])
+
+    # iterate over every degree pair (k,l) and add the number of edges given
+    # for each pair.
+    for k in nkk:
+        for l in nkk[k]:
+            n_edges_add = nkk[k][l]
+
+            if n_edges_add > 0:
+                # chords contains a random set of potential edges.
+                chords = set()
+
+                k_len = nk_out[k]
+                l_len = nk_in[l]
+                chords_sample = seed.sample(
+                    range(k_len * l_len), n_edges_add + non_chords.get((k, l), 0)
+                )
+
+                num = 0
+                while len(chords) < n_edges_add:
+                    i = h_degree_nodelist_out[k][chords_sample[num] % k_len]
+                    j = h_degree_nodelist_in[l][chords_sample[num] // k_len]
+                    num += 1
+                    if i != j:
+                        chords.add((i, j))
+
+                # k_unsat and l_unsat consist of nodes of in/out degree k and l
+                # that are unsaturated i.e. those nodes that have at least one
+                # available stub
+                k_unsat = h_degree_nodelist_out_unsat[k]
+                l_unsat = h_degree_nodelist_in_unsat[l]
+
+                while n_edges_add > 0:
+                    v, w = chords.pop()
+                    chords.add((v, w))
+
+                    # if node v has no free stubs then do neighbor switch.
+                    if h_node_residual_out[v] == 0:
+                        _v = _directed_neighbor_switch(
+                            G,
+                            v,
+                            k_unsat,
+                            h_node_residual_out,
+                            chords,
+                            h_partition_in,
+                            l,
+                        )
+                        if _v is not None:
+                            v = _v
+
+                    # if node w has no free stubs then do neighbor switch.
+                    if h_node_residual_in[w] == 0:
+                        _w = _directed_neighbor_switch_rev(
+                            G,
+                            w,
+                            l_unsat,
+                            h_node_residual_in,
+                            chords,
+                            h_partition_out,
+                            k,
+                        )
+                        if _w is not None:
+                            w = _w
+
+                    # add edge (v,w) and update data structures.
+                    G.add_edge(v, w)
+                    h_node_residual_out[v] -= 1
+                    h_node_residual_in[w] -= 1
+                    n_edges_add -= 1
+                    chords.discard((v, w))
+
+                    if h_node_residual_out[v] == 0:
+                        k_unsat.discard(v)
+                    if h_node_residual_in[w] == 0:
+                        l_unsat.discard(w)
+    return G

minigpt2/lib/python3.10/site-packages/networkx/generators/line.py

@@ -0,0 +1,500 @@
+"""Functions for generating line graphs."""
+
+from collections import defaultdict
+from functools import partial
+from itertools import combinations
+
+import networkx as nx
+from networkx.utils import arbitrary_element
+from networkx.utils.decorators import not_implemented_for
+
+__all__ = ["line_graph", "inverse_line_graph"]
+
+
+@nx._dispatchable(returns_graph=True)
+def line_graph(G, create_using=None):
+    r"""Returns the line graph of the graph or digraph `G`.
+
+    The line graph of a graph `G` has a node for each edge in `G` and an
+    edge joining those nodes if the two edges in `G` share a common node. For
+    directed graphs, nodes are adjacent exactly when the edges they represent
+    form a directed path of length two.
+
+    The nodes of the line graph are 2-tuples of nodes in the original graph (or
+    3-tuples for multigraphs, with the key of the edge as the third element).
+
+    For information about self-loops and more discussion, see the **Notes**
+    section below.
+
+    Parameters
+    ----------
+    G : graph
+        A NetworkX Graph, DiGraph, MultiGraph, or MultiDigraph.
+    create_using : NetworkX graph constructor, optional (default=nx.Graph)
+       Graph type to create. If graph instance, then cleared before populated.
+
+    Returns
+    -------
+    L : graph
+        The line graph of G.
+
+    Examples
+    --------
+    >>> G = nx.star_graph(3)
+    >>> L = nx.line_graph(G)
+    >>> print(sorted(map(sorted, L.edges())))  # makes a 3-clique, K3
+    [[(0, 1), (0, 2)], [(0, 1), (0, 3)], [(0, 2), (0, 3)]]
+
+    Edge attributes from `G` are not copied over as node attributes in `L`, but
+    attributes can be copied manually:
+
+    >>> G = nx.path_graph(4)
+    >>> G.add_edges_from((u, v, {"tot": u + v}) for u, v in G.edges)
+    >>> G.edges(data=True)
+    EdgeDataView([(0, 1, {'tot': 1}), (1, 2, {'tot': 3}), (2, 3, {'tot': 5})])
+    >>> H = nx.line_graph(G)
+    >>> H.add_nodes_from((node, G.edges[node]) for node in H)
+    >>> H.nodes(data=True)
+    NodeDataView({(0, 1): {'tot': 1}, (2, 3): {'tot': 5}, (1, 2): {'tot': 3}})
+
+    Notes
+    -----
+    Graph, node, and edge data are not propagated to the new graph. For
+    undirected graphs, the nodes in G must be sortable, otherwise the
+    constructed line graph may not be correct.
+
+    *Self-loops in undirected graphs*
+
+    For an undirected graph `G` without multiple edges, each edge can be
+    written as a set `\{u, v\}`.  Its line graph `L` has the edges of `G` as
+    its nodes. If `x` and `y` are two nodes in `L`, then `\{x, y\}` is an edge
+    in `L` if and only if the intersection of `x` and `y` is nonempty. Thus,
+    the set of all edges is determined by the set of all pairwise intersections
+    of edges in `G`.
+
+    Trivially, every edge in G would have a nonzero intersection with itself,
+    and so every node in `L` should have a self-loop. This is not so
+    interesting, and the original context of line graphs was with simple
+    graphs, which had no self-loops or multiple edges. The line graph was also
+    meant to be a simple graph and thus, self-loops in `L` are not part of the
+    standard definition of a line graph. In a pairwise intersection matrix,
+    this is analogous to excluding the diagonal entries from the line graph
+    definition.
+
+    Self-loops and multiple edges in `G` add nodes to `L` in a natural way, and
+    do not require any fundamental changes to the definition. It might be
+    argued that the self-loops we excluded before should now be included.
+    However, the self-loops are still "trivial" in some sense and thus, are
+    usually excluded.
+
+    *Self-loops in directed graphs*
+
+    For a directed graph `G` without multiple edges, each edge can be written
+    as a tuple `(u, v)`. Its line graph `L` has the edges of `G` as its
+    nodes. If `x` and `y` are two nodes in `L`, then `(x, y)` is an edge in `L`
+    if and only if the tail of `x` matches the head of `y`, for example, if `x
+    = (a, b)` and `y = (b, c)` for some vertices `a`, `b`, and `c` in `G`.
+
+    Due to the directed nature of the edges, it is no longer the case that
+    every edge in `G` should have a self-loop in `L`. Now, the only time
+    self-loops arise is if a node in `G` itself has a self-loop.  So such
+    self-loops are no longer "trivial" but instead, represent essential
+    features of the topology of `G`. For this reason, the historical
+    development of line digraphs is such that self-loops are included. When the
+    graph `G` has multiple edges, once again only superficial changes are
+    required to the definition.
+
+    References
+    ----------
+    * Harary, Frank, and Norman, Robert Z., "Some properties of line digraphs",
+      Rend. Circ. Mat. Palermo, II. Ser. 9 (1960), 161--168.
+    * Hemminger, R. L.; Beineke, L. W. (1978), "Line graphs and line digraphs",
+      in Beineke, L. W.; Wilson, R. J., Selected Topics in Graph Theory,
+      Academic Press Inc., pp. 271--305.
+
+    """
+    if G.is_directed():
+        L = _lg_directed(G, create_using=create_using)
+    else:
+        L = _lg_undirected(G, selfloops=False, create_using=create_using)
+    return L
+
+
+def _lg_directed(G, create_using=None):
+    """Returns the line graph L of the (multi)digraph G.
+
+    Edges in G appear as nodes in L, represented as tuples of the form (u,v)
+    or (u,v,key) if G is a multidigraph. A node in L corresponding to the edge
+    (u,v) is connected to every node corresponding to an edge (v,w).
+
+    Parameters
+    ----------
+    G : digraph
+        A directed graph or directed multigraph.
+    create_using : NetworkX graph constructor, optional
+       Graph type to create. If graph instance, then cleared before populated.
+       Default is to use the same graph class as `G`.
+
+    """
+    L = nx.empty_graph(0, create_using, default=G.__class__)
+
+    # Create a graph specific edge function.
+    get_edges = partial(G.edges, keys=True) if G.is_multigraph() else G.edges
+
+    for from_node in get_edges():
+        # from_node is: (u,v) or (u,v,key)
+        L.add_node(from_node)
+        for to_node in get_edges(from_node[1]):
+            L.add_edge(from_node, to_node)
+
+    return L
+
+
+def _lg_undirected(G, selfloops=False, create_using=None):
+    """Returns the line graph L of the (multi)graph G.
+
+    Edges in G appear as nodes in L, represented as sorted tuples of the form
+    (u,v), or (u,v,key) if G is a multigraph. A node in L corresponding to
+    the edge {u,v} is connected to every node corresponding to an edge that
+    involves u or v.
+
+    Parameters
+    ----------
+    G : graph
+        An undirected graph or multigraph.
+    selfloops : bool
+        If `True`, then self-loops are included in the line graph. If `False`,
+        they are excluded.
+    create_using : NetworkX graph constructor, optional (default=nx.Graph)
+       Graph type to create. If graph instance, then cleared before populated.
+
+    Notes
+    -----
+    The standard algorithm for line graphs of undirected graphs does not
+    produce self-loops.
+
+    """
+    L = nx.empty_graph(0, create_using, default=G.__class__)
+
+    # Graph specific functions for edges.
+    get_edges = partial(G.edges, keys=True) if G.is_multigraph() else G.edges
+
+    # Determine if we include self-loops or not.
+    shift = 0 if selfloops else 1
+
+    # Introduce numbering of nodes
+    node_index = {n: i for i, n in enumerate(G)}
+
+    # Lift canonical representation of nodes to edges in line graph
+    edge_key_function = lambda edge: (node_index[edge[0]], node_index[edge[1]])
+
+    edges = set()
+    for u in G:
+        # Label nodes as a sorted tuple of nodes in original graph.
+        # Decide on representation of {u, v} as (u, v) or (v, u) depending on node_index.
+        # -> This ensures a canonical representation and avoids comparing values of different types.
+        nodes = [tuple(sorted(x[:2], key=node_index.get)) + x[2:] for x in get_edges(u)]
+
+        if len(nodes) == 1:
+            # Then the edge will be an isolated node in L.
+            L.add_node(nodes[0])
+
+        # Add a clique of `nodes` to graph. To prevent double adding edges,
+        # especially important for multigraphs, we store the edges in
+        # canonical form in a set.
+        for i, a in enumerate(nodes):
+            edges.update(
+                [
+                    tuple(sorted((a, b), key=edge_key_function))
+                    for b in nodes[i + shift :]
+                ]
+            )
+
+    L.add_edges_from(edges)
+    return L
+
+
+@not_implemented_for("directed")
+@not_implemented_for("multigraph")
+@nx._dispatchable(returns_graph=True)
+def inverse_line_graph(G):
+    """Returns the inverse line graph of graph G.
+
+    If H is a graph, and G is the line graph of H, such that G = L(H).
+    Then H is the inverse line graph of G.
+
+    Not all graphs are line graphs and these do not have an inverse line graph.
+    In these cases this function raises a NetworkXError.
+
+    Parameters
+    ----------
+    G : graph
+        A NetworkX Graph
+
+    Returns
+    -------
+    H : graph
+        The inverse line graph of G.
+
+    Raises
+    ------
+    NetworkXNotImplemented
+        If G is directed or a multigraph
+
+    NetworkXError
+        If G is not a line graph
+
+    Notes
+    -----
+    This is an implementation of the Roussopoulos algorithm[1]_.
+
+    If G consists of multiple components, then the algorithm doesn't work.
+    You should invert every component separately:
+
+    >>> K5 = nx.complete_graph(5)
+    >>> P4 = nx.Graph([("a", "b"), ("b", "c"), ("c", "d")])
+    >>> G = nx.union(K5, P4)
+    >>> root_graphs = []
+    >>> for comp in nx.connected_components(G):
+    ...     root_graphs.append(nx.inverse_line_graph(G.subgraph(comp)))
+    >>> len(root_graphs)
+    2
+
+    References
+    ----------
+    .. [1] Roussopoulos, N.D. , "A max {m, n} algorithm for determining the graph H from
+       its line graph G", Information Processing Letters 2, (1973), 108--112, ISSN 0020-0190,
+       `DOI link <https://doi.org/10.1016/0020-0190(73)90029-X>`_
+
+    """
+    if G.number_of_nodes() == 0:
+        return nx.empty_graph(1)
+    elif G.number_of_nodes() == 1:
+        v = arbitrary_element(G)
+        a = (v, 0)
+        b = (v, 1)
+        H = nx.Graph([(a, b)])
+        return H
+    elif G.number_of_nodes() > 1 and G.number_of_edges() == 0:
+        msg = (
+            "inverse_line_graph() doesn't work on an edgeless graph. "
+            "Please use this function on each component separately."
+        )
+        raise nx.NetworkXError(msg)
+
+    if nx.number_of_selfloops(G) != 0:
+        msg = (
+            "A line graph as generated by NetworkX has no selfloops, so G has no "
+            "inverse line graph. Please remove the selfloops from G and try again."
+        )
+        raise nx.NetworkXError(msg)
+
+    starting_cell = _select_starting_cell(G)
+    P = _find_partition(G, starting_cell)
+    # count how many times each vertex appears in the partition set
+    P_count = {u: 0 for u in G.nodes}
+    for p in P:
+        for u in p:
+            P_count[u] += 1
+
+    if max(P_count.values()) > 2:
+        msg = "G is not a line graph (vertex found in more than two partition cells)"
+        raise nx.NetworkXError(msg)
+    W = tuple((u,) for u in P_count if P_count[u] == 1)
+    H = nx.Graph()
+    H.add_nodes_from(P)
+    H.add_nodes_from(W)
+    for a, b in combinations(H.nodes, 2):
+        if any(a_bit in b for a_bit in a):
+            H.add_edge(a, b)
+    return H
+
+
+def _triangles(G, e):
+    """Return list of all triangles containing edge e"""
+    u, v = e
+    if u not in G:
+        raise nx.NetworkXError(f"Vertex {u} not in graph")
+    if v not in G[u]:
+        raise nx.NetworkXError(f"Edge ({u}, {v}) not in graph")
+    triangle_list = []
+    for x in G[u]:
+        if x in G[v]:
+            triangle_list.append((u, v, x))
+    return triangle_list
+
+
+def _odd_triangle(G, T):
+    """Test whether T is an odd triangle in G
+
+    Parameters
+    ----------
+    G : NetworkX Graph
+    T : 3-tuple of vertices forming triangle in G
+
+    Returns
+    -------
+    True is T is an odd triangle
+    False otherwise
+
+    Raises
+    ------
+    NetworkXError
+        T is not a triangle in G
+
+    Notes
+    -----
+    An odd triangle is one in which there exists another vertex in G which is
+    adjacent to either exactly one or exactly all three of the vertices in the
+    triangle.
+
+    """
+    for u in T:
+        if u not in G.nodes():
+            raise nx.NetworkXError(f"Vertex {u} not in graph")
+    for e in list(combinations(T, 2)):
+        if e[0] not in G[e[1]]:
+            raise nx.NetworkXError(f"Edge ({e[0]}, {e[1]}) not in graph")
+
+    T_nbrs = defaultdict(int)
+    for t in T:
+        for v in G[t]:
+            if v not in T:
+                T_nbrs[v] += 1
+    return any(T_nbrs[v] in [1, 3] for v in T_nbrs)
+
+
+def _find_partition(G, starting_cell):
+    """Find a partition of the vertices of G into cells of complete graphs
+
+    Parameters
+    ----------
+    G : NetworkX Graph
+    starting_cell : tuple of vertices in G which form a cell
+
+    Returns
+    -------
+    List of tuples of vertices of G
+
+    Raises
+    ------
+    NetworkXError
+        If a cell is not a complete subgraph then G is not a line graph
+    """
+    G_partition = G.copy()
+    P = [starting_cell]  # partition set
+    G_partition.remove_edges_from(list(combinations(starting_cell, 2)))
+    # keep list of partitioned nodes which might have an edge in G_partition
+    partitioned_vertices = list(starting_cell)
+    while G_partition.number_of_edges() > 0:
+        # there are still edges left and so more cells to be made
+        u = partitioned_vertices.pop()
+        deg_u = len(G_partition[u])
+        if deg_u != 0:
+            # if u still has edges then we need to find its other cell
+            # this other cell must be a complete subgraph or else G is
+            # not a line graph
+            new_cell = [u] + list(G_partition[u])
+            for u in new_cell:
+                for v in new_cell:
+                    if (u != v) and (v not in G_partition[u]):
+                        msg = (
+                            "G is not a line graph "
+                            "(partition cell not a complete subgraph)"
+                        )
+                        raise nx.NetworkXError(msg)
+            P.append(tuple(new_cell))
+            G_partition.remove_edges_from(list(combinations(new_cell, 2)))
+            partitioned_vertices += new_cell
+    return P
+
+
+def _select_starting_cell(G, starting_edge=None):
+    """Select a cell to initiate _find_partition
+
+    Parameters
+    ----------
+    G : NetworkX Graph
+    starting_edge: an edge to build the starting cell from
+
+    Returns
+    -------
+    Tuple of vertices in G
+
+    Raises
+    ------
+    NetworkXError
+        If it is determined that G is not a line graph
+
+    Notes
+    -----
+    If starting edge not specified then pick an arbitrary edge - doesn't
+    matter which. However, this function may call itself requiring a
+    specific starting edge. Note that the r, s notation for counting
+    triangles is the same as in the Roussopoulos paper cited above.
+    """
+    if starting_edge is None:
+        e = arbitrary_element(G.edges())
+    else:
+        e = starting_edge
+        if e[0] not in G.nodes():
+            raise nx.NetworkXError(f"Vertex {e[0]} not in graph")
+        if e[1] not in G[e[0]]:
+            msg = f"starting_edge ({e[0]}, {e[1]}) is not in the Graph"
+            raise nx.NetworkXError(msg)
+    e_triangles = _triangles(G, e)
+    r = len(e_triangles)
+    if r == 0:
+        # there are no triangles containing e, so the starting cell is just e
+        starting_cell = e
+    elif r == 1:
+        # there is exactly one triangle, T, containing e. If other 2 edges
+        # of T belong only to this triangle then T is starting cell
+        T = e_triangles[0]
+        a, b, c = T
+        # ab was original edge so check the other 2 edges
+        ac_edges = len(_triangles(G, (a, c)))
+        bc_edges = len(_triangles(G, (b, c)))
+        if ac_edges == 1:
+            if bc_edges == 1:
+                starting_cell = T
+            else:
+                return _select_starting_cell(G, starting_edge=(b, c))
+        else:
+            return _select_starting_cell(G, starting_edge=(a, c))
+    else:
+        # r >= 2 so we need to count the number of odd triangles, s
+        s = 0
+        odd_triangles = []
+        for T in e_triangles:
+            if _odd_triangle(G, T):
+                s += 1
+                odd_triangles.append(T)
+        if r == 2 and s == 0:
+            # in this case either triangle works, so just use T
+            starting_cell = T
+        elif r - 1 <= s <= r:
+            # check if odd triangles containing e form complete subgraph
+            triangle_nodes = set()
+            for T in odd_triangles:
+                for x in T:
+                    triangle_nodes.add(x)
+
+            for u in triangle_nodes:
+                for v in triangle_nodes:
+                    if u != v and (v not in G[u]):
+                        msg = (
+                            "G is not a line graph (odd triangles "
+                            "do not form complete subgraph)"
+                        )
+                        raise nx.NetworkXError(msg)
+            # otherwise then we can use this as the starting cell
+            starting_cell = tuple(triangle_nodes)
+
+        else:
+            msg = (
+                "G is not a line graph (incorrect number of "
+                "odd triangles around starting edge)"
+            )
+            raise nx.NetworkXError(msg)
+    return starting_cell

minigpt2/lib/python3.10/site-packages/networkx/generators/random_graphs.py

@@ -0,0 +1,1400 @@
+"""
+Generators for random graphs.
+
+"""
+
+import itertools
+import math
+from collections import defaultdict
+
+import networkx as nx
+from networkx.utils import py_random_state
+
+from ..utils.misc import check_create_using
+from .classic import complete_graph, empty_graph, path_graph, star_graph
+from .degree_seq import degree_sequence_tree
+
+__all__ = [
+    "fast_gnp_random_graph",
+    "gnp_random_graph",
+    "dense_gnm_random_graph",
+    "gnm_random_graph",
+    "erdos_renyi_graph",
+    "binomial_graph",
+    "newman_watts_strogatz_graph",
+    "watts_strogatz_graph",
+    "connected_watts_strogatz_graph",
+    "random_regular_graph",
+    "barabasi_albert_graph",
+    "dual_barabasi_albert_graph",
+    "extended_barabasi_albert_graph",
+    "powerlaw_cluster_graph",
+    "random_lobster",
+    "random_shell_graph",
+    "random_powerlaw_tree",
+    "random_powerlaw_tree_sequence",
+    "random_kernel_graph",
+]
+
+
+@py_random_state(2)
+@nx._dispatchable(graphs=None, returns_graph=True)
+def fast_gnp_random_graph(n, p, seed=None, directed=False, *, create_using=None):
+    """Returns a $G_{n,p}$ random graph, also known as an Erdős-Rényi graph or
+    a binomial graph.
+
+    Parameters
+    ----------
+    n : int
+        The number of nodes.
+    p : float
+        Probability for edge creation.
+    seed : integer, random_state, or None (default)
+        Indicator of random number generation state.
+        See :ref:`Randomness<randomness>`.
+    directed : bool, optional (default=False)
+        If True, this function returns a directed graph.
+    create_using : Graph constructor, optional (default=nx.Graph or nx.DiGraph)
+        Graph type to create. If graph instance, then cleared before populated.
+        Multigraph types are not supported and raise a ``NetworkXError``.
+        By default NetworkX Graph or DiGraph are used depending on `directed`.
+
+    Notes
+    -----
+    The $G_{n,p}$ graph algorithm chooses each of the $[n (n - 1)] / 2$
+    (undirected) or $n (n - 1)$ (directed) possible edges with probability $p$.
+
+    This algorithm [1]_ runs in $O(n + m)$ time, where `m` is the expected number of
+    edges, which equals $p n (n - 1) / 2$. This should be faster than
+    :func:`gnp_random_graph` when $p$ is small and the expected number of edges
+    is small (that is, the graph is sparse).
+
+    See Also
+    --------
+    gnp_random_graph
+
+    References
+    ----------
+    .. [1] Vladimir Batagelj and Ulrik Brandes,
+       "Efficient generation of large random networks",
+       Phys. Rev. E, 71, 036113, 2005.
+    """
+    default = nx.DiGraph if directed else nx.Graph
+    create_using = check_create_using(
+        create_using, directed=directed, multigraph=False, default=default
+    )
+    if p <= 0 or p >= 1:
+        return nx.gnp_random_graph(
+            n, p, seed=seed, directed=directed, create_using=create_using
+        )
+
+    G = empty_graph(n, create_using=create_using)
+
+    lp = math.log(1.0 - p)
+
+    if directed:
+        v = 1
+        w = -1
+        while v < n:
+            lr = math.log(1.0 - seed.random())
+            w = w + 1 + int(lr / lp)
+            while w >= v and v < n:
+                w = w - v
+                v = v + 1
+            if v < n:
+                G.add_edge(w, v)
+
+    # Nodes in graph are from 0,n-1 (start with v as the second node index).
+    v = 1
+    w = -1
+    while v < n:
+        lr = math.log(1.0 - seed.random())
+        w = w + 1 + int(lr / lp)
+        while w >= v and v < n:
+            w = w - v
+            v = v + 1
+        if v < n:
+            G.add_edge(v, w)
+    return G
+
+
+@py_random_state(2)
+@nx._dispatchable(graphs=None, returns_graph=True)
+def gnp_random_graph(n, p, seed=None, directed=False, *, create_using=None):
+    """Returns a $G_{n,p}$ random graph, also known as an Erdős-Rényi graph
+    or a binomial graph.
+
+    The $G_{n,p}$ model chooses each of the possible edges with probability $p$.
+
+    Parameters
+    ----------
+    n : int
+        The number of nodes.
+    p : float
+        Probability for edge creation.
+    seed : integer, random_state, or None (default)
+        Indicator of random number generation state.
+        See :ref:`Randomness<randomness>`.
+    directed : bool, optional (default=False)
+        If True, this function returns a directed graph.
+    create_using : Graph constructor, optional (default=nx.Graph or nx.DiGraph)
+        Graph type to create. If graph instance, then cleared before populated.
+        Multigraph types are not supported and raise a ``NetworkXError``.
+        By default NetworkX Graph or DiGraph are used depending on `directed`.
+
+    See Also
+    --------
+    fast_gnp_random_graph
+
+    Notes
+    -----
+    This algorithm [2]_ runs in $O(n^2)$ time.  For sparse graphs (that is, for
+    small values of $p$), :func:`fast_gnp_random_graph` is a faster algorithm.
+
+    :func:`binomial_graph` and :func:`erdos_renyi_graph` are
+    aliases for :func:`gnp_random_graph`.
+
+    >>> nx.binomial_graph is nx.gnp_random_graph
+    True
+    >>> nx.erdos_renyi_graph is nx.gnp_random_graph
+    True
+
+    References
+    ----------
+    .. [1] P. Erdős and A. Rényi, On Random Graphs, Publ. Math. 6, 290 (1959).
+    .. [2] E. N. Gilbert, Random Graphs, Ann. Math. Stat., 30, 1141 (1959).
+    """
+    default = nx.DiGraph if directed else nx.Graph
+    create_using = check_create_using(
+        create_using, directed=directed, multigraph=False, default=default
+    )
+    if p >= 1:
+        return complete_graph(n, create_using=create_using)
+
+    G = nx.empty_graph(n, create_using=create_using)
+    if p <= 0:
+        return G
+
+    edgetool = itertools.permutations if directed else itertools.combinations
+    for e in edgetool(range(n), 2):
+        if seed.random() < p:
+            G.add_edge(*e)
+    return G
+
+
+# add some aliases to common names
+binomial_graph = gnp_random_graph
+erdos_renyi_graph = gnp_random_graph
+
+
+@py_random_state(2)
+@nx._dispatchable(graphs=None, returns_graph=True)
+def dense_gnm_random_graph(n, m, seed=None, *, create_using=None):
+    """Returns a $G_{n,m}$ random graph.
+
+    In the $G_{n,m}$ model, a graph is chosen uniformly at random from the set
+    of all graphs with $n$ nodes and $m$ edges.
+
+    This algorithm should be faster than :func:`gnm_random_graph` for dense
+    graphs.
+
+    Parameters
+    ----------
+    n : int
+        The number of nodes.
+    m : int
+        The number of edges.
+    seed : integer, random_state, or None (default)
+        Indicator of random number generation state.
+        See :ref:`Randomness<randomness>`.
+    create_using : Graph constructor, optional (default=nx.Graph)
+        Graph type to create. If graph instance, then cleared before populated.
+        Multigraph and directed types are not supported and raise a ``NetworkXError``.
+
+    See Also
+    --------
+    gnm_random_graph
+
+    Notes
+    -----
+    Algorithm by Keith M. Briggs Mar 31, 2006.
+    Inspired by Knuth's Algorithm S (Selection sampling technique),
+    in section 3.4.2 of [1]_.
+
+    References
+    ----------
+    .. [1] Donald E. Knuth, The Art of Computer Programming,
+        Volume 2/Seminumerical algorithms, Third Edition, Addison-Wesley, 1997.
+    """
+    create_using = check_create_using(create_using, directed=False, multigraph=False)
+    mmax = n * (n - 1) // 2
+    if m >= mmax:
+        return complete_graph(n, create_using)
+    G = empty_graph(n, create_using)
+
+    if n == 1:
+        return G
+
+    u = 0
+    v = 1
+    t = 0
+    k = 0
+    while True:
+        if seed.randrange(mmax - t) < m - k:
+            G.add_edge(u, v)
+            k += 1
+            if k == m:
+                return G
+        t += 1
+        v += 1
+        if v == n:  # go to next row of adjacency matrix
+            u += 1
+            v = u + 1
+
+
+@py_random_state(2)
+@nx._dispatchable(graphs=None, returns_graph=True)
+def gnm_random_graph(n, m, seed=None, directed=False, *, create_using=None):
+    """Returns a $G_{n,m}$ random graph.
+
+    In the $G_{n,m}$ model, a graph is chosen uniformly at random from the set
+    of all graphs with $n$ nodes and $m$ edges.
+
+    This algorithm should be faster than :func:`dense_gnm_random_graph` for
+    sparse graphs.
+
+    Parameters
+    ----------
+    n : int
+        The number of nodes.
+    m : int
+        The number of edges.
+    seed : integer, random_state, or None (default)
+        Indicator of random number generation state.
+        See :ref:`Randomness<randomness>`.
+    directed : bool, optional (default=False)
+        If True return a directed graph
+    create_using : Graph constructor, optional (default=nx.Graph or nx.DiGraph)
+        Graph type to create. If graph instance, then cleared before populated.
+        Multigraph types are not supported and raise a ``NetworkXError``.
+        By default NetworkX Graph or DiGraph are used depending on `directed`.
+
+    See also
+    --------
+    dense_gnm_random_graph
+
+    """
+    default = nx.DiGraph if directed else nx.Graph
+    create_using = check_create_using(
+        create_using, directed=directed, multigraph=False, default=default
+    )
+    if n == 1:
+        return nx.empty_graph(n, create_using=create_using)
+    max_edges = n * (n - 1) if directed else n * (n - 1) / 2.0
+    if m >= max_edges:
+        return complete_graph(n, create_using=create_using)
+
+    G = nx.empty_graph(n, create_using=create_using)
+    nlist = list(G)
+    edge_count = 0
+    while edge_count < m:
+        # generate random edge,u,v
+        u = seed.choice(nlist)
+        v = seed.choice(nlist)
+        if u == v or G.has_edge(u, v):
+            continue
+        else:
+            G.add_edge(u, v)
+            edge_count = edge_count + 1
+    return G
+
+
+@py_random_state(3)
+@nx._dispatchable(graphs=None, returns_graph=True)
+def newman_watts_strogatz_graph(n, k, p, seed=None, *, create_using=None):
+    """Returns a Newman–Watts–Strogatz small-world graph.
+
+    Parameters
+    ----------
+    n : int
+        The number of nodes.
+    k : int
+        Each node is joined with its `k` nearest neighbors in a ring
+        topology.
+    p : float
+        The probability of adding a new edge for each edge.
+    seed : integer, random_state, or None (default)
+        Indicator of random number generation state.
+        See :ref:`Randomness<randomness>`.
+    create_using : Graph constructor, optional (default=nx.Graph)
+        Graph type to create. If graph instance, then cleared before populated.
+        Multigraph and directed types are not supported and raise a ``NetworkXError``.
+
+    Notes
+    -----
+    First create a ring over $n$ nodes [1]_.  Then each node in the ring is
+    connected with its $k$ nearest neighbors (or $k - 1$ neighbors if $k$
+    is odd).  Then shortcuts are created by adding new edges as follows: for
+    each edge $(u, v)$ in the underlying "$n$-ring with $k$ nearest
+    neighbors" with probability $p$ add a new edge $(u, w)$ with
+    randomly-chosen existing node $w$.  In contrast with
+    :func:`watts_strogatz_graph`, no edges are removed.
+
+    See Also
+    --------
+    watts_strogatz_graph
+
+    References
+    ----------
+    .. [1] M. E. J. Newman and D. J. Watts,
+       Renormalization group analysis of the small-world network model,
+       Physics Letters A, 263, 341, 1999.
+       https://doi.org/10.1016/S0375-9601(99)00757-4
+    """
+    create_using = check_create_using(create_using, directed=False, multigraph=False)
+    if k > n:
+        raise nx.NetworkXError("k>=n, choose smaller k or larger n")
+
+    # If k == n the graph return is a complete graph
+    if k == n:
+        return nx.complete_graph(n, create_using)
+
+    G = empty_graph(n, create_using)
+    nlist = list(G.nodes())
+    fromv = nlist
+    # connect the k/2 neighbors
+    for j in range(1, k // 2 + 1):
+        tov = fromv[j:] + fromv[0:j]  # the first j are now last
+        for i in range(len(fromv)):
+            G.add_edge(fromv[i], tov[i])
+    # for each edge u-v, with probability p, randomly select existing
+    # node w and add new edge u-w
+    e = list(G.edges())
+    for u, v in e:
+        if seed.random() < p:
+            w = seed.choice(nlist)
+            # no self-loops and reject if edge u-w exists
+            # is that the correct NWS model?
+            while w == u or G.has_edge(u, w):
+                w = seed.choice(nlist)
+                if G.degree(u) >= n - 1:
+                    break  # skip this rewiring
+            else:
+                G.add_edge(u, w)
+    return G
+
+
+@py_random_state(3)
+@nx._dispatchable(graphs=None, returns_graph=True)
+def watts_strogatz_graph(n, k, p, seed=None, *, create_using=None):
+    """Returns a Watts–Strogatz small-world graph.
+
+    Parameters
+    ----------
+    n : int
+        The number of nodes
+    k : int
+        Each node is joined with its `k` nearest neighbors in a ring
+        topology.
+    p : float
+        The probability of rewiring each edge
+    seed : integer, random_state, or None (default)
+        Indicator of random number generation state.
+        See :ref:`Randomness<randomness>`.
+    create_using : Graph constructor, optional (default=nx.Graph)
+        Graph type to create. If graph instance, then cleared before populated.
+        Multigraph and directed types are not supported and raise a ``NetworkXError``.
+
+    See Also
+    --------
+    newman_watts_strogatz_graph
+    connected_watts_strogatz_graph
+
+    Notes
+    -----
+    First create a ring over $n$ nodes [1]_.  Then each node in the ring is joined
+    to its $k$ nearest neighbors (or $k - 1$ neighbors if $k$ is odd).
+    Then shortcuts are created by replacing some edges as follows: for each
+    edge $(u, v)$ in the underlying "$n$-ring with $k$ nearest neighbors"
+    with probability $p$ replace it with a new edge $(u, w)$ with uniformly
+    random choice of existing node $w$.
+
+    In contrast with :func:`newman_watts_strogatz_graph`, the random rewiring
+    does not increase the number of edges. The rewired graph is not guaranteed
+    to be connected as in :func:`connected_watts_strogatz_graph`.
+
+    References
+    ----------
+    .. [1] Duncan J. Watts and Steven H. Strogatz,
+       Collective dynamics of small-world networks,
+       Nature, 393, pp. 440--442, 1998.
+    """
+    create_using = check_create_using(create_using, directed=False, multigraph=False)
+    if k > n:
+        raise nx.NetworkXError("k>n, choose smaller k or larger n")
+
+    # If k == n, the graph is complete not Watts-Strogatz
+    if k == n:
+        G = nx.complete_graph(n, create_using)
+        return G
+
+    G = nx.empty_graph(n, create_using=create_using)
+    nodes = list(range(n))  # nodes are labeled 0 to n-1
+    # connect each node to k/2 neighbors
+    for j in range(1, k // 2 + 1):
+        targets = nodes[j:] + nodes[0:j]  # first j nodes are now last in list
+        G.add_edges_from(zip(nodes, targets))
+    # rewire edges from each node
+    # loop over all nodes in order (label) and neighbors in order (distance)
+    # no self loops or multiple edges allowed
+    for j in range(1, k // 2 + 1):  # outer loop is neighbors
+        targets = nodes[j:] + nodes[0:j]  # first j nodes are now last in list
+        # inner loop in node order
+        for u, v in zip(nodes, targets):
+            if seed.random() < p:
+                w = seed.choice(nodes)
+                # Enforce no self-loops or multiple edges
+                while w == u or G.has_edge(u, w):
+                    w = seed.choice(nodes)
+                    if G.degree(u) >= n - 1:
+                        break  # skip this rewiring
+                else:
+                    G.remove_edge(u, v)
+                    G.add_edge(u, w)
+    return G
+
+
+@py_random_state(4)
+@nx._dispatchable(graphs=None, returns_graph=True)
+def connected_watts_strogatz_graph(n, k, p, tries=100, seed=None, *, create_using=None):
+    """Returns a connected Watts–Strogatz small-world graph.
+
+    Attempts to generate a connected graph by repeated generation of
+    Watts–Strogatz small-world graphs.  An exception is raised if the maximum
+    number of tries is exceeded.
+
+    Parameters
+    ----------
+    n : int
+        The number of nodes
+    k : int
+        Each node is joined with its `k` nearest neighbors in a ring
+        topology.
+    p : float
+        The probability of rewiring each edge
+    tries : int
+        Number of attempts to generate a connected graph.
+    seed : integer, random_state, or None (default)
+        Indicator of random number generation state.
+        See :ref:`Randomness<randomness>`.
+    create_using : Graph constructor, optional (default=nx.Graph)
+        Graph type to create. If graph instance, then cleared before populated.
+        Multigraph and directed types are not supported and raise a ``NetworkXError``.
+
+    Notes
+    -----
+    First create a ring over $n$ nodes [1]_.  Then each node in the ring is joined
+    to its $k$ nearest neighbors (or $k - 1$ neighbors if $k$ is odd).
+    Then shortcuts are created by replacing some edges as follows: for each
+    edge $(u, v)$ in the underlying "$n$-ring with $k$ nearest neighbors"
+    with probability $p$ replace it with a new edge $(u, w)$ with uniformly
+    random choice of existing node $w$.
+    The entire process is repeated until a connected graph results.
+
+    See Also
+    --------
+    newman_watts_strogatz_graph
+    watts_strogatz_graph
+
+    References
+    ----------
+    .. [1] Duncan J. Watts and Steven H. Strogatz,
+       Collective dynamics of small-world networks,
+       Nature, 393, pp. 440--442, 1998.
+    """
+    for i in range(tries):
+        # seed is an RNG so should change sequence each call
+        G = watts_strogatz_graph(n, k, p, seed, create_using=create_using)
+        if nx.is_connected(G):
+            return G
+    raise nx.NetworkXError("Maximum number of tries exceeded")
+
+
+@py_random_state(2)
+@nx._dispatchable(graphs=None, returns_graph=True)
+def random_regular_graph(d, n, seed=None, *, create_using=None):
+    r"""Returns a random $d$-regular graph on $n$ nodes.
+
+    A regular graph is a graph where each node has the same number of neighbors.
+
+    The resulting graph has no self-loops or parallel edges.
+
+    Parameters
+    ----------
+    d : int
+      The degree of each node.
+    n : integer
+      The number of nodes. The value of $n \times d$ must be even.
+    seed : integer, random_state, or None (default)
+        Indicator of random number generation state.
+        See :ref:`Randomness<randomness>`.
+    create_using : Graph constructor, optional (default=nx.Graph)
+        Graph type to create. If graph instance, then cleared before populated.
+        Multigraph and directed types are not supported and raise a ``NetworkXError``.
+
+    Notes
+    -----
+    The nodes are numbered from $0$ to $n - 1$.
+
+    Kim and Vu's paper [2]_ shows that this algorithm samples in an
+    asymptotically uniform way from the space of random graphs when
+    $d = O(n^{1 / 3 - \epsilon})$.
+
+    Raises
+    ------
+
+    NetworkXError
+        If $n \times d$ is odd or $d$ is greater than or equal to $n$.
+
+    References
+    ----------
+    .. [1] A. Steger and N. Wormald,
+       Generating random regular graphs quickly,
+       Probability and Computing 8 (1999), 377-396, 1999.
+       https://doi.org/10.1017/S0963548399003867
+
+    .. [2] Jeong Han Kim and Van H. Vu,
+       Generating random regular graphs,
+       Proceedings of the thirty-fifth ACM symposium on Theory of computing,
+       San Diego, CA, USA, pp 213--222, 2003.
+       http://portal.acm.org/citation.cfm?id=780542.780576
+    """
+    create_using = check_create_using(create_using, directed=False, multigraph=False)
+    if (n * d) % 2 != 0:
+        raise nx.NetworkXError("n * d must be even")
+
+    if not 0 <= d < n:
+        raise nx.NetworkXError("the 0 <= d < n inequality must be satisfied")
+
+    G = nx.empty_graph(n, create_using=create_using)
+
+    if d == 0:
+        return G
+
+    def _suitable(edges, potential_edges):
+        # Helper subroutine to check if there are suitable edges remaining
+        # If False, the generation of the graph has failed
+        if not potential_edges:
+            return True
+        for s1 in potential_edges:
+            for s2 in potential_edges:
+                # Two iterators on the same dictionary are guaranteed
+                # to visit it in the same order if there are no
+                # intervening modifications.
+                if s1 == s2:
+                    # Only need to consider s1-s2 pair one time
+                    break
+                if s1 > s2:
+                    s1, s2 = s2, s1
+                if (s1, s2) not in edges:
+                    return True
+        return False
+
+    def _try_creation():
+        # Attempt to create an edge set
+
+        edges = set()
+        stubs = list(range(n)) * d
+
+        while stubs:
+            potential_edges = defaultdict(lambda: 0)
+            seed.shuffle(stubs)
+            stubiter = iter(stubs)
+            for s1, s2 in zip(stubiter, stubiter):
+                if s1 > s2:
+                    s1, s2 = s2, s1
+                if s1 != s2 and ((s1, s2) not in edges):
+                    edges.add((s1, s2))
+                else:
+                    potential_edges[s1] += 1
+                    potential_edges[s2] += 1
+
+            if not _suitable(edges, potential_edges):
+                return None  # failed to find suitable edge set
+
+            stubs = [
+                node
+                for node, potential in potential_edges.items()
+                for _ in range(potential)
+            ]
+        return edges
+
+    # Even though a suitable edge set exists,
+    # the generation of such a set is not guaranteed.
+    # Try repeatedly to find one.
+    edges = _try_creation()
+    while edges is None:
+        edges = _try_creation()
+    G.add_edges_from(edges)
+
+    return G
+
+
+def _random_subset(seq, m, rng):
+    """Return m unique elements from seq.
+
+    This differs from random.sample which can return repeated
+    elements if seq holds repeated elements.
+
+    Note: rng is a random.Random or numpy.random.RandomState instance.
+    """
+    targets = set()
+    while len(targets) < m:
+        x = rng.choice(seq)
+        targets.add(x)
+    return targets
+
+
+@py_random_state(2)
+@nx._dispatchable(graphs=None, returns_graph=True)
+def barabasi_albert_graph(n, m, seed=None, initial_graph=None, *, create_using=None):
+    """Returns a random graph using Barabási–Albert preferential attachment
+
+    A graph of $n$ nodes is grown by attaching new nodes each with $m$
+    edges that are preferentially attached to existing nodes with high degree.
+
+    Parameters
+    ----------
+    n : int
+        Number of nodes
+    m : int
+        Number of edges to attach from a new node to existing nodes
+    seed : integer, random_state, or None (default)
+        Indicator of random number generation state.
+        See :ref:`Randomness<randomness>`.
+    initial_graph : Graph or None (default)
+        Initial network for Barabási–Albert algorithm.
+        It should be a connected graph for most use cases.
+        A copy of `initial_graph` is used.
+        If None, starts from a star graph on (m+1) nodes.
+    create_using : Graph constructor, optional (default=nx.Graph)
+        Graph type to create. If graph instance, then cleared before populated.
+        Multigraph and directed types are not supported and raise a ``NetworkXError``.
+
+    Returns
+    -------
+    G : Graph
+
+    Raises
+    ------
+    NetworkXError
+        If `m` does not satisfy ``1 <= m < n``, or
+        the initial graph number of nodes m0 does not satisfy ``m <= m0 <= n``.
+
+    References
+    ----------
+    .. [1] A. L. Barabási and R. Albert "Emergence of scaling in
+       random networks", Science 286, pp 509-512, 1999.
+    """
+    create_using = check_create_using(create_using, directed=False, multigraph=False)
+    if m < 1 or m >= n:
+        raise nx.NetworkXError(
+            f"Barabási–Albert network must have m >= 1 and m < n, m = {m}, n = {n}"
+        )
+
+    if initial_graph is None:
+        # Default initial graph : star graph on (m + 1) nodes
+        G = star_graph(m, create_using)
+    else:
+        if len(initial_graph) < m or len(initial_graph) > n:
+            raise nx.NetworkXError(
+                f"Barabási–Albert initial graph needs between m={m} and n={n} nodes"
+            )
+        G = initial_graph.copy()
+
+    # List of existing nodes, with nodes repeated once for each adjacent edge
+    repeated_nodes = [n for n, d in G.degree() for _ in range(d)]
+    # Start adding the other n - m0 nodes.
+    source = len(G)
+    while source < n:
+        # Now choose m unique nodes from the existing nodes
+        # Pick uniformly from repeated_nodes (preferential attachment)
+        targets = _random_subset(repeated_nodes, m, seed)
+        # Add edges to m nodes from the source.
+        G.add_edges_from(zip([source] * m, targets))
+        # Add one node to the list for each new edge just created.
+        repeated_nodes.extend(targets)
+        # And the new node "source" has m edges to add to the list.
+        repeated_nodes.extend([source] * m)
+
+        source += 1
+    return G
+
+
+@py_random_state(4)
+@nx._dispatchable(graphs=None, returns_graph=True)
+def dual_barabasi_albert_graph(
+    n, m1, m2, p, seed=None, initial_graph=None, *, create_using=None
+):
+    """Returns a random graph using dual Barabási–Albert preferential attachment
+
+    A graph of $n$ nodes is grown by attaching new nodes each with either $m_1$
+    edges (with probability $p$) or $m_2$ edges (with probability $1-p$) that
+    are preferentially attached to existing nodes with high degree.
+
+    Parameters
+    ----------
+    n : int
+        Number of nodes
+    m1 : int
+        Number of edges to link each new node to existing nodes with probability $p$
+    m2 : int
+        Number of edges to link each new node to existing nodes with probability $1-p$
+    p : float
+        The probability of attaching $m_1$ edges (as opposed to $m_2$ edges)
+    seed : integer, random_state, or None (default)
+        Indicator of random number generation state.
+        See :ref:`Randomness<randomness>`.
+    initial_graph : Graph or None (default)
+        Initial network for Barabási–Albert algorithm.
+        A copy of `initial_graph` is used.
+        It should be connected for most use cases.
+        If None, starts from an star graph on max(m1, m2) + 1 nodes.
+    create_using : Graph constructor, optional (default=nx.Graph)
+        Graph type to create. If graph instance, then cleared before populated.
+        Multigraph and directed types are not supported and raise a ``NetworkXError``.
+
+    Returns
+    -------
+    G : Graph
+
+    Raises
+    ------
+    NetworkXError
+        If `m1` and `m2` do not satisfy ``1 <= m1,m2 < n``, or
+        `p` does not satisfy ``0 <= p <= 1``, or
+        the initial graph number of nodes m0 does not satisfy m1, m2 <= m0 <= n.
+
+    References
+    ----------
+    .. [1] N. Moshiri "The dual-Barabasi-Albert model", arXiv:1810.10538.
+    """
+    create_using = check_create_using(create_using, directed=False, multigraph=False)
+    if m1 < 1 or m1 >= n:
+        raise nx.NetworkXError(
+            f"Dual Barabási–Albert must have m1 >= 1 and m1 < n, m1 = {m1}, n = {n}"
+        )
+    if m2 < 1 or m2 >= n:
+        raise nx.NetworkXError(
+            f"Dual Barabási–Albert must have m2 >= 1 and m2 < n, m2 = {m2}, n = {n}"
+        )
+    if p < 0 or p > 1:
+        raise nx.NetworkXError(
+            f"Dual Barabási–Albert network must have 0 <= p <= 1, p = {p}"
+        )
+
+    # For simplicity, if p == 0 or 1, just return BA
+    if p == 1:
+        return barabasi_albert_graph(n, m1, seed, create_using=create_using)
+    elif p == 0:
+        return barabasi_albert_graph(n, m2, seed, create_using=create_using)
+
+    if initial_graph is None:
+        # Default initial graph : star graph on max(m1, m2) nodes
+        G = star_graph(max(m1, m2), create_using)
+    else:
+        if len(initial_graph) < max(m1, m2) or len(initial_graph) > n:
+            raise nx.NetworkXError(
+                f"Barabási–Albert initial graph must have between "
+                f"max(m1, m2) = {max(m1, m2)} and n = {n} nodes"
+            )
+        G = initial_graph.copy()
+
+    # Target nodes for new edges
+    targets = list(G)
+    # List of existing nodes, with nodes repeated once for each adjacent edge
+    repeated_nodes = [n for n, d in G.degree() for _ in range(d)]
+    # Start adding the remaining nodes.
+    source = len(G)
+    while source < n:
+        # Pick which m to use (m1 or m2)
+        if seed.random() < p:
+            m = m1
+        else:
+            m = m2
+        # Now choose m unique nodes from the existing nodes
+        # Pick uniformly from repeated_nodes (preferential attachment)
+        targets = _random_subset(repeated_nodes, m, seed)
+        # Add edges to m nodes from the source.
+        G.add_edges_from(zip([source] * m, targets))
+        # Add one node to the list for each new edge just created.
+        repeated_nodes.extend(targets)
+        # And the new node "source" has m edges to add to the list.
+        repeated_nodes.extend([source] * m)
+
+        source += 1
+    return G
+
+
+@py_random_state(4)
+@nx._dispatchable(graphs=None, returns_graph=True)
+def extended_barabasi_albert_graph(n, m, p, q, seed=None, *, create_using=None):
+    """Returns an extended Barabási–Albert model graph.
+
+    An extended Barabási–Albert model graph is a random graph constructed
+    using preferential attachment. The extended model allows new edges,
+    rewired edges or new nodes. Based on the probabilities $p$ and $q$
+    with $p + q < 1$, the growing behavior of the graph is determined as:
+
+    1) With $p$ probability, $m$ new edges are added to the graph,
+    starting from randomly chosen existing nodes and attached preferentially at the
+    other end.
+
+    2) With $q$ probability, $m$ existing edges are rewired
+    by randomly choosing an edge and rewiring one end to a preferentially chosen node.
+
+    3) With $(1 - p - q)$ probability, $m$ new nodes are added to the graph
+    with edges attached preferentially.
+
+    When $p = q = 0$, the model behaves just like the Barabási–Alber model.
+
+    Parameters
+    ----------
+    n : int
+        Number of nodes
+    m : int
+        Number of edges with which a new node attaches to existing nodes
+    p : float
+        Probability value for adding an edge between existing nodes. p + q < 1
+    q : float
+        Probability value of rewiring of existing edges. p + q < 1
+    seed : integer, random_state, or None (default)
+        Indicator of random number generation state.
+        See :ref:`Randomness<randomness>`.
+    create_using : Graph constructor, optional (default=nx.Graph)
+        Graph type to create. If graph instance, then cleared before populated.
+        Multigraph and directed types are not supported and raise a ``NetworkXError``.
+
+    Returns
+    -------
+    G : Graph
+
+    Raises
+    ------
+    NetworkXError
+        If `m` does not satisfy ``1 <= m < n`` or ``1 >= p + q``
+
+    References
+    ----------
+    .. [1] Albert, R., & Barabási, A. L. (2000)
+       Topology of evolving networks: local events and universality
+       Physical review letters, 85(24), 5234.
+    """
+    create_using = check_create_using(create_using, directed=False, multigraph=False)
+    if m < 1 or m >= n:
+        msg = f"Extended Barabasi-Albert network needs m>=1 and m<n, m={m}, n={n}"
+        raise nx.NetworkXError(msg)
+    if p + q >= 1:
+        msg = f"Extended Barabasi-Albert network needs p + q <= 1, p={p}, q={q}"
+        raise nx.NetworkXError(msg)
+
+    # Add m initial nodes (m0 in barabasi-speak)
+    G = empty_graph(m, create_using)
+
+    # List of nodes to represent the preferential attachment random selection.
+    # At the creation of the graph, all nodes are added to the list
+    # so that even nodes that are not connected have a chance to get selected,
+    # for rewiring and adding of edges.
+    # With each new edge, nodes at the ends of the edge are added to the list.
+    attachment_preference = []
+    attachment_preference.extend(range(m))
+
+    # Start adding the other n-m nodes. The first node is m.
+    new_node = m
+    while new_node < n:
+        a_probability = seed.random()
+
+        # Total number of edges of a Clique of all the nodes
+        clique_degree = len(G) - 1
+        clique_size = (len(G) * clique_degree) / 2
+
+        # Adding m new edges, if there is room to add them
+        if a_probability < p and G.size() <= clique_size - m:
+            # Select the nodes where an edge can be added
+            eligible_nodes = [nd for nd, deg in G.degree() if deg < clique_degree]
+            for i in range(m):
+                # Choosing a random source node from eligible_nodes
+                src_node = seed.choice(eligible_nodes)
+
+                # Picking a possible node that is not 'src_node' or
+                # neighbor with 'src_node', with preferential attachment
+                prohibited_nodes = list(G[src_node])
+                prohibited_nodes.append(src_node)
+                # This will raise an exception if the sequence is empty
+                dest_node = seed.choice(
+                    [nd for nd in attachment_preference if nd not in prohibited_nodes]
+                )
+                # Adding the new edge
+                G.add_edge(src_node, dest_node)
+
+                # Appending both nodes to add to their preferential attachment
+                attachment_preference.append(src_node)
+                attachment_preference.append(dest_node)
+
+                # Adjusting the eligible nodes. Degree may be saturated.
+                if G.degree(src_node) == clique_degree:
+                    eligible_nodes.remove(src_node)
+                if G.degree(dest_node) == clique_degree and dest_node in eligible_nodes:
+                    eligible_nodes.remove(dest_node)
+
+        # Rewiring m edges, if there are enough edges
+        elif p <= a_probability < (p + q) and m <= G.size() < clique_size:
+            # Selecting nodes that have at least 1 edge but that are not
+            # fully connected to ALL other nodes (center of star).
+            # These nodes are the pivot nodes of the edges to rewire
+            eligible_nodes = [nd for nd, deg in G.degree() if 0 < deg < clique_degree]
+            for i in range(m):
+                # Choosing a random source node
+                node = seed.choice(eligible_nodes)
+
+                # The available nodes do have a neighbor at least.
+                nbr_nodes = list(G[node])
+
+                # Choosing the other end that will get detached
+                src_node = seed.choice(nbr_nodes)
+
+                # Picking a target node that is not 'node' or
+                # neighbor with 'node', with preferential attachment
+                nbr_nodes.append(node)
+                dest_node = seed.choice(
+                    [nd for nd in attachment_preference if nd not in nbr_nodes]
+                )
+                # Rewire
+                G.remove_edge(node, src_node)
+                G.add_edge(node, dest_node)
+
+                # Adjusting the preferential attachment list
+                attachment_preference.remove(src_node)
+                attachment_preference.append(dest_node)
+
+                # Adjusting the eligible nodes.
+                # nodes may be saturated or isolated.
+                if G.degree(src_node) == 0 and src_node in eligible_nodes:
+                    eligible_nodes.remove(src_node)
+                if dest_node in eligible_nodes:
+                    if G.degree(dest_node) == clique_degree:
+                        eligible_nodes.remove(dest_node)
+                else:
+                    if G.degree(dest_node) == 1:
+                        eligible_nodes.append(dest_node)
+
+        # Adding new node with m edges
+        else:
+            # Select the edges' nodes by preferential attachment
+            targets = _random_subset(attachment_preference, m, seed)
+            G.add_edges_from(zip([new_node] * m, targets))
+
+            # Add one node to the list for each new edge just created.
+            attachment_preference.extend(targets)
+            # The new node has m edges to it, plus itself: m + 1
+            attachment_preference.extend([new_node] * (m + 1))
+            new_node += 1
+    return G
+
+
+@py_random_state(3)
+@nx._dispatchable(graphs=None, returns_graph=True)
+def powerlaw_cluster_graph(n, m, p, seed=None, *, create_using=None):
+    """Holme and Kim algorithm for growing graphs with powerlaw
+    degree distribution and approximate average clustering.
+
+    Parameters
+    ----------
+    n : int
+        the number of nodes
+    m : int
+        the number of random edges to add for each new node
+    p : float,
+        Probability of adding a triangle after adding a random edge
+    seed : integer, random_state, or None (default)
+        Indicator of random number generation state.
+        See :ref:`Randomness<randomness>`.
+    create_using : Graph constructor, optional (default=nx.Graph)
+        Graph type to create. If graph instance, then cleared before populated.
+        Multigraph and directed types are not supported and raise a ``NetworkXError``.
+
+    Notes
+    -----
+    The average clustering has a hard time getting above a certain
+    cutoff that depends on `m`.  This cutoff is often quite low.  The
+    transitivity (fraction of triangles to possible triangles) seems to
+    decrease with network size.
+
+    It is essentially the Barabási–Albert (BA) growth model with an
+    extra step that each random edge is followed by a chance of
+    making an edge to one of its neighbors too (and thus a triangle).
+
+    This algorithm improves on BA in the sense that it enables a
+    higher average clustering to be attained if desired.
+
+    It seems possible to have a disconnected graph with this algorithm
+    since the initial `m` nodes may not be all linked to a new node
+    on the first iteration like the BA model.
+
+    Raises
+    ------
+    NetworkXError
+        If `m` does not satisfy ``1 <= m <= n`` or `p` does not
+        satisfy ``0 <= p <= 1``.
+
+    References
+    ----------
+    .. [1] P. Holme and B. J. Kim,
+       "Growing scale-free networks with tunable clustering",
+       Phys. Rev. E, 65, 026107, 2002.
+    """
+    create_using = check_create_using(create_using, directed=False, multigraph=False)
+    if m < 1 or n < m:
+        raise nx.NetworkXError(f"NetworkXError must have m>1 and m<n, m={m},n={n}")
+
+    if p > 1 or p < 0:
+        raise nx.NetworkXError(f"NetworkXError p must be in [0,1], p={p}")
+
+    G = empty_graph(m, create_using)  # add m initial nodes (m0 in barabasi-speak)
+    repeated_nodes = list(G)  # list of existing nodes to sample from
+    # with nodes repeated once for each adjacent edge
+    source = m  # next node is m
+    while source < n:  # Now add the other n-1 nodes
+        possible_targets = _random_subset(repeated_nodes, m, seed)
+        # do one preferential attachment for new node
+        target = possible_targets.pop()
+        G.add_edge(source, target)
+        repeated_nodes.append(target)  # add one node to list for each new link
+        count = 1
+        while count < m:  # add m-1 more new links
+            if seed.random() < p:  # clustering step: add triangle
+                neighborhood = [
+                    nbr
+                    for nbr in G.neighbors(target)
+                    if not G.has_edge(source, nbr) and nbr != source
+                ]
+                if neighborhood:  # if there is a neighbor without a link
+                    nbr = seed.choice(neighborhood)
+                    G.add_edge(source, nbr)  # add triangle
+                    repeated_nodes.append(nbr)
+                    count = count + 1
+                    continue  # go to top of while loop
+            # else do preferential attachment step if above fails
+            target = possible_targets.pop()
+            G.add_edge(source, target)
+            repeated_nodes.append(target)
+            count = count + 1
+
+        repeated_nodes.extend([source] * m)  # add source node to list m times
+        source += 1
+    return G
+
+
+@py_random_state(3)
+@nx._dispatchable(graphs=None, returns_graph=True)
+def random_lobster(n, p1, p2, seed=None, *, create_using=None):
+    """Returns a random lobster graph.
+
+    A lobster is a tree that reduces to a caterpillar when pruning all
+    leaf nodes. A caterpillar is a tree that reduces to a path graph
+    when pruning all leaf nodes; setting `p2` to zero produces a caterpillar.
+
+    This implementation iterates on the probabilities `p1` and `p2` to add
+    edges at levels 1 and 2, respectively. Graphs are therefore constructed
+    iteratively with uniform randomness at each level rather than being selected
+    uniformly at random from the set of all possible lobsters.
+
+    Parameters
+    ----------
+    n : int
+        The expected number of nodes in the backbone
+    p1 : float
+        Probability of adding an edge to the backbone
+    p2 : float
+        Probability of adding an edge one level beyond backbone
+    seed : integer, random_state, or None (default)
+        Indicator of random number generation state.
+        See :ref:`Randomness<randomness>`.
+    create_using : Graph constructor, optional (default=nx.Grap)
+        Graph type to create. If graph instance, then cleared before populated.
+        Multigraph and directed types are not supported and raise a ``NetworkXError``.
+
+    Raises
+    ------
+    NetworkXError
+        If `p1` or `p2` parameters are >= 1 because the while loops would never finish.
+    """
+    create_using = check_create_using(create_using, directed=False, multigraph=False)
+    p1, p2 = abs(p1), abs(p2)
+    if any(p >= 1 for p in [p1, p2]):
+        raise nx.NetworkXError("Probability values for `p1` and `p2` must both be < 1.")
+
+    # a necessary ingredient in any self-respecting graph library
+    llen = int(2 * seed.random() * n + 0.5)
+    L = path_graph(llen, create_using)
+    # build caterpillar: add edges to path graph with probability p1
+    current_node = llen - 1
+    for n in range(llen):
+        while seed.random() < p1:  # add fuzzy caterpillar parts
+            current_node += 1
+            L.add_edge(n, current_node)
+            cat_node = current_node
+            while seed.random() < p2:  # add crunchy lobster bits
+                current_node += 1
+                L.add_edge(cat_node, current_node)
+    return L  # voila, un lobster!
+
+
+@py_random_state(1)
+@nx._dispatchable(graphs=None, returns_graph=True)
+def random_shell_graph(constructor, seed=None, *, create_using=None):
+    """Returns a random shell graph for the constructor given.
+
+    Parameters
+    ----------
+    constructor : list of three-tuples
+        Represents the parameters for a shell, starting at the center
+        shell.  Each element of the list must be of the form `(n, m,
+        d)`, where `n` is the number of nodes in the shell, `m` is
+        the number of edges in the shell, and `d` is the ratio of
+        inter-shell (next) edges to intra-shell edges. If `d` is zero,
+        there will be no intra-shell edges, and if `d` is one there
+        will be all possible intra-shell edges.
+    seed : integer, random_state, or None (default)
+        Indicator of random number generation state.
+        See :ref:`Randomness<randomness>`.
+    create_using : Graph constructor, optional (default=nx.Graph)
+        Graph type to create. Graph instances are not supported.
+        Multigraph and directed types are not supported and raise a ``NetworkXError``.
+
+    Examples
+    --------
+    >>> constructor = [(10, 20, 0.8), (20, 40, 0.8)]
+    >>> G = nx.random_shell_graph(constructor)
+
+    """
+    create_using = check_create_using(create_using, directed=False, multigraph=False)
+    G = empty_graph(0, create_using)
+
+    glist = []
+    intra_edges = []
+    nnodes = 0
+    # create gnm graphs for each shell
+    for n, m, d in constructor:
+        inter_edges = int(m * d)
+        intra_edges.append(m - inter_edges)
+        g = nx.convert_node_labels_to_integers(
+            gnm_random_graph(n, inter_edges, seed=seed, create_using=G.__class__),
+            first_label=nnodes,
+        )
+        glist.append(g)
+        nnodes += n
+        G = nx.operators.union(G, g)
+
+    # connect the shells randomly
+    for gi in range(len(glist) - 1):
+        nlist1 = list(glist[gi])
+        nlist2 = list(glist[gi + 1])
+        total_edges = intra_edges[gi]
+        edge_count = 0
+        while edge_count < total_edges:
+            u = seed.choice(nlist1)
+            v = seed.choice(nlist2)
+            if u == v or G.has_edge(u, v):
+                continue
+            else:
+                G.add_edge(u, v)
+                edge_count = edge_count + 1
+    return G
+
+
+@py_random_state(2)
+@nx._dispatchable(graphs=None, returns_graph=True)
+def random_powerlaw_tree(n, gamma=3, seed=None, tries=100, *, create_using=None):
+    """Returns a tree with a power law degree distribution.
+
+    Parameters
+    ----------
+    n : int
+        The number of nodes.
+    gamma : float
+        Exponent of the power law.
+    seed : integer, random_state, or None (default)
+        Indicator of random number generation state.
+        See :ref:`Randomness<randomness>`.
+    tries : int
+        Number of attempts to adjust the sequence to make it a tree.
+    create_using : Graph constructor, optional (default=nx.Graph)
+        Graph type to create. If graph instance, then cleared before populated.
+        Multigraph and directed types are not supported and raise a ``NetworkXError``.
+
+    Raises
+    ------
+    NetworkXError
+        If no valid sequence is found within the maximum number of
+        attempts.
+
+    Notes
+    -----
+    A trial power law degree sequence is chosen and then elements are
+    swapped with new elements from a powerlaw distribution until the
+    sequence makes a tree (by checking, for example, that the number of
+    edges is one smaller than the number of nodes).
+
+    """
+    create_using = check_create_using(create_using, directed=False, multigraph=False)
+    # This call may raise a NetworkXError if the number of tries is succeeded.
+    seq = random_powerlaw_tree_sequence(n, gamma=gamma, seed=seed, tries=tries)
+    G = degree_sequence_tree(seq, create_using)
+    return G
+
+
+@py_random_state(2)
+@nx._dispatchable(graphs=None)
+def random_powerlaw_tree_sequence(n, gamma=3, seed=None, tries=100):
+    """Returns a degree sequence for a tree with a power law distribution.
+
+    Parameters
+    ----------
+    n : int,
+        The number of nodes.
+    gamma : float
+        Exponent of the power law.
+    seed : integer, random_state, or None (default)
+        Indicator of random number generation state.
+        See :ref:`Randomness<randomness>`.
+    tries : int
+        Number of attempts to adjust the sequence to make it a tree.
+
+    Raises
+    ------
+    NetworkXError
+        If no valid sequence is found within the maximum number of
+        attempts.
+
+    Notes
+    -----
+    A trial power law degree sequence is chosen and then elements are
+    swapped with new elements from a power law distribution until
+    the sequence makes a tree (by checking, for example, that the number of
+    edges is one smaller than the number of nodes).
+
+    """
+    # get trial sequence
+    z = nx.utils.powerlaw_sequence(n, exponent=gamma, seed=seed)
+    # round to integer values in the range [0,n]
+    zseq = [min(n, max(round(s), 0)) for s in z]
+
+    # another sequence to swap values from
+    z = nx.utils.powerlaw_sequence(tries, exponent=gamma, seed=seed)
+    # round to integer values in the range [0,n]
+    swap = [min(n, max(round(s), 0)) for s in z]
+
+    for deg in swap:
+        # If this degree sequence can be the degree sequence of a tree, return
+        # it. It can be a tree if the number of edges is one fewer than the
+        # number of nodes, or in other words, `n - sum(zseq) / 2 == 1`. We
+        # use an equivalent condition below that avoids floating point
+        # operations.
+        if 2 * n - sum(zseq) == 2:
+            return zseq
+        index = seed.randint(0, n - 1)
+        zseq[index] = swap.pop()
+
+    raise nx.NetworkXError(
+        f"Exceeded max ({tries}) attempts for a valid tree sequence."
+    )
+
+
+@py_random_state(3)
+@nx._dispatchable(graphs=None, returns_graph=True)
+def random_kernel_graph(
+    n, kernel_integral, kernel_root=None, seed=None, *, create_using=None
+):
+    r"""Returns an random graph based on the specified kernel.
+
+    The algorithm chooses each of the $[n(n-1)]/2$ possible edges with
+    probability specified by a kernel $\kappa(x,y)$ [1]_.  The kernel
+    $\kappa(x,y)$ must be a symmetric (in $x,y$), non-negative,
+    bounded function.
+
+    Parameters
+    ----------
+    n : int
+        The number of nodes
+    kernel_integral : function
+        Function that returns the definite integral of the kernel $\kappa(x,y)$,
+        $F(y,a,b) := \int_a^b \kappa(x,y)dx$
+    kernel_root: function (optional)
+        Function that returns the root $b$ of the equation $F(y,a,b) = r$.
+        If None, the root is found using :func:`scipy.optimize.brentq`
+        (this requires SciPy).
+    seed : integer, random_state, or None (default)
+        Indicator of random number generation state.
+        See :ref:`Randomness<randomness>`.
+    create_using : Graph constructor, optional (default=nx.Graph)
+        Graph type to create. If graph instance, then cleared before populated.
+        Multigraph and directed types are not supported and raise a ``NetworkXError``.
+
+    Notes
+    -----
+    The kernel is specified through its definite integral which must be
+    provided as one of the arguments. If the integral and root of the
+    kernel integral can be found in $O(1)$ time then this algorithm runs in
+    time $O(n+m)$ where m is the expected number of edges [2]_.
+
+    The nodes are set to integers from $0$ to $n-1$.
+
+    Examples
+    --------
+    Generate an Erdős–Rényi random graph $G(n,c/n)$, with kernel
+    $\kappa(x,y)=c$ where $c$ is the mean expected degree.
+
+    >>> def integral(u, w, z):
+    ...     return c * (z - w)
+    >>> def root(u, w, r):
+    ...     return r / c + w
+    >>> c = 1
+    >>> graph = nx.random_kernel_graph(1000, integral, root)
+
+    See Also
+    --------
+    gnp_random_graph
+    expected_degree_graph
+
+    References
+    ----------
+    .. [1] Bollobás, Béla,  Janson, S. and Riordan, O.
+       "The phase transition in inhomogeneous random graphs",
+       *Random Structures Algorithms*, 31, 3--122, 2007.
+
+    .. [2] Hagberg A, Lemons N (2015),
+       "Fast Generation of Sparse Random Kernel Graphs".
+       PLoS ONE 10(9): e0135177, 2015. doi:10.1371/journal.pone.0135177
+    """
+    create_using = check_create_using(create_using, directed=False, multigraph=False)
+    if kernel_root is None:
+        import scipy as sp
+
+        def kernel_root(y, a, r):
+            def my_function(b):
+                return kernel_integral(y, a, b) - r
+
+            return sp.optimize.brentq(my_function, a, 1)
+
+    graph = nx.empty_graph(create_using=create_using)
+    graph.add_nodes_from(range(n))
+    (i, j) = (1, 1)
+    while i < n:
+        r = -math.log(1 - seed.random())  # (1-seed.random()) in (0, 1]
+        if kernel_integral(i / n, j / n, 1) <= r:
+            i, j = i + 1, i + 1
+        else:
+            j = math.ceil(n * kernel_root(i / n, j / n, r))
+            graph.add_edge(i - 1, j - 1)
+    return graph

minigpt2/lib/python3.10/site-packages/networkx/generators/small.py

@@ -0,0 +1,993 @@
+"""
+Various small and named graphs, together with some compact generators.
+
+"""
+
+__all__ = [
+    "LCF_graph",
+    "bull_graph",
+    "chvatal_graph",
+    "cubical_graph",
+    "desargues_graph",
+    "diamond_graph",
+    "dodecahedral_graph",
+    "frucht_graph",
+    "heawood_graph",
+    "hoffman_singleton_graph",
+    "house_graph",
+    "house_x_graph",
+    "icosahedral_graph",
+    "krackhardt_kite_graph",
+    "moebius_kantor_graph",
+    "octahedral_graph",
+    "pappus_graph",
+    "petersen_graph",
+    "sedgewick_maze_graph",
+    "tetrahedral_graph",
+    "truncated_cube_graph",
+    "truncated_tetrahedron_graph",
+    "tutte_graph",
+]
+
+from functools import wraps
+
+import networkx as nx
+from networkx.exception import NetworkXError
+from networkx.generators.classic import (
+    complete_graph,
+    cycle_graph,
+    empty_graph,
+    path_graph,
+)
+
+
+def _raise_on_directed(func):
+    """
+    A decorator which inspects the `create_using` argument and raises a
+    NetworkX exception when `create_using` is a DiGraph (class or instance) for
+    graph generators that do not support directed outputs.
+    """
+
+    @wraps(func)
+    def wrapper(*args, **kwargs):
+        if kwargs.get("create_using") is not None:
+            G = nx.empty_graph(create_using=kwargs["create_using"])
+            if G.is_directed():
+                raise NetworkXError("Directed Graph not supported")
+        return func(*args, **kwargs)
+
+    return wrapper
+
+
+@nx._dispatchable(graphs=None, returns_graph=True)
+def LCF_graph(n, shift_list, repeats, create_using=None):
+    """
+    Return the cubic graph specified in LCF notation.
+
+    LCF (Lederberg-Coxeter-Fruchte) notation[1]_ is a compressed
+    notation used in the generation of various cubic Hamiltonian
+    graphs of high symmetry. See, for example, `dodecahedral_graph`,
+    `desargues_graph`, `heawood_graph` and `pappus_graph`.
+
+    Nodes are drawn from ``range(n)``. Each node ``n_i`` is connected with
+    node ``n_i + shift % n`` where ``shift`` is given by cycling through
+    the input `shift_list` `repeat` s times.
+
+    Parameters
+    ----------
+    n : int
+       The starting graph is the `n`-cycle with nodes ``0, ..., n-1``.
+       The null graph is returned if `n` < 1.
+
+    shift_list : list
+       A list of integer shifts mod `n`, ``[s1, s2, .., sk]``
+
+    repeats : int
+       Integer specifying the number of times that shifts in `shift_list`
+       are successively applied to each current node in the n-cycle
+       to generate an edge between ``n_current`` and ``n_current + shift mod n``.
+
+    Returns
+    -------
+    G : Graph
+       A graph instance created from the specified LCF notation.
+
+    Examples
+    --------
+    The utility graph $K_{3,3}$
+
+    >>> G = nx.LCF_graph(6, [3, -3], 3)
+    >>> G.edges()
+    EdgeView([(0, 1), (0, 5), (0, 3), (1, 2), (1, 4), (2, 3), (2, 5), (3, 4), (4, 5)])
+
+    The Heawood graph:
+
+    >>> G = nx.LCF_graph(14, [5, -5], 7)
+    >>> nx.is_isomorphic(G, nx.heawood_graph())
+    True
+
+    References
+    ----------
+    .. [1] https://en.wikipedia.org/wiki/LCF_notation
+
+    """
+    if n <= 0:
+        return empty_graph(0, create_using)
+
+    # start with the n-cycle
+    G = cycle_graph(n, create_using)
+    if G.is_directed():
+        raise NetworkXError("Directed Graph not supported")
+    G.name = "LCF_graph"
+    nodes = sorted(G)
+
+    n_extra_edges = repeats * len(shift_list)
+    # edges are added n_extra_edges times
+    # (not all of these need be new)
+    if n_extra_edges < 1:
+        return G
+
+    for i in range(n_extra_edges):
+        shift = shift_list[i % len(shift_list)]  # cycle through shift_list
+        v1 = nodes[i % n]  # cycle repeatedly through nodes
+        v2 = nodes[(i + shift) % n]
+        G.add_edge(v1, v2)
+    return G
+
+
+# -------------------------------------------------------------------------------
+#   Various small and named graphs
+# -------------------------------------------------------------------------------
+
+
+@_raise_on_directed
+@nx._dispatchable(graphs=None, returns_graph=True)
+def bull_graph(create_using=None):
+    """
+    Returns the Bull Graph
+
+    The Bull Graph has 5 nodes and 5 edges. It is a planar undirected
+    graph in the form of a triangle with two disjoint pendant edges [1]_
+    The name comes from the triangle and pendant edges representing
+    respectively the body and legs of a bull.
+
+    Parameters
+    ----------
+    create_using : NetworkX graph constructor, optional (default=nx.Graph)
+       Graph type to create. If graph instance, then cleared before populated.
+
+    Returns
+    -------
+    G : networkx Graph
+        A bull graph with 5 nodes
+
+    References
+    ----------
+    .. [1] https://en.wikipedia.org/wiki/Bull_graph.
+
+    """
+    G = nx.from_dict_of_lists(
+        {0: [1, 2], 1: [0, 2, 3], 2: [0, 1, 4], 3: [1], 4: [2]},
+        create_using=create_using,
+    )
+    G.name = "Bull Graph"
+    return G
+
+
+@_raise_on_directed
+@nx._dispatchable(graphs=None, returns_graph=True)
+def chvatal_graph(create_using=None):
+    """
+    Returns the Chvátal Graph
+
+    The Chvátal Graph is an undirected graph with 12 nodes and 24 edges [1]_.
+    It has 370 distinct (directed) Hamiltonian cycles, giving a unique generalized
+    LCF notation of order 4, two of order 6 , and 43 of order 1 [2]_.
+
+    Parameters
+    ----------
+    create_using : NetworkX graph constructor, optional (default=nx.Graph)
+       Graph type to create. If graph instance, then cleared before populated.
+
+    Returns
+    -------
+    G : networkx Graph
+        The Chvátal graph with 12 nodes and 24 edges
+
+    References
+    ----------
+    .. [1] https://en.wikipedia.org/wiki/Chv%C3%A1tal_graph
+    .. [2] https://mathworld.wolfram.com/ChvatalGraph.html
+
+    """
+    G = nx.from_dict_of_lists(
+        {
+            0: [1, 4, 6, 9],
+            1: [2, 5, 7],
+            2: [3, 6, 8],
+            3: [4, 7, 9],
+            4: [5, 8],
+            5: [10, 11],
+            6: [10, 11],
+            7: [8, 11],
+            8: [10],
+            9: [10, 11],
+        },
+        create_using=create_using,
+    )
+    G.name = "Chvatal Graph"
+    return G
+
+
+@_raise_on_directed
+@nx._dispatchable(graphs=None, returns_graph=True)
+def cubical_graph(create_using=None):
+    """
+    Returns the 3-regular Platonic Cubical Graph
+
+    The skeleton of the cube (the nodes and edges) form a graph, with 8
+    nodes, and 12 edges. It is a special case of the hypercube graph.
+    It is one of 5 Platonic graphs, each a skeleton of its
+    Platonic solid [1]_.
+    Such graphs arise in parallel processing in computers.
+
+    Parameters
+    ----------
+    create_using : NetworkX graph constructor, optional (default=nx.Graph)
+       Graph type to create. If graph instance, then cleared before populated.
+
+    Returns
+    -------
+    G : networkx Graph
+        A cubical graph with 8 nodes and 12 edges
+
+    References
+    ----------
+    .. [1] https://en.wikipedia.org/wiki/Cube#Cubical_graph
+
+    """
+    G = nx.from_dict_of_lists(
+        {
+            0: [1, 3, 4],
+            1: [0, 2, 7],
+            2: [1, 3, 6],
+            3: [0, 2, 5],
+            4: [0, 5, 7],
+            5: [3, 4, 6],
+            6: [2, 5, 7],
+            7: [1, 4, 6],
+        },
+        create_using=create_using,
+    )
+    G.name = "Platonic Cubical Graph"
+    return G
+
+
+@nx._dispatchable(graphs=None, returns_graph=True)
+def desargues_graph(create_using=None):
+    """
+    Returns the Desargues Graph
+
+    The Desargues Graph is a non-planar, distance-transitive cubic graph
+    with 20 nodes and 30 edges [1]_.
+    It is a symmetric graph. It can be represented in LCF notation
+    as [5,-5,9,-9]^5 [2]_.
+
+    Parameters
+    ----------
+    create_using : NetworkX graph constructor, optional (default=nx.Graph)
+       Graph type to create. If graph instance, then cleared before populated.
+
+    Returns
+    -------
+    G : networkx Graph
+        Desargues Graph with 20 nodes and 30 edges
+
+    References
+    ----------
+    .. [1] https://en.wikipedia.org/wiki/Desargues_graph
+    .. [2] https://mathworld.wolfram.com/DesarguesGraph.html
+    """
+    G = LCF_graph(20, [5, -5, 9, -9], 5, create_using)
+    G.name = "Desargues Graph"
+    return G
+
+
+@_raise_on_directed
+@nx._dispatchable(graphs=None, returns_graph=True)
+def diamond_graph(create_using=None):
+    """
+    Returns the Diamond graph
+
+    The Diamond Graph is  planar undirected graph with 4 nodes and 5 edges.
+    It is also sometimes known as the double triangle graph or kite graph [1]_.
+
+    Parameters
+    ----------
+    create_using : NetworkX graph constructor, optional (default=nx.Graph)
+       Graph type to create. If graph instance, then cleared before populated.
+
+    Returns
+    -------
+    G : networkx Graph
+        Diamond Graph with 4 nodes and 5 edges
+
+    References
+    ----------
+    .. [1] https://mathworld.wolfram.com/DiamondGraph.html
+    """
+    G = nx.from_dict_of_lists(
+        {0: [1, 2], 1: [0, 2, 3], 2: [0, 1, 3], 3: [1, 2]}, create_using=create_using
+    )
+    G.name = "Diamond Graph"
+    return G
+
+
+@nx._dispatchable(graphs=None, returns_graph=True)
+def dodecahedral_graph(create_using=None):
+    """
+    Returns the Platonic Dodecahedral graph.
+
+    The dodecahedral graph has 20 nodes and 30 edges. The skeleton of the
+    dodecahedron forms a graph. It is one of 5 Platonic graphs [1]_.
+    It can be described in LCF notation as:
+    ``[10, 7, 4, -4, -7, 10, -4, 7, -7, 4]^2`` [2]_.
+
+    Parameters
+    ----------
+    create_using : NetworkX graph constructor, optional (default=nx.Graph)
+       Graph type to create. If graph instance, then cleared before populated.
+
+    Returns
+    -------
+    G : networkx Graph
+        Dodecahedral Graph with 20 nodes and 30 edges
+
+    References
+    ----------
+    .. [1] https://en.wikipedia.org/wiki/Regular_dodecahedron#Dodecahedral_graph
+    .. [2] https://mathworld.wolfram.com/DodecahedralGraph.html
+
+    """
+    G = LCF_graph(20, [10, 7, 4, -4, -7, 10, -4, 7, -7, 4], 2, create_using)
+    G.name = "Dodecahedral Graph"
+    return G
+
+
+@nx._dispatchable(graphs=None, returns_graph=True)
+def frucht_graph(create_using=None):
+    """
+    Returns the Frucht Graph.
+
+    The Frucht Graph is the smallest cubical graph whose
+    automorphism group consists only of the identity element [1]_.
+    It has 12 nodes and 18 edges and no nontrivial symmetries.
+    It is planar and Hamiltonian [2]_.
+
+    Parameters
+    ----------
+    create_using : NetworkX graph constructor, optional (default=nx.Graph)
+       Graph type to create. If graph instance, then cleared before populated.
+
+    Returns
+    -------
+    G : networkx Graph
+        Frucht Graph with 12 nodes and 18 edges
+
+    References
+    ----------
+    .. [1] https://en.wikipedia.org/wiki/Frucht_graph
+    .. [2] https://mathworld.wolfram.com/FruchtGraph.html
+
+    """
+    G = cycle_graph(7, create_using)
+    G.add_edges_from(
+        [
+            [0, 7],
+            [1, 7],
+            [2, 8],
+            [3, 9],
+            [4, 9],
+            [5, 10],
+            [6, 10],
+            [7, 11],
+            [8, 11],
+            [8, 9],
+            [10, 11],
+        ]
+    )
+
+    G.name = "Frucht Graph"
+    return G
+
+
+@nx._dispatchable(graphs=None, returns_graph=True)
+def heawood_graph(create_using=None):
+    """
+    Returns the Heawood Graph, a (3,6) cage.
+
+    The Heawood Graph is an undirected graph with 14 nodes and 21 edges,
+    named after Percy John Heawood [1]_.
+    It is cubic symmetric, nonplanar, Hamiltonian, and can be represented
+    in LCF notation as ``[5,-5]^7`` [2]_.
+    It is the unique (3,6)-cage: the regular cubic graph of girth 6 with
+    minimal number of vertices [3]_.
+
+    Parameters
+    ----------
+    create_using : NetworkX graph constructor, optional (default=nx.Graph)
+       Graph type to create. If graph instance, then cleared before populated.
+
+    Returns
+    -------
+    G : networkx Graph
+        Heawood Graph with 14 nodes and 21 edges
+
+    References
+    ----------
+    .. [1] https://en.wikipedia.org/wiki/Heawood_graph
+    .. [2] https://mathworld.wolfram.com/HeawoodGraph.html
+    .. [3] https://www.win.tue.nl/~aeb/graphs/Heawood.html
+
+    """
+    G = LCF_graph(14, [5, -5], 7, create_using)
+    G.name = "Heawood Graph"
+    return G
+
+
+@nx._dispatchable(graphs=None, returns_graph=True)
+def hoffman_singleton_graph():
+    """
+    Returns the Hoffman-Singleton Graph.
+
+    The Hoffman–Singleton graph is a symmetrical undirected graph
+    with 50 nodes and 175 edges.
+    All indices lie in ``Z % 5``: that is, the integers mod 5 [1]_.
+    It is the only regular graph of vertex degree 7, diameter 2, and girth 5.
+    It is the unique (7,5)-cage graph and Moore graph, and contains many
+    copies of the Petersen graph [2]_.
+
+    Returns
+    -------
+    G : networkx Graph
+        Hoffman–Singleton Graph with 50 nodes and 175 edges
+
+    Notes
+    -----
+    Constructed from pentagon and pentagram as follows: Take five pentagons $P_h$
+    and five pentagrams $Q_i$ . Join vertex $j$ of $P_h$ to vertex $h·i+j$ of $Q_i$ [3]_.
+
+    References
+    ----------
+    .. [1] https://blogs.ams.org/visualinsight/2016/02/01/hoffman-singleton-graph/
+    .. [2] https://mathworld.wolfram.com/Hoffman-SingletonGraph.html
+    .. [3] https://en.wikipedia.org/wiki/Hoffman%E2%80%93Singleton_graph
+
+    """
+    G = nx.Graph()
+    for i in range(5):
+        for j in range(5):
+            G.add_edge(("pentagon", i, j), ("pentagon", i, (j - 1) % 5))
+            G.add_edge(("pentagon", i, j), ("pentagon", i, (j + 1) % 5))
+            G.add_edge(("pentagram", i, j), ("pentagram", i, (j - 2) % 5))
+            G.add_edge(("pentagram", i, j), ("pentagram", i, (j + 2) % 5))
+            for k in range(5):
+                G.add_edge(("pentagon", i, j), ("pentagram", k, (i * k + j) % 5))
+    G = nx.convert_node_labels_to_integers(G)
+    G.name = "Hoffman-Singleton Graph"
+    return G
+
+
+@_raise_on_directed
+@nx._dispatchable(graphs=None, returns_graph=True)
+def house_graph(create_using=None):
+    """
+    Returns the House graph (square with triangle on top)
+
+    The house graph is a simple undirected graph with
+    5 nodes and 6 edges [1]_.
+
+    Parameters
+    ----------
+    create_using : NetworkX graph constructor, optional (default=nx.Graph)
+       Graph type to create. If graph instance, then cleared before populated.
+
+    Returns
+    -------
+    G : networkx Graph
+        House graph in the form of a square with a triangle on top
+
+    References
+    ----------
+    .. [1] https://mathworld.wolfram.com/HouseGraph.html
+    """
+    G = nx.from_dict_of_lists(
+        {0: [1, 2], 1: [0, 3], 2: [0, 3, 4], 3: [1, 2, 4], 4: [2, 3]},
+        create_using=create_using,
+    )
+    G.name = "House Graph"
+    return G
+
+
+@_raise_on_directed
+@nx._dispatchable(graphs=None, returns_graph=True)
+def house_x_graph(create_using=None):
+    """
+    Returns the House graph with a cross inside the house square.
+
+    The House X-graph is the House graph plus the two edges connecting diagonally
+    opposite vertices of the square base. It is also one of the two graphs
+    obtained by removing two edges from the pentatope graph [1]_.
+
+    Parameters
+    ----------
+    create_using : NetworkX graph constructor, optional (default=nx.Graph)
+       Graph type to create. If graph instance, then cleared before populated.
+
+    Returns
+    -------
+    G : networkx Graph
+        House graph with diagonal vertices connected
+
+    References
+    ----------
+    .. [1] https://mathworld.wolfram.com/HouseGraph.html
+    """
+    G = house_graph(create_using)
+    G.add_edges_from([(0, 3), (1, 2)])
+    G.name = "House-with-X-inside Graph"
+    return G
+
+
+@_raise_on_directed
+@nx._dispatchable(graphs=None, returns_graph=True)
+def icosahedral_graph(create_using=None):
+    """
+    Returns the Platonic Icosahedral graph.
+
+    The icosahedral graph has 12 nodes and 30 edges. It is a Platonic graph
+    whose nodes have the connectivity of the icosahedron. It is undirected,
+    regular and Hamiltonian [1]_.
+
+    Parameters
+    ----------
+    create_using : NetworkX graph constructor, optional (default=nx.Graph)
+       Graph type to create. If graph instance, then cleared before populated.
+
+    Returns
+    -------
+    G : networkx Graph
+        Icosahedral graph with 12 nodes and 30 edges.
+
+    References
+    ----------
+    .. [1] https://mathworld.wolfram.com/IcosahedralGraph.html
+    """
+    G = nx.from_dict_of_lists(
+        {
+            0: [1, 5, 7, 8, 11],
+            1: [2, 5, 6, 8],
+            2: [3, 6, 8, 9],
+            3: [4, 6, 9, 10],
+            4: [5, 6, 10, 11],
+            5: [6, 11],
+            7: [8, 9, 10, 11],
+            8: [9],
+            9: [10],
+            10: [11],
+        },
+        create_using=create_using,
+    )
+    G.name = "Platonic Icosahedral Graph"
+    return G
+
+
+@_raise_on_directed
+@nx._dispatchable(graphs=None, returns_graph=True)
+def krackhardt_kite_graph(create_using=None):
+    """
+    Returns the Krackhardt Kite Social Network.
+
+    A 10 actor social network introduced by David Krackhardt
+    to illustrate different centrality measures [1]_.
+
+    Parameters
+    ----------
+    create_using : NetworkX graph constructor, optional (default=nx.Graph)
+       Graph type to create. If graph instance, then cleared before populated.
+
+    Returns
+    -------
+    G : networkx Graph
+        Krackhardt Kite graph with 10 nodes and 18 edges
+
+    Notes
+    -----
+    The traditional labeling is:
+    Andre=1, Beverley=2, Carol=3, Diane=4,
+    Ed=5, Fernando=6, Garth=7, Heather=8, Ike=9, Jane=10.
+
+    References
+    ----------
+    .. [1] Krackhardt, David. "Assessing the Political Landscape: Structure,
+       Cognition, and Power in Organizations". Administrative Science Quarterly.
+       35 (2): 342–369. doi:10.2307/2393394. JSTOR 2393394. June 1990.
+
+    """
+    G = nx.from_dict_of_lists(
+        {
+            0: [1, 2, 3, 5],
+            1: [0, 3, 4, 6],
+            2: [0, 3, 5],
+            3: [0, 1, 2, 4, 5, 6],
+            4: [1, 3, 6],
+            5: [0, 2, 3, 6, 7],
+            6: [1, 3, 4, 5, 7],
+            7: [5, 6, 8],
+            8: [7, 9],
+            9: [8],
+        },
+        create_using=create_using,
+    )
+    G.name = "Krackhardt Kite Social Network"
+    return G
+
+
+@nx._dispatchable(graphs=None, returns_graph=True)
+def moebius_kantor_graph(create_using=None):
+    """
+    Returns the Moebius-Kantor graph.
+
+    The Möbius-Kantor graph is the cubic symmetric graph on 16 nodes.
+    Its LCF notation is [5,-5]^8, and it is isomorphic to the generalized
+    Petersen graph [1]_.
+
+    Parameters
+    ----------
+    create_using : NetworkX graph constructor, optional (default=nx.Graph)
+       Graph type to create. If graph instance, then cleared before populated.
+
+    Returns
+    -------
+    G : networkx Graph
+        Moebius-Kantor graph
+
+    References
+    ----------
+    .. [1] https://en.wikipedia.org/wiki/M%C3%B6bius%E2%80%93Kantor_graph
+
+    """
+    G = LCF_graph(16, [5, -5], 8, create_using)
+    G.name = "Moebius-Kantor Graph"
+    return G
+
+
+@_raise_on_directed
+@nx._dispatchable(graphs=None, returns_graph=True)
+def octahedral_graph(create_using=None):
+    """
+    Returns the Platonic Octahedral graph.
+
+    The octahedral graph is the 6-node 12-edge Platonic graph having the
+    connectivity of the octahedron [1]_. If 6 couples go to a party,
+    and each person shakes hands with every person except his or her partner,
+    then this graph describes the set of handshakes that take place;
+    for this reason it is also called the cocktail party graph [2]_.
+
+    Parameters
+    ----------
+    create_using : NetworkX graph constructor, optional (default=nx.Graph)
+       Graph type to create. If graph instance, then cleared before populated.
+
+    Returns
+    -------
+    G : networkx Graph
+        Octahedral graph
+
+    References
+    ----------
+    .. [1] https://mathworld.wolfram.com/OctahedralGraph.html
+    .. [2] https://en.wikipedia.org/wiki/Tur%C3%A1n_graph#Special_cases
+
+    """
+    G = nx.from_dict_of_lists(
+        {0: [1, 2, 3, 4], 1: [2, 3, 5], 2: [4, 5], 3: [4, 5], 4: [5]},
+        create_using=create_using,
+    )
+    G.name = "Platonic Octahedral Graph"
+    return G
+
+
+@nx._dispatchable(graphs=None, returns_graph=True)
+def pappus_graph():
+    """
+    Returns the Pappus graph.
+
+    The Pappus graph is a cubic symmetric distance-regular graph with 18 nodes
+    and 27 edges. It is Hamiltonian and can be represented in LCF notation as
+    [5,7,-7,7,-7,-5]^3 [1]_.
+
+    Returns
+    -------
+    G : networkx Graph
+        Pappus graph
+
+    References
+    ----------
+    .. [1] https://en.wikipedia.org/wiki/Pappus_graph
+    """
+    G = LCF_graph(18, [5, 7, -7, 7, -7, -5], 3)
+    G.name = "Pappus Graph"
+    return G
+
+
+@_raise_on_directed
+@nx._dispatchable(graphs=None, returns_graph=True)
+def petersen_graph(create_using=None):
+    """
+    Returns the Petersen graph.
+
+    The Peterson graph is a cubic, undirected graph with 10 nodes and 15 edges [1]_.
+    Julius Petersen constructed the graph as the smallest counterexample
+    against the claim that a connected bridgeless cubic graph
+    has an edge colouring with three colours [2]_.
+
+    Parameters
+    ----------
+    create_using : NetworkX graph constructor, optional (default=nx.Graph)
+       Graph type to create. If graph instance, then cleared before populated.
+
+    Returns
+    -------
+    G : networkx Graph
+        Petersen graph
+
+    References
+    ----------
+    .. [1] https://en.wikipedia.org/wiki/Petersen_graph
+    .. [2] https://www.win.tue.nl/~aeb/drg/graphs/Petersen.html
+    """
+    G = nx.from_dict_of_lists(
+        {
+            0: [1, 4, 5],
+            1: [0, 2, 6],
+            2: [1, 3, 7],
+            3: [2, 4, 8],
+            4: [3, 0, 9],
+            5: [0, 7, 8],
+            6: [1, 8, 9],
+            7: [2, 5, 9],
+            8: [3, 5, 6],
+            9: [4, 6, 7],
+        },
+        create_using=create_using,
+    )
+    G.name = "Petersen Graph"
+    return G
+
+
+@nx._dispatchable(graphs=None, returns_graph=True)
+def sedgewick_maze_graph(create_using=None):
+    """
+    Return a small maze with a cycle.
+
+    This is the maze used in Sedgewick, 3rd Edition, Part 5, Graph
+    Algorithms, Chapter 18, e.g. Figure 18.2 and following [1]_.
+    Nodes are numbered 0,..,7
+
+    Parameters
+    ----------
+    create_using : NetworkX graph constructor, optional (default=nx.Graph)
+       Graph type to create. If graph instance, then cleared before populated.
+
+    Returns
+    -------
+    G : networkx Graph
+        Small maze with a cycle
+
+    References
+    ----------
+    .. [1] Figure 18.2, Chapter 18, Graph Algorithms (3rd Ed), Sedgewick
+    """
+    G = empty_graph(0, create_using)
+    G.add_nodes_from(range(8))
+    G.add_edges_from([[0, 2], [0, 7], [0, 5]])
+    G.add_edges_from([[1, 7], [2, 6]])
+    G.add_edges_from([[3, 4], [3, 5]])
+    G.add_edges_from([[4, 5], [4, 7], [4, 6]])
+    G.name = "Sedgewick Maze"
+    return G
+
+
+@nx._dispatchable(graphs=None, returns_graph=True)
+def tetrahedral_graph(create_using=None):
+    """
+    Returns the 3-regular Platonic Tetrahedral graph.
+
+    Tetrahedral graph has 4 nodes and 6 edges. It is a
+    special case of the complete graph, K4, and wheel graph, W4.
+    It is one of the 5 platonic graphs [1]_.
+
+    Parameters
+    ----------
+    create_using : NetworkX graph constructor, optional (default=nx.Graph)
+       Graph type to create. If graph instance, then cleared before populated.
+
+    Returns
+    -------
+    G : networkx Graph
+        Tetrahedral Graph
+
+    References
+    ----------
+    .. [1] https://en.wikipedia.org/wiki/Tetrahedron#Tetrahedral_graph
+
+    """
+    G = complete_graph(4, create_using)
+    G.name = "Platonic Tetrahedral Graph"
+    return G
+
+
+@_raise_on_directed
+@nx._dispatchable(graphs=None, returns_graph=True)
+def truncated_cube_graph(create_using=None):
+    """
+    Returns the skeleton of the truncated cube.
+
+    The truncated cube is an Archimedean solid with 14 regular
+    faces (6 octagonal and 8 triangular), 36 edges and 24 nodes [1]_.
+    The truncated cube is created by truncating (cutting off) the tips
+    of the cube one third of the way into each edge [2]_.
+
+    Parameters
+    ----------
+    create_using : NetworkX graph constructor, optional (default=nx.Graph)
+       Graph type to create. If graph instance, then cleared before populated.
+
+    Returns
+    -------
+    G : networkx Graph
+        Skeleton of the truncated cube
+
+    References
+    ----------
+    .. [1] https://en.wikipedia.org/wiki/Truncated_cube
+    .. [2] https://www.coolmath.com/reference/polyhedra-truncated-cube
+
+    """
+    G = nx.from_dict_of_lists(
+        {
+            0: [1, 2, 4],
+            1: [11, 14],
+            2: [3, 4],
+            3: [6, 8],
+            4: [5],
+            5: [16, 18],
+            6: [7, 8],
+            7: [10, 12],
+            8: [9],
+            9: [17, 20],
+            10: [11, 12],
+            11: [14],
+            12: [13],
+            13: [21, 22],
+            14: [15],
+            15: [19, 23],
+            16: [17, 18],
+            17: [20],
+            18: [19],
+            19: [23],
+            20: [21],
+            21: [22],
+            22: [23],
+        },
+        create_using=create_using,
+    )
+    G.name = "Truncated Cube Graph"
+    return G
+
+
+@nx._dispatchable(graphs=None, returns_graph=True)
+def truncated_tetrahedron_graph(create_using=None):
+    """
+    Returns the skeleton of the truncated Platonic tetrahedron.
+
+    The truncated tetrahedron is an Archimedean solid with 4 regular hexagonal faces,
+    4 equilateral triangle faces, 12 nodes and 18 edges. It can be constructed by truncating
+    all 4 vertices of a regular tetrahedron at one third of the original edge length [1]_.
+
+    Parameters
+    ----------
+    create_using : NetworkX graph constructor, optional (default=nx.Graph)
+       Graph type to create. If graph instance, then cleared before populated.
+
+    Returns
+    -------
+    G : networkx Graph
+        Skeleton of the truncated tetrahedron
+
+    References
+    ----------
+    .. [1] https://en.wikipedia.org/wiki/Truncated_tetrahedron
+
+    """
+    G = path_graph(12, create_using)
+    G.add_edges_from([(0, 2), (0, 9), (1, 6), (3, 11), (4, 11), (5, 7), (8, 10)])
+    G.name = "Truncated Tetrahedron Graph"
+    return G
+
+
+@_raise_on_directed
+@nx._dispatchable(graphs=None, returns_graph=True)
+def tutte_graph(create_using=None):
+    """
+    Returns the Tutte graph.
+
+    The Tutte graph is a cubic polyhedral, non-Hamiltonian graph. It has
+    46 nodes and 69 edges.
+    It is a counterexample to Tait's conjecture that every 3-regular polyhedron
+    has a Hamiltonian cycle.
+    It can be realized geometrically from a tetrahedron by multiply truncating
+    three of its vertices [1]_.
+
+    Parameters
+    ----------
+    create_using : NetworkX graph constructor, optional (default=nx.Graph)
+       Graph type to create. If graph instance, then cleared before populated.
+
+    Returns
+    -------
+    G : networkx Graph
+        Tutte graph
+
+    References
+    ----------
+    .. [1] https://en.wikipedia.org/wiki/Tutte_graph
+    """
+    G = nx.from_dict_of_lists(
+        {
+            0: [1, 2, 3],
+            1: [4, 26],
+            2: [10, 11],
+            3: [18, 19],
+            4: [5, 33],
+            5: [6, 29],
+            6: [7, 27],
+            7: [8, 14],
+            8: [9, 38],
+            9: [10, 37],
+            10: [39],
+            11: [12, 39],
+            12: [13, 35],
+            13: [14, 15],
+            14: [34],
+            15: [16, 22],
+            16: [17, 44],
+            17: [18, 43],
+            18: [45],
+            19: [20, 45],
+            20: [21, 41],
+            21: [22, 23],
+            22: [40],
+            23: [24, 27],
+            24: [25, 32],
+            25: [26, 31],
+            26: [33],
+            27: [28],
+            28: [29, 32],
+            29: [30],
+            30: [31, 33],
+            31: [32],
+            34: [35, 38],
+            35: [36],
+            36: [37, 39],
+            37: [38],
+            40: [41, 44],
+            41: [42],
+            42: [43, 45],
+            43: [44],
+        },
+        create_using=create_using,
+    )
+    G.name = "Tutte's Graph"
+    return G

minigpt2/lib/python3.10/site-packages/networkx/generators/tests/test_atlas.py

@@ -0,0 +1,75 @@
+from itertools import groupby
+
+import pytest
+
+import networkx as nx
+from networkx import graph_atlas, graph_atlas_g
+from networkx.generators.atlas import NUM_GRAPHS
+from networkx.utils import edges_equal, nodes_equal, pairwise
+
+
+class TestAtlasGraph:
+    """Unit tests for the :func:`~networkx.graph_atlas` function."""
+
+    def test_index_too_small(self):
+        with pytest.raises(ValueError):
+            graph_atlas(-1)
+
+    def test_index_too_large(self):
+        with pytest.raises(ValueError):
+            graph_atlas(NUM_GRAPHS)
+
+    def test_graph(self):
+        G = graph_atlas(6)
+        assert nodes_equal(G.nodes(), range(3))
+        assert edges_equal(G.edges(), [(0, 1), (0, 2)])
+
+
+class TestAtlasGraphG:
+    """Unit tests for the :func:`~networkx.graph_atlas_g` function."""
+
+    @classmethod
+    def setup_class(cls):
+        cls.GAG = graph_atlas_g()
+
+    def test_sizes(self):
+        G = self.GAG[0]
+        assert G.number_of_nodes() == 0
+        assert G.number_of_edges() == 0
+
+        G = self.GAG[7]
+        assert G.number_of_nodes() == 3
+        assert G.number_of_edges() == 3
+
+    def test_names(self):
+        for i, G in enumerate(self.GAG):
+            assert int(G.name[1:]) == i
+
+    def test_nondecreasing_nodes(self):
+        # check for nondecreasing number of nodes
+        for n1, n2 in pairwise(map(len, self.GAG)):
+            assert n2 <= n1 + 1
+
+    def test_nondecreasing_edges(self):
+        # check for nondecreasing number of edges (for fixed number of
+        # nodes)
+        for n, group in groupby(self.GAG, key=nx.number_of_nodes):
+            for m1, m2 in pairwise(map(nx.number_of_edges, group)):
+                assert m2 <= m1 + 1
+
+    def test_nondecreasing_degree_sequence(self):
+        # Check for lexicographically nondecreasing degree sequences
+        # (for fixed number of nodes and edges).
+        #
+        # There are three exceptions to this rule in the order given in
+        # the "Atlas of Graphs" book, so we need to manually exclude
+        # those.
+        exceptions = [("G55", "G56"), ("G1007", "G1008"), ("G1012", "G1013")]
+        for n, group in groupby(self.GAG, key=nx.number_of_nodes):
+            for m, group in groupby(group, key=nx.number_of_edges):
+                for G1, G2 in pairwise(group):
+                    if (G1.name, G2.name) in exceptions:
+                        continue
+                    d1 = sorted(d for v, d in G1.degree())
+                    d2 = sorted(d for v, d in G2.degree())
+                    assert d1 <= d2

minigpt2/lib/python3.10/site-packages/networkx/generators/tests/test_classic.py

@@ -0,0 +1,640 @@
+"""
+====================
+Generators - Classic
+====================
+
+Unit tests for various classic graph generators in generators/classic.py
+"""
+
+import itertools
+import typing
+
+import pytest
+
+import networkx as nx
+from networkx.algorithms.isomorphism.isomorph import graph_could_be_isomorphic
+from networkx.utils import edges_equal, nodes_equal
+
+is_isomorphic = graph_could_be_isomorphic
+
+
+class TestGeneratorClassic:
+    def test_balanced_tree(self):
+        # balanced_tree(r,h) is a tree with (r**(h+1)-1)/(r-1) edges
+        for r, h in [(2, 2), (3, 3), (6, 2)]:
+            t = nx.balanced_tree(r, h)
+            order = t.order()
+            assert order == (r ** (h + 1) - 1) / (r - 1)
+            assert nx.is_connected(t)
+            assert t.size() == order - 1
+            dh = nx.degree_histogram(t)
+            assert dh[0] == 0  # no nodes of 0
+            assert dh[1] == r**h  # nodes of degree 1 are leaves
+            assert dh[r] == 1  # root is degree r
+            assert dh[r + 1] == order - r**h - 1  # everyone else is degree r+1
+            assert len(dh) == r + 2
+
+    def test_balanced_tree_star(self):
+        # balanced_tree(r,1) is the r-star
+        t = nx.balanced_tree(r=2, h=1)
+        assert is_isomorphic(t, nx.star_graph(2))
+        t = nx.balanced_tree(r=5, h=1)
+        assert is_isomorphic(t, nx.star_graph(5))
+        t = nx.balanced_tree(r=10, h=1)
+        assert is_isomorphic(t, nx.star_graph(10))
+
+    def test_balanced_tree_path(self):
+        """Tests that the balanced tree with branching factor one is the
+        path graph.
+
+        """
+        # A tree of height four has five levels.
+        T = nx.balanced_tree(1, 4)
+        P = nx.path_graph(5)
+        assert is_isomorphic(T, P)
+
+    def test_full_rary_tree(self):
+        r = 2
+        n = 9
+        t = nx.full_rary_tree(r, n)
+        assert t.order() == n
+        assert nx.is_connected(t)
+        dh = nx.degree_histogram(t)
+        assert dh[0] == 0  # no nodes of 0
+        assert dh[1] == 5  # nodes of degree 1 are leaves
+        assert dh[r] == 1  # root is degree r
+        assert dh[r + 1] == 9 - 5 - 1  # everyone else is degree r+1
+        assert len(dh) == r + 2
+
+    def test_full_rary_tree_balanced(self):
+        t = nx.full_rary_tree(2, 15)
+        th = nx.balanced_tree(2, 3)
+        assert is_isomorphic(t, th)
+
+    def test_full_rary_tree_path(self):
+        t = nx.full_rary_tree(1, 10)
+        assert is_isomorphic(t, nx.path_graph(10))
+
+    def test_full_rary_tree_empty(self):
+        t = nx.full_rary_tree(0, 10)
+        assert is_isomorphic(t, nx.empty_graph(10))
+        t = nx.full_rary_tree(3, 0)
+        assert is_isomorphic(t, nx.empty_graph(0))
+
+    def test_full_rary_tree_3_20(self):
+        t = nx.full_rary_tree(3, 20)
+        assert t.order() == 20
+
+    def test_barbell_graph(self):
+        # number of nodes = 2*m1 + m2 (2 m1-complete graphs + m2-path + 2 edges)
+        # number of edges = 2*(nx.number_of_edges(m1-complete graph) + m2 + 1
+        m1 = 3
+        m2 = 5
+        b = nx.barbell_graph(m1, m2)
+        assert nx.number_of_nodes(b) == 2 * m1 + m2
+        assert nx.number_of_edges(b) == m1 * (m1 - 1) + m2 + 1
+
+        m1 = 4
+        m2 = 10
+        b = nx.barbell_graph(m1, m2)
+        assert nx.number_of_nodes(b) == 2 * m1 + m2
+        assert nx.number_of_edges(b) == m1 * (m1 - 1) + m2 + 1
+
+        m1 = 3
+        m2 = 20
+        b = nx.barbell_graph(m1, m2)
+        assert nx.number_of_nodes(b) == 2 * m1 + m2
+        assert nx.number_of_edges(b) == m1 * (m1 - 1) + m2 + 1
+
+        # Raise NetworkXError if m1<2
+        m1 = 1
+        m2 = 20
+        pytest.raises(nx.NetworkXError, nx.barbell_graph, m1, m2)
+
+        # Raise NetworkXError if m2<0
+        m1 = 5
+        m2 = -2
+        pytest.raises(nx.NetworkXError, nx.barbell_graph, m1, m2)
+
+        # nx.barbell_graph(2,m) = nx.path_graph(m+4)
+        m1 = 2
+        m2 = 5
+        b = nx.barbell_graph(m1, m2)
+        assert is_isomorphic(b, nx.path_graph(m2 + 4))
+
+        m1 = 2
+        m2 = 10
+        b = nx.barbell_graph(m1, m2)
+        assert is_isomorphic(b, nx.path_graph(m2 + 4))
+
+        m1 = 2
+        m2 = 20
+        b = nx.barbell_graph(m1, m2)
+        assert is_isomorphic(b, nx.path_graph(m2 + 4))
+
+        pytest.raises(
+            nx.NetworkXError, nx.barbell_graph, m1, m2, create_using=nx.DiGraph()
+        )
+
+        mb = nx.barbell_graph(m1, m2, create_using=nx.MultiGraph())
+        assert edges_equal(mb.edges(), b.edges())
+
+    def test_binomial_tree(self):
+        graphs = (None, nx.Graph, nx.DiGraph, nx.MultiGraph, nx.MultiDiGraph)
+        for create_using in graphs:
+            for n in range(4):
+                b = nx.binomial_tree(n, create_using)
+                assert nx.number_of_nodes(b) == 2**n
+                assert nx.number_of_edges(b) == (2**n - 1)
+
+    def test_complete_graph(self):
+        # complete_graph(m) is a connected graph with
+        # m nodes and  m*(m+1)/2 edges
+        for m in [0, 1, 3, 5]:
+            g = nx.complete_graph(m)
+            assert nx.number_of_nodes(g) == m
+            assert nx.number_of_edges(g) == m * (m - 1) // 2
+
+        mg = nx.complete_graph(m, create_using=nx.MultiGraph)
+        assert edges_equal(mg.edges(), g.edges())
+
+        g = nx.complete_graph("abc")
+        assert nodes_equal(g.nodes(), ["a", "b", "c"])
+        assert g.size() == 3
+
+        # creates a self-loop... should it? <backward compatible says yes>
+        g = nx.complete_graph("abcb")
+        assert nodes_equal(g.nodes(), ["a", "b", "c"])
+        assert g.size() == 4
+
+        g = nx.complete_graph("abcb", create_using=nx.MultiGraph)
+        assert nodes_equal(g.nodes(), ["a", "b", "c"])
+        assert g.size() == 6
+
+    def test_complete_digraph(self):
+        # complete_graph(m) is a connected graph with
+        # m nodes and  m*(m+1)/2 edges
+        for m in [0, 1, 3, 5]:
+            g = nx.complete_graph(m, create_using=nx.DiGraph)
+            assert nx.number_of_nodes(g) == m
+            assert nx.number_of_edges(g) == m * (m - 1)
+
+        g = nx.complete_graph("abc", create_using=nx.DiGraph)
+        assert len(g) == 3
+        assert g.size() == 6
+        assert g.is_directed()
+
+    def test_circular_ladder_graph(self):
+        G = nx.circular_ladder_graph(5)
+        pytest.raises(
+            nx.NetworkXError, nx.circular_ladder_graph, 5, create_using=nx.DiGraph
+        )
+        mG = nx.circular_ladder_graph(5, create_using=nx.MultiGraph)
+        assert edges_equal(mG.edges(), G.edges())
+
+    def test_circulant_graph(self):
+        # Ci_n(1) is the cycle graph for all n
+        Ci6_1 = nx.circulant_graph(6, [1])
+        C6 = nx.cycle_graph(6)
+        assert edges_equal(Ci6_1.edges(), C6.edges())
+
+        # Ci_n(1, 2, ..., n div 2) is the complete graph for all n
+        Ci7 = nx.circulant_graph(7, [1, 2, 3])
+        K7 = nx.complete_graph(7)
+        assert edges_equal(Ci7.edges(), K7.edges())
+
+        # Ci_6(1, 3) is K_3,3 i.e. the utility graph
+        Ci6_1_3 = nx.circulant_graph(6, [1, 3])
+        K3_3 = nx.complete_bipartite_graph(3, 3)
+        assert is_isomorphic(Ci6_1_3, K3_3)
+
+    def test_cycle_graph(self):
+        G = nx.cycle_graph(4)
+        assert edges_equal(G.edges(), [(0, 1), (0, 3), (1, 2), (2, 3)])
+        mG = nx.cycle_graph(4, create_using=nx.MultiGraph)
+        assert edges_equal(mG.edges(), [(0, 1), (0, 3), (1, 2), (2, 3)])
+        G = nx.cycle_graph(4, create_using=nx.DiGraph)
+        assert not G.has_edge(2, 1)
+        assert G.has_edge(1, 2)
+        assert G.is_directed()
+
+        G = nx.cycle_graph("abc")
+        assert len(G) == 3
+        assert G.size() == 3
+        G = nx.cycle_graph("abcb")
+        assert len(G) == 3
+        assert G.size() == 2
+        g = nx.cycle_graph("abc", nx.DiGraph)
+        assert len(g) == 3
+        assert g.size() == 3
+        assert g.is_directed()
+        g = nx.cycle_graph("abcb", nx.DiGraph)
+        assert len(g) == 3
+        assert g.size() == 4
+
+    def test_dorogovtsev_goltsev_mendes_graph(self):
+        G = nx.dorogovtsev_goltsev_mendes_graph(0)
+        assert edges_equal(G.edges(), [(0, 1)])
+        assert nodes_equal(list(G), [0, 1])
+        G = nx.dorogovtsev_goltsev_mendes_graph(1)
+        assert edges_equal(G.edges(), [(0, 1), (0, 2), (1, 2)])
+        assert nx.average_clustering(G) == 1.0
+        assert nx.average_shortest_path_length(G) == 1.0
+        assert sorted(nx.triangles(G).values()) == [1, 1, 1]
+        assert nx.is_planar(G)
+        G = nx.dorogovtsev_goltsev_mendes_graph(2)
+        assert nx.number_of_nodes(G) == 6
+        assert nx.number_of_edges(G) == 9
+        assert nx.average_clustering(G) == 0.75
+        assert nx.average_shortest_path_length(G) == 1.4
+        assert nx.is_planar(G)
+        G = nx.dorogovtsev_goltsev_mendes_graph(10)
+        assert nx.number_of_nodes(G) == 29526
+        assert nx.number_of_edges(G) == 59049
+        assert G.degree(0) == 1024
+        assert G.degree(1) == 1024
+        assert G.degree(2) == 1024
+
+        with pytest.raises(nx.NetworkXError, match=r"n must be greater than"):
+            nx.dorogovtsev_goltsev_mendes_graph(-1)
+        with pytest.raises(nx.NetworkXError, match=r"directed graph not supported"):
+            nx.dorogovtsev_goltsev_mendes_graph(7, create_using=nx.DiGraph)
+        with pytest.raises(nx.NetworkXError, match=r"multigraph not supported"):
+            nx.dorogovtsev_goltsev_mendes_graph(7, create_using=nx.MultiGraph)
+        with pytest.raises(nx.NetworkXError):
+            nx.dorogovtsev_goltsev_mendes_graph(7, create_using=nx.MultiDiGraph)
+
+    def test_create_using(self):
+        G = nx.empty_graph()
+        assert isinstance(G, nx.Graph)
+        pytest.raises(TypeError, nx.empty_graph, create_using=0.0)
+        pytest.raises(TypeError, nx.empty_graph, create_using="Graph")
+
+        G = nx.empty_graph(create_using=nx.MultiGraph)
+        assert isinstance(G, nx.MultiGraph)
+        G = nx.empty_graph(create_using=nx.DiGraph)
+        assert isinstance(G, nx.DiGraph)
+
+        G = nx.empty_graph(create_using=nx.DiGraph, default=nx.MultiGraph)
+        assert isinstance(G, nx.DiGraph)
+        G = nx.empty_graph(create_using=None, default=nx.MultiGraph)
+        assert isinstance(G, nx.MultiGraph)
+        G = nx.empty_graph(default=nx.MultiGraph)
+        assert isinstance(G, nx.MultiGraph)
+
+        G = nx.path_graph(5)
+        H = nx.empty_graph(create_using=G)
+        assert not H.is_multigraph()
+        assert not H.is_directed()
+        assert len(H) == 0
+        assert G is H
+
+        H = nx.empty_graph(create_using=nx.MultiGraph())
+        assert H.is_multigraph()
+        assert not H.is_directed()
+        assert G is not H
+
+        # test for subclasses that also use typing.Protocol. See gh-6243
+        class Mixin(typing.Protocol):
+            pass
+
+        class MyGraph(Mixin, nx.DiGraph):
+            pass
+
+        G = nx.empty_graph(create_using=MyGraph)
+
+    def test_empty_graph(self):
+        G = nx.empty_graph()
+        assert nx.number_of_nodes(G) == 0
+        G = nx.empty_graph(42)
+        assert nx.number_of_nodes(G) == 42
+        assert nx.number_of_edges(G) == 0
+
+        G = nx.empty_graph("abc")
+        assert len(G) == 3
+        assert G.size() == 0
+
+        # create empty digraph
+        G = nx.empty_graph(42, create_using=nx.DiGraph(name="duh"))
+        assert nx.number_of_nodes(G) == 42
+        assert nx.number_of_edges(G) == 0
+        assert isinstance(G, nx.DiGraph)
+
+        # create empty multigraph
+        G = nx.empty_graph(42, create_using=nx.MultiGraph(name="duh"))
+        assert nx.number_of_nodes(G) == 42
+        assert nx.number_of_edges(G) == 0
+        assert isinstance(G, nx.MultiGraph)
+
+        # create empty graph from another
+        pete = nx.petersen_graph()
+        G = nx.empty_graph(42, create_using=pete)
+        assert nx.number_of_nodes(G) == 42
+        assert nx.number_of_edges(G) == 0
+        assert isinstance(G, nx.Graph)
+
+    def test_ladder_graph(self):
+        for i, G in [
+            (0, nx.empty_graph(0)),
+            (1, nx.path_graph(2)),
+            (2, nx.hypercube_graph(2)),
+            (10, nx.grid_graph([2, 10])),
+        ]:
+            assert is_isomorphic(nx.ladder_graph(i), G)
+
+        pytest.raises(nx.NetworkXError, nx.ladder_graph, 2, create_using=nx.DiGraph)
+
+        g = nx.ladder_graph(2)
+        mg = nx.ladder_graph(2, create_using=nx.MultiGraph)
+        assert edges_equal(mg.edges(), g.edges())
+
+    @pytest.mark.parametrize(("m", "n"), [(3, 5), (4, 10), (3, 20)])
+    def test_lollipop_graph_right_sizes(self, m, n):
+        G = nx.lollipop_graph(m, n)
+        assert nx.number_of_nodes(G) == m + n
+        assert nx.number_of_edges(G) == m * (m - 1) / 2 + n
+
+    @pytest.mark.parametrize(("m", "n"), [("ab", ""), ("abc", "defg")])
+    def test_lollipop_graph_size_node_sequence(self, m, n):
+        G = nx.lollipop_graph(m, n)
+        assert nx.number_of_nodes(G) == len(m) + len(n)
+        assert nx.number_of_edges(G) == len(m) * (len(m) - 1) / 2 + len(n)
+
+    def test_lollipop_graph_exceptions(self):
+        # Raise NetworkXError if m<2
+        pytest.raises(nx.NetworkXError, nx.lollipop_graph, -1, 2)
+        pytest.raises(nx.NetworkXError, nx.lollipop_graph, 1, 20)
+        pytest.raises(nx.NetworkXError, nx.lollipop_graph, "", 20)
+        pytest.raises(nx.NetworkXError, nx.lollipop_graph, "a", 20)
+
+        # Raise NetworkXError if n<0
+        pytest.raises(nx.NetworkXError, nx.lollipop_graph, 5, -2)
+
+        # raise NetworkXError if create_using is directed
+        with pytest.raises(nx.NetworkXError):
+            nx.lollipop_graph(2, 20, create_using=nx.DiGraph)
+        with pytest.raises(nx.NetworkXError):
+            nx.lollipop_graph(2, 20, create_using=nx.MultiDiGraph)
+
+    @pytest.mark.parametrize(("m", "n"), [(2, 0), (2, 5), (2, 10), ("ab", 20)])
+    def test_lollipop_graph_same_as_path_when_m1_is_2(self, m, n):
+        G = nx.lollipop_graph(m, n)
+        assert is_isomorphic(G, nx.path_graph(n + 2))
+
+    def test_lollipop_graph_for_multigraph(self):
+        G = nx.lollipop_graph(5, 20)
+        MG = nx.lollipop_graph(5, 20, create_using=nx.MultiGraph)
+        assert edges_equal(MG.edges(), G.edges())
+
+    @pytest.mark.parametrize(
+        ("m", "n"),
+        [(4, "abc"), ("abcd", 3), ([1, 2, 3, 4], "abc"), ("abcd", [1, 2, 3])],
+    )
+    def test_lollipop_graph_mixing_input_types(self, m, n):
+        expected = nx.compose(nx.complete_graph(4), nx.path_graph(range(100, 103)))
+        expected.add_edge(0, 100)  # Connect complete graph and path graph
+        assert is_isomorphic(nx.lollipop_graph(m, n), expected)
+
+    def test_lollipop_graph_non_builtin_ints(self):
+        np = pytest.importorskip("numpy")
+        G = nx.lollipop_graph(np.int32(4), np.int64(3))
+        expected = nx.compose(nx.complete_graph(4), nx.path_graph(range(100, 103)))
+        expected.add_edge(0, 100)  # Connect complete graph and path graph
+        assert is_isomorphic(G, expected)
+
+    def test_null_graph(self):
+        assert nx.number_of_nodes(nx.null_graph()) == 0
+
+    def test_path_graph(self):
+        p = nx.path_graph(0)
+        assert is_isomorphic(p, nx.null_graph())
+
+        p = nx.path_graph(1)
+        assert is_isomorphic(p, nx.empty_graph(1))
+
+        p = nx.path_graph(10)
+        assert nx.is_connected(p)
+        assert sorted(d for n, d in p.degree()) == [1, 1, 2, 2, 2, 2, 2, 2, 2, 2]
+        assert p.order() - 1 == p.size()
+
+        dp = nx.path_graph(3, create_using=nx.DiGraph)
+        assert dp.has_edge(0, 1)
+        assert not dp.has_edge(1, 0)
+
+        mp = nx.path_graph(10, create_using=nx.MultiGraph)
+        assert edges_equal(mp.edges(), p.edges())
+
+        G = nx.path_graph("abc")
+        assert len(G) == 3
+        assert G.size() == 2
+        G = nx.path_graph("abcb")
+        assert len(G) == 3
+        assert G.size() == 2
+        g = nx.path_graph("abc", nx.DiGraph)
+        assert len(g) == 3
+        assert g.size() == 2
+        assert g.is_directed()
+        g = nx.path_graph("abcb", nx.DiGraph)
+        assert len(g) == 3
+        assert g.size() == 3
+
+        G = nx.path_graph((1, 2, 3, 2, 4))
+        assert G.has_edge(2, 4)
+
+    def test_star_graph(self):
+        assert is_isomorphic(nx.star_graph(""), nx.empty_graph(0))
+        assert is_isomorphic(nx.star_graph([]), nx.empty_graph(0))
+        assert is_isomorphic(nx.star_graph(0), nx.empty_graph(1))
+        assert is_isomorphic(nx.star_graph(1), nx.path_graph(2))
+        assert is_isomorphic(nx.star_graph(2), nx.path_graph(3))
+        assert is_isomorphic(nx.star_graph(5), nx.complete_bipartite_graph(1, 5))
+
+        s = nx.star_graph(10)
+        assert sorted(d for n, d in s.degree()) == [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 10]
+
+        pytest.raises(nx.NetworkXError, nx.star_graph, 10, create_using=nx.DiGraph)
+
+        ms = nx.star_graph(10, create_using=nx.MultiGraph)
+        assert edges_equal(ms.edges(), s.edges())
+
+        G = nx.star_graph("abc")
+        assert len(G) == 3
+        assert G.size() == 2
+
+        G = nx.star_graph("abcb")
+        assert len(G) == 3
+        assert G.size() == 2
+        G = nx.star_graph("abcb", create_using=nx.MultiGraph)
+        assert len(G) == 3
+        assert G.size() == 3
+
+        G = nx.star_graph("abcdefg")
+        assert len(G) == 7
+        assert G.size() == 6
+
+    def test_non_int_integers_for_star_graph(self):
+        np = pytest.importorskip("numpy")
+        G = nx.star_graph(np.int32(3))
+        assert len(G) == 4
+        assert G.size() == 3
+
+    @pytest.mark.parametrize(("m", "n"), [(3, 0), (3, 5), (4, 10), (3, 20)])
+    def test_tadpole_graph_right_sizes(self, m, n):
+        G = nx.tadpole_graph(m, n)
+        assert nx.number_of_nodes(G) == m + n
+        assert nx.number_of_edges(G) == m + n - (m == 2)
+
+    @pytest.mark.parametrize(("m", "n"), [("ab", ""), ("ab", "c"), ("abc", "defg")])
+    def test_tadpole_graph_size_node_sequences(self, m, n):
+        G = nx.tadpole_graph(m, n)
+        assert nx.number_of_nodes(G) == len(m) + len(n)
+        assert nx.number_of_edges(G) == len(m) + len(n) - (len(m) == 2)
+
+    def test_tadpole_graph_exceptions(self):
+        # Raise NetworkXError if m<2
+        pytest.raises(nx.NetworkXError, nx.tadpole_graph, -1, 3)
+        pytest.raises(nx.NetworkXError, nx.tadpole_graph, 0, 3)
+        pytest.raises(nx.NetworkXError, nx.tadpole_graph, 1, 3)
+
+        # Raise NetworkXError if n<0
+        pytest.raises(nx.NetworkXError, nx.tadpole_graph, 5, -2)
+
+        # Raise NetworkXError for digraphs
+        with pytest.raises(nx.NetworkXError):
+            nx.tadpole_graph(2, 20, create_using=nx.DiGraph)
+        with pytest.raises(nx.NetworkXError):
+            nx.tadpole_graph(2, 20, create_using=nx.MultiDiGraph)
+
+    @pytest.mark.parametrize(("m", "n"), [(2, 0), (2, 5), (2, 10), ("ab", 20)])
+    def test_tadpole_graph_same_as_path_when_m_is_2(self, m, n):
+        G = nx.tadpole_graph(m, n)
+        assert is_isomorphic(G, nx.path_graph(n + 2))
+
+    @pytest.mark.parametrize("m", [4, 7])
+    def test_tadpole_graph_same_as_cycle_when_m2_is_0(self, m):
+        G = nx.tadpole_graph(m, 0)
+        assert is_isomorphic(G, nx.cycle_graph(m))
+
+    def test_tadpole_graph_for_multigraph(self):
+        G = nx.tadpole_graph(5, 20)
+        MG = nx.tadpole_graph(5, 20, create_using=nx.MultiGraph)
+        assert edges_equal(MG.edges(), G.edges())
+
+    @pytest.mark.parametrize(
+        ("m", "n"),
+        [(4, "abc"), ("abcd", 3), ([1, 2, 3, 4], "abc"), ("abcd", [1, 2, 3])],
+    )
+    def test_tadpole_graph_mixing_input_types(self, m, n):
+        expected = nx.compose(nx.cycle_graph(4), nx.path_graph(range(100, 103)))
+        expected.add_edge(0, 100)  # Connect cycle and path
+        assert is_isomorphic(nx.tadpole_graph(m, n), expected)
+
+    def test_tadpole_graph_non_builtin_integers(self):
+        np = pytest.importorskip("numpy")
+        G = nx.tadpole_graph(np.int32(4), np.int64(3))
+        expected = nx.compose(nx.cycle_graph(4), nx.path_graph(range(100, 103)))
+        expected.add_edge(0, 100)  # Connect cycle and path
+        assert is_isomorphic(G, expected)
+
+    def test_trivial_graph(self):
+        assert nx.number_of_nodes(nx.trivial_graph()) == 1
+
+    def test_turan_graph(self):
+        assert nx.number_of_edges(nx.turan_graph(13, 4)) == 63
+        assert is_isomorphic(
+            nx.turan_graph(13, 4), nx.complete_multipartite_graph(3, 4, 3, 3)
+        )
+
+    def test_wheel_graph(self):
+        for n, G in [
+            ("", nx.null_graph()),
+            (0, nx.null_graph()),
+            (1, nx.empty_graph(1)),
+            (2, nx.path_graph(2)),
+            (3, nx.complete_graph(3)),
+            (4, nx.complete_graph(4)),
+        ]:
+            g = nx.wheel_graph(n)
+            assert is_isomorphic(g, G)
+
+        g = nx.wheel_graph(10)
+        assert sorted(d for n, d in g.degree()) == [3, 3, 3, 3, 3, 3, 3, 3, 3, 9]
+
+        pytest.raises(nx.NetworkXError, nx.wheel_graph, 10, create_using=nx.DiGraph)
+
+        mg = nx.wheel_graph(10, create_using=nx.MultiGraph())
+        assert edges_equal(mg.edges(), g.edges())
+
+        G = nx.wheel_graph("abc")
+        assert len(G) == 3
+        assert G.size() == 3
+
+        G = nx.wheel_graph("abcb")
+        assert len(G) == 3
+        assert G.size() == 4
+        G = nx.wheel_graph("abcb", nx.MultiGraph)
+        assert len(G) == 3
+        assert G.size() == 6
+
+    def test_non_int_integers_for_wheel_graph(self):
+        np = pytest.importorskip("numpy")
+        G = nx.wheel_graph(np.int32(3))
+        assert len(G) == 3
+        assert G.size() == 3
+
+    def test_complete_0_partite_graph(self):
+        """Tests that the complete 0-partite graph is the null graph."""
+        G = nx.complete_multipartite_graph()
+        H = nx.null_graph()
+        assert nodes_equal(G, H)
+        assert edges_equal(G.edges(), H.edges())
+
+    def test_complete_1_partite_graph(self):
+        """Tests that the complete 1-partite graph is the empty graph."""
+        G = nx.complete_multipartite_graph(3)
+        H = nx.empty_graph(3)
+        assert nodes_equal(G, H)
+        assert edges_equal(G.edges(), H.edges())
+
+    def test_complete_2_partite_graph(self):
+        """Tests that the complete 2-partite graph is the complete bipartite
+        graph.
+
+        """
+        G = nx.complete_multipartite_graph(2, 3)
+        H = nx.complete_bipartite_graph(2, 3)
+        assert nodes_equal(G, H)
+        assert edges_equal(G.edges(), H.edges())
+
+    def test_complete_multipartite_graph(self):
+        """Tests for generating the complete multipartite graph."""
+        G = nx.complete_multipartite_graph(2, 3, 4)
+        blocks = [(0, 1), (2, 3, 4), (5, 6, 7, 8)]
+        # Within each block, no two vertices should be adjacent.
+        for block in blocks:
+            for u, v in itertools.combinations_with_replacement(block, 2):
+                assert v not in G[u]
+                assert G.nodes[u] == G.nodes[v]
+        # Across blocks, all vertices should be adjacent.
+        for block1, block2 in itertools.combinations(blocks, 2):
+            for u, v in itertools.product(block1, block2):
+                assert v in G[u]
+                assert G.nodes[u] != G.nodes[v]
+        with pytest.raises(nx.NetworkXError, match="Negative number of nodes"):
+            nx.complete_multipartite_graph(2, -3, 4)
+
+    def test_kneser_graph(self):
+        # the petersen graph is a special case of the kneser graph when n=5 and k=2
+        assert is_isomorphic(nx.kneser_graph(5, 2), nx.petersen_graph())
+
+        # when k is 1, the kneser graph returns a complete graph with n vertices
+        for i in range(1, 7):
+            assert is_isomorphic(nx.kneser_graph(i, 1), nx.complete_graph(i))
+
+        # the kneser graph of n and n-1 is the empty graph with n vertices
+        for j in range(3, 7):
+            assert is_isomorphic(nx.kneser_graph(j, j - 1), nx.empty_graph(j))
+
+        # in general the number of edges of the kneser graph is equal to
+        # (n choose k) times (n-k choose k) divided by 2
+        assert nx.number_of_edges(nx.kneser_graph(8, 3)) == 280

minigpt2/lib/python3.10/site-packages/networkx/generators/tests/test_cographs.py

@@ -0,0 +1,18 @@
+"""Unit tests for the :mod:`networkx.generators.cographs` module."""
+
+import networkx as nx
+
+
+def test_random_cograph():
+    n = 3
+    G = nx.random_cograph(n)
+
+    assert len(G) == 2**n
+
+    # Every connected subgraph of G has diameter <= 2
+    if nx.is_connected(G):
+        assert nx.diameter(G) <= 2
+    else:
+        components = nx.connected_components(G)
+        for component in components:
+            assert nx.diameter(G.subgraph(component)) <= 2

minigpt2/lib/python3.10/site-packages/networkx/generators/tests/test_community.py

@@ -0,0 +1,362 @@
+import pytest
+
+import networkx as nx
+
+
+def test_random_partition_graph():
+    G = nx.random_partition_graph([3, 3, 3], 1, 0, seed=42)
+    C = G.graph["partition"]
+    assert C == [{0, 1, 2}, {3, 4, 5}, {6, 7, 8}]
+    assert len(G) == 9
+    assert len(list(G.edges())) == 9
+
+    G = nx.random_partition_graph([3, 3, 3], 0, 1)
+    C = G.graph["partition"]
+    assert C == [{0, 1, 2}, {3, 4, 5}, {6, 7, 8}]
+    assert len(G) == 9
+    assert len(list(G.edges())) == 27
+
+    G = nx.random_partition_graph([3, 3, 3], 1, 0, directed=True)
+    C = G.graph["partition"]
+    assert C == [{0, 1, 2}, {3, 4, 5}, {6, 7, 8}]
+    assert len(G) == 9
+    assert len(list(G.edges())) == 18
+
+    G = nx.random_partition_graph([3, 3, 3], 0, 1, directed=True)
+    C = G.graph["partition"]
+    assert C == [{0, 1, 2}, {3, 4, 5}, {6, 7, 8}]
+    assert len(G) == 9
+    assert len(list(G.edges())) == 54
+
+    G = nx.random_partition_graph([1, 2, 3, 4, 5], 0.5, 0.1)
+    C = G.graph["partition"]
+    assert C == [{0}, {1, 2}, {3, 4, 5}, {6, 7, 8, 9}, {10, 11, 12, 13, 14}]
+    assert len(G) == 15
+
+    rpg = nx.random_partition_graph
+    pytest.raises(nx.NetworkXError, rpg, [1, 2, 3], 1.1, 0.1)
+    pytest.raises(nx.NetworkXError, rpg, [1, 2, 3], -0.1, 0.1)
+    pytest.raises(nx.NetworkXError, rpg, [1, 2, 3], 0.1, 1.1)
+    pytest.raises(nx.NetworkXError, rpg, [1, 2, 3], 0.1, -0.1)
+
+
+def test_planted_partition_graph():
+    G = nx.planted_partition_graph(4, 3, 1, 0, seed=42)
+    C = G.graph["partition"]
+    assert len(C) == 4
+    assert len(G) == 12
+    assert len(list(G.edges())) == 12
+
+    G = nx.planted_partition_graph(4, 3, 0, 1)
+    C = G.graph["partition"]
+    assert len(C) == 4
+    assert len(G) == 12
+    assert len(list(G.edges())) == 54
+
+    G = nx.planted_partition_graph(10, 4, 0.5, 0.1, seed=42)
+    C = G.graph["partition"]
+    assert len(C) == 10
+    assert len(G) == 40
+
+    G = nx.planted_partition_graph(4, 3, 1, 0, directed=True)
+    C = G.graph["partition"]
+    assert len(C) == 4
+    assert len(G) == 12
+    assert len(list(G.edges())) == 24
+
+    G = nx.planted_partition_graph(4, 3, 0, 1, directed=True)
+    C = G.graph["partition"]
+    assert len(C) == 4
+    assert len(G) == 12
+    assert len(list(G.edges())) == 108
+
+    G = nx.planted_partition_graph(10, 4, 0.5, 0.1, seed=42, directed=True)
+    C = G.graph["partition"]
+    assert len(C) == 10
+    assert len(G) == 40
+
+    ppg = nx.planted_partition_graph
+    pytest.raises(nx.NetworkXError, ppg, 3, 3, 1.1, 0.1)
+    pytest.raises(nx.NetworkXError, ppg, 3, 3, -0.1, 0.1)
+    pytest.raises(nx.NetworkXError, ppg, 3, 3, 0.1, 1.1)
+    pytest.raises(nx.NetworkXError, ppg, 3, 3, 0.1, -0.1)
+
+
+def test_relaxed_caveman_graph():
+    G = nx.relaxed_caveman_graph(4, 3, 0)
+    assert len(G) == 12
+    G = nx.relaxed_caveman_graph(4, 3, 1)
+    assert len(G) == 12
+    G = nx.relaxed_caveman_graph(4, 3, 0.5)
+    assert len(G) == 12
+    G = nx.relaxed_caveman_graph(4, 3, 0.5, seed=42)
+    assert len(G) == 12
+
+
+def test_connected_caveman_graph():
+    G = nx.connected_caveman_graph(4, 3)
+    assert len(G) == 12
+
+    G = nx.connected_caveman_graph(1, 5)
+    K5 = nx.complete_graph(5)
+    K5.remove_edge(3, 4)
+    assert nx.is_isomorphic(G, K5)
+
+    # need at least 2 nodes in each clique
+    pytest.raises(nx.NetworkXError, nx.connected_caveman_graph, 4, 1)
+
+
+def test_caveman_graph():
+    G = nx.caveman_graph(4, 3)
+    assert len(G) == 12
+
+    G = nx.caveman_graph(5, 1)
+    E5 = nx.empty_graph(5)
+    assert nx.is_isomorphic(G, E5)
+
+    G = nx.caveman_graph(1, 5)
+    K5 = nx.complete_graph(5)
+    assert nx.is_isomorphic(G, K5)
+
+
+def test_gaussian_random_partition_graph():
+    G = nx.gaussian_random_partition_graph(100, 10, 10, 0.3, 0.01)
+    assert len(G) == 100
+    G = nx.gaussian_random_partition_graph(100, 10, 10, 0.3, 0.01, directed=True)
+    assert len(G) == 100
+    G = nx.gaussian_random_partition_graph(
+        100, 10, 10, 0.3, 0.01, directed=False, seed=42
+    )
+    assert len(G) == 100
+    assert not isinstance(G, nx.DiGraph)
+    G = nx.gaussian_random_partition_graph(
+        100, 10, 10, 0.3, 0.01, directed=True, seed=42
+    )
+    assert len(G) == 100
+    assert isinstance(G, nx.DiGraph)
+    pytest.raises(
+        nx.NetworkXError, nx.gaussian_random_partition_graph, 100, 101, 10, 1, 0
+    )
+    # Test when clusters are likely less than 1
+    G = nx.gaussian_random_partition_graph(10, 0.5, 0.5, 0.5, 0.5, seed=1)
+    assert len(G) == 10
+
+
+def test_ring_of_cliques():
+    for i in range(2, 20, 3):
+        for j in range(2, 20, 3):
+            G = nx.ring_of_cliques(i, j)
+            assert G.number_of_nodes() == i * j
+            if i != 2 or j != 1:
+                expected_num_edges = i * (((j * (j - 1)) // 2) + 1)
+            else:
+                # the edge that already exists cannot be duplicated
+                expected_num_edges = i * (((j * (j - 1)) // 2) + 1) - 1
+            assert G.number_of_edges() == expected_num_edges
+    with pytest.raises(
+        nx.NetworkXError, match="A ring of cliques must have at least two cliques"
+    ):
+        nx.ring_of_cliques(1, 5)
+    with pytest.raises(
+        nx.NetworkXError, match="The cliques must have at least two nodes"
+    ):
+        nx.ring_of_cliques(3, 0)
+
+
+def test_windmill_graph():
+    for n in range(2, 20, 3):
+        for k in range(2, 20, 3):
+            G = nx.windmill_graph(n, k)
+            assert G.number_of_nodes() == (k - 1) * n + 1
+            assert G.number_of_edges() == n * k * (k - 1) / 2
+            assert G.degree(0) == G.number_of_nodes() - 1
+            for i in range(1, G.number_of_nodes()):
+                assert G.degree(i) == k - 1
+    with pytest.raises(
+        nx.NetworkXError, match="A windmill graph must have at least two cliques"
+    ):
+        nx.windmill_graph(1, 3)
+    with pytest.raises(
+        nx.NetworkXError, match="The cliques must have at least two nodes"
+    ):
+        nx.windmill_graph(3, 0)
+
+
+def test_stochastic_block_model():
+    sizes = [75, 75, 300]
+    probs = [[0.25, 0.05, 0.02], [0.05, 0.35, 0.07], [0.02, 0.07, 0.40]]
+    G = nx.stochastic_block_model(sizes, probs, seed=0)
+    C = G.graph["partition"]
+    assert len(C) == 3
+    assert len(G) == 450
+    assert G.size() == 22160
+
+    GG = nx.stochastic_block_model(sizes, probs, range(450), seed=0)
+    assert G.nodes == GG.nodes
+
+    # Test Exceptions
+    sbm = nx.stochastic_block_model
+    badnodelist = list(range(400))  # not enough nodes to match sizes
+    badprobs1 = [[0.25, 0.05, 1.02], [0.05, 0.35, 0.07], [0.02, 0.07, 0.40]]
+    badprobs2 = [[0.25, 0.05, 0.02], [0.05, -0.35, 0.07], [0.02, 0.07, 0.40]]
+    probs_rect1 = [[0.25, 0.05, 0.02], [0.05, -0.35, 0.07]]
+    probs_rect2 = [[0.25, 0.05], [0.05, -0.35], [0.02, 0.07]]
+    asymprobs = [[0.25, 0.05, 0.01], [0.05, -0.35, 0.07], [0.02, 0.07, 0.40]]
+    pytest.raises(nx.NetworkXException, sbm, sizes, badprobs1)
+    pytest.raises(nx.NetworkXException, sbm, sizes, badprobs2)
+    pytest.raises(nx.NetworkXException, sbm, sizes, probs_rect1, directed=True)
+    pytest.raises(nx.NetworkXException, sbm, sizes, probs_rect2, directed=True)
+    pytest.raises(nx.NetworkXException, sbm, sizes, asymprobs, directed=False)
+    pytest.raises(nx.NetworkXException, sbm, sizes, probs, badnodelist)
+    nodelist = [0] + list(range(449))  # repeated node name in nodelist
+    pytest.raises(nx.NetworkXException, sbm, sizes, probs, nodelist)
+
+    # Extra keyword arguments test
+    GG = nx.stochastic_block_model(sizes, probs, seed=0, selfloops=True)
+    assert G.nodes == GG.nodes
+    GG = nx.stochastic_block_model(sizes, probs, selfloops=True, directed=True)
+    assert G.nodes == GG.nodes
+    GG = nx.stochastic_block_model(sizes, probs, seed=0, sparse=False)
+    assert G.nodes == GG.nodes
+
+
+def test_generator():
+    n = 250
+    tau1 = 3
+    tau2 = 1.5
+    mu = 0.1
+    G = nx.LFR_benchmark_graph(
+        n, tau1, tau2, mu, average_degree=5, min_community=20, seed=10
+    )
+    assert len(G) == 250
+    C = {frozenset(G.nodes[v]["community"]) for v in G}
+    assert nx.community.is_partition(G.nodes(), C)
+
+
+def test_invalid_tau1():
+    with pytest.raises(nx.NetworkXError, match="tau2 must be greater than one"):
+        n = 100
+        tau1 = 2
+        tau2 = 1
+        mu = 0.1
+        nx.LFR_benchmark_graph(n, tau1, tau2, mu, min_degree=2)
+
+
+def test_invalid_tau2():
+    with pytest.raises(nx.NetworkXError, match="tau1 must be greater than one"):
+        n = 100
+        tau1 = 1
+        tau2 = 2
+        mu = 0.1
+        nx.LFR_benchmark_graph(n, tau1, tau2, mu, min_degree=2)
+
+
+def test_mu_too_large():
+    with pytest.raises(nx.NetworkXError, match="mu must be in the interval \\[0, 1\\]"):
+        n = 100
+        tau1 = 2
+        tau2 = 2
+        mu = 1.1
+        nx.LFR_benchmark_graph(n, tau1, tau2, mu, min_degree=2)
+
+
+def test_mu_too_small():
+    with pytest.raises(nx.NetworkXError, match="mu must be in the interval \\[0, 1\\]"):
+        n = 100
+        tau1 = 2
+        tau2 = 2
+        mu = -1
+        nx.LFR_benchmark_graph(n, tau1, tau2, mu, min_degree=2)
+
+
+def test_both_degrees_none():
+    with pytest.raises(
+        nx.NetworkXError,
+        match="Must assign exactly one of min_degree and average_degree",
+    ):
+        n = 100
+        tau1 = 2
+        tau2 = 2
+        mu = 1
+        nx.LFR_benchmark_graph(n, tau1, tau2, mu)
+
+
+def test_neither_degrees_none():
+    with pytest.raises(
+        nx.NetworkXError,
+        match="Must assign exactly one of min_degree and average_degree",
+    ):
+        n = 100
+        tau1 = 2
+        tau2 = 2
+        mu = 1
+        nx.LFR_benchmark_graph(n, tau1, tau2, mu, min_degree=2, average_degree=5)
+
+
+def test_max_iters_exceeded():
+    with pytest.raises(
+        nx.ExceededMaxIterations,
+        match="Could not assign communities; try increasing min_community",
+    ):
+        n = 10
+        tau1 = 2
+        tau2 = 2
+        mu = 0.1
+        nx.LFR_benchmark_graph(n, tau1, tau2, mu, min_degree=2, max_iters=10, seed=1)
+
+
+def test_max_deg_out_of_range():
+    with pytest.raises(
+        nx.NetworkXError, match="max_degree must be in the interval \\(0, n\\]"
+    ):
+        n = 10
+        tau1 = 2
+        tau2 = 2
+        mu = 0.1
+        nx.LFR_benchmark_graph(
+            n, tau1, tau2, mu, max_degree=n + 1, max_iters=10, seed=1
+        )
+
+
+def test_max_community():
+    n = 250
+    tau1 = 3
+    tau2 = 1.5
+    mu = 0.1
+    G = nx.LFR_benchmark_graph(
+        n,
+        tau1,
+        tau2,
+        mu,
+        average_degree=5,
+        max_degree=100,
+        min_community=50,
+        max_community=200,
+        seed=10,
+    )
+    assert len(G) == 250
+    C = {frozenset(G.nodes[v]["community"]) for v in G}
+    assert nx.community.is_partition(G.nodes(), C)
+
+
+def test_powerlaw_iterations_exceeded():
+    with pytest.raises(
+        nx.ExceededMaxIterations, match="Could not create power law sequence"
+    ):
+        n = 100
+        tau1 = 2
+        tau2 = 2
+        mu = 1
+        nx.LFR_benchmark_graph(n, tau1, tau2, mu, min_degree=2, max_iters=0)
+
+
+def test_no_scipy_zeta():
+    zeta2 = 1.6449340668482264
+    assert abs(zeta2 - nx.generators.community._hurwitz_zeta(2, 1, 0.0001)) < 0.01
+
+
+def test_generate_min_degree_itr():
+    with pytest.raises(
+        nx.ExceededMaxIterations, match="Could not match average_degree"
+    ):
+        nx.generators.community._generate_min_degree(2, 2, 1, 0.01, 0)

minigpt2/lib/python3.10/site-packages/networkx/generators/tests/test_degree_seq.py

@@ -0,0 +1,230 @@
+import pytest
+
+import networkx as nx
+
+
+class TestConfigurationModel:
+    """Unit tests for the :func:`~networkx.configuration_model`
+    function.
+
+    """
+
+    def test_empty_degree_sequence(self):
+        """Tests that an empty degree sequence yields the null graph."""
+        G = nx.configuration_model([])
+        assert len(G) == 0
+
+    def test_degree_zero(self):
+        """Tests that a degree sequence of all zeros yields the empty
+        graph.
+
+        """
+        G = nx.configuration_model([0, 0, 0])
+        assert len(G) == 3
+        assert G.number_of_edges() == 0
+
+    def test_degree_sequence(self):
+        """Tests that the degree sequence of the generated graph matches
+        the input degree sequence.
+
+        """
+        deg_seq = [5, 3, 3, 3, 3, 2, 2, 2, 1, 1, 1]
+        G = nx.configuration_model(deg_seq, seed=12345678)
+        assert sorted((d for n, d in G.degree()), reverse=True) == [
+            5,
+            3,
+            3,
+            3,
+            3,
+            2,
+            2,
+            2,
+            1,
+            1,
+            1,
+        ]
+        assert sorted((d for n, d in G.degree(range(len(deg_seq)))), reverse=True) == [
+            5,
+            3,
+            3,
+            3,
+            3,
+            2,
+            2,
+            2,
+            1,
+            1,
+            1,
+        ]
+
+    def test_random_seed(self):
+        """Tests that each call with the same random seed generates the
+        same graph.
+
+        """
+        deg_seq = [3] * 12
+        G1 = nx.configuration_model(deg_seq, seed=1000)
+        G2 = nx.configuration_model(deg_seq, seed=1000)
+        assert nx.is_isomorphic(G1, G2)
+        G1 = nx.configuration_model(deg_seq, seed=10)
+        G2 = nx.configuration_model(deg_seq, seed=10)
+        assert nx.is_isomorphic(G1, G2)
+
+    def test_directed_disallowed(self):
+        """Tests that attempting to create a configuration model graph
+        using a directed graph yields an exception.
+
+        """
+        with pytest.raises(nx.NetworkXNotImplemented):
+            nx.configuration_model([], create_using=nx.DiGraph())
+
+    def test_odd_degree_sum(self):
+        """Tests that a degree sequence whose sum is odd yields an
+        exception.
+
+        """
+        with pytest.raises(nx.NetworkXError):
+            nx.configuration_model([1, 2])
+
+
+def test_directed_configuration_raise_unequal():
+    with pytest.raises(nx.NetworkXError):
+        zin = [5, 3, 3, 3, 3, 2, 2, 2, 1, 1]
+        zout = [5, 3, 3, 3, 3, 2, 2, 2, 1, 2]
+        nx.directed_configuration_model(zin, zout)
+
+
+def test_directed_configuration_model():
+    G = nx.directed_configuration_model([], [], seed=0)
+    assert len(G) == 0
+
+
+def test_simple_directed_configuration_model():
+    G = nx.directed_configuration_model([1, 1], [1, 1], seed=0)
+    assert len(G) == 2
+
+
+def test_expected_degree_graph_empty():
+    # empty graph has empty degree sequence
+    deg_seq = []
+    G = nx.expected_degree_graph(deg_seq)
+    assert dict(G.degree()) == {}
+
+
+def test_expected_degree_graph():
+    # test that fixed seed delivers the same graph
+    deg_seq = [3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3]
+    G1 = nx.expected_degree_graph(deg_seq, seed=1000)
+    assert len(G1) == 12
+
+    G2 = nx.expected_degree_graph(deg_seq, seed=1000)
+    assert nx.is_isomorphic(G1, G2)
+
+    G1 = nx.expected_degree_graph(deg_seq, seed=10)
+    G2 = nx.expected_degree_graph(deg_seq, seed=10)
+    assert nx.is_isomorphic(G1, G2)
+
+
+def test_expected_degree_graph_selfloops():
+    deg_seq = [3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3]
+    G1 = nx.expected_degree_graph(deg_seq, seed=1000, selfloops=False)
+    G2 = nx.expected_degree_graph(deg_seq, seed=1000, selfloops=False)
+    assert nx.is_isomorphic(G1, G2)
+    assert len(G1) == 12
+
+
+def test_expected_degree_graph_skew():
+    deg_seq = [10, 2, 2, 2, 2]
+    G1 = nx.expected_degree_graph(deg_seq, seed=1000)
+    G2 = nx.expected_degree_graph(deg_seq, seed=1000)
+    assert nx.is_isomorphic(G1, G2)
+    assert len(G1) == 5
+
+
+def test_havel_hakimi_construction():
+    G = nx.havel_hakimi_graph([])
+    assert len(G) == 0
+
+    z = [1000, 3, 3, 3, 3, 2, 2, 2, 1, 1, 1]
+    pytest.raises(nx.NetworkXError, nx.havel_hakimi_graph, z)
+    z = ["A", 3, 3, 3, 3, 2, 2, 2, 1, 1, 1]
+    pytest.raises(nx.NetworkXError, nx.havel_hakimi_graph, z)
+
+    z = [5, 4, 3, 3, 3, 2, 2, 2]
+    G = nx.havel_hakimi_graph(z)
+    G = nx.configuration_model(z)
+    z = [6, 5, 4, 4, 2, 1, 1, 1]
+    pytest.raises(nx.NetworkXError, nx.havel_hakimi_graph, z)
+
+    z = [10, 3, 3, 3, 3, 2, 2, 2, 2, 2, 2]
+
+    G = nx.havel_hakimi_graph(z)
+
+    pytest.raises(nx.NetworkXError, nx.havel_hakimi_graph, z, create_using=nx.DiGraph())
+
+
+def test_directed_havel_hakimi():
+    # Test range of valid directed degree sequences
+    n, r = 100, 10
+    p = 1.0 / r
+    for i in range(r):
+        G1 = nx.erdos_renyi_graph(n, p * (i + 1), None, True)
+        din1 = [d for n, d in G1.in_degree()]
+        dout1 = [d for n, d in G1.out_degree()]
+        G2 = nx.directed_havel_hakimi_graph(din1, dout1)
+        din2 = [d for n, d in G2.in_degree()]
+        dout2 = [d for n, d in G2.out_degree()]
+        assert sorted(din1) == sorted(din2)
+        assert sorted(dout1) == sorted(dout2)
+
+    # Test non-graphical sequence
+    dout = [1000, 3, 3, 3, 3, 2, 2, 2, 1, 1, 1]
+    din = [103, 102, 102, 102, 102, 102, 102, 102, 102, 102]
+    pytest.raises(nx.exception.NetworkXError, nx.directed_havel_hakimi_graph, din, dout)
+    # Test valid sequences
+    dout = [1, 1, 1, 1, 1, 2, 2, 2, 3, 4]
+    din = [2, 2, 2, 2, 2, 2, 2, 2, 0, 2]
+    G2 = nx.directed_havel_hakimi_graph(din, dout)
+    dout2 = (d for n, d in G2.out_degree())
+    din2 = (d for n, d in G2.in_degree())
+    assert sorted(dout) == sorted(dout2)
+    assert sorted(din) == sorted(din2)
+    # Test unequal sums
+    din = [2, 2, 2, 2, 2, 2, 2, 2, 2, 2]
+    pytest.raises(nx.exception.NetworkXError, nx.directed_havel_hakimi_graph, din, dout)
+    # Test for negative values
+    din = [2, 2, 2, 2, 2, 2, 2, 2, 2, 2, -2]
+    pytest.raises(nx.exception.NetworkXError, nx.directed_havel_hakimi_graph, din, dout)
+
+
+def test_degree_sequence_tree():
+    z = [1, 1, 1, 1, 1, 2, 2, 2, 3, 4]
+    G = nx.degree_sequence_tree(z)
+    assert len(G) == len(z)
+    assert len(list(G.edges())) == sum(z) / 2
+
+    pytest.raises(
+        nx.NetworkXError, nx.degree_sequence_tree, z, create_using=nx.DiGraph()
+    )
+
+    z = [1, 1, 1, 1, 1, 1, 2, 2, 2, 3, 4]
+    pytest.raises(nx.NetworkXError, nx.degree_sequence_tree, z)
+
+
+def test_random_degree_sequence_graph():
+    d = [1, 2, 2, 3]
+    G = nx.random_degree_sequence_graph(d, seed=42)
+    assert d == sorted(d for n, d in G.degree())
+
+
+def test_random_degree_sequence_graph_raise():
+    z = [1, 1, 1, 1, 1, 1, 2, 2, 2, 3, 4]
+    pytest.raises(nx.NetworkXUnfeasible, nx.random_degree_sequence_graph, z)
+
+
+def test_random_degree_sequence_large():
+    G1 = nx.fast_gnp_random_graph(100, 0.1, seed=42)
+    d1 = (d for n, d in G1.degree())
+    G2 = nx.random_degree_sequence_graph(d1, seed=42)
+    d2 = (d for n, d in G2.degree())
+    assert sorted(d1) == sorted(d2)

minigpt2/lib/python3.10/site-packages/networkx/generators/tests/test_directed.py

@@ -0,0 +1,163 @@
+"""Generators - Directed Graphs
+----------------------------
+"""
+
+import pytest
+
+import networkx as nx
+from networkx.classes import Graph, MultiDiGraph
+from networkx.generators.directed import (
+    gn_graph,
+    gnc_graph,
+    gnr_graph,
+    random_k_out_graph,
+    random_uniform_k_out_graph,
+    scale_free_graph,
+)
+
+
+class TestGeneratorsDirected:
+    def test_smoke_test_random_graphs(self):
+        gn_graph(100)
+        gnr_graph(100, 0.5)
+        gnc_graph(100)
+        scale_free_graph(100)
+
+        gn_graph(100, seed=42)
+        gnr_graph(100, 0.5, seed=42)
+        gnc_graph(100, seed=42)
+        scale_free_graph(100, seed=42)
+
+    def test_create_using_keyword_arguments(self):
+        pytest.raises(nx.NetworkXError, gn_graph, 100, create_using=Graph())
+        pytest.raises(nx.NetworkXError, gnr_graph, 100, 0.5, create_using=Graph())
+        pytest.raises(nx.NetworkXError, gnc_graph, 100, create_using=Graph())
+        G = gn_graph(100, seed=1)
+        MG = gn_graph(100, create_using=MultiDiGraph(), seed=1)
+        assert sorted(G.edges()) == sorted(MG.edges())
+        G = gnr_graph(100, 0.5, seed=1)
+        MG = gnr_graph(100, 0.5, create_using=MultiDiGraph(), seed=1)
+        assert sorted(G.edges()) == sorted(MG.edges())
+        G = gnc_graph(100, seed=1)
+        MG = gnc_graph(100, create_using=MultiDiGraph(), seed=1)
+        assert sorted(G.edges()) == sorted(MG.edges())
+
+        G = scale_free_graph(
+            100,
+            alpha=0.3,
+            beta=0.4,
+            gamma=0.3,
+            delta_in=0.3,
+            delta_out=0.1,
+            initial_graph=nx.cycle_graph(4, create_using=MultiDiGraph),
+            seed=1,
+        )
+        pytest.raises(ValueError, scale_free_graph, 100, 0.5, 0.4, 0.3)
+        pytest.raises(ValueError, scale_free_graph, 100, alpha=-0.3)
+        pytest.raises(ValueError, scale_free_graph, 100, beta=-0.3)
+        pytest.raises(ValueError, scale_free_graph, 100, gamma=-0.3)
+
+    def test_parameters(self):
+        G = nx.DiGraph()
+        G.add_node(0)
+
+        def kernel(x):
+            return x
+
+        assert nx.is_isomorphic(gn_graph(1), G)
+        assert nx.is_isomorphic(gn_graph(1, kernel=kernel), G)
+        assert nx.is_isomorphic(gnc_graph(1), G)
+        assert nx.is_isomorphic(gnr_graph(1, 0.5), G)
+
+
+def test_scale_free_graph_negative_delta():
+    with pytest.raises(ValueError, match="delta_in must be >= 0."):
+        scale_free_graph(10, delta_in=-1)
+    with pytest.raises(ValueError, match="delta_out must be >= 0."):
+        scale_free_graph(10, delta_out=-1)
+
+
+def test_non_numeric_ordering():
+    G = MultiDiGraph([("a", "b"), ("b", "c"), ("c", "a")])
+    s = scale_free_graph(3, initial_graph=G)
+    assert len(s) == 3
+    assert len(s.edges) == 3
+
+
+@pytest.mark.parametrize("ig", (nx.Graph(), nx.DiGraph([(0, 1)])))
+def test_scale_free_graph_initial_graph_kwarg(ig):
+    with pytest.raises(nx.NetworkXError):
+        scale_free_graph(100, initial_graph=ig)
+
+
+class TestRandomKOutGraph:
+    """Unit tests for the
+    :func:`~networkx.generators.directed.random_k_out_graph` function.
+
+    """
+
+    def test_regularity(self):
+        """Tests that the generated graph is `k`-out-regular."""
+        n = 10
+        k = 3
+        alpha = 1
+        G = random_k_out_graph(n, k, alpha)
+        assert all(d == k for v, d in G.out_degree())
+        G = random_k_out_graph(n, k, alpha, seed=42)
+        assert all(d == k for v, d in G.out_degree())
+
+    def test_no_self_loops(self):
+        """Tests for forbidding self-loops."""
+        n = 10
+        k = 3
+        alpha = 1
+        G = random_k_out_graph(n, k, alpha, self_loops=False)
+        assert nx.number_of_selfloops(G) == 0
+
+    def test_negative_alpha(self):
+        with pytest.raises(ValueError, match="alpha must be positive"):
+            random_k_out_graph(10, 3, -1)
+
+
+class TestUniformRandomKOutGraph:
+    """Unit tests for the
+    :func:`~networkx.generators.directed.random_uniform_k_out_graph`
+    function.
+
+    """
+
+    def test_regularity(self):
+        """Tests that the generated graph is `k`-out-regular."""
+        n = 10
+        k = 3
+        G = random_uniform_k_out_graph(n, k)
+        assert all(d == k for v, d in G.out_degree())
+        G = random_uniform_k_out_graph(n, k, seed=42)
+        assert all(d == k for v, d in G.out_degree())
+
+    def test_no_self_loops(self):
+        """Tests for forbidding self-loops."""
+        n = 10
+        k = 3
+        G = random_uniform_k_out_graph(n, k, self_loops=False)
+        assert nx.number_of_selfloops(G) == 0
+        assert all(d == k for v, d in G.out_degree())
+
+    def test_with_replacement(self):
+        n = 10
+        k = 3
+        G = random_uniform_k_out_graph(n, k, with_replacement=True)
+        assert G.is_multigraph()
+        assert all(d == k for v, d in G.out_degree())
+        n = 10
+        k = 9
+        G = random_uniform_k_out_graph(n, k, with_replacement=False, self_loops=False)
+        assert nx.number_of_selfloops(G) == 0
+        assert all(d == k for v, d in G.out_degree())
+
+    def test_without_replacement(self):
+        n = 10
+        k = 3
+        G = random_uniform_k_out_graph(n, k, with_replacement=False)
+        assert not G.is_multigraph()
+        assert all(d == k for v, d in G.out_degree())

minigpt2/lib/python3.10/site-packages/networkx/generators/tests/test_duplication.py

@@ -0,0 +1,103 @@
+"""Unit tests for the :mod:`networkx.generators.duplication` module."""
+
+import pytest
+
+import networkx as nx
+
+
+class TestDuplicationDivergenceGraph:
+    """Unit tests for the
+    :func:`networkx.generators.duplication.duplication_divergence_graph`
+    function.
+
+    """
+
+    def test_final_size(self):
+        G = nx.duplication_divergence_graph(3, p=1)
+        assert len(G) == 3
+        G = nx.duplication_divergence_graph(3, p=1, seed=42)
+        assert len(G) == 3
+
+    def test_probability_too_large(self):
+        with pytest.raises(nx.NetworkXError):
+            nx.duplication_divergence_graph(3, p=2)
+
+    def test_probability_too_small(self):
+        with pytest.raises(nx.NetworkXError):
+            nx.duplication_divergence_graph(3, p=-1)
+
+    def test_non_extreme_probability_value(self):
+        G = nx.duplication_divergence_graph(6, p=0.3, seed=42)
+        assert len(G) == 6
+        assert list(G.degree()) == [(0, 2), (1, 3), (2, 2), (3, 3), (4, 1), (5, 1)]
+
+    def test_minimum_desired_nodes(self):
+        with pytest.raises(
+            nx.NetworkXError, match=".*n must be greater than or equal to 2"
+        ):
+            nx.duplication_divergence_graph(1, p=1)
+
+    def test_create_using(self):
+        class DummyGraph(nx.Graph):
+            pass
+
+        class DummyDiGraph(nx.DiGraph):
+            pass
+
+        G = nx.duplication_divergence_graph(6, 0.3, seed=42, create_using=DummyGraph)
+        assert isinstance(G, DummyGraph)
+        with pytest.raises(nx.NetworkXError, match="create_using must not be directed"):
+            nx.duplication_divergence_graph(6, 0.3, seed=42, create_using=DummyDiGraph)
+
+
+class TestPartialDuplicationGraph:
+    """Unit tests for the
+    :func:`networkx.generators.duplication.partial_duplication_graph`
+    function.
+
+    """
+
+    def test_final_size(self):
+        N = 10
+        n = 5
+        p = 0.5
+        q = 0.5
+        G = nx.partial_duplication_graph(N, n, p, q)
+        assert len(G) == N
+        G = nx.partial_duplication_graph(N, n, p, q, seed=42)
+        assert len(G) == N
+
+    def test_initial_clique_size(self):
+        N = 10
+        n = 10
+        p = 0.5
+        q = 0.5
+        G = nx.partial_duplication_graph(N, n, p, q)
+        assert len(G) == n
+
+    def test_invalid_initial_size(self):
+        with pytest.raises(nx.NetworkXError):
+            N = 5
+            n = 10
+            p = 0.5
+            q = 0.5
+            G = nx.partial_duplication_graph(N, n, p, q)
+
+    def test_invalid_probabilities(self):
+        N = 1
+        n = 1
+        for p, q in [(0.5, 2), (0.5, -1), (2, 0.5), (-1, 0.5)]:
+            args = (N, n, p, q)
+            pytest.raises(nx.NetworkXError, nx.partial_duplication_graph, *args)
+
+    def test_create_using(self):
+        class DummyGraph(nx.Graph):
+            pass
+
+        class DummyDiGraph(nx.DiGraph):
+            pass
+
+        G = nx.partial_duplication_graph(10, 5, 0.5, 0.5, create_using=DummyGraph)
+        assert isinstance(G, DummyGraph)
+        with pytest.raises(nx.NetworkXError, match="create_using must not be directed"):
+            nx.partial_duplication_graph(10, 5, 0.5, 0.5, create_using=DummyDiGraph)

minigpt2/lib/python3.10/site-packages/networkx/generators/tests/test_ego.py

@@ -0,0 +1,39 @@
+"""
+ego graph
+---------
+"""
+
+import networkx as nx
+from networkx.utils import edges_equal, nodes_equal
+
+
+class TestGeneratorEgo:
+    def test_ego(self):
+        G = nx.star_graph(3)
+        H = nx.ego_graph(G, 0)
+        assert nx.is_isomorphic(G, H)
+        G.add_edge(1, 11)
+        G.add_edge(2, 22)
+        G.add_edge(3, 33)
+        H = nx.ego_graph(G, 0)
+        assert nx.is_isomorphic(nx.star_graph(3), H)
+        G = nx.path_graph(3)
+        H = nx.ego_graph(G, 0)
+        assert edges_equal(H.edges(), [(0, 1)])
+        H = nx.ego_graph(G, 0, undirected=True)
+        assert edges_equal(H.edges(), [(0, 1)])
+        H = nx.ego_graph(G, 0, center=False)
+        assert edges_equal(H.edges(), [])
+
+    def test_ego_distance(self):
+        G = nx.Graph()
+        G.add_edge(0, 1, weight=2, distance=1)
+        G.add_edge(1, 2, weight=2, distance=2)
+        G.add_edge(2, 3, weight=2, distance=1)
+        assert nodes_equal(nx.ego_graph(G, 0, radius=3).nodes(), [0, 1, 2, 3])
+        eg = nx.ego_graph(G, 0, radius=3, distance="weight")
+        assert nodes_equal(eg.nodes(), [0, 1])
+        eg = nx.ego_graph(G, 0, radius=3, distance="weight", undirected=True)
+        assert nodes_equal(eg.nodes(), [0, 1])
+        eg = nx.ego_graph(G, 0, radius=3, distance="distance")
+        assert nodes_equal(eg.nodes(), [0, 1, 2])