Initial & Programe PY

credential.txt

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+Félicitations !
+Nous sommes ravis de vous annoncer que vous avez franchi avec succès la première étape. 
+Pour continuer, veuillez utiliser le mot de passe chiffré en Hill avec la clé IJDK et passer par l'autre service ouvert sur la machine.
+
+Mot de passe : yzhxvq
+
+Cordialement,
+Deby

env/Lib/site-packages/_distutils_hack/__init__.py

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+# don't import any costly modules
+import sys
+import os
+
+
+is_pypy = '__pypy__' in sys.builtin_module_names
+
+
+def warn_distutils_present():
+    if 'distutils' not in sys.modules:
+        return
+    if is_pypy and sys.version_info < (3, 7):
+        # PyPy for 3.6 unconditionally imports distutils, so bypass the warning
+        # https://foss.heptapod.net/pypy/pypy/-/blob/be829135bc0d758997b3566062999ee8b23872b4/lib-python/3/site.py#L250
+        return
+    import warnings
+
+    warnings.warn(
+        "Distutils was imported before Setuptools, but importing Setuptools "
+        "also replaces the `distutils` module in `sys.modules`. This may lead "
+        "to undesirable behaviors or errors. To avoid these issues, avoid "
+        "using distutils directly, ensure that setuptools is installed in the "
+        "traditional way (e.g. not an editable install), and/or make sure "
+        "that setuptools is always imported before distutils."
+    )
+
+
+def clear_distutils():
+    if 'distutils' not in sys.modules:
+        return
+    import warnings
+
+    warnings.warn("Setuptools is replacing distutils.")
+    mods = [
+        name
+        for name in sys.modules
+        if name == "distutils" or name.startswith("distutils.")
+    ]
+    for name in mods:
+        del sys.modules[name]
+
+
+def enabled():
+    """
+    Allow selection of distutils by environment variable.
+    """
+    which = os.environ.get('SETUPTOOLS_USE_DISTUTILS', 'local')
+    return which == 'local'
+
+
+def ensure_local_distutils():
+    import importlib
+
+    clear_distutils()
+
+    # With the DistutilsMetaFinder in place,
+    # perform an import to cause distutils to be
+    # loaded from setuptools._distutils. Ref #2906.
+    with shim():
+        importlib.import_module('distutils')
+
+    # check that submodules load as expected
+    core = importlib.import_module('distutils.core')
+    assert '_distutils' in core.__file__, core.__file__
+    assert 'setuptools._distutils.log' not in sys.modules
+
+
+def do_override():
+    """
+    Ensure that the local copy of distutils is preferred over stdlib.
+
+    See https://github.com/pypa/setuptools/issues/417#issuecomment-392298401
+    for more motivation.
+    """
+    if enabled():
+        warn_distutils_present()
+        ensure_local_distutils()
+
+
+class _TrivialRe:
+    def __init__(self, *patterns):
+        self._patterns = patterns
+
+    def match(self, string):
+        return all(pat in string for pat in self._patterns)
+
+
+class DistutilsMetaFinder:
+    def find_spec(self, fullname, path, target=None):
+        # optimization: only consider top level modules and those
+        # found in the CPython test suite.
+        if path is not None and not fullname.startswith('test.'):
+            return
+
+        method_name = 'spec_for_{fullname}'.format(**locals())
+        method = getattr(self, method_name, lambda: None)
+        return method()
+
+    def spec_for_distutils(self):
+        if self.is_cpython():
+            return
+
+        import importlib
+        import importlib.abc
+        import importlib.util
+
+        try:
+            mod = importlib.import_module('setuptools._distutils')
+        except Exception:
+            # There are a couple of cases where setuptools._distutils
+            # may not be present:
+            # - An older Setuptools without a local distutils is
+            #   taking precedence. Ref #2957.
+            # - Path manipulation during sitecustomize removes
+            #   setuptools from the path but only after the hook
+            #   has been loaded. Ref #2980.
+            # In either case, fall back to stdlib behavior.
+            return
+
+        class DistutilsLoader(importlib.abc.Loader):
+            def create_module(self, spec):
+                mod.__name__ = 'distutils'
+                return mod
+
+            def exec_module(self, module):
+                pass
+
+        return importlib.util.spec_from_loader(
+            'distutils', DistutilsLoader(), origin=mod.__file__
+        )
+
+    @staticmethod
+    def is_cpython():
+        """
+        Suppress supplying distutils for CPython (build and tests).
+        Ref #2965 and #3007.
+        """
+        return os.path.isfile('pybuilddir.txt')
+
+    def spec_for_pip(self):
+        """
+        Ensure stdlib distutils when running under pip.
+        See pypa/pip#8761 for rationale.
+        """
+        if self.pip_imported_during_build():
+            return
+        clear_distutils()
+        self.spec_for_distutils = lambda: None
+
+    @classmethod
+    def pip_imported_during_build(cls):
+        """
+        Detect if pip is being imported in a build script. Ref #2355.
+        """
+        import traceback
+
+        return any(
+            cls.frame_file_is_setup(frame) for frame, line in traceback.walk_stack(None)
+        )
+
+    @staticmethod
+    def frame_file_is_setup(frame):
+        """
+        Return True if the indicated frame suggests a setup.py file.
+        """
+        # some frames may not have __file__ (#2940)
+        return frame.f_globals.get('__file__', '').endswith('setup.py')
+
+    def spec_for_sensitive_tests(self):
+        """
+        Ensure stdlib distutils when running select tests under CPython.
+
+        python/cpython#91169
+        """
+        clear_distutils()
+        self.spec_for_distutils = lambda: None
+
+    sensitive_tests = (
+        [
+            'test.test_distutils',
+            'test.test_peg_generator',
+            'test.test_importlib',
+        ]
+        if sys.version_info < (3, 10)
+        else [
+            'test.test_distutils',
+        ]
+    )
+
+
+for name in DistutilsMetaFinder.sensitive_tests:
+    setattr(
+        DistutilsMetaFinder,
+        f'spec_for_{name}',
+        DistutilsMetaFinder.spec_for_sensitive_tests,
+    )
+
+
+DISTUTILS_FINDER = DistutilsMetaFinder()
+
+
+def add_shim():
+    DISTUTILS_FINDER in sys.meta_path or insert_shim()
+
+
+class shim:
+    def __enter__(self):
+        insert_shim()
+
+    def __exit__(self, exc, value, tb):
+        remove_shim()
+
+
+def insert_shim():
+    sys.meta_path.insert(0, DISTUTILS_FINDER)
+
+
+def remove_shim():
+    try:
+        sys.meta_path.remove(DISTUTILS_FINDER)
+    except ValueError:
+        pass

env/Lib/site-packages/_distutils_hack/override.py

@@ -0,0 +1 @@
+__import__('_distutils_hack').do_override()

env/Lib/site-packages/distutils-precedence.pth

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+import os; var = 'SETUPTOOLS_USE_DISTUTILS'; enabled = os.environ.get(var, 'local') == 'local'; enabled and __import__('_distutils_hack').add_shim(); 

env/Lib/site-packages/numpy-2.2.6.dist-info/DELVEWHEEL

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+Version: 1.10.1
+Arguments: ['C:\\Users\\runneradmin\\AppData\\Local\\Temp\\cibw-run-7xf_vzet\\cp310-win_amd64\\build\\venv\\Scripts\\delvewheel', 'repair', '--add-path', 'C:/a/numpy/numpy/.openblas/lib', '-w', 'C:\\Users\\runneradmin\\AppData\\Local\\Temp\\cibw-run-7xf_vzet\\cp310-win_amd64\\repaired_wheel', 'C:\\Users\\runneradmin\\AppData\\Local\\Temp\\cibw-run-7xf_vzet\\cp310-win_amd64\\built_wheel\\numpy-2.2.6-cp310-cp310-win_amd64.whl']

env/Lib/site-packages/numpy-2.2.6.dist-info/INSTALLER

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+pip

env/Lib/site-packages/numpy-2.2.6.dist-info/LICENSE.txt

@@ -0,0 +1,950 @@
+Copyright (c) 2005-2024, NumPy Developers.
+All rights reserved.
+
+Redistribution and use in source and binary forms, with or without
+modification, are permitted provided that the following conditions are
+met:
+
+    * Redistributions of source code must retain the above copyright
+       notice, this list of conditions and the following disclaimer.
+
+    * Redistributions in binary form must reproduce the above
+       copyright notice, this list of conditions and the following
+       disclaimer in the documentation and/or other materials provided
+       with the distribution.
+
+    * Neither the name of the NumPy Developers nor the names of any
+       contributors may be used to endorse or promote products derived
+       from this software without specific prior written permission.
+
+THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
+"AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
+LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
+A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT
+OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
+SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT
+LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
+DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
+THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+(INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+
+----
+
+The NumPy repository and source distributions bundle several libraries that are
+compatibly licensed.  We list these here.
+
+Name: lapack-lite
+Files: numpy/linalg/lapack_lite/*
+License: BSD-3-Clause
+  For details, see numpy/linalg/lapack_lite/LICENSE.txt
+
+Name: dragon4
+Files: numpy/_core/src/multiarray/dragon4.c
+License: MIT
+  For license text, see numpy/_core/src/multiarray/dragon4.c
+
+Name: libdivide
+Files: numpy/_core/include/numpy/libdivide/*
+License: Zlib
+  For license text, see numpy/_core/include/numpy/libdivide/LICENSE.txt
+
+
+Note that the following files are vendored in the repository and sdist but not
+installed in built numpy packages:
+
+Name: Meson
+Files: vendored-meson/meson/*
+License: Apache 2.0
+  For license text, see vendored-meson/meson/COPYING
+
+Name: spin
+Files: .spin/cmds.py
+License: BSD-3
+  For license text, see .spin/LICENSE
+
+Name: tempita
+Files: numpy/_build_utils/tempita/*
+License: MIT
+  For details, see numpy/_build_utils/tempita/LICENCE.txt
+
+----
+
+This binary distribution of NumPy also bundles the following software:
+
+
+Name: OpenBLAS
+Files: numpy.libs\libscipy_openblas*.dll
+Description: bundled as a dynamically linked library
+Availability: https://github.com/OpenMathLib/OpenBLAS/
+License: BSD-3-Clause
+  Copyright (c) 2011-2014, The OpenBLAS Project
+  All rights reserved.
+
+  Redistribution and use in source and binary forms, with or without
+  modification, are permitted provided that the following conditions are
+  met:
+
+     1. Redistributions of source code must retain the above copyright
+        notice, this list of conditions and the following disclaimer.
+
+     2. Redistributions in binary form must reproduce the above copyright
+        notice, this list of conditions and the following disclaimer in
+        the documentation and/or other materials provided with the
+        distribution.
+     3. Neither the name of the OpenBLAS project nor the names of
+        its contributors may be used to endorse or promote products
+        derived from this software without specific prior written
+        permission.
+
+  THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
+  AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
+  IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
+  ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE
+  LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
+  DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
+  SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
+  CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
+  OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE
+  USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+
+
+Name: LAPACK
+Files: numpy.libs\libscipy_openblas*.dll
+Description: bundled in OpenBLAS
+Availability: https://github.com/OpenMathLib/OpenBLAS/
+License: BSD-3-Clause-Attribution
+  Copyright (c) 1992-2013 The University of Tennessee and The University
+                          of Tennessee Research Foundation.  All rights
+                          reserved.
+  Copyright (c) 2000-2013 The University of California Berkeley. All
+                          rights reserved.
+  Copyright (c) 2006-2013 The University of Colorado Denver.  All rights
+                          reserved.
+
+  $COPYRIGHT$
+
+  Additional copyrights may follow
+
+  $HEADER$
+
+  Redistribution and use in source and binary forms, with or without
+  modification, are permitted provided that the following conditions are
+  met:
+
+  - Redistributions of source code must retain the above copyright
+    notice, this list of conditions and the following disclaimer.
+
+  - Redistributions in binary form must reproduce the above copyright
+    notice, this list of conditions and the following disclaimer listed
+    in this license in the documentation and/or other materials
+    provided with the distribution.
+
+  - Neither the name of the copyright holders nor the names of its
+    contributors may be used to endorse or promote products derived from
+    this software without specific prior written permission.
+
+  The copyright holders provide no reassurances that the source code
+  provided does not infringe any patent, copyright, or any other
+  intellectual property rights of third parties.  The copyright holders
+  disclaim any liability to any recipient for claims brought against
+  recipient by any third party for infringement of that parties
+  intellectual property rights.
+
+  THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
+  "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
+  LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
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+  OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
+  SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT
+  LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
+  DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
+  THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+  (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+  OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+
+
+Name: GCC runtime library
+Files: numpy.libs\libscipy_openblas*.dll
+Description: statically linked to files compiled with gcc
+Availability: https://gcc.gnu.org/git/?p=gcc.git;a=tree;f=libgfortran
+License: GPL-3.0-with-GCC-exception
+  Copyright (C) 2002-2017 Free Software Foundation, Inc.
+
+  Libgfortran is free software; you can redistribute it and/or modify
+  it under the terms of the GNU General Public License as published by
+  the Free Software Foundation; either version 3, or (at your option)
+  any later version.
+
+  Libgfortran is distributed in the hope that it will be useful,
+  but WITHOUT ANY WARRANTY; without even the implied warranty of
+  MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+  GNU General Public License for more details.
+
+  Under Section 7 of GPL version 3, you are granted additional
+  permissions described in the GCC Runtime Library Exception, version
+  3.1, as published by the Free Software Foundation.
+
+  You should have received a copy of the GNU General Public License and
+  a copy of the GCC Runtime Library Exception along with this program;
+  see the files COPYING3 and COPYING.RUNTIME respectively.  If not, see
+  <http://www.gnu.org/licenses/>.
+
+----
+
+Full text of license texts referred to above follows (that they are
+listed below does not necessarily imply the conditions apply to the
+present binary release):
+
+----
+
+GCC RUNTIME LIBRARY EXCEPTION
+
+Version 3.1, 31 March 2009
+
+Copyright (C) 2009 Free Software Foundation, Inc. <http://fsf.org/>
+
+Everyone is permitted to copy and distribute verbatim copies of this
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+
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+
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+
+----
+
+                    GNU GENERAL PUBLIC LICENSE
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+
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+
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+<http://www.gnu.org/philosophy/why-not-lgpl.html>.
+

env/Lib/site-packages/numpy-2.2.6.dist-info/WHEEL

@@ -0,0 +1,4 @@
+Wheel-Version: 1.0
+Generator: meson
+Root-Is-Purelib: false
+Tag: cp310-cp310-win_amd64

env/Lib/site-packages/numpy-2.2.6.dist-info/entry_points.txt

@@ -0,0 +1,10 @@
+[array_api]
+numpy = numpy
+
+[pyinstaller40]
+hook-dirs = numpy:_pyinstaller_hooks_dir
+
+[console_scripts]
+f2py = numpy.f2py.f2py2e:main
+numpy-config = numpy._configtool:main
+

env/Lib/site-packages/numpy/__config__.py

@@ -0,0 +1,170 @@
+# This file is generated by numpy's build process
+# It contains system_info results at the time of building this package.
+from enum import Enum
+from numpy._core._multiarray_umath import (
+    __cpu_features__,
+    __cpu_baseline__,
+    __cpu_dispatch__,
+)
+
+__all__ = ["show_config"]
+_built_with_meson = True
+
+
+class DisplayModes(Enum):
+    stdout = "stdout"
+    dicts = "dicts"
+
+
+def _cleanup(d):
+    """
+    Removes empty values in a `dict` recursively
+    This ensures we remove values that Meson could not provide to CONFIG
+    """
+    if isinstance(d, dict):
+        return {k: _cleanup(v) for k, v in d.items() if v and _cleanup(v)}
+    else:
+        return d
+
+
+CONFIG = _cleanup(
+    {
+        "Compilers": {
+            "c": {
+                "name": "msvc",
+                "linker": r"link",
+                "version": "19.29.30159",
+                "commands": r"cl",
+                "args": r"",
+                "linker args": r"",
+            },
+            "cython": {
+                "name": "cython",
+                "linker": r"cython",
+                "version": "3.1.0",
+                "commands": r"cython",
+                "args": r"",
+                "linker args": r"",
+            },
+            "c++": {
+                "name": "msvc",
+                "linker": r"link",
+                "version": "19.29.30159",
+                "commands": r"cl",
+                "args": r"",
+                "linker args": r"",
+            },
+        },
+        "Machine Information": {
+            "host": {
+                "cpu": "x86_64",
+                "family": "x86_64",
+                "endian": "little",
+                "system": "windows",
+            },
+            "build": {
+                "cpu": "x86_64",
+                "family": "x86_64",
+                "endian": "little",
+                "system": "windows",
+            },
+            "cross-compiled": bool("False".lower().replace("false", "")),
+        },
+        "Build Dependencies": {
+            "blas": {
+                "name": "scipy-openblas",
+                "found": bool("True".lower().replace("false", "")),
+                "version": "0.3.29",
+                "detection method": "pkgconfig",
+                "include directory": r"C:/Users/runneradmin/AppData/Local/Temp/cibw-run-7xf_vzet/cp310-win_amd64/build/venv/Lib/site-packages/scipy_openblas64/include",
+                "lib directory": r"C:/Users/runneradmin/AppData/Local/Temp/cibw-run-7xf_vzet/cp310-win_amd64/build/venv/Lib/site-packages/scipy_openblas64/lib",
+                "openblas configuration": r"OpenBLAS 0.3.29  USE64BITINT DYNAMIC_ARCH NO_AFFINITY Haswell MAX_THREADS=24",
+                "pc file directory": r"C:/a/numpy/numpy/.openblas",
+            },
+            "lapack": {
+                "name": "scipy-openblas",
+                "found": bool("True".lower().replace("false", "")),
+                "version": "0.3.29",
+                "detection method": "pkgconfig",
+                "include directory": r"C:/Users/runneradmin/AppData/Local/Temp/cibw-run-7xf_vzet/cp310-win_amd64/build/venv/Lib/site-packages/scipy_openblas64/include",
+                "lib directory": r"C:/Users/runneradmin/AppData/Local/Temp/cibw-run-7xf_vzet/cp310-win_amd64/build/venv/Lib/site-packages/scipy_openblas64/lib",
+                "openblas configuration": r"OpenBLAS 0.3.29  USE64BITINT DYNAMIC_ARCH NO_AFFINITY Haswell MAX_THREADS=24",
+                "pc file directory": r"C:/a/numpy/numpy/.openblas",
+            },
+        },
+        "Python Information": {
+            "path": r"C:\Users\runneradmin\AppData\Local\Temp\build-env-r6iiznem\Scripts\python.exe",
+            "version": "3.10",
+        },
+        "SIMD Extensions": {
+            "baseline": __cpu_baseline__,
+            "found": [
+                feature for feature in __cpu_dispatch__ if __cpu_features__[feature]
+            ],
+            "not found": [
+                feature for feature in __cpu_dispatch__ if not __cpu_features__[feature]
+            ],
+        },
+    }
+)
+
+
+def _check_pyyaml():
+    import yaml
+
+    return yaml
+
+
+def show(mode=DisplayModes.stdout.value):
+    """
+    Show libraries and system information on which NumPy was built
+    and is being used
+
+    Parameters
+    ----------
+    mode : {`'stdout'`, `'dicts'`}, optional.
+        Indicates how to display the config information.
+        `'stdout'` prints to console, `'dicts'` returns a dictionary
+        of the configuration.
+
+    Returns
+    -------
+    out : {`dict`, `None`}
+        If mode is `'dicts'`, a dict is returned, else None
+
+    See Also
+    --------
+    get_include : Returns the directory containing NumPy C
+                  header files.
+
+    Notes
+    -----
+    1. The `'stdout'` mode will give more readable
+       output if ``pyyaml`` is installed
+
+    """
+    if mode == DisplayModes.stdout.value:
+        try:  # Non-standard library, check import
+            yaml = _check_pyyaml()
+
+            print(yaml.dump(CONFIG))
+        except ModuleNotFoundError:
+            import warnings
+            import json
+
+            warnings.warn("Install `pyyaml` for better output", stacklevel=1)
+            print(json.dumps(CONFIG, indent=2))
+    elif mode == DisplayModes.dicts.value:
+        return CONFIG
+    else:
+        raise AttributeError(
+            f"Invalid `mode`, use one of: {', '.join([e.value for e in DisplayModes])}"
+        )
+
+
+def show_config(mode=DisplayModes.stdout.value):
+    return show(mode)
+
+
+show_config.__doc__ = show.__doc__
+show_config.__module__ = "numpy"

env/Lib/site-packages/numpy/__config__.pyi

@@ -0,0 +1,102 @@
+from enum import Enum
+from types import ModuleType
+from typing import Final, Literal as L, TypedDict, overload, type_check_only
+from typing_extensions import NotRequired
+
+_CompilerConfigDictValue = TypedDict(
+    "_CompilerConfigDictValue",
+    {
+        "name": str,
+        "linker": str,
+        "version": str,
+        "commands": str,
+        "args": str,
+        "linker args": str,
+    },
+)
+_CompilerConfigDict = TypedDict(
+    "_CompilerConfigDict",
+    {
+        "c": _CompilerConfigDictValue,
+        "cython": _CompilerConfigDictValue,
+        "c++": _CompilerConfigDictValue,
+    },
+)
+_MachineInformationDict = TypedDict(
+    "_MachineInformationDict",
+    {
+        "host":_MachineInformationDictValue,
+        "build": _MachineInformationDictValue,
+        "cross-compiled": NotRequired[L[True]],
+    },
+)
+
+@type_check_only
+class _MachineInformationDictValue(TypedDict):
+    cpu: str
+    family: str
+    endian: L["little", "big"]
+    system: str
+
+_BuildDependenciesDictValue = TypedDict(
+    "_BuildDependenciesDictValue",
+    {
+        "name": str,
+        "found": NotRequired[L[True]],
+        "version": str,
+        "include directory": str,
+        "lib directory": str,
+        "openblas configuration": str,
+        "pc file directory": str,
+    },
+)
+
+class _BuildDependenciesDict(TypedDict):
+    blas: _BuildDependenciesDictValue
+    lapack: _BuildDependenciesDictValue
+
+class _PythonInformationDict(TypedDict):
+    path: str
+    version: str
+
+_SIMDExtensionsDict = TypedDict(
+    "_SIMDExtensionsDict",
+    {
+        "baseline": list[str],
+        "found": list[str],
+        "not found": list[str],
+    },
+)
+
+_ConfigDict = TypedDict(
+    "_ConfigDict",
+    {
+        "Compilers": _CompilerConfigDict,
+        "Machine Information": _MachineInformationDict,
+        "Build Dependencies": _BuildDependenciesDict,
+        "Python Information": _PythonInformationDict,
+        "SIMD Extensions": _SIMDExtensionsDict,
+    },
+)
+
+###
+
+__all__ = ["show_config"]
+
+CONFIG: Final[_ConfigDict] = ...
+
+class DisplayModes(Enum):
+    stdout = "stdout"
+    dicts = "dicts"
+
+def _check_pyyaml() -> ModuleType: ...
+
+@overload
+def show(mode: L["stdout"] = "stdout") -> None: ...
+@overload
+def show(mode: L["dicts"]) -> _ConfigDict: ...
+
+@overload
+def show_config(mode: L["stdout"] = "stdout") -> None: ...
+@overload
+def show_config(mode: L["dicts"]) -> _ConfigDict: ...

env/Lib/site-packages/numpy/__init__.py

@@ -0,0 +1,560 @@
+"""
+NumPy
+=====
+
+Provides
+  1. An array object of arbitrary homogeneous items
+  2. Fast mathematical operations over arrays
+  3. Linear Algebra, Fourier Transforms, Random Number Generation
+
+How to use the documentation
+----------------------------
+Documentation is available in two forms: docstrings provided
+with the code, and a loose standing reference guide, available from
+`the NumPy homepage <https://numpy.org>`_.
+
+We recommend exploring the docstrings using
+`IPython <https://ipython.org>`_, an advanced Python shell with
+TAB-completion and introspection capabilities.  See below for further
+instructions.
+
+The docstring examples assume that `numpy` has been imported as ``np``::
+
+  >>> import numpy as np
+
+Code snippets are indicated by three greater-than signs::
+
+  >>> x = 42
+  >>> x = x + 1
+
+Use the built-in ``help`` function to view a function's docstring::
+
+  >>> help(np.sort)
+  ... # doctest: +SKIP
+
+For some objects, ``np.info(obj)`` may provide additional help.  This is
+particularly true if you see the line "Help on ufunc object:" at the top
+of the help() page.  Ufuncs are implemented in C, not Python, for speed.
+The native Python help() does not know how to view their help, but our
+np.info() function does.
+
+Available subpackages
+---------------------
+lib
+    Basic functions used by several sub-packages.
+random
+    Core Random Tools
+linalg
+    Core Linear Algebra Tools
+fft
+    Core FFT routines
+polynomial
+    Polynomial tools
+testing
+    NumPy testing tools
+distutils
+    Enhancements to distutils with support for
+    Fortran compilers support and more (for Python <= 3.11)
+
+Utilities
+---------
+test
+    Run numpy unittests
+show_config
+    Show numpy build configuration
+__version__
+    NumPy version string
+
+Viewing documentation using IPython
+-----------------------------------
+
+Start IPython and import `numpy` usually under the alias ``np``: `import
+numpy as np`.  Then, directly past or use the ``%cpaste`` magic to paste
+examples into the shell.  To see which functions are available in `numpy`,
+type ``np.<TAB>`` (where ``<TAB>`` refers to the TAB key), or use
+``np.*cos*?<ENTER>`` (where ``<ENTER>`` refers to the ENTER key) to narrow
+down the list.  To view the docstring for a function, use
+``np.cos?<ENTER>`` (to view the docstring) and ``np.cos??<ENTER>`` (to view
+the source code).
+
+Copies vs. in-place operation
+-----------------------------
+Most of the functions in `numpy` return a copy of the array argument
+(e.g., `np.sort`).  In-place versions of these functions are often
+available as array methods, i.e. ``x = np.array([1,2,3]); x.sort()``.
+Exceptions to this rule are documented.
+
+"""
+
+
+# start delvewheel patch
+def _delvewheel_patch_1_10_1():
+    import os
+    if os.path.isdir(libs_dir := os.path.abspath(os.path.join(os.path.dirname(__file__), os.pardir, 'numpy.libs'))):
+        os.add_dll_directory(libs_dir)
+
+
+_delvewheel_patch_1_10_1()
+del _delvewheel_patch_1_10_1
+# end delvewheel patch
+
+import os
+import sys
+import warnings
+
+from ._globals import _NoValue, _CopyMode
+from ._expired_attrs_2_0 import __expired_attributes__
+
+
+# If a version with git hash was stored, use that instead
+from . import version
+from .version import __version__
+
+# We first need to detect if we're being called as part of the numpy setup
+# procedure itself in a reliable manner.
+try:
+    __NUMPY_SETUP__
+except NameError:
+    __NUMPY_SETUP__ = False
+
+if __NUMPY_SETUP__:
+    sys.stderr.write('Running from numpy source directory.\n')
+else:
+    # Allow distributors to run custom init code before importing numpy._core
+    from . import _distributor_init
+
+    try:
+        from numpy.__config__ import show_config
+    except ImportError as e:
+        msg = """Error importing numpy: you should not try to import numpy from
+        its source directory; please exit the numpy source tree, and relaunch
+        your python interpreter from there."""
+        raise ImportError(msg) from e
+
+    from . import _core
+    from ._core import (
+        False_, ScalarType, True_,
+        abs, absolute, acos, acosh, add, all, allclose,
+        amax, amin, any, arange, arccos, arccosh, arcsin, arcsinh,
+        arctan, arctan2, arctanh, argmax, argmin, argpartition, argsort,
+        argwhere, around, array, array2string, array_equal, array_equiv,
+        array_repr, array_str, asanyarray, asarray, ascontiguousarray,
+        asfortranarray, asin, asinh, atan, atanh, atan2, astype, atleast_1d,
+        atleast_2d, atleast_3d, base_repr, binary_repr, bitwise_and,
+        bitwise_count, bitwise_invert, bitwise_left_shift, bitwise_not,
+        bitwise_or, bitwise_right_shift, bitwise_xor, block, bool, bool_,
+        broadcast, busday_count, busday_offset, busdaycalendar, byte, bytes_,
+        can_cast, cbrt, cdouble, ceil, character, choose, clip, clongdouble,
+        complex128, complex64, complexfloating, compress, concat, concatenate,
+        conj, conjugate, convolve, copysign, copyto, correlate, cos, cosh,
+        count_nonzero, cross, csingle, cumprod, cumsum, cumulative_prod,
+        cumulative_sum, datetime64, datetime_as_string, datetime_data,
+        deg2rad, degrees, diagonal, divide, divmod, dot, double, dtype, e,
+        einsum, einsum_path, empty, empty_like, equal, errstate, euler_gamma,
+        exp, exp2, expm1, fabs, finfo, flatiter, flatnonzero, flexible,
+        float16, float32, float64, float_power, floating, floor, floor_divide,
+        fmax, fmin, fmod, format_float_positional, format_float_scientific,
+        frexp, from_dlpack, frombuffer, fromfile, fromfunction, fromiter,
+        frompyfunc, fromstring, full, full_like, gcd, generic, geomspace,
+        get_printoptions, getbufsize, geterr, geterrcall, greater,
+        greater_equal, half, heaviside, hstack, hypot, identity, iinfo,
+        indices, inexact, inf, inner, int16, int32, int64, int8, int_, intc,
+        integer, intp, invert, is_busday, isclose, isdtype, isfinite,
+        isfortran, isinf, isnan, isnat, isscalar, issubdtype, lcm, ldexp,
+        left_shift, less, less_equal, lexsort, linspace, little_endian, log,
+        log10, log1p, log2, logaddexp, logaddexp2, logical_and, logical_not,
+        logical_or, logical_xor, logspace, long, longdouble, longlong, matmul,
+        matvec, matrix_transpose, max, maximum, may_share_memory, mean, memmap,
+        min, min_scalar_type, minimum, mod, modf, moveaxis, multiply, nan,
+        ndarray, ndim, nditer, negative, nested_iters, newaxis, nextafter,
+        nonzero, not_equal, number, object_, ones, ones_like, outer, partition,
+        permute_dims, pi, positive, pow, power, printoptions, prod,
+        promote_types, ptp, put, putmask, rad2deg, radians, ravel, recarray,
+        reciprocal, record, remainder, repeat, require, reshape, resize,
+        result_type, right_shift, rint, roll, rollaxis, round, sctypeDict,
+        searchsorted, set_printoptions, setbufsize, seterr, seterrcall, shape,
+        shares_memory, short, sign, signbit, signedinteger, sin, single, sinh,
+        size, sort, spacing, sqrt, square, squeeze, stack, std,
+        str_, subtract, sum, swapaxes, take, tan, tanh, tensordot,
+        timedelta64, trace, transpose, true_divide, trunc, typecodes, ubyte,
+        ufunc, uint, uint16, uint32, uint64, uint8, uintc, uintp, ulong,
+        ulonglong, unsignedinteger, unstack, ushort, var, vdot, vecdot,
+        vecmat, void, vstack, where, zeros, zeros_like
+    )
+
+    # NOTE: It's still under discussion whether these aliases
+    # should be removed.
+    for ta in ["float96", "float128", "complex192", "complex256"]:
+        try:
+            globals()[ta] = getattr(_core, ta)
+        except AttributeError:
+            pass
+    del ta
+
+    from . import lib
+    from .lib import scimath as emath
+    from .lib._histograms_impl import (
+        histogram, histogram_bin_edges, histogramdd
+    )
+    from .lib._nanfunctions_impl import (
+        nanargmax, nanargmin, nancumprod, nancumsum, nanmax, nanmean,
+        nanmedian, nanmin, nanpercentile, nanprod, nanquantile, nanstd,
+        nansum, nanvar
+    )
+    from .lib._function_base_impl import (
+        select, piecewise, trim_zeros, copy, iterable, percentile, diff,
+        gradient, angle, unwrap, sort_complex, flip, rot90, extract, place,
+        vectorize, asarray_chkfinite, average, bincount, digitize, cov,
+        corrcoef, median, sinc, hamming, hanning, bartlett, blackman,
+        kaiser, trapezoid, trapz, i0, meshgrid, delete, insert, append,
+        interp, quantile
+    )
+    from .lib._twodim_base_impl import (
+        diag, diagflat, eye, fliplr, flipud, tri, triu, tril, vander,
+        histogram2d, mask_indices, tril_indices, tril_indices_from,
+        triu_indices, triu_indices_from
+    )
+    from .lib._shape_base_impl import (
+        apply_over_axes, apply_along_axis, array_split, column_stack, dsplit,
+        dstack, expand_dims, hsplit, kron, put_along_axis, row_stack, split,
+        take_along_axis, tile, vsplit
+    )
+    from .lib._type_check_impl import (
+        iscomplexobj, isrealobj, imag, iscomplex, isreal, nan_to_num, real,
+        real_if_close, typename, mintypecode, common_type
+    )
+    from .lib._arraysetops_impl import (
+        ediff1d, in1d, intersect1d, isin, setdiff1d, setxor1d, union1d,
+        unique, unique_all, unique_counts, unique_inverse, unique_values
+    )
+    from .lib._ufunclike_impl import fix, isneginf, isposinf
+    from .lib._arraypad_impl import pad
+    from .lib._utils_impl import (
+        show_runtime, get_include, info
+    )
+    from .lib._stride_tricks_impl import (
+        broadcast_arrays, broadcast_shapes, broadcast_to
+    )
+    from .lib._polynomial_impl import (
+        poly, polyint, polyder, polyadd, polysub, polymul, polydiv, polyval,
+        polyfit, poly1d, roots
+    )
+    from .lib._npyio_impl import (
+        savetxt, loadtxt, genfromtxt, load, save, savez, packbits,
+        savez_compressed, unpackbits, fromregex
+    )
+    from .lib._index_tricks_impl import (
+        diag_indices_from, diag_indices, fill_diagonal, ndindex, ndenumerate,
+        ix_, c_, r_, s_, ogrid, mgrid, unravel_index, ravel_multi_index,
+        index_exp
+    )
+
+    from . import matrixlib as _mat
+    from .matrixlib import (
+        asmatrix, bmat, matrix
+    )
+
+    # public submodules are imported lazily, therefore are accessible from
+    # __getattr__. Note that `distutils` (deprecated) and `array_api`
+    # (experimental label) are not added here, because `from numpy import *`
+    # must not raise any warnings - that's too disruptive.
+    __numpy_submodules__ = {
+        "linalg", "fft", "dtypes", "random", "polynomial", "ma",
+        "exceptions", "lib", "ctypeslib", "testing", "typing",
+        "f2py", "test", "rec", "char", "core", "strings",
+    }
+
+    # We build warning messages for former attributes
+    _msg = (
+        "module 'numpy' has no attribute '{n}'.\n"
+        "`np.{n}` was a deprecated alias for the builtin `{n}`. "
+        "To avoid this error in existing code, use `{n}` by itself. "
+        "Doing this will not modify any behavior and is safe. {extended_msg}\n"
+        "The aliases was originally deprecated in NumPy 1.20; for more "
+        "details and guidance see the original release note at:\n"
+        "    https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations")
+
+    _specific_msg = (
+        "If you specifically wanted the numpy scalar type, use `np.{}` here.")
+
+    _int_extended_msg = (
+        "When replacing `np.{}`, you may wish to use e.g. `np.int64` "
+        "or `np.int32` to specify the precision. If you wish to review "
+        "your current use, check the release note link for "
+        "additional information.")
+
+    _type_info = [
+        ("object", ""),  # The NumPy scalar only exists by name.
+        ("float", _specific_msg.format("float64")),
+        ("complex", _specific_msg.format("complex128")),
+        ("str", _specific_msg.format("str_")),
+        ("int", _int_extended_msg.format("int"))]
+
+    __former_attrs__ = {
+         n: _msg.format(n=n, extended_msg=extended_msg)
+         for n, extended_msg in _type_info
+     }
+
+
+    # Some of these could be defined right away, but most were aliases to
+    # the Python objects and only removed in NumPy 1.24.  Defining them should
+    # probably wait for NumPy 1.26 or 2.0.
+    # When defined, these should possibly not be added to `__all__` to avoid
+    # import with `from numpy import *`.
+    __future_scalars__ = {"str", "bytes", "object"}
+
+    __array_api_version__ = "2023.12"
+
+    from ._array_api_info import __array_namespace_info__
+
+    # now that numpy core module is imported, can initialize limits
+    _core.getlimits._register_known_types()
+
+    __all__ = list(
+        __numpy_submodules__ |
+        set(_core.__all__) |
+        set(_mat.__all__) |
+        set(lib._histograms_impl.__all__) |
+        set(lib._nanfunctions_impl.__all__) |
+        set(lib._function_base_impl.__all__) |
+        set(lib._twodim_base_impl.__all__) |
+        set(lib._shape_base_impl.__all__) |
+        set(lib._type_check_impl.__all__) |
+        set(lib._arraysetops_impl.__all__) |
+        set(lib._ufunclike_impl.__all__) |
+        set(lib._arraypad_impl.__all__) |
+        set(lib._utils_impl.__all__) |
+        set(lib._stride_tricks_impl.__all__) |
+        set(lib._polynomial_impl.__all__) |
+        set(lib._npyio_impl.__all__) |
+        set(lib._index_tricks_impl.__all__) |
+        {"emath", "show_config", "__version__", "__array_namespace_info__"}
+    )
+
+    # Filter out Cython harmless warnings
+    warnings.filterwarnings("ignore", message="numpy.dtype size changed")
+    warnings.filterwarnings("ignore", message="numpy.ufunc size changed")
+    warnings.filterwarnings("ignore", message="numpy.ndarray size changed")
+
+    def __getattr__(attr):
+        # Warn for expired attributes
+        import warnings
+
+        if attr == "linalg":
+            import numpy.linalg as linalg
+            return linalg
+        elif attr == "fft":
+            import numpy.fft as fft
+            return fft
+        elif attr == "dtypes":
+            import numpy.dtypes as dtypes
+            return dtypes
+        elif attr == "random":
+            import numpy.random as random
+            return random
+        elif attr == "polynomial":
+            import numpy.polynomial as polynomial
+            return polynomial
+        elif attr == "ma":
+            import numpy.ma as ma
+            return ma
+        elif attr == "ctypeslib":
+            import numpy.ctypeslib as ctypeslib
+            return ctypeslib
+        elif attr == "exceptions":
+            import numpy.exceptions as exceptions
+            return exceptions
+        elif attr == "testing":
+            import numpy.testing as testing
+            return testing
+        elif attr == "matlib":
+            import numpy.matlib as matlib
+            return matlib
+        elif attr == "f2py":
+            import numpy.f2py as f2py
+            return f2py
+        elif attr == "typing":
+            import numpy.typing as typing
+            return typing
+        elif attr == "rec":
+            import numpy.rec as rec
+            return rec
+        elif attr == "char":
+            import numpy.char as char
+            return char
+        elif attr == "array_api":
+            raise AttributeError("`numpy.array_api` is not available from "
+                                 "numpy 2.0 onwards", name=None)
+        elif attr == "core":
+            import numpy.core as core
+            return core
+        elif attr == "strings":
+            import numpy.strings as strings
+            return strings
+        elif attr == "distutils":
+            if 'distutils' in __numpy_submodules__:
+                import numpy.distutils as distutils
+                return distutils
+            else:
+                raise AttributeError("`numpy.distutils` is not available from "
+                                     "Python 3.12 onwards", name=None)
+
+        if attr in __future_scalars__:
+            # And future warnings for those that will change, but also give
+            # the AttributeError
+            warnings.warn(
+                f"In the future `np.{attr}` will be defined as the "
+                "corresponding NumPy scalar.", FutureWarning, stacklevel=2)
+
+        if attr in __former_attrs__:
+            raise AttributeError(__former_attrs__[attr], name=None)
+
+        if attr in __expired_attributes__:
+            raise AttributeError(
+                f"`np.{attr}` was removed in the NumPy 2.0 release. "
+                f"{__expired_attributes__[attr]}",
+                name=None
+            )
+
+        if attr == "chararray":
+            warnings.warn(
+                "`np.chararray` is deprecated and will be removed from "
+                "the main namespace in the future. Use an array with a string "
+                "or bytes dtype instead.", DeprecationWarning, stacklevel=2)
+            import numpy.char as char
+            return char.chararray
+
+        raise AttributeError("module {!r} has no attribute "
+                             "{!r}".format(__name__, attr))
+
+    def __dir__():
+        public_symbols = (
+            globals().keys() | __numpy_submodules__
+        )
+        public_symbols -= {
+            "matrixlib", "matlib", "tests", "conftest", "version",
+            "compat", "distutils", "array_api"
+        }
+        return list(public_symbols)
+
+    # Pytest testing
+    from numpy._pytesttester import PytestTester
+    test = PytestTester(__name__)
+    del PytestTester
+
+    def _sanity_check():
+        """
+        Quick sanity checks for common bugs caused by environment.
+        There are some cases e.g. with wrong BLAS ABI that cause wrong
+        results under specific runtime conditions that are not necessarily
+        achieved during test suite runs, and it is useful to catch those early.
+
+        See https://github.com/numpy/numpy/issues/8577 and other
+        similar bug reports.
+
+        """
+        try:
+            x = ones(2, dtype=float32)
+            if not abs(x.dot(x) - float32(2.0)) < 1e-5:
+                raise AssertionError
+        except AssertionError:
+            msg = ("The current Numpy installation ({!r}) fails to "
+                   "pass simple sanity checks. This can be caused for example "
+                   "by incorrect BLAS library being linked in, or by mixing "
+                   "package managers (pip, conda, apt, ...). Search closed "
+                   "numpy issues for similar problems.")
+            raise RuntimeError(msg.format(__file__)) from None
+
+    _sanity_check()
+    del _sanity_check
+
+    def _mac_os_check():
+        """
+        Quick Sanity check for Mac OS look for accelerate build bugs.
+        Testing numpy polyfit calls init_dgelsd(LAPACK)
+        """
+        try:
+            c = array([3., 2., 1.])
+            x = linspace(0, 2, 5)
+            y = polyval(c, x)
+            _ = polyfit(x, y, 2, cov=True)
+        except ValueError:
+            pass
+
+    if sys.platform == "darwin":
+        from . import exceptions
+        with warnings.catch_warnings(record=True) as w:
+            _mac_os_check()
+            # Throw runtime error, if the test failed Check for warning and error_message
+            if len(w) > 0:
+                for _wn in w:
+                    if _wn.category is exceptions.RankWarning:
+                        # Ignore other warnings, they may not be relevant (see gh-25433).
+                        error_message = (
+                            f"{_wn.category.__name__}: {_wn.message}"
+                        )
+                        msg = (
+                            "Polyfit sanity test emitted a warning, most likely due "
+                            "to using a buggy Accelerate backend."
+                            "\nIf you compiled yourself, more information is available at:"
+                            "\nhttps://numpy.org/devdocs/building/index.html"
+                            "\nOtherwise report this to the vendor "
+                            "that provided NumPy.\n\n{}\n".format(error_message))
+                        raise RuntimeError(msg)
+                del _wn
+            del w
+    del _mac_os_check
+
+    def hugepage_setup():
+        """
+        We usually use madvise hugepages support, but on some old kernels it
+        is slow and thus better avoided. Specifically kernel version 4.6
+        had a bug fix which probably fixed this:
+        https://github.com/torvalds/linux/commit/7cf91a98e607c2f935dbcc177d70011e95b8faff
+        """
+        use_hugepage = os.environ.get("NUMPY_MADVISE_HUGEPAGE", None)
+        if sys.platform == "linux" and use_hugepage is None:
+            # If there is an issue with parsing the kernel version,
+            # set use_hugepage to 0. Usage of LooseVersion will handle
+            # the kernel version parsing better, but avoided since it
+            # will increase the import time.
+            # See: #16679 for related discussion.
+            try:
+                use_hugepage = 1
+                kernel_version = os.uname().release.split(".")[:2]
+                kernel_version = tuple(int(v) for v in kernel_version)
+                if kernel_version < (4, 6):
+                    use_hugepage = 0
+            except ValueError:
+                use_hugepage = 0
+        elif use_hugepage is None:
+            # This is not Linux, so it should not matter, just enable anyway
+            use_hugepage = 1
+        else:
+            use_hugepage = int(use_hugepage)
+        return use_hugepage
+
+    # Note that this will currently only make a difference on Linux
+    _core.multiarray._set_madvise_hugepage(hugepage_setup())
+    del hugepage_setup
+
+    # Give a warning if NumPy is reloaded or imported on a sub-interpreter
+    # We do this from python, since the C-module may not be reloaded and
+    # it is tidier organized.
+    _core.multiarray._multiarray_umath._reload_guard()
+
+    # TODO: Remove the environment variable entirely now that it is "weak"
+    if (os.environ.get("NPY_PROMOTION_STATE", "weak") != "weak"):
+        warnings.warn(
+            "NPY_PROMOTION_STATE was a temporary feature for NumPy 2.0 "
+            "transition and is ignored after NumPy 2.2.",
+            UserWarning, stacklevel=2)
+
+    # Tell PyInstaller where to find hook-numpy.py
+    def _pyinstaller_hooks_dir():
+        from pathlib import Path
+        return [str(Path(__file__).with_name("_pyinstaller").resolve())]
+
+
+# Remove symbols imported for internal use
+del os, sys, warnings

env/Lib/site-packages/numpy/_array_api_info.py

@@ -0,0 +1,346 @@
+"""
+Array API Inspection namespace
+
+This is the namespace for inspection functions as defined by the array API
+standard. See
+https://data-apis.org/array-api/latest/API_specification/inspection.html for
+more details.
+
+"""
+from numpy._core import (
+    dtype,
+    bool,
+    intp,
+    int8,
+    int16,
+    int32,
+    int64,
+    uint8,
+    uint16,
+    uint32,
+    uint64,
+    float32,
+    float64,
+    complex64,
+    complex128,
+)
+
+
+class __array_namespace_info__:
+    """
+    Get the array API inspection namespace for NumPy.
+
+    The array API inspection namespace defines the following functions:
+
+    - capabilities()
+    - default_device()
+    - default_dtypes()
+    - dtypes()
+    - devices()
+
+    See
+    https://data-apis.org/array-api/latest/API_specification/inspection.html
+    for more details.
+
+    Returns
+    -------
+    info : ModuleType
+        The array API inspection namespace for NumPy.
+
+    Examples
+    --------
+    >>> info = np.__array_namespace_info__()
+    >>> info.default_dtypes()
+    {'real floating': numpy.float64,
+     'complex floating': numpy.complex128,
+     'integral': numpy.int64,
+     'indexing': numpy.int64}
+
+    """
+
+    __module__ = 'numpy'
+
+    def capabilities(self):
+        """
+        Return a dictionary of array API library capabilities.
+
+        The resulting dictionary has the following keys:
+
+        - **"boolean indexing"**: boolean indicating whether an array library
+          supports boolean indexing. Always ``True`` for NumPy.
+
+        - **"data-dependent shapes"**: boolean indicating whether an array
+          library supports data-dependent output shapes. Always ``True`` for
+          NumPy.
+
+        See
+        https://data-apis.org/array-api/latest/API_specification/generated/array_api.info.capabilities.html
+        for more details.
+
+        See Also
+        --------
+        __array_namespace_info__.default_device,
+        __array_namespace_info__.default_dtypes,
+        __array_namespace_info__.dtypes,
+        __array_namespace_info__.devices
+
+        Returns
+        -------
+        capabilities : dict
+            A dictionary of array API library capabilities.
+
+        Examples
+        --------
+        >>> info = np.__array_namespace_info__()
+        >>> info.capabilities()
+        {'boolean indexing': True,
+         'data-dependent shapes': True}
+
+        """
+        return {
+            "boolean indexing": True,
+            "data-dependent shapes": True,
+            # 'max rank' will be part of the 2024.12 standard
+            # "max rank": 64,
+        }
+
+    def default_device(self):
+        """
+        The default device used for new NumPy arrays.
+
+        For NumPy, this always returns ``'cpu'``.
+
+        See Also
+        --------
+        __array_namespace_info__.capabilities,
+        __array_namespace_info__.default_dtypes,
+        __array_namespace_info__.dtypes,
+        __array_namespace_info__.devices
+
+        Returns
+        -------
+        device : str
+            The default device used for new NumPy arrays.
+
+        Examples
+        --------
+        >>> info = np.__array_namespace_info__()
+        >>> info.default_device()
+        'cpu'
+
+        """
+        return "cpu"
+
+    def default_dtypes(self, *, device=None):
+        """
+        The default data types used for new NumPy arrays.
+
+        For NumPy, this always returns the following dictionary:
+
+        - **"real floating"**: ``numpy.float64``
+        - **"complex floating"**: ``numpy.complex128``
+        - **"integral"**: ``numpy.intp``
+        - **"indexing"**: ``numpy.intp``
+
+        Parameters
+        ----------
+        device : str, optional
+            The device to get the default data types for. For NumPy, only
+            ``'cpu'`` is allowed.
+
+        Returns
+        -------
+        dtypes : dict
+            A dictionary describing the default data types used for new NumPy
+            arrays.
+
+        See Also
+        --------
+        __array_namespace_info__.capabilities,
+        __array_namespace_info__.default_device,
+        __array_namespace_info__.dtypes,
+        __array_namespace_info__.devices
+
+        Examples
+        --------
+        >>> info = np.__array_namespace_info__()
+        >>> info.default_dtypes()
+        {'real floating': numpy.float64,
+         'complex floating': numpy.complex128,
+         'integral': numpy.int64,
+         'indexing': numpy.int64}
+
+        """
+        if device not in ["cpu", None]:
+            raise ValueError(
+                'Device not understood. Only "cpu" is allowed, but received:'
+                f' {device}'
+            )
+        return {
+            "real floating": dtype(float64),
+            "complex floating": dtype(complex128),
+            "integral": dtype(intp),
+            "indexing": dtype(intp),
+        }
+
+    def dtypes(self, *, device=None, kind=None):
+        """
+        The array API data types supported by NumPy.
+
+        Note that this function only returns data types that are defined by
+        the array API.
+
+        Parameters
+        ----------
+        device : str, optional
+            The device to get the data types for. For NumPy, only ``'cpu'`` is
+            allowed.
+        kind : str or tuple of str, optional
+            The kind of data types to return. If ``None``, all data types are
+            returned. If a string, only data types of that kind are returned.
+            If a tuple, a dictionary containing the union of the given kinds
+            is returned. The following kinds are supported:
+
+            - ``'bool'``: boolean data types (i.e., ``bool``).
+            - ``'signed integer'``: signed integer data types (i.e., ``int8``,
+              ``int16``, ``int32``, ``int64``).
+            - ``'unsigned integer'``: unsigned integer data types (i.e.,
+              ``uint8``, ``uint16``, ``uint32``, ``uint64``).
+            - ``'integral'``: integer data types. Shorthand for ``('signed
+              integer', 'unsigned integer')``.
+            - ``'real floating'``: real-valued floating-point data types
+              (i.e., ``float32``, ``float64``).
+            - ``'complex floating'``: complex floating-point data types (i.e.,
+              ``complex64``, ``complex128``).
+            - ``'numeric'``: numeric data types. Shorthand for ``('integral',
+              'real floating', 'complex floating')``.
+
+        Returns
+        -------
+        dtypes : dict
+            A dictionary mapping the names of data types to the corresponding
+            NumPy data types.
+
+        See Also
+        --------
+        __array_namespace_info__.capabilities,
+        __array_namespace_info__.default_device,
+        __array_namespace_info__.default_dtypes,
+        __array_namespace_info__.devices
+
+        Examples
+        --------
+        >>> info = np.__array_namespace_info__()
+        >>> info.dtypes(kind='signed integer')
+        {'int8': numpy.int8,
+         'int16': numpy.int16,
+         'int32': numpy.int32,
+         'int64': numpy.int64}
+
+        """
+        if device not in ["cpu", None]:
+            raise ValueError(
+                'Device not understood. Only "cpu" is allowed, but received:'
+                f' {device}'
+            )
+        if kind is None:
+            return {
+                "bool": dtype(bool),
+                "int8": dtype(int8),
+                "int16": dtype(int16),
+                "int32": dtype(int32),
+                "int64": dtype(int64),
+                "uint8": dtype(uint8),
+                "uint16": dtype(uint16),
+                "uint32": dtype(uint32),
+                "uint64": dtype(uint64),
+                "float32": dtype(float32),
+                "float64": dtype(float64),
+                "complex64": dtype(complex64),
+                "complex128": dtype(complex128),
+            }
+        if kind == "bool":
+            return {"bool": bool}
+        if kind == "signed integer":
+            return {
+                "int8": dtype(int8),
+                "int16": dtype(int16),
+                "int32": dtype(int32),
+                "int64": dtype(int64),
+            }
+        if kind == "unsigned integer":
+            return {
+                "uint8": dtype(uint8),
+                "uint16": dtype(uint16),
+                "uint32": dtype(uint32),
+                "uint64": dtype(uint64),
+            }
+        if kind == "integral":
+            return {
+                "int8": dtype(int8),
+                "int16": dtype(int16),
+                "int32": dtype(int32),
+                "int64": dtype(int64),
+                "uint8": dtype(uint8),
+                "uint16": dtype(uint16),
+                "uint32": dtype(uint32),
+                "uint64": dtype(uint64),
+            }
+        if kind == "real floating":
+            return {
+                "float32": dtype(float32),
+                "float64": dtype(float64),
+            }
+        if kind == "complex floating":
+            return {
+                "complex64": dtype(complex64),
+                "complex128": dtype(complex128),
+            }
+        if kind == "numeric":
+            return {
+                "int8": dtype(int8),
+                "int16": dtype(int16),
+                "int32": dtype(int32),
+                "int64": dtype(int64),
+                "uint8": dtype(uint8),
+                "uint16": dtype(uint16),
+                "uint32": dtype(uint32),
+                "uint64": dtype(uint64),
+                "float32": dtype(float32),
+                "float64": dtype(float64),
+                "complex64": dtype(complex64),
+                "complex128": dtype(complex128),
+            }
+        if isinstance(kind, tuple):
+            res = {}
+            for k in kind:
+                res.update(self.dtypes(kind=k))
+            return res
+        raise ValueError(f"unsupported kind: {kind!r}")
+
+    def devices(self):
+        """
+        The devices supported by NumPy.
+
+        For NumPy, this always returns ``['cpu']``.
+
+        Returns
+        -------
+        devices : list of str
+            The devices supported by NumPy.
+
+        See Also
+        --------
+        __array_namespace_info__.capabilities,
+        __array_namespace_info__.default_device,
+        __array_namespace_info__.default_dtypes,
+        __array_namespace_info__.dtypes
+
+        Examples
+        --------
+        >>> info = np.__array_namespace_info__()
+        >>> info.devices()
+        ['cpu']
+
+        """
+        return ["cpu"]

env/Lib/site-packages/numpy/_array_api_info.pyi

@@ -0,0 +1,210 @@
+from typing import (
+    ClassVar,
+    Literal,
+    TypeAlias,
+    TypedDict,
+    TypeVar,
+    final,
+    overload,
+    type_check_only,
+)
+from typing_extensions import Never
+
+import numpy as np
+
+
+_Device: TypeAlias = Literal["cpu"]
+_DeviceLike: TypeAlias = None | _Device
+
+_Capabilities = TypedDict(
+    "_Capabilities",
+    {
+        "boolean indexing": Literal[True],
+        "data-dependent shapes": Literal[True],
+    },
+)
+
+_DefaultDTypes = TypedDict(
+    "_DefaultDTypes",
+    {
+        "real floating": np.dtype[np.float64],
+        "complex floating": np.dtype[np.complex128],
+        "integral": np.dtype[np.intp],
+        "indexing": np.dtype[np.intp],
+    },
+)
+
+
+_KindBool: TypeAlias = Literal["bool"]
+_KindInt: TypeAlias = Literal["signed integer"]
+_KindUInt: TypeAlias = Literal["unsigned integer"]
+_KindInteger: TypeAlias = Literal["integral"]
+_KindFloat: TypeAlias = Literal["real floating"]
+_KindComplex: TypeAlias = Literal["complex floating"]
+_KindNumber: TypeAlias = Literal["numeric"]
+_Kind: TypeAlias = (
+    _KindBool
+    | _KindInt
+    | _KindUInt
+    | _KindInteger
+    | _KindFloat
+    | _KindComplex
+    | _KindNumber
+)
+
+
+_T1 = TypeVar("_T1")
+_T2 = TypeVar("_T2")
+_T3 = TypeVar("_T3")
+_Permute1: TypeAlias = _T1 | tuple[_T1]
+_Permute2: TypeAlias = tuple[_T1, _T2] | tuple[_T2, _T1]
+_Permute3: TypeAlias = (
+    tuple[_T1, _T2, _T3] | tuple[_T1, _T3, _T2]
+    | tuple[_T2, _T1, _T3] | tuple[_T2, _T3, _T1]
+    | tuple[_T3, _T1, _T2] | tuple[_T3, _T2, _T1]
+)
+
+@type_check_only
+class _DTypesBool(TypedDict):
+    bool: np.dtype[np.bool]
+
+@type_check_only
+class _DTypesInt(TypedDict):
+    int8: np.dtype[np.int8]
+    int16: np.dtype[np.int16]
+    int32: np.dtype[np.int32]
+    int64: np.dtype[np.int64]
+
+@type_check_only
+class _DTypesUInt(TypedDict):
+    uint8: np.dtype[np.uint8]
+    uint16: np.dtype[np.uint16]
+    uint32: np.dtype[np.uint32]
+    uint64: np.dtype[np.uint64]
+
+@type_check_only
+class _DTypesInteger(_DTypesInt, _DTypesUInt): ...
+
+@type_check_only
+class _DTypesFloat(TypedDict):
+    float32: np.dtype[np.float32]
+    float64: np.dtype[np.float64]
+
+@type_check_only
+class _DTypesComplex(TypedDict):
+    complex64: np.dtype[np.complex64]
+    complex128: np.dtype[np.complex128]
+
+@type_check_only
+class _DTypesNumber(_DTypesInteger, _DTypesFloat, _DTypesComplex): ...
+
+@type_check_only
+class _DTypes(_DTypesBool, _DTypesNumber): ...
+
+@type_check_only
+class _DTypesUnion(TypedDict, total=False):
+    bool: np.dtype[np.bool]
+    int8: np.dtype[np.int8]
+    int16: np.dtype[np.int16]
+    int32: np.dtype[np.int32]
+    int64: np.dtype[np.int64]
+    uint8: np.dtype[np.uint8]
+    uint16: np.dtype[np.uint16]
+    uint32: np.dtype[np.uint32]
+    uint64: np.dtype[np.uint64]
+    float32: np.dtype[np.float32]
+    float64: np.dtype[np.float64]
+    complex64: np.dtype[np.complex64]
+    complex128: np.dtype[np.complex128]
+
+_EmptyDict: TypeAlias = dict[Never, Never]
+
+@final
+class __array_namespace_info__:
+    __module__: ClassVar[Literal['numpy']]
+
+    def capabilities(self) -> _Capabilities: ...
+    def default_device(self) -> _Device: ...
+    def default_dtypes(
+        self,
+        *,
+        device: _DeviceLike = ...,
+    ) -> _DefaultDTypes: ...
+    def devices(self) -> list[_Device]: ...
+
+    @overload
+    def dtypes(
+        self,
+        *,
+        device: _DeviceLike = ...,
+        kind: None = ...,
+    ) -> _DTypes: ...
+    @overload
+    def dtypes(
+        self,
+        *,
+        device: _DeviceLike = ...,
+        kind: _Permute1[_KindBool],
+    ) -> _DTypesBool: ...
+    @overload
+    def dtypes(
+        self,
+        *,
+        device: _DeviceLike = ...,
+        kind: _Permute1[_KindInt],
+    ) -> _DTypesInt: ...
+    @overload
+    def dtypes(
+        self,
+        *,
+        device: _DeviceLike = ...,
+        kind: _Permute1[_KindUInt],
+    ) -> _DTypesUInt: ...
+    @overload
+    def dtypes(
+        self,
+        *,
+        device: _DeviceLike = ...,
+        kind: _Permute1[_KindFloat],
+    ) -> _DTypesFloat: ...
+    @overload
+    def dtypes(
+        self,
+        *,
+        device: _DeviceLike = ...,
+        kind: _Permute1[_KindComplex],
+    ) -> _DTypesComplex: ...
+    @overload
+    def dtypes(
+        self,
+        *,
+        device: _DeviceLike = ...,
+        kind: (
+            _Permute1[_KindInteger]
+            | _Permute2[_KindInt, _KindUInt]
+        ),
+    ) -> _DTypesInteger: ...
+    @overload
+    def dtypes(
+        self,
+        *,
+        device: _DeviceLike = ...,
+        kind: (
+            _Permute1[_KindNumber]
+            | _Permute3[_KindInteger, _KindFloat, _KindComplex]
+        ),
+    ) -> _DTypesNumber: ...
+    @overload
+    def dtypes(
+        self,
+        *,
+        device: _DeviceLike = ...,
+        kind: tuple[()],
+    ) -> _EmptyDict: ...
+    @overload
+    def dtypes(
+        self,
+        *,
+        device: _DeviceLike = ...,
+        kind: tuple[_Kind, ...],
+    ) -> _DTypesUnion: ...

env/Lib/site-packages/numpy/_configtool.py

@@ -0,0 +1,39 @@
+import argparse
+from pathlib import Path
+import sys
+
+from .version import __version__
+from .lib._utils_impl import get_include
+
+
+def main() -> None:
+    parser = argparse.ArgumentParser()
+    parser.add_argument(
+        "--version",
+        action="version",
+        version=__version__,
+        help="Print the version and exit.",
+    )
+    parser.add_argument(
+        "--cflags",
+        action="store_true",
+        help="Compile flag needed when using the NumPy headers.",
+    )
+    parser.add_argument(
+        "--pkgconfigdir",
+        action="store_true",
+        help=("Print the pkgconfig directory in which `numpy.pc` is stored "
+              "(useful for setting $PKG_CONFIG_PATH)."),
+    )
+    args = parser.parse_args()
+    if not sys.argv[1:]:
+        parser.print_help()
+    if args.cflags:
+        print("-I" + get_include())
+    if args.pkgconfigdir:
+        _path = Path(get_include()) / '..' / 'lib' / 'pkgconfig'
+        print(_path.resolve())
+
+
+if __name__ == "__main__":
+    main()

env/Lib/site-packages/numpy/_configtool.pyi

@@ -0,0 +1 @@
+def main() -> None: ...

env/Lib/site-packages/numpy/_core/__init__.py

@@ -0,0 +1,180 @@
+"""
+Contains the core of NumPy: ndarray, ufuncs, dtypes, etc.
+
+Please note that this module is private.  All functions and objects
+are available in the main ``numpy`` namespace - use that instead.
+
+"""
+
+import os
+
+from numpy.version import version as __version__
+
+
+# disables OpenBLAS affinity setting of the main thread that limits
+# python threads or processes to one core
+env_added = []
+for envkey in ['OPENBLAS_MAIN_FREE', 'GOTOBLAS_MAIN_FREE']:
+    if envkey not in os.environ:
+        os.environ[envkey] = '1'
+        env_added.append(envkey)
+
+try:
+    from . import multiarray
+except ImportError as exc:
+    import sys
+    msg = """
+
+IMPORTANT: PLEASE READ THIS FOR ADVICE ON HOW TO SOLVE THIS ISSUE!
+
+Importing the numpy C-extensions failed. This error can happen for
+many reasons, often due to issues with your setup or how NumPy was
+installed.
+
+We have compiled some common reasons and troubleshooting tips at:
+
+    https://numpy.org/devdocs/user/troubleshooting-importerror.html
+
+Please note and check the following:
+
+  * The Python version is: Python%d.%d from "%s"
+  * The NumPy version is: "%s"
+
+and make sure that they are the versions you expect.
+Please carefully study the documentation linked above for further help.
+
+Original error was: %s
+""" % (sys.version_info[0], sys.version_info[1], sys.executable,
+        __version__, exc)
+    raise ImportError(msg)
+finally:
+    for envkey in env_added:
+        del os.environ[envkey]
+del envkey
+del env_added
+del os
+
+from . import umath
+
+# Check that multiarray,umath are pure python modules wrapping
+# _multiarray_umath and not either of the old c-extension modules
+if not (hasattr(multiarray, '_multiarray_umath') and
+        hasattr(umath, '_multiarray_umath')):
+    import sys
+    path = sys.modules['numpy'].__path__
+    msg = ("Something is wrong with the numpy installation. "
+        "While importing we detected an older version of "
+        "numpy in {}. One method of fixing this is to repeatedly uninstall "
+        "numpy until none is found, then reinstall this version.")
+    raise ImportError(msg.format(path))
+
+from . import numerictypes as nt
+from .numerictypes import sctypes, sctypeDict
+multiarray.set_typeDict(nt.sctypeDict)
+from . import numeric
+from .numeric import *
+from . import fromnumeric
+from .fromnumeric import *
+from .records import record, recarray
+# Note: module name memmap is overwritten by a class with same name
+from .memmap import *
+from . import function_base
+from .function_base import *
+from . import _machar
+from . import getlimits
+from .getlimits import *
+from . import shape_base
+from .shape_base import *
+from . import einsumfunc
+from .einsumfunc import *
+del nt
+
+from .numeric import absolute as abs
+
+# do this after everything else, to minimize the chance of this misleadingly
+# appearing in an import-time traceback
+from . import _add_newdocs
+from . import _add_newdocs_scalars
+# add these for module-freeze analysis (like PyInstaller)
+from . import _dtype_ctypes
+from . import _internal
+from . import _dtype
+from . import _methods
+
+acos = numeric.arccos
+acosh = numeric.arccosh
+asin = numeric.arcsin
+asinh = numeric.arcsinh
+atan = numeric.arctan
+atanh = numeric.arctanh
+atan2 = numeric.arctan2
+concat = numeric.concatenate
+bitwise_left_shift = numeric.left_shift
+bitwise_invert = numeric.invert
+bitwise_right_shift = numeric.right_shift
+permute_dims = numeric.transpose
+pow = numeric.power
+
+__all__ = [
+    "abs", "acos", "acosh", "asin", "asinh", "atan", "atanh", "atan2",
+    "bitwise_invert", "bitwise_left_shift", "bitwise_right_shift", "concat",
+    "pow", "permute_dims", "memmap", "sctypeDict", "record", "recarray"
+]
+__all__ += numeric.__all__
+__all__ += function_base.__all__
+__all__ += getlimits.__all__
+__all__ += shape_base.__all__
+__all__ += einsumfunc.__all__
+
+
+def _ufunc_reduce(func):
+    # Report the `__name__`. pickle will try to find the module. Note that
+    # pickle supports for this `__name__` to be a `__qualname__`. It may
+    # make sense to add a `__qualname__` to ufuncs, to allow this more
+    # explicitly (Numba has ufuncs as attributes).
+    # See also: https://github.com/dask/distributed/issues/3450
+    return func.__name__
+
+
+def _DType_reconstruct(scalar_type):
+    # This is a work-around to pickle type(np.dtype(np.float64)), etc.
+    # and it should eventually be replaced with a better solution, e.g. when
+    # DTypes become HeapTypes.
+    return type(dtype(scalar_type))
+
+
+def _DType_reduce(DType):
+    # As types/classes, most DTypes can simply be pickled by their name:
+    if not DType._legacy or DType.__module__ == "numpy.dtypes":
+        return DType.__name__
+
+    # However, user defined legacy dtypes (like rational) do not end up in
+    # `numpy.dtypes` as module and do not have a public class at all.
+    # For these, we pickle them by reconstructing them from the scalar type:
+    scalar_type = DType.type
+    return _DType_reconstruct, (scalar_type,)
+
+
+def __getattr__(name):
+    # Deprecated 2022-11-22, NumPy 1.25.
+    if name == "MachAr":
+        import warnings
+        warnings.warn(
+            "The `np._core.MachAr` is considered private API (NumPy 1.24)",
+            DeprecationWarning, stacklevel=2,
+        )
+        return _machar.MachAr
+    raise AttributeError(f"Module {__name__!r} has no attribute {name!r}")
+
+
+import copyreg
+
+copyreg.pickle(ufunc, _ufunc_reduce)
+copyreg.pickle(type(dtype), _DType_reduce, _DType_reconstruct)
+
+# Unclutter namespace (must keep _*_reconstruct for unpickling)
+del copyreg, _ufunc_reduce, _DType_reduce
+
+from numpy._pytesttester import PytestTester
+test = PytestTester(__name__)
+del PytestTester

env/Lib/site-packages/numpy/_core/__init__.pyi

@@ -0,0 +1,2 @@
+# NOTE: The `np._core` namespace is deliberately kept empty due to it
+# being private

env/Lib/site-packages/numpy/_core/_add_newdocs.pyi

@@ -0,0 +1,3 @@
+from .overrides import get_array_function_like_doc as get_array_function_like_doc
+
+def refer_to_array_attribute(attr: str, method: bool = True) -> tuple[str, str]: ...

env/Lib/site-packages/numpy/_core/_add_newdocs_scalars.py

@@ -0,0 +1,389 @@
+"""
+This file is separate from ``_add_newdocs.py`` so that it can be mocked out by
+our sphinx ``conf.py`` during doc builds, where we want to avoid showing
+platform-dependent information.
+"""
+import sys
+import os
+from numpy._core import dtype
+from numpy._core import numerictypes as _numerictypes
+from numpy._core.function_base import add_newdoc
+
+##############################################################################
+#
+# Documentation for concrete scalar classes
+#
+##############################################################################
+
+def numeric_type_aliases(aliases):
+    def type_aliases_gen():
+        for alias, doc in aliases:
+            try:
+                alias_type = getattr(_numerictypes, alias)
+            except AttributeError:
+                # The set of aliases that actually exist varies between platforms
+                pass
+            else:
+                yield (alias_type, alias, doc)
+    return list(type_aliases_gen())
+
+
+possible_aliases = numeric_type_aliases([
+    ('int8', '8-bit signed integer (``-128`` to ``127``)'),
+    ('int16', '16-bit signed integer (``-32_768`` to ``32_767``)'),
+    ('int32', '32-bit signed integer (``-2_147_483_648`` to ``2_147_483_647``)'),
+    ('int64', '64-bit signed integer (``-9_223_372_036_854_775_808`` to ``9_223_372_036_854_775_807``)'),
+    ('intp', 'Signed integer large enough to fit pointer, compatible with C ``intptr_t``'),
+    ('uint8', '8-bit unsigned integer (``0`` to ``255``)'),
+    ('uint16', '16-bit unsigned integer (``0`` to ``65_535``)'),
+    ('uint32', '32-bit unsigned integer (``0`` to ``4_294_967_295``)'),
+    ('uint64', '64-bit unsigned integer (``0`` to ``18_446_744_073_709_551_615``)'),
+    ('uintp', 'Unsigned integer large enough to fit pointer, compatible with C ``uintptr_t``'),
+    ('float16', '16-bit-precision floating-point number type: sign bit, 5 bits exponent, 10 bits mantissa'),
+    ('float32', '32-bit-precision floating-point number type: sign bit, 8 bits exponent, 23 bits mantissa'),
+    ('float64', '64-bit precision floating-point number type: sign bit, 11 bits exponent, 52 bits mantissa'),
+    ('float96', '96-bit extended-precision floating-point number type'),
+    ('float128', '128-bit extended-precision floating-point number type'),
+    ('complex64', 'Complex number type composed of 2 32-bit-precision floating-point numbers'),
+    ('complex128', 'Complex number type composed of 2 64-bit-precision floating-point numbers'),
+    ('complex192', 'Complex number type composed of 2 96-bit extended-precision floating-point numbers'),
+    ('complex256', 'Complex number type composed of 2 128-bit extended-precision floating-point numbers'),
+    ])
+
+
+def _get_platform_and_machine():
+    try:
+        system, _, _, _, machine = os.uname()
+    except AttributeError:
+        system = sys.platform
+        if system == 'win32':
+            machine = os.environ.get('PROCESSOR_ARCHITEW6432', '') \
+                    or os.environ.get('PROCESSOR_ARCHITECTURE', '')
+        else:
+            machine = 'unknown'
+    return system, machine
+
+
+_system, _machine = _get_platform_and_machine()
+_doc_alias_string = f":Alias on this platform ({_system} {_machine}):"
+
+
+def add_newdoc_for_scalar_type(obj, fixed_aliases, doc):
+    # note: `:field: value` is rST syntax which renders as field lists.
+    o = getattr(_numerictypes, obj)
+
+    character_code = dtype(o).char
+    canonical_name_doc = "" if obj == o.__name__ else \
+                        f":Canonical name: `numpy.{obj}`\n    "
+    if fixed_aliases:
+        alias_doc = ''.join(f":Alias: `numpy.{alias}`\n    "
+                            for alias in fixed_aliases)
+    else:
+        alias_doc = ''
+    alias_doc += ''.join(f"{_doc_alias_string} `numpy.{alias}`: {doc}.\n    "
+                         for (alias_type, alias, doc) in possible_aliases if alias_type is o)
+
+    docstring = f"""
+    {doc.strip()}
+
+    :Character code: ``'{character_code}'``
+    {canonical_name_doc}{alias_doc}
+    """
+
+    add_newdoc('numpy._core.numerictypes', obj, docstring)
+
+
+_bool_docstring = (
+    """
+    Boolean type (True or False), stored as a byte.
+
+    .. warning::
+
+       The :class:`bool` type is not a subclass of the :class:`int_` type
+       (the :class:`bool` is not even a number type). This is different
+       than Python's default implementation of :class:`bool` as a
+       sub-class of :class:`int`.
+    """
+)
+
+add_newdoc_for_scalar_type('bool', [], _bool_docstring)
+
+add_newdoc_for_scalar_type('bool_', [], _bool_docstring)
+
+add_newdoc_for_scalar_type('byte', [],
+    """
+    Signed integer type, compatible with C ``char``.
+    """)
+
+add_newdoc_for_scalar_type('short', [],
+    """
+    Signed integer type, compatible with C ``short``.
+    """)
+
+add_newdoc_for_scalar_type('intc', [],
+    """
+    Signed integer type, compatible with C ``int``.
+    """)
+
+# TODO: These docs probably need an if to highlight the default rather than
+#       the C-types (and be correct).
+add_newdoc_for_scalar_type('int_', [],
+    """
+    Default signed integer type, 64bit on 64bit systems and 32bit on 32bit
+    systems.
+    """)
+
+add_newdoc_for_scalar_type('longlong', [],
+    """
+    Signed integer type, compatible with C ``long long``.
+    """)
+
+add_newdoc_for_scalar_type('ubyte', [],
+    """
+    Unsigned integer type, compatible with C ``unsigned char``.
+    """)
+
+add_newdoc_for_scalar_type('ushort', [],
+    """
+    Unsigned integer type, compatible with C ``unsigned short``.
+    """)
+
+add_newdoc_for_scalar_type('uintc', [],
+    """
+    Unsigned integer type, compatible with C ``unsigned int``.
+    """)
+
+add_newdoc_for_scalar_type('uint', [],
+    """
+    Unsigned signed integer type, 64bit on 64bit systems and 32bit on 32bit
+    systems.
+    """)
+
+add_newdoc_for_scalar_type('ulonglong', [],
+    """
+    Signed integer type, compatible with C ``unsigned long long``.
+    """)
+
+add_newdoc_for_scalar_type('half', [],
+    """
+    Half-precision floating-point number type.
+    """)
+
+add_newdoc_for_scalar_type('single', [],
+    """
+    Single-precision floating-point number type, compatible with C ``float``.
+    """)
+
+add_newdoc_for_scalar_type('double', [],
+    """
+    Double-precision floating-point number type, compatible with Python
+    :class:`float` and C ``double``.
+    """)
+
+add_newdoc_for_scalar_type('longdouble', [],
+    """
+    Extended-precision floating-point number type, compatible with C
+    ``long double`` but not necessarily with IEEE 754 quadruple-precision.
+    """)
+
+add_newdoc_for_scalar_type('csingle', [],
+    """
+    Complex number type composed of two single-precision floating-point
+    numbers.
+    """)
+
+add_newdoc_for_scalar_type('cdouble', [],
+    """
+    Complex number type composed of two double-precision floating-point
+    numbers, compatible with Python :class:`complex`.
+    """)
+
+add_newdoc_for_scalar_type('clongdouble', [],
+    """
+    Complex number type composed of two extended-precision floating-point
+    numbers.
+    """)
+
+add_newdoc_for_scalar_type('object_', [],
+    """
+    Any Python object.
+    """)
+
+add_newdoc_for_scalar_type('str_', [],
+    r"""
+    A unicode string.
+
+    This type strips trailing null codepoints.
+
+    >>> s = np.str_("abc\x00")
+    >>> s
+    'abc'
+
+    Unlike the builtin :class:`str`, this supports the
+    :ref:`python:bufferobjects`, exposing its contents as UCS4:
+
+    >>> m = memoryview(np.str_("abc"))
+    >>> m.format
+    '3w'
+    >>> m.tobytes()
+    b'a\x00\x00\x00b\x00\x00\x00c\x00\x00\x00'
+    """)
+
+add_newdoc_for_scalar_type('bytes_', [],
+    r"""
+    A byte string.
+
+    When used in arrays, this type strips trailing null bytes.
+    """)
+
+add_newdoc_for_scalar_type('void', [],
+    r"""
+    np.void(length_or_data, /, dtype=None)
+
+    Create a new structured or unstructured void scalar.
+
+    Parameters
+    ----------
+    length_or_data : int, array-like, bytes-like, object
+       One of multiple meanings (see notes).  The length or
+       bytes data of an unstructured void.  Or alternatively,
+       the data to be stored in the new scalar when `dtype`
+       is provided.
+       This can be an array-like, in which case an array may
+       be returned.
+    dtype : dtype, optional
+       If provided the dtype of the new scalar.  This dtype must
+       be "void" dtype (i.e. a structured or unstructured void,
+       see also :ref:`defining-structured-types`).
+
+       .. versionadded:: 1.24
+
+    Notes
+    -----
+    For historical reasons and because void scalars can represent both
+    arbitrary byte data and structured dtypes, the void constructor
+    has three calling conventions:
+
+    1. ``np.void(5)`` creates a ``dtype="V5"`` scalar filled with five
+       ``\0`` bytes.  The 5 can be a Python or NumPy integer.
+    2. ``np.void(b"bytes-like")`` creates a void scalar from the byte string.
+       The dtype itemsize will match the byte string length, here ``"V10"``.
+    3. When a ``dtype=`` is passed the call is roughly the same as an
+       array creation.  However, a void scalar rather than array is returned.
+
+    Please see the examples which show all three different conventions.
+
+    Examples
+    --------
+    >>> np.void(5)
+    np.void(b'\x00\x00\x00\x00\x00')
+    >>> np.void(b'abcd')
+    np.void(b'\x61\x62\x63\x64')
+    >>> np.void((3.2, b'eggs'), dtype="d,S5")
+    np.void((3.2, b'eggs'), dtype=[('f0', '<f8'), ('f1', 'S5')])
+    >>> np.void(3, dtype=[('x', np.int8), ('y', np.int8)])
+    np.void((3, 3), dtype=[('x', 'i1'), ('y', 'i1')])
+
+    """)
+
+add_newdoc_for_scalar_type('datetime64', [],
+    """
+    If created from a 64-bit integer, it represents an offset from
+    ``1970-01-01T00:00:00``.
+    If created from string, the string can be in ISO 8601 date
+    or datetime format.
+
+    When parsing a string to create a datetime object, if the string contains
+    a trailing timezone (A 'Z' or a timezone offset), the timezone will be
+    dropped and a User Warning is given.
+
+    Datetime64 objects should be considered to be UTC and therefore have an
+    offset of +0000.
+
+    >>> np.datetime64(10, 'Y')
+    np.datetime64('1980')
+    >>> np.datetime64('1980', 'Y')
+    np.datetime64('1980')
+    >>> np.datetime64(10, 'D')
+    np.datetime64('1970-01-11')
+
+    See :ref:`arrays.datetime` for more information.
+    """)
+
+add_newdoc_for_scalar_type('timedelta64', [],
+    """
+    A timedelta stored as a 64-bit integer.
+
+    See :ref:`arrays.datetime` for more information.
+    """)
+
+add_newdoc('numpy._core.numerictypes', "integer", ('is_integer',
+    """
+    integer.is_integer() -> bool
+
+    Return ``True`` if the number is finite with integral value.
+
+    .. versionadded:: 1.22
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> np.int64(-2).is_integer()
+    True
+    >>> np.uint32(5).is_integer()
+    True
+    """))
+
+# TODO: work out how to put this on the base class, np.floating
+for float_name in ('half', 'single', 'double', 'longdouble'):
+    add_newdoc('numpy._core.numerictypes', float_name, ('as_integer_ratio',
+        """
+        {ftype}.as_integer_ratio() -> (int, int)
+
+        Return a pair of integers, whose ratio is exactly equal to the original
+        floating point number, and with a positive denominator.
+        Raise `OverflowError` on infinities and a `ValueError` on NaNs.
+
+        >>> np.{ftype}(10.0).as_integer_ratio()
+        (10, 1)
+        >>> np.{ftype}(0.0).as_integer_ratio()
+        (0, 1)
+        >>> np.{ftype}(-.25).as_integer_ratio()
+        (-1, 4)
+        """.format(ftype=float_name)))
+
+    add_newdoc('numpy._core.numerictypes', float_name, ('is_integer',
+        f"""
+        {float_name}.is_integer() -> bool
+
+        Return ``True`` if the floating point number is finite with integral
+        value, and ``False`` otherwise.
+
+        .. versionadded:: 1.22
+
+        Examples
+        --------
+        >>> np.{float_name}(-2.0).is_integer()
+        True
+        >>> np.{float_name}(3.2).is_integer()
+        False
+        """))
+
+for int_name in ('int8', 'uint8', 'int16', 'uint16', 'int32', 'uint32',
+        'int64', 'uint64', 'int64', 'uint64', 'int64', 'uint64'):
+    # Add negative examples for signed cases by checking typecode
+    add_newdoc('numpy._core.numerictypes', int_name, ('bit_count',
+        f"""
+        {int_name}.bit_count() -> int
+
+        Computes the number of 1-bits in the absolute value of the input.
+        Analogous to the builtin `int.bit_count` or ``popcount`` in C++.
+
+        Examples
+        --------
+        >>> np.{int_name}(127).bit_count()
+        7""" +
+        (f"""
+        >>> np.{int_name}(-127).bit_count()
+        7
+        """ if dtype(int_name).char.islower() else "")))

env/Lib/site-packages/numpy/_core/_add_newdocs_scalars.pyi

@@ -0,0 +1,16 @@
+from collections.abc import Iterable
+from typing import Final
+
+import numpy as np
+
+possible_aliases: Final[list[tuple[type[np.number], str, str]]] = ...
+_system: Final[str] = ...
+_machine: Final[str] = ...
+_doc_alias_string: Final[str] = ...
+_bool_docstring: Final[str] = ...
+int_name: str = ...
+float_name: str = ...
+
+def numeric_type_aliases(aliases: list[tuple[str, str]]) -> list[tuple[type[np.number], str, str]]: ...
+def add_newdoc_for_scalar_type(obj: str, fixed_aliases: Iterable[str], doc: str) -> None: ...
+def _get_platform_and_machine() -> tuple[str, str]: ...

env/Lib/site-packages/numpy/_core/_asarray.py

@@ -0,0 +1,135 @@
+"""
+Functions in the ``as*array`` family that promote array-likes into arrays.
+
+`require` fits this category despite its name not matching this pattern.
+"""
+from .overrides import (
+    array_function_dispatch,
+    finalize_array_function_like,
+    set_module,
+)
+from .multiarray import array, asanyarray
+
+
+__all__ = ["require"]
+
+
+POSSIBLE_FLAGS = {
+    'C': 'C', 'C_CONTIGUOUS': 'C', 'CONTIGUOUS': 'C',
+    'F': 'F', 'F_CONTIGUOUS': 'F', 'FORTRAN': 'F',
+    'A': 'A', 'ALIGNED': 'A',
+    'W': 'W', 'WRITEABLE': 'W',
+    'O': 'O', 'OWNDATA': 'O',
+    'E': 'E', 'ENSUREARRAY': 'E'
+}
+
+
+@finalize_array_function_like
+@set_module('numpy')
+def require(a, dtype=None, requirements=None, *, like=None):
+    """
+    Return an ndarray of the provided type that satisfies requirements.
+
+    This function is useful to be sure that an array with the correct flags
+    is returned for passing to compiled code (perhaps through ctypes).
+
+    Parameters
+    ----------
+    a : array_like
+       The object to be converted to a type-and-requirement-satisfying array.
+    dtype : data-type
+       The required data-type. If None preserve the current dtype. If your
+       application requires the data to be in native byteorder, include
+       a byteorder specification as a part of the dtype specification.
+    requirements : str or sequence of str
+       The requirements list can be any of the following
+
+       * 'F_CONTIGUOUS' ('F') - ensure a Fortran-contiguous array
+       * 'C_CONTIGUOUS' ('C') - ensure a C-contiguous array
+       * 'ALIGNED' ('A')      - ensure a data-type aligned array
+       * 'WRITEABLE' ('W')    - ensure a writable array
+       * 'OWNDATA' ('O')      - ensure an array that owns its own data
+       * 'ENSUREARRAY', ('E') - ensure a base array, instead of a subclass
+    ${ARRAY_FUNCTION_LIKE}
+
+        .. versionadded:: 1.20.0
+
+    Returns
+    -------
+    out : ndarray
+        Array with specified requirements and type if given.
+
+    See Also
+    --------
+    asarray : Convert input to an ndarray.
+    asanyarray : Convert to an ndarray, but pass through ndarray subclasses.
+    ascontiguousarray : Convert input to a contiguous array.
+    asfortranarray : Convert input to an ndarray with column-major
+                     memory order.
+    ndarray.flags : Information about the memory layout of the array.
+
+    Notes
+    -----
+    The returned array will be guaranteed to have the listed requirements
+    by making a copy if needed.
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> x = np.arange(6).reshape(2,3)
+    >>> x.flags
+      C_CONTIGUOUS : True
+      F_CONTIGUOUS : False
+      OWNDATA : False
+      WRITEABLE : True
+      ALIGNED : True
+      WRITEBACKIFCOPY : False
+
+    >>> y = np.require(x, dtype=np.float32, requirements=['A', 'O', 'W', 'F'])
+    >>> y.flags
+      C_CONTIGUOUS : False
+      F_CONTIGUOUS : True
+      OWNDATA : True
+      WRITEABLE : True
+      ALIGNED : True
+      WRITEBACKIFCOPY : False
+
+    """
+    if like is not None:
+        return _require_with_like(
+            like,
+            a,
+            dtype=dtype,
+            requirements=requirements,
+        )
+
+    if not requirements:
+        return asanyarray(a, dtype=dtype)
+
+    requirements = {POSSIBLE_FLAGS[x.upper()] for x in requirements}
+
+    if 'E' in requirements:
+        requirements.remove('E')
+        subok = False
+    else:
+        subok = True
+
+    order = 'A'
+    if requirements >= {'C', 'F'}:
+        raise ValueError('Cannot specify both "C" and "F" order')
+    elif 'F' in requirements:
+        order = 'F'
+        requirements.remove('F')
+    elif 'C' in requirements:
+        order = 'C'
+        requirements.remove('C')
+
+    arr = array(a, dtype=dtype, order=order, copy=None, subok=subok)
+
+    for prop in requirements:
+        if not arr.flags[prop]:
+            return arr.copy(order)
+    return arr
+
+
+_require_with_like = array_function_dispatch()(require)

env/Lib/site-packages/numpy/_core/_asarray.pyi

@@ -0,0 +1,41 @@
+from collections.abc import Iterable
+from typing import Any, TypeAlias, TypeVar, overload, Literal
+
+from numpy._typing import NDArray, DTypeLike, _SupportsArrayFunc
+
+_ArrayType = TypeVar("_ArrayType", bound=NDArray[Any])
+
+_Requirements: TypeAlias = Literal[
+    "C", "C_CONTIGUOUS", "CONTIGUOUS",
+    "F", "F_CONTIGUOUS", "FORTRAN",
+    "A", "ALIGNED",
+    "W", "WRITEABLE",
+    "O", "OWNDATA"
+]
+_E: TypeAlias = Literal["E", "ENSUREARRAY"]
+_RequirementsWithE: TypeAlias = _Requirements | _E
+
+@overload
+def require(
+    a: _ArrayType,
+    dtype: None = ...,
+    requirements: None | _Requirements | Iterable[_Requirements] = ...,
+    *,
+    like: _SupportsArrayFunc = ...
+) -> _ArrayType: ...
+@overload
+def require(
+    a: object,
+    dtype: DTypeLike = ...,
+    requirements: _E | Iterable[_RequirementsWithE] = ...,
+    *,
+    like: _SupportsArrayFunc = ...
+) -> NDArray[Any]: ...
+@overload
+def require(
+    a: object,
+    dtype: DTypeLike = ...,
+    requirements: None | _Requirements | Iterable[_Requirements] = ...,
+    *,
+    like: _SupportsArrayFunc = ...
+) -> NDArray[Any]: ...

env/Lib/site-packages/numpy/_core/_dtype.py

@@ -0,0 +1,374 @@
+"""
+A place for code to be called from the implementation of np.dtype
+
+String handling is much easier to do correctly in python.
+"""
+import numpy as np
+
+
+_kind_to_stem = {
+    'u': 'uint',
+    'i': 'int',
+    'c': 'complex',
+    'f': 'float',
+    'b': 'bool',
+    'V': 'void',
+    'O': 'object',
+    'M': 'datetime',
+    'm': 'timedelta',
+    'S': 'bytes',
+    'U': 'str',
+}
+
+
+def _kind_name(dtype):
+    try:
+        return _kind_to_stem[dtype.kind]
+    except KeyError as e:
+        raise RuntimeError(
+            "internal dtype error, unknown kind {!r}"
+            .format(dtype.kind)
+        ) from None
+
+
+def __str__(dtype):
+    if dtype.fields is not None:
+        return _struct_str(dtype, include_align=True)
+    elif dtype.subdtype:
+        return _subarray_str(dtype)
+    elif issubclass(dtype.type, np.flexible) or not dtype.isnative:
+        return dtype.str
+    else:
+        return dtype.name
+
+
+def __repr__(dtype):
+    arg_str = _construction_repr(dtype, include_align=False)
+    if dtype.isalignedstruct:
+        arg_str = arg_str + ", align=True"
+    return "dtype({})".format(arg_str)
+
+
+def _unpack_field(dtype, offset, title=None):
+    """
+    Helper function to normalize the items in dtype.fields.
+
+    Call as:
+
+    dtype, offset, title = _unpack_field(*dtype.fields[name])
+    """
+    return dtype, offset, title
+
+
+def _isunsized(dtype):
+    # PyDataType_ISUNSIZED
+    return dtype.itemsize == 0
+
+
+def _construction_repr(dtype, include_align=False, short=False):
+    """
+    Creates a string repr of the dtype, excluding the 'dtype()' part
+    surrounding the object. This object may be a string, a list, or
+    a dict depending on the nature of the dtype. This
+    is the object passed as the first parameter to the dtype
+    constructor, and if no additional constructor parameters are
+    given, will reproduce the exact memory layout.
+
+    Parameters
+    ----------
+    short : bool
+        If true, this creates a shorter repr using 'kind' and 'itemsize',
+        instead of the longer type name.
+
+    include_align : bool
+        If true, this includes the 'align=True' parameter
+        inside the struct dtype construction dict when needed. Use this flag
+        if you want a proper repr string without the 'dtype()' part around it.
+
+        If false, this does not preserve the
+        'align=True' parameter or sticky NPY_ALIGNED_STRUCT flag for
+        struct arrays like the regular repr does, because the 'align'
+        flag is not part of first dtype constructor parameter. This
+        mode is intended for a full 'repr', where the 'align=True' is
+        provided as the second parameter.
+    """
+    if dtype.fields is not None:
+        return _struct_str(dtype, include_align=include_align)
+    elif dtype.subdtype:
+        return _subarray_str(dtype)
+    else:
+        return _scalar_str(dtype, short=short)
+
+
+def _scalar_str(dtype, short):
+    byteorder = _byte_order_str(dtype)
+
+    if dtype.type == np.bool:
+        if short:
+            return "'?'"
+        else:
+            return "'bool'"
+
+    elif dtype.type == np.object_:
+        # The object reference may be different sizes on different
+        # platforms, so it should never include the itemsize here.
+        return "'O'"
+
+    elif dtype.type == np.bytes_:
+        if _isunsized(dtype):
+            return "'S'"
+        else:
+            return "'S%d'" % dtype.itemsize
+
+    elif dtype.type == np.str_:
+        if _isunsized(dtype):
+            return "'%sU'" % byteorder
+        else:
+            return "'%sU%d'" % (byteorder, dtype.itemsize / 4)
+
+    elif dtype.type == str:
+        return "'T'"
+
+    elif not type(dtype)._legacy:
+        return f"'{byteorder}{type(dtype).__name__}{dtype.itemsize * 8}'"
+
+    # unlike the other types, subclasses of void are preserved - but
+    # historically the repr does not actually reveal the subclass
+    elif issubclass(dtype.type, np.void):
+        if _isunsized(dtype):
+            return "'V'"
+        else:
+            return "'V%d'" % dtype.itemsize
+
+    elif dtype.type == np.datetime64:
+        return "'%sM8%s'" % (byteorder, _datetime_metadata_str(dtype))
+
+    elif dtype.type == np.timedelta64:
+        return "'%sm8%s'" % (byteorder, _datetime_metadata_str(dtype))
+
+    elif np.issubdtype(dtype, np.number):
+        # Short repr with endianness, like '<f8'
+        if short or dtype.byteorder not in ('=', '|'):
+            return "'%s%c%d'" % (byteorder, dtype.kind, dtype.itemsize)
+
+        # Longer repr, like 'float64'
+        else:
+            return "'%s%d'" % (_kind_name(dtype), 8*dtype.itemsize)
+
+    elif dtype.isbuiltin == 2:
+        return dtype.type.__name__
+
+    else:
+        raise RuntimeError(
+            "Internal error: NumPy dtype unrecognized type number")
+
+
+def _byte_order_str(dtype):
+    """ Normalize byteorder to '<' or '>' """
+    # hack to obtain the native and swapped byte order characters
+    swapped = np.dtype(int).newbyteorder('S')
+    native = swapped.newbyteorder('S')
+
+    byteorder = dtype.byteorder
+    if byteorder == '=':
+        return native.byteorder
+    if byteorder == 'S':
+        # TODO: this path can never be reached
+        return swapped.byteorder
+    elif byteorder == '|':
+        return ''
+    else:
+        return byteorder
+
+
+def _datetime_metadata_str(dtype):
+    # TODO: this duplicates the C metastr_to_unicode functionality
+    unit, count = np.datetime_data(dtype)
+    if unit == 'generic':
+        return ''
+    elif count == 1:
+        return '[{}]'.format(unit)
+    else:
+        return '[{}{}]'.format(count, unit)
+
+
+def _struct_dict_str(dtype, includealignedflag):
+    # unpack the fields dictionary into ls
+    names = dtype.names
+    fld_dtypes = []
+    offsets = []
+    titles = []
+    for name in names:
+        fld_dtype, offset, title = _unpack_field(*dtype.fields[name])
+        fld_dtypes.append(fld_dtype)
+        offsets.append(offset)
+        titles.append(title)
+
+    # Build up a string to make the dictionary
+
+    if np._core.arrayprint._get_legacy_print_mode() <= 121:
+        colon = ":"
+        fieldsep = ","
+    else:
+        colon = ": "
+        fieldsep = ", "
+
+    # First, the names
+    ret = "{'names'%s[" % colon
+    ret += fieldsep.join(repr(name) for name in names)
+
+    # Second, the formats
+    ret += "], 'formats'%s[" % colon
+    ret += fieldsep.join(
+        _construction_repr(fld_dtype, short=True) for fld_dtype in fld_dtypes)
+
+    # Third, the offsets
+    ret += "], 'offsets'%s[" % colon
+    ret += fieldsep.join("%d" % offset for offset in offsets)
+
+    # Fourth, the titles
+    if any(title is not None for title in titles):
+        ret += "], 'titles'%s[" % colon
+        ret += fieldsep.join(repr(title) for title in titles)
+
+    # Fifth, the itemsize
+    ret += "], 'itemsize'%s%d" % (colon, dtype.itemsize)
+
+    if (includealignedflag and dtype.isalignedstruct):
+        # Finally, the aligned flag
+        ret += ", 'aligned'%sTrue}" % colon
+    else:
+        ret += "}"
+
+    return ret
+
+
+def _aligned_offset(offset, alignment):
+    # round up offset:
+    return - (-offset // alignment) * alignment
+
+
+def _is_packed(dtype):
+    """
+    Checks whether the structured data type in 'dtype'
+    has a simple layout, where all the fields are in order,
+    and follow each other with no alignment padding.
+
+    When this returns true, the dtype can be reconstructed
+    from a list of the field names and dtypes with no additional
+    dtype parameters.
+
+    Duplicates the C `is_dtype_struct_simple_unaligned_layout` function.
+    """
+    align = dtype.isalignedstruct
+    max_alignment = 1
+    total_offset = 0
+    for name in dtype.names:
+        fld_dtype, fld_offset, title = _unpack_field(*dtype.fields[name])
+
+        if align:
+            total_offset = _aligned_offset(total_offset, fld_dtype.alignment)
+            max_alignment = max(max_alignment, fld_dtype.alignment)
+
+        if fld_offset != total_offset:
+            return False
+        total_offset += fld_dtype.itemsize
+
+    if align:
+        total_offset = _aligned_offset(total_offset, max_alignment)
+
+    return total_offset == dtype.itemsize
+
+
+def _struct_list_str(dtype):
+    items = []
+    for name in dtype.names:
+        fld_dtype, fld_offset, title = _unpack_field(*dtype.fields[name])
+
+        item = "("
+        if title is not None:
+            item += "({!r}, {!r}), ".format(title, name)
+        else:
+            item += "{!r}, ".format(name)
+        # Special case subarray handling here
+        if fld_dtype.subdtype is not None:
+            base, shape = fld_dtype.subdtype
+            item += "{}, {}".format(
+                _construction_repr(base, short=True),
+                shape
+            )
+        else:
+            item += _construction_repr(fld_dtype, short=True)
+
+        item += ")"
+        items.append(item)
+
+    return "[" + ", ".join(items) + "]"
+
+
+def _struct_str(dtype, include_align):
+    # The list str representation can't include the 'align=' flag,
+    # so if it is requested and the struct has the aligned flag set,
+    # we must use the dict str instead.
+    if not (include_align and dtype.isalignedstruct) and _is_packed(dtype):
+        sub = _struct_list_str(dtype)
+
+    else:
+        sub = _struct_dict_str(dtype, include_align)
+
+    # If the data type isn't the default, void, show it
+    if dtype.type != np.void:
+        return "({t.__module__}.{t.__name__}, {f})".format(t=dtype.type, f=sub)
+    else:
+        return sub
+
+
+def _subarray_str(dtype):
+    base, shape = dtype.subdtype
+    return "({}, {})".format(
+        _construction_repr(base, short=True),
+        shape
+    )
+
+
+def _name_includes_bit_suffix(dtype):
+    if dtype.type == np.object_:
+        # pointer size varies by system, best to omit it
+        return False
+    elif dtype.type == np.bool:
+        # implied
+        return False
+    elif dtype.type is None:
+        return True
+    elif np.issubdtype(dtype, np.flexible) and _isunsized(dtype):
+        # unspecified
+        return False
+    else:
+        return True
+
+
+def _name_get(dtype):
+    # provides dtype.name.__get__, documented as returning a "bit name"
+
+    if dtype.isbuiltin == 2:
+        # user dtypes don't promise to do anything special
+        return dtype.type.__name__
+
+    if not type(dtype)._legacy:
+        name = type(dtype).__name__
+
+    elif issubclass(dtype.type, np.void):
+        # historically, void subclasses preserve their name, eg `record64`
+        name = dtype.type.__name__
+    else:
+        name = _kind_name(dtype)
+
+    # append bit counts
+    if _name_includes_bit_suffix(dtype):
+        name += "{}".format(dtype.itemsize * 8)
+
+    # append metadata to datetimes
+    if dtype.type in (np.datetime64, np.timedelta64):
+        name += _datetime_metadata_str(dtype)
+
+    return name

env/Lib/site-packages/numpy/_core/_dtype.pyi

@@ -0,0 +1,58 @@
+from typing import Any, Final, TypeAlias, TypedDict, overload, type_check_only
+from typing import Literal as L
+
+from typing_extensions import ReadOnly, TypeVar
+
+import numpy as np
+
+###
+
+_T = TypeVar("_T")
+
+_Name: TypeAlias = L["uint", "int", "complex", "float", "bool", "void", "object", "datetime", "timedelta", "bytes", "str"]
+
+@type_check_only
+class _KindToStemType(TypedDict):
+    u: ReadOnly[L["uint"]]
+    i: ReadOnly[L["int"]]
+    c: ReadOnly[L["complex"]]
+    f: ReadOnly[L["float"]]
+    b: ReadOnly[L["bool"]]
+    V: ReadOnly[L["void"]]
+    O: ReadOnly[L["object"]]
+    M: ReadOnly[L["datetime"]]
+    m: ReadOnly[L["timedelta"]]
+    S: ReadOnly[L["bytes"]]
+    U: ReadOnly[L["str"]]
+
+###
+
+_kind_to_stem: Final[_KindToStemType] = ...
+
+#
+def _kind_name(dtype: np.dtype[Any]) -> _Name: ...
+def __str__(dtype: np.dtype[Any]) -> str: ...
+def __repr__(dtype: np.dtype[Any]) -> str: ...
+
+#
+def _isunsized(dtype: np.dtype[Any]) -> bool: ...
+def _is_packed(dtype: np.dtype[Any]) -> bool: ...
+def _name_includes_bit_suffix(dtype: np.dtype[Any]) -> bool: ...
+
+#
+def _construction_repr(dtype: np.dtype[Any], include_align: bool = False, short: bool = False) -> str: ...
+def _scalar_str(dtype: np.dtype[Any], short: bool) -> str: ...
+def _byte_order_str(dtype: np.dtype[Any]) -> str: ...
+def _datetime_metadata_str(dtype: np.dtype[Any]) -> str: ...
+def _struct_dict_str(dtype: np.dtype[Any], includealignedflag: bool) -> str: ...
+def _struct_list_str(dtype: np.dtype[Any]) -> str: ...
+def _struct_str(dtype: np.dtype[Any], include_align: bool) -> str: ...
+def _subarray_str(dtype: np.dtype[Any]) -> str: ...
+def _name_get(dtype: np.dtype[Any]) -> str: ...
+
+#
+@overload
+def _unpack_field(dtype: np.dtype[Any], offset: int, title: _T) -> tuple[np.dtype[Any], int, _T]: ...
+@overload
+def _unpack_field(dtype: np.dtype[Any], offset: int, title: None = None) -> tuple[np.dtype[Any], int, None]: ...
+def _aligned_offset(offset: int, alignment: int) -> int: ...

env/Lib/site-packages/numpy/_core/_dtype_ctypes.py

@@ -0,0 +1,120 @@
+"""
+Conversion from ctypes to dtype.
+
+In an ideal world, we could achieve this through the PEP3118 buffer protocol,
+something like::
+
+    def dtype_from_ctypes_type(t):
+        # needed to ensure that the shape of `t` is within memoryview.format
+        class DummyStruct(ctypes.Structure):
+            _fields_ = [('a', t)]
+
+        # empty to avoid memory allocation
+        ctype_0 = (DummyStruct * 0)()
+        mv = memoryview(ctype_0)
+
+        # convert the struct, and slice back out the field
+        return _dtype_from_pep3118(mv.format)['a']
+
+Unfortunately, this fails because:
+
+* ctypes cannot handle length-0 arrays with PEP3118 (bpo-32782)
+* PEP3118 cannot represent unions, but both numpy and ctypes can
+* ctypes cannot handle big-endian structs with PEP3118 (bpo-32780)
+"""
+
+# We delay-import ctypes for distributions that do not include it.
+# While this module is not used unless the user passes in ctypes
+# members, it is eagerly imported from numpy/_core/__init__.py.
+import numpy as np
+
+
+def _from_ctypes_array(t):
+    return np.dtype((dtype_from_ctypes_type(t._type_), (t._length_,)))
+
+
+def _from_ctypes_structure(t):
+    for item in t._fields_:
+        if len(item) > 2:
+            raise TypeError(
+                "ctypes bitfields have no dtype equivalent")
+
+    if hasattr(t, "_pack_"):
+        import ctypes
+        formats = []
+        offsets = []
+        names = []
+        current_offset = 0
+        for fname, ftyp in t._fields_:
+            names.append(fname)
+            formats.append(dtype_from_ctypes_type(ftyp))
+            # Each type has a default offset, this is platform dependent
+            # for some types.
+            effective_pack = min(t._pack_, ctypes.alignment(ftyp))
+            current_offset = (
+                (current_offset + effective_pack - 1) // effective_pack
+            ) * effective_pack
+            offsets.append(current_offset)
+            current_offset += ctypes.sizeof(ftyp)
+
+        return np.dtype(dict(
+            formats=formats,
+            offsets=offsets,
+            names=names,
+            itemsize=ctypes.sizeof(t)))
+    else:
+        fields = []
+        for fname, ftyp in t._fields_:
+            fields.append((fname, dtype_from_ctypes_type(ftyp)))
+
+        # by default, ctypes structs are aligned
+        return np.dtype(fields, align=True)
+
+
+def _from_ctypes_scalar(t):
+    """
+    Return the dtype type with endianness included if it's the case
+    """
+    if getattr(t, '__ctype_be__', None) is t:
+        return np.dtype('>' + t._type_)
+    elif getattr(t, '__ctype_le__', None) is t:
+        return np.dtype('<' + t._type_)
+    else:
+        return np.dtype(t._type_)
+
+
+def _from_ctypes_union(t):
+    import ctypes
+    formats = []
+    offsets = []
+    names = []
+    for fname, ftyp in t._fields_:
+        names.append(fname)
+        formats.append(dtype_from_ctypes_type(ftyp))
+        offsets.append(0)  # Union fields are offset to 0
+
+    return np.dtype(dict(
+        formats=formats,
+        offsets=offsets,
+        names=names,
+        itemsize=ctypes.sizeof(t)))
+
+
+def dtype_from_ctypes_type(t):
+    """
+    Construct a dtype object from a ctypes type
+    """
+    import _ctypes
+    if issubclass(t, _ctypes.Array):
+        return _from_ctypes_array(t)
+    elif issubclass(t, _ctypes._Pointer):
+        raise TypeError("ctypes pointers have no dtype equivalent")
+    elif issubclass(t, _ctypes.Structure):
+        return _from_ctypes_structure(t)
+    elif issubclass(t, _ctypes.Union):
+        return _from_ctypes_union(t)
+    elif isinstance(getattr(t, '_type_', None), str):
+        return _from_ctypes_scalar(t)
+    else:
+        raise NotImplementedError(
+            "Unknown ctypes type {}".format(t.__name__))

env/Lib/site-packages/numpy/_core/_dtype_ctypes.pyi

@@ -0,0 +1,83 @@
+import _ctypes
+import ctypes as ct
+from typing import Any, overload
+
+import numpy as np
+
+#
+@overload
+def dtype_from_ctypes_type(t: type[_ctypes.Array[Any] | _ctypes.Structure]) -> np.dtype[np.void]: ...
+@overload
+def dtype_from_ctypes_type(t: type[ct.c_bool]) -> np.dtype[np.bool]: ...
+@overload
+def dtype_from_ctypes_type(t: type[ct.c_int8 | ct.c_byte]) -> np.dtype[np.int8]: ...
+@overload
+def dtype_from_ctypes_type(t: type[ct.c_uint8 | ct.c_ubyte]) -> np.dtype[np.uint8]: ...
+@overload
+def dtype_from_ctypes_type(t: type[ct.c_int16 | ct.c_short]) -> np.dtype[np.int16]: ...
+@overload
+def dtype_from_ctypes_type(t: type[ct.c_uint16 | ct.c_ushort]) -> np.dtype[np.uint16]: ...
+@overload
+def dtype_from_ctypes_type(t: type[ct.c_int32 | ct.c_int]) -> np.dtype[np.int32]: ...
+@overload
+def dtype_from_ctypes_type(t: type[ct.c_uint32 | ct.c_uint]) -> np.dtype[np.uint32]: ...
+@overload
+def dtype_from_ctypes_type(t: type[ct.c_ssize_t | ct.c_long]) -> np.dtype[np.int32 | np.int64]: ...
+@overload
+def dtype_from_ctypes_type(t: type[ct.c_size_t | ct.c_ulong]) -> np.dtype[np.uint32 | np.uint64]: ...
+@overload
+def dtype_from_ctypes_type(t: type[ct.c_int64 | ct.c_longlong]) -> np.dtype[np.int64]: ...
+@overload
+def dtype_from_ctypes_type(t: type[ct.c_uint64 | ct.c_ulonglong]) -> np.dtype[np.uint64]: ...
+@overload
+def dtype_from_ctypes_type(t: type[ct.c_float]) -> np.dtype[np.float32]: ...
+@overload
+def dtype_from_ctypes_type(t: type[ct.c_double]) -> np.dtype[np.float64]: ...
+@overload
+def dtype_from_ctypes_type(t: type[ct.c_longdouble]) -> np.dtype[np.longdouble]: ...
+@overload
+def dtype_from_ctypes_type(t: type[ct.c_char]) -> np.dtype[np.bytes_]: ...
+@overload
+def dtype_from_ctypes_type(t: type[ct.py_object[Any]]) -> np.dtype[np.object_]: ...
+
+# NOTE: the complex ctypes on python>=3.14 are not yet supported at runtim, see
+# https://github.com/numpy/numpy/issues/28360
+
+#
+def _from_ctypes_array(t: type[_ctypes.Array[Any]]) -> np.dtype[np.void]: ...
+def _from_ctypes_structure(t: type[_ctypes.Structure]) -> np.dtype[np.void]: ...
+def _from_ctypes_union(t: type[_ctypes.Union]) -> np.dtype[np.void]: ...
+
+# keep in sync with `dtype_from_ctypes_type` (minus the first overload)
+@overload
+def _from_ctypes_scalar(t: type[ct.c_bool]) -> np.dtype[np.bool]: ...
+@overload
+def _from_ctypes_scalar(t: type[ct.c_int8 | ct.c_byte]) -> np.dtype[np.int8]: ...
+@overload
+def _from_ctypes_scalar(t: type[ct.c_uint8 | ct.c_ubyte]) -> np.dtype[np.uint8]: ...
+@overload
+def _from_ctypes_scalar(t: type[ct.c_int16 | ct.c_short]) -> np.dtype[np.int16]: ...
+@overload
+def _from_ctypes_scalar(t: type[ct.c_uint16 | ct.c_ushort]) -> np.dtype[np.uint16]: ...
+@overload
+def _from_ctypes_scalar(t: type[ct.c_int32 | ct.c_int]) -> np.dtype[np.int32]: ...
+@overload
+def _from_ctypes_scalar(t: type[ct.c_uint32 | ct.c_uint]) -> np.dtype[np.uint32]: ...
+@overload
+def _from_ctypes_scalar(t: type[ct.c_ssize_t | ct.c_long]) -> np.dtype[np.int32 | np.int64]: ...
+@overload
+def _from_ctypes_scalar(t: type[ct.c_size_t | ct.c_ulong]) -> np.dtype[np.uint32 | np.uint64]: ...
+@overload
+def _from_ctypes_scalar(t: type[ct.c_int64 | ct.c_longlong]) -> np.dtype[np.int64]: ...
+@overload
+def _from_ctypes_scalar(t: type[ct.c_uint64 | ct.c_ulonglong]) -> np.dtype[np.uint64]: ...
+@overload
+def _from_ctypes_scalar(t: type[ct.c_float]) -> np.dtype[np.float32]: ...
+@overload
+def _from_ctypes_scalar(t: type[ct.c_double]) -> np.dtype[np.float64]: ...
+@overload
+def _from_ctypes_scalar(t: type[ct.c_longdouble]) -> np.dtype[np.longdouble]: ...
+@overload
+def _from_ctypes_scalar(t: type[ct.c_char]) -> np.dtype[np.bytes_]: ...
+@overload
+def _from_ctypes_scalar(t: type[ct.py_object[Any]]) -> np.dtype[np.object_]: ...

env/Lib/site-packages/numpy/_core/_exceptions.py

@@ -0,0 +1,172 @@
+"""
+Various richly-typed exceptions, that also help us deal with string formatting
+in python where it's easier.
+
+By putting the formatting in `__str__`, we also avoid paying the cost for
+users who silence the exceptions.
+"""
+from .._utils import set_module
+
+def _unpack_tuple(tup):
+    if len(tup) == 1:
+        return tup[0]
+    else:
+        return tup
+
+
+def _display_as_base(cls):
+    """
+    A decorator that makes an exception class look like its base.
+
+    We use this to hide subclasses that are implementation details - the user
+    should catch the base type, which is what the traceback will show them.
+
+    Classes decorated with this decorator are subject to removal without a
+    deprecation warning.
+    """
+    assert issubclass(cls, Exception)
+    cls.__name__ = cls.__base__.__name__
+    return cls
+
+
+class UFuncTypeError(TypeError):
+    """ Base class for all ufunc exceptions """
+    def __init__(self, ufunc):
+        self.ufunc = ufunc
+
+
+@_display_as_base
+class _UFuncNoLoopError(UFuncTypeError):
+    """ Thrown when a ufunc loop cannot be found """
+    def __init__(self, ufunc, dtypes):
+        super().__init__(ufunc)
+        self.dtypes = tuple(dtypes)
+
+    def __str__(self):
+        return (
+            "ufunc {!r} did not contain a loop with signature matching types "
+            "{!r} -> {!r}"
+        ).format(
+            self.ufunc.__name__,
+            _unpack_tuple(self.dtypes[:self.ufunc.nin]),
+            _unpack_tuple(self.dtypes[self.ufunc.nin:])
+        )
+
+
+@_display_as_base
+class _UFuncBinaryResolutionError(_UFuncNoLoopError):
+    """ Thrown when a binary resolution fails """
+    def __init__(self, ufunc, dtypes):
+        super().__init__(ufunc, dtypes)
+        assert len(self.dtypes) == 2
+
+    def __str__(self):
+        return (
+            "ufunc {!r} cannot use operands with types {!r} and {!r}"
+        ).format(
+            self.ufunc.__name__, *self.dtypes
+        )
+
+
+@_display_as_base
+class _UFuncCastingError(UFuncTypeError):
+    def __init__(self, ufunc, casting, from_, to):
+        super().__init__(ufunc)
+        self.casting = casting
+        self.from_ = from_
+        self.to = to
+
+
+@_display_as_base
+class _UFuncInputCastingError(_UFuncCastingError):
+    """ Thrown when a ufunc input cannot be casted """
+    def __init__(self, ufunc, casting, from_, to, i):
+        super().__init__(ufunc, casting, from_, to)
+        self.in_i = i
+
+    def __str__(self):
+        # only show the number if more than one input exists
+        i_str = "{} ".format(self.in_i) if self.ufunc.nin != 1 else ""
+        return (
+            "Cannot cast ufunc {!r} input {}from {!r} to {!r} with casting "
+            "rule {!r}"
+        ).format(
+            self.ufunc.__name__, i_str, self.from_, self.to, self.casting
+        )
+
+
+@_display_as_base
+class _UFuncOutputCastingError(_UFuncCastingError):
+    """ Thrown when a ufunc output cannot be casted """
+    def __init__(self, ufunc, casting, from_, to, i):
+        super().__init__(ufunc, casting, from_, to)
+        self.out_i = i
+
+    def __str__(self):
+        # only show the number if more than one output exists
+        i_str = "{} ".format(self.out_i) if self.ufunc.nout != 1 else ""
+        return (
+            "Cannot cast ufunc {!r} output {}from {!r} to {!r} with casting "
+            "rule {!r}"
+        ).format(
+            self.ufunc.__name__, i_str, self.from_, self.to, self.casting
+        )
+
+
+@_display_as_base
+class _ArrayMemoryError(MemoryError):
+    """ Thrown when an array cannot be allocated"""
+    def __init__(self, shape, dtype):
+        self.shape = shape
+        self.dtype = dtype
+
+    @property
+    def _total_size(self):
+        num_bytes = self.dtype.itemsize
+        for dim in self.shape:
+            num_bytes *= dim
+        return num_bytes
+
+    @staticmethod
+    def _size_to_string(num_bytes):
+        """ Convert a number of bytes into a binary size string """
+
+        # https://en.wikipedia.org/wiki/Binary_prefix
+        LOG2_STEP = 10
+        STEP = 1024
+        units = ['bytes', 'KiB', 'MiB', 'GiB', 'TiB', 'PiB', 'EiB']
+
+        unit_i = max(num_bytes.bit_length() - 1, 1) // LOG2_STEP
+        unit_val = 1 << (unit_i * LOG2_STEP)
+        n_units = num_bytes / unit_val
+        del unit_val
+
+        # ensure we pick a unit that is correct after rounding
+        if round(n_units) == STEP:
+            unit_i += 1
+            n_units /= STEP
+
+        # deal with sizes so large that we don't have units for them
+        if unit_i >= len(units):
+            new_unit_i = len(units) - 1
+            n_units *= 1 << ((unit_i - new_unit_i) * LOG2_STEP)
+            unit_i = new_unit_i
+
+        unit_name = units[unit_i]
+        # format with a sensible number of digits
+        if unit_i == 0:
+            # no decimal point on bytes
+            return '{:.0f} {}'.format(n_units, unit_name)
+        elif round(n_units) < 1000:
+            # 3 significant figures, if none are dropped to the left of the .
+            return '{:#.3g} {}'.format(n_units, unit_name)
+        else:
+            # just give all the digits otherwise
+            return '{:#.0f} {}'.format(n_units, unit_name)
+
+    def __str__(self):
+        size_str = self._size_to_string(self._total_size)
+        return (
+            "Unable to allocate {} for an array with shape {} and data type {}"
+            .format(size_str, self.shape, self.dtype)
+        )

env/Lib/site-packages/numpy/_core/_exceptions.pyi

@@ -0,0 +1,73 @@
+from collections.abc import Iterable
+from typing import Any, Final, overload
+
+from typing_extensions import TypeVar, Unpack
+
+import numpy as np
+from numpy import _CastingKind
+from numpy._utils import set_module as set_module
+
+###
+
+_T = TypeVar("_T")
+_TupleT = TypeVar("_TupleT", bound=tuple[()] | tuple[Any, Any, Unpack[tuple[Any, ...]]])
+_ExceptionT = TypeVar("_ExceptionT", bound=Exception)
+
+###
+
+class UFuncTypeError(TypeError):
+    ufunc: Final[np.ufunc]
+    def __init__(self, /, ufunc: np.ufunc) -> None: ...
+
+class _UFuncNoLoopError(UFuncTypeError):
+    dtypes: tuple[np.dtype[Any], ...]
+    def __init__(self, /, ufunc: np.ufunc, dtypes: Iterable[np.dtype[Any]]) -> None: ...
+
+class _UFuncBinaryResolutionError(_UFuncNoLoopError):
+    dtypes: tuple[np.dtype[Any], np.dtype[Any]]
+    def __init__(self, /, ufunc: np.ufunc, dtypes: Iterable[np.dtype[Any]]) -> None: ...
+
+class _UFuncCastingError(UFuncTypeError):
+    casting: Final[_CastingKind]
+    from_: Final[np.dtype[Any]]
+    to: Final[np.dtype[Any]]
+    def __init__(self, /, ufunc: np.ufunc, casting: _CastingKind, from_: np.dtype[Any], to: np.dtype[Any]) -> None: ...
+
+class _UFuncInputCastingError(_UFuncCastingError):
+    in_i: Final[int]
+    def __init__(
+        self,
+        /,
+        ufunc: np.ufunc,
+        casting: _CastingKind,
+        from_: np.dtype[Any],
+        to: np.dtype[Any],
+        i: int,
+    ) -> None: ...
+
+class _UFuncOutputCastingError(_UFuncCastingError):
+    out_i: Final[int]
+    def __init__(
+        self,
+        /,
+        ufunc: np.ufunc,
+        casting: _CastingKind,
+        from_: np.dtype[Any],
+        to: np.dtype[Any],
+        i: int,
+    ) -> None: ...
+
+class _ArrayMemoryError(MemoryError):
+    shape: tuple[int, ...]
+    dtype: np.dtype[Any]
+    def __init__(self, /, shape: tuple[int, ...], dtype: np.dtype[Any]) -> None: ...
+    @property
+    def _total_size(self) -> int: ...
+    @staticmethod
+    def _size_to_string(num_bytes: int) -> str: ...
+
+@overload
+def _unpack_tuple(tup: tuple[_T]) -> _T: ...
+@overload
+def _unpack_tuple(tup: _TupleT) -> _TupleT: ...
+def _display_as_base(cls: type[_ExceptionT]) -> type[_ExceptionT]: ...

env/Lib/site-packages/numpy/_core/_internal.py

@@ -0,0 +1,963 @@
+"""
+A place for internal code
+
+Some things are more easily handled Python.
+
+"""
+import ast
+import math
+import re
+import sys
+import warnings
+
+from ..exceptions import DTypePromotionError
+from .multiarray import dtype, array, ndarray, promote_types, StringDType
+from numpy import _NoValue
+try:
+    import ctypes
+except ImportError:
+    ctypes = None
+
+IS_PYPY = sys.implementation.name == 'pypy'
+
+if sys.byteorder == 'little':
+    _nbo = '<'
+else:
+    _nbo = '>'
+
+def _makenames_list(adict, align):
+    allfields = []
+
+    for fname, obj in adict.items():
+        n = len(obj)
+        if not isinstance(obj, tuple) or n not in (2, 3):
+            raise ValueError("entry not a 2- or 3- tuple")
+        if n > 2 and obj[2] == fname:
+            continue
+        num = int(obj[1])
+        if num < 0:
+            raise ValueError("invalid offset.")
+        format = dtype(obj[0], align=align)
+        if n > 2:
+            title = obj[2]
+        else:
+            title = None
+        allfields.append((fname, format, num, title))
+    # sort by offsets
+    allfields.sort(key=lambda x: x[2])
+    names = [x[0] for x in allfields]
+    formats = [x[1] for x in allfields]
+    offsets = [x[2] for x in allfields]
+    titles = [x[3] for x in allfields]
+
+    return names, formats, offsets, titles
+
+# Called in PyArray_DescrConverter function when
+#  a dictionary without "names" and "formats"
+#  fields is used as a data-type descriptor.
+def _usefields(adict, align):
+    try:
+        names = adict[-1]
+    except KeyError:
+        names = None
+    if names is None:
+        names, formats, offsets, titles = _makenames_list(adict, align)
+    else:
+        formats = []
+        offsets = []
+        titles = []
+        for name in names:
+            res = adict[name]
+            formats.append(res[0])
+            offsets.append(res[1])
+            if len(res) > 2:
+                titles.append(res[2])
+            else:
+                titles.append(None)
+
+    return dtype({"names": names,
+                  "formats": formats,
+                  "offsets": offsets,
+                  "titles": titles}, align)
+
+
+# construct an array_protocol descriptor list
+#  from the fields attribute of a descriptor
+# This calls itself recursively but should eventually hit
+#  a descriptor that has no fields and then return
+#  a simple typestring
+
+def _array_descr(descriptor):
+    fields = descriptor.fields
+    if fields is None:
+        subdtype = descriptor.subdtype
+        if subdtype is None:
+            if descriptor.metadata is None:
+                return descriptor.str
+            else:
+                new = descriptor.metadata.copy()
+                if new:
+                    return (descriptor.str, new)
+                else:
+                    return descriptor.str
+        else:
+            return (_array_descr(subdtype[0]), subdtype[1])
+
+    names = descriptor.names
+    ordered_fields = [fields[x] + (x,) for x in names]
+    result = []
+    offset = 0
+    for field in ordered_fields:
+        if field[1] > offset:
+            num = field[1] - offset
+            result.append(('', f'|V{num}'))
+            offset += num
+        elif field[1] < offset:
+            raise ValueError(
+                "dtype.descr is not defined for types with overlapping or "
+                "out-of-order fields")
+        if len(field) > 3:
+            name = (field[2], field[3])
+        else:
+            name = field[2]
+        if field[0].subdtype:
+            tup = (name, _array_descr(field[0].subdtype[0]),
+                   field[0].subdtype[1])
+        else:
+            tup = (name, _array_descr(field[0]))
+        offset += field[0].itemsize
+        result.append(tup)
+
+    if descriptor.itemsize > offset:
+        num = descriptor.itemsize - offset
+        result.append(('', f'|V{num}'))
+
+    return result
+
+
+# format_re was originally from numarray by J. Todd Miller
+
+format_re = re.compile(r'(?P<order1>[<>|=]?)'
+                       r'(?P<repeats> *[(]?[ ,0-9]*[)]? *)'
+                       r'(?P<order2>[<>|=]?)'
+                       r'(?P<dtype>[A-Za-z0-9.?]*(?:\[[a-zA-Z0-9,.]+\])?)')
+sep_re = re.compile(r'\s*,\s*')
+space_re = re.compile(r'\s+$')
+
+# astr is a string (perhaps comma separated)
+
+_convorder = {'=': _nbo}
+
+def _commastring(astr):
+    startindex = 0
+    result = []
+    islist = False
+    while startindex < len(astr):
+        mo = format_re.match(astr, pos=startindex)
+        try:
+            (order1, repeats, order2, dtype) = mo.groups()
+        except (TypeError, AttributeError):
+            raise ValueError(
+                f'format number {len(result)+1} of "{astr}" is not recognized'
+                ) from None
+        startindex = mo.end()
+        # Separator or ending padding
+        if startindex < len(astr):
+            if space_re.match(astr, pos=startindex):
+                startindex = len(astr)
+            else:
+                mo = sep_re.match(astr, pos=startindex)
+                if not mo:
+                    raise ValueError(
+                        'format number %d of "%s" is not recognized' %
+                        (len(result)+1, astr))
+                startindex = mo.end()
+                islist = True
+
+        if order2 == '':
+            order = order1
+        elif order1 == '':
+            order = order2
+        else:
+            order1 = _convorder.get(order1, order1)
+            order2 = _convorder.get(order2, order2)
+            if (order1 != order2):
+                raise ValueError(
+                    'inconsistent byte-order specification %s and %s' %
+                    (order1, order2))
+            order = order1
+
+        if order in ('|', '=', _nbo):
+            order = ''
+        dtype = order + dtype
+        if repeats == '':
+            newitem = dtype
+        else:
+            if (repeats[0] == "(" and repeats[-1] == ")"
+                    and repeats[1:-1].strip() != ""
+                    and "," not in repeats):
+                warnings.warn(
+                    'Passing in a parenthesized single number for repeats '
+                    'is deprecated; pass either a single number or indicate '
+                    'a tuple with a comma, like "(2,)".', DeprecationWarning,
+                    stacklevel=2)
+            newitem = (dtype, ast.literal_eval(repeats))
+
+        result.append(newitem)
+
+    return result if islist else result[0]
+
+class dummy_ctype:
+
+    def __init__(self, cls):
+        self._cls = cls
+
+    def __mul__(self, other):
+        return self
+
+    def __call__(self, *other):
+        return self._cls(other)
+
+    def __eq__(self, other):
+        return self._cls == other._cls
+
+    def __ne__(self, other):
+        return self._cls != other._cls
+
+def _getintp_ctype():
+    val = _getintp_ctype.cache
+    if val is not None:
+        return val
+    if ctypes is None:
+        import numpy as np
+        val = dummy_ctype(np.intp)
+    else:
+        char = dtype('n').char
+        if char == 'i':
+            val = ctypes.c_int
+        elif char == 'l':
+            val = ctypes.c_long
+        elif char == 'q':
+            val = ctypes.c_longlong
+        else:
+            val = ctypes.c_long
+    _getintp_ctype.cache = val
+    return val
+
+
+_getintp_ctype.cache = None
+
+# Used for .ctypes attribute of ndarray
+
+class _missing_ctypes:
+    def cast(self, num, obj):
+        return num.value
+
+    class c_void_p:
+        def __init__(self, ptr):
+            self.value = ptr
+
+
+class _ctypes:
+    def __init__(self, array, ptr=None):
+        self._arr = array
+
+        if ctypes:
+            self._ctypes = ctypes
+            self._data = self._ctypes.c_void_p(ptr)
+        else:
+            # fake a pointer-like object that holds onto the reference
+            self._ctypes = _missing_ctypes()
+            self._data = self._ctypes.c_void_p(ptr)
+            self._data._objects = array
+
+        if self._arr.ndim == 0:
+            self._zerod = True
+        else:
+            self._zerod = False
+
+    def data_as(self, obj):
+        """
+        Return the data pointer cast to a particular c-types object.
+        For example, calling ``self._as_parameter_`` is equivalent to
+        ``self.data_as(ctypes.c_void_p)``. Perhaps you want to use
+        the data as a pointer to a ctypes array of floating-point data:
+        ``self.data_as(ctypes.POINTER(ctypes.c_double))``.
+
+        The returned pointer will keep a reference to the array.
+        """
+        # _ctypes.cast function causes a circular reference of self._data in
+        # self._data._objects. Attributes of self._data cannot be released
+        # until gc.collect is called. Make a copy of the pointer first then
+        # let it hold the array reference. This is a workaround to circumvent
+        # the CPython bug https://bugs.python.org/issue12836.
+        ptr = self._ctypes.cast(self._data, obj)
+        ptr._arr = self._arr
+        return ptr
+
+    def shape_as(self, obj):
+        """
+        Return the shape tuple as an array of some other c-types
+        type. For example: ``self.shape_as(ctypes.c_short)``.
+        """
+        if self._zerod:
+            return None
+        return (obj*self._arr.ndim)(*self._arr.shape)
+
+    def strides_as(self, obj):
+        """
+        Return the strides tuple as an array of some other
+        c-types type. For example: ``self.strides_as(ctypes.c_longlong)``.
+        """
+        if self._zerod:
+            return None
+        return (obj*self._arr.ndim)(*self._arr.strides)
+
+    @property
+    def data(self):
+        """
+        A pointer to the memory area of the array as a Python integer.
+        This memory area may contain data that is not aligned, or not in
+        correct byte-order. The memory area may not even be writeable.
+        The array flags and data-type of this array should be respected
+        when passing this attribute to arbitrary C-code to avoid trouble
+        that can include Python crashing. User Beware! The value of this
+        attribute is exactly the same as:
+        ``self._array_interface_['data'][0]``.
+
+        Note that unlike ``data_as``, a reference won't be kept to the array:
+        code like ``ctypes.c_void_p((a + b).ctypes.data)`` will result in a
+        pointer to a deallocated array, and should be spelt
+        ``(a + b).ctypes.data_as(ctypes.c_void_p)``
+        """
+        return self._data.value
+
+    @property
+    def shape(self):
+        """
+        (c_intp*self.ndim): A ctypes array of length self.ndim where
+        the basetype is the C-integer corresponding to ``dtype('p')`` on this
+        platform (see `~numpy.ctypeslib.c_intp`). This base-type could be
+        `ctypes.c_int`, `ctypes.c_long`, or `ctypes.c_longlong` depending on
+        the platform. The ctypes array contains the shape of
+        the underlying array.
+        """
+        return self.shape_as(_getintp_ctype())
+
+    @property
+    def strides(self):
+        """
+        (c_intp*self.ndim): A ctypes array of length self.ndim where
+        the basetype is the same as for the shape attribute. This ctypes
+        array contains the strides information from the underlying array.
+        This strides information is important for showing how many bytes
+        must be jumped to get to the next element in the array.
+        """
+        return self.strides_as(_getintp_ctype())
+
+    @property
+    def _as_parameter_(self):
+        """
+        Overrides the ctypes semi-magic method
+
+        Enables `c_func(some_array.ctypes)`
+        """
+        return self.data_as(ctypes.c_void_p)
+
+    # Numpy 1.21.0, 2021-05-18
+
+    def get_data(self):
+        """Deprecated getter for the `_ctypes.data` property.
+
+        .. deprecated:: 1.21
+        """
+        warnings.warn('"get_data" is deprecated. Use "data" instead',
+                      DeprecationWarning, stacklevel=2)
+        return self.data
+
+    def get_shape(self):
+        """Deprecated getter for the `_ctypes.shape` property.
+
+        .. deprecated:: 1.21
+        """
+        warnings.warn('"get_shape" is deprecated. Use "shape" instead',
+                      DeprecationWarning, stacklevel=2)
+        return self.shape
+
+    def get_strides(self):
+        """Deprecated getter for the `_ctypes.strides` property.
+
+        .. deprecated:: 1.21
+        """
+        warnings.warn('"get_strides" is deprecated. Use "strides" instead',
+                      DeprecationWarning, stacklevel=2)
+        return self.strides
+
+    def get_as_parameter(self):
+        """Deprecated getter for the `_ctypes._as_parameter_` property.
+
+        .. deprecated:: 1.21
+        """
+        warnings.warn(
+            '"get_as_parameter" is deprecated. Use "_as_parameter_" instead',
+            DeprecationWarning, stacklevel=2,
+        )
+        return self._as_parameter_
+
+
+def _newnames(datatype, order):
+    """
+    Given a datatype and an order object, return a new names tuple, with the
+    order indicated
+    """
+    oldnames = datatype.names
+    nameslist = list(oldnames)
+    if isinstance(order, str):
+        order = [order]
+    seen = set()
+    if isinstance(order, (list, tuple)):
+        for name in order:
+            try:
+                nameslist.remove(name)
+            except ValueError:
+                if name in seen:
+                    raise ValueError(f"duplicate field name: {name}") from None
+                else:
+                    raise ValueError(f"unknown field name: {name}") from None
+            seen.add(name)
+        return tuple(list(order) + nameslist)
+    raise ValueError(f"unsupported order value: {order}")
+
+def _copy_fields(ary):
+    """Return copy of structured array with padding between fields removed.
+
+    Parameters
+    ----------
+    ary : ndarray
+       Structured array from which to remove padding bytes
+
+    Returns
+    -------
+    ary_copy : ndarray
+       Copy of ary with padding bytes removed
+    """
+    dt = ary.dtype
+    copy_dtype = {'names': dt.names,
+                  'formats': [dt.fields[name][0] for name in dt.names]}
+    return array(ary, dtype=copy_dtype, copy=True)
+
+def _promote_fields(dt1, dt2):
+    """ Perform type promotion for two structured dtypes.
+
+    Parameters
+    ----------
+    dt1 : structured dtype
+        First dtype.
+    dt2 : structured dtype
+        Second dtype.
+
+    Returns
+    -------
+    out : dtype
+        The promoted dtype
+
+    Notes
+    -----
+    If one of the inputs is aligned, the result will be.  The titles of
+    both descriptors must match (point to the same field).
+    """
+    # Both must be structured and have the same names in the same order
+    if (dt1.names is None or dt2.names is None) or dt1.names != dt2.names:
+        raise DTypePromotionError(
+                f"field names `{dt1.names}` and `{dt2.names}` mismatch.")
+
+    # if both are identical, we can (maybe!) just return the same dtype.
+    identical = dt1 is dt2
+    new_fields = []
+    for name in dt1.names:
+        field1 = dt1.fields[name]
+        field2 = dt2.fields[name]
+        new_descr = promote_types(field1[0], field2[0])
+        identical = identical and new_descr is field1[0]
+
+        # Check that the titles match (if given):
+        if field1[2:] != field2[2:]:
+            raise DTypePromotionError(
+                    f"field titles of field '{name}' mismatch")
+        if len(field1) == 2:
+            new_fields.append((name, new_descr))
+        else:
+            new_fields.append(((field1[2], name), new_descr))
+
+    res = dtype(new_fields, align=dt1.isalignedstruct or dt2.isalignedstruct)
+
+    # Might as well preserve identity (and metadata) if the dtype is identical
+    # and the itemsize, offsets are also unmodified.  This could probably be
+    # sped up, but also probably just be removed entirely.
+    if identical and res.itemsize == dt1.itemsize:
+        for name in dt1.names:
+            if dt1.fields[name][1] != res.fields[name][1]:
+                return res  # the dtype changed.
+        return dt1
+
+    return res
+
+
+def _getfield_is_safe(oldtype, newtype, offset):
+    """ Checks safety of getfield for object arrays.
+
+    As in _view_is_safe, we need to check that memory containing objects is not
+    reinterpreted as a non-object datatype and vice versa.
+
+    Parameters
+    ----------
+    oldtype : data-type
+        Data type of the original ndarray.
+    newtype : data-type
+        Data type of the field being accessed by ndarray.getfield
+    offset : int
+        Offset of the field being accessed by ndarray.getfield
+
+    Raises
+    ------
+    TypeError
+        If the field access is invalid
+
+    """
+    if newtype.hasobject or oldtype.hasobject:
+        if offset == 0 and newtype == oldtype:
+            return
+        if oldtype.names is not None:
+            for name in oldtype.names:
+                if (oldtype.fields[name][1] == offset and
+                        oldtype.fields[name][0] == newtype):
+                    return
+        raise TypeError("Cannot get/set field of an object array")
+    return
+
+def _view_is_safe(oldtype, newtype):
+    """ Checks safety of a view involving object arrays, for example when
+    doing::
+
+        np.zeros(10, dtype=oldtype).view(newtype)
+
+    Parameters
+    ----------
+    oldtype : data-type
+        Data type of original ndarray
+    newtype : data-type
+        Data type of the view
+
+    Raises
+    ------
+    TypeError
+        If the new type is incompatible with the old type.
+
+    """
+
+    # if the types are equivalent, there is no problem.
+    # for example: dtype((np.record, 'i4,i4')) == dtype((np.void, 'i4,i4'))
+    if oldtype == newtype:
+        return
+
+    if newtype.hasobject or oldtype.hasobject:
+        raise TypeError("Cannot change data-type for array of references.")
+    return
+
+
+# Given a string containing a PEP 3118 format specifier,
+# construct a NumPy dtype
+
+_pep3118_native_map = {
+    '?': '?',
+    'c': 'S1',
+    'b': 'b',
+    'B': 'B',
+    'h': 'h',
+    'H': 'H',
+    'i': 'i',
+    'I': 'I',
+    'l': 'l',
+    'L': 'L',
+    'q': 'q',
+    'Q': 'Q',
+    'e': 'e',
+    'f': 'f',
+    'd': 'd',
+    'g': 'g',
+    'Zf': 'F',
+    'Zd': 'D',
+    'Zg': 'G',
+    's': 'S',
+    'w': 'U',
+    'O': 'O',
+    'x': 'V',  # padding
+}
+_pep3118_native_typechars = ''.join(_pep3118_native_map.keys())
+
+_pep3118_standard_map = {
+    '?': '?',
+    'c': 'S1',
+    'b': 'b',
+    'B': 'B',
+    'h': 'i2',
+    'H': 'u2',
+    'i': 'i4',
+    'I': 'u4',
+    'l': 'i4',
+    'L': 'u4',
+    'q': 'i8',
+    'Q': 'u8',
+    'e': 'f2',
+    'f': 'f',
+    'd': 'd',
+    'Zf': 'F',
+    'Zd': 'D',
+    's': 'S',
+    'w': 'U',
+    'O': 'O',
+    'x': 'V',  # padding
+}
+_pep3118_standard_typechars = ''.join(_pep3118_standard_map.keys())
+
+_pep3118_unsupported_map = {
+    'u': 'UCS-2 strings',
+    '&': 'pointers',
+    't': 'bitfields',
+    'X': 'function pointers',
+}
+
+class _Stream:
+    def __init__(self, s):
+        self.s = s
+        self.byteorder = '@'
+
+    def advance(self, n):
+        res = self.s[:n]
+        self.s = self.s[n:]
+        return res
+
+    def consume(self, c):
+        if self.s[:len(c)] == c:
+            self.advance(len(c))
+            return True
+        return False
+
+    def consume_until(self, c):
+        if callable(c):
+            i = 0
+            while i < len(self.s) and not c(self.s[i]):
+                i = i + 1
+            return self.advance(i)
+        else:
+            i = self.s.index(c)
+            res = self.advance(i)
+            self.advance(len(c))
+            return res
+
+    @property
+    def next(self):
+        return self.s[0]
+
+    def __bool__(self):
+        return bool(self.s)
+
+
+def _dtype_from_pep3118(spec):
+    stream = _Stream(spec)
+    dtype, align = __dtype_from_pep3118(stream, is_subdtype=False)
+    return dtype
+
+def __dtype_from_pep3118(stream, is_subdtype):
+    field_spec = dict(
+        names=[],
+        formats=[],
+        offsets=[],
+        itemsize=0
+    )
+    offset = 0
+    common_alignment = 1
+    is_padding = False
+
+    # Parse spec
+    while stream:
+        value = None
+
+        # End of structure, bail out to upper level
+        if stream.consume('}'):
+            break
+
+        # Sub-arrays (1)
+        shape = None
+        if stream.consume('('):
+            shape = stream.consume_until(')')
+            shape = tuple(map(int, shape.split(',')))
+
+        # Byte order
+        if stream.next in ('@', '=', '<', '>', '^', '!'):
+            byteorder = stream.advance(1)
+            if byteorder == '!':
+                byteorder = '>'
+            stream.byteorder = byteorder
+
+        # Byte order characters also control native vs. standard type sizes
+        if stream.byteorder in ('@', '^'):
+            type_map = _pep3118_native_map
+            type_map_chars = _pep3118_native_typechars
+        else:
+            type_map = _pep3118_standard_map
+            type_map_chars = _pep3118_standard_typechars
+
+        # Item sizes
+        itemsize_str = stream.consume_until(lambda c: not c.isdigit())
+        if itemsize_str:
+            itemsize = int(itemsize_str)
+        else:
+            itemsize = 1
+
+        # Data types
+        is_padding = False
+
+        if stream.consume('T{'):
+            value, align = __dtype_from_pep3118(
+                stream, is_subdtype=True)
+        elif stream.next in type_map_chars:
+            if stream.next == 'Z':
+                typechar = stream.advance(2)
+            else:
+                typechar = stream.advance(1)
+
+            is_padding = (typechar == 'x')
+            dtypechar = type_map[typechar]
+            if dtypechar in 'USV':
+                dtypechar += '%d' % itemsize
+                itemsize = 1
+            numpy_byteorder = {'@': '=', '^': '='}.get(
+                stream.byteorder, stream.byteorder)
+            value = dtype(numpy_byteorder + dtypechar)
+            align = value.alignment
+        elif stream.next in _pep3118_unsupported_map:
+            desc = _pep3118_unsupported_map[stream.next]
+            raise NotImplementedError(
+                "Unrepresentable PEP 3118 data type {!r} ({})"
+                .format(stream.next, desc))
+        else:
+            raise ValueError(
+                "Unknown PEP 3118 data type specifier %r" % stream.s
+            )
+
+        #
+        # Native alignment may require padding
+        #
+        # Here we assume that the presence of a '@' character implicitly
+        # implies that the start of the array is *already* aligned.
+        #
+        extra_offset = 0
+        if stream.byteorder == '@':
+            start_padding = (-offset) % align
+            intra_padding = (-value.itemsize) % align
+
+            offset += start_padding
+
+            if intra_padding != 0:
+                if itemsize > 1 or (shape is not None and _prod(shape) > 1):
+                    # Inject internal padding to the end of the sub-item
+                    value = _add_trailing_padding(value, intra_padding)
+                else:
+                    # We can postpone the injection of internal padding,
+                    # as the item appears at most once
+                    extra_offset += intra_padding
+
+            # Update common alignment
+            common_alignment = _lcm(align, common_alignment)
+
+        # Convert itemsize to sub-array
+        if itemsize != 1:
+            value = dtype((value, (itemsize,)))
+
+        # Sub-arrays (2)
+        if shape is not None:
+            value = dtype((value, shape))
+
+        # Field name
+        if stream.consume(':'):
+            name = stream.consume_until(':')
+        else:
+            name = None
+
+        if not (is_padding and name is None):
+            if name is not None and name in field_spec['names']:
+                raise RuntimeError(
+                    f"Duplicate field name '{name}' in PEP3118 format"
+                )
+            field_spec['names'].append(name)
+            field_spec['formats'].append(value)
+            field_spec['offsets'].append(offset)
+
+        offset += value.itemsize
+        offset += extra_offset
+
+        field_spec['itemsize'] = offset
+
+    # extra final padding for aligned types
+    if stream.byteorder == '@':
+        field_spec['itemsize'] += (-offset) % common_alignment
+
+    # Check if this was a simple 1-item type, and unwrap it
+    if (field_spec['names'] == [None]
+            and field_spec['offsets'][0] == 0
+            and field_spec['itemsize'] == field_spec['formats'][0].itemsize
+            and not is_subdtype):
+        ret = field_spec['formats'][0]
+    else:
+        _fix_names(field_spec)
+        ret = dtype(field_spec)
+
+    # Finished
+    return ret, common_alignment
+
+def _fix_names(field_spec):
+    """ Replace names which are None with the next unused f%d name """
+    names = field_spec['names']
+    for i, name in enumerate(names):
+        if name is not None:
+            continue
+
+        j = 0
+        while True:
+            name = f'f{j}'
+            if name not in names:
+                break
+            j = j + 1
+        names[i] = name
+
+def _add_trailing_padding(value, padding):
+    """Inject the specified number of padding bytes at the end of a dtype"""
+    if value.fields is None:
+        field_spec = dict(
+            names=['f0'],
+            formats=[value],
+            offsets=[0],
+            itemsize=value.itemsize
+        )
+    else:
+        fields = value.fields
+        names = value.names
+        field_spec = dict(
+            names=names,
+            formats=[fields[name][0] for name in names],
+            offsets=[fields[name][1] for name in names],
+            itemsize=value.itemsize
+        )
+
+    field_spec['itemsize'] += padding
+    return dtype(field_spec)
+
+def _prod(a):
+    p = 1
+    for x in a:
+        p *= x
+    return p
+
+def _gcd(a, b):
+    """Calculate the greatest common divisor of a and b"""
+    if not (math.isfinite(a) and math.isfinite(b)):
+        raise ValueError('Can only find greatest common divisor of '
+                         f'finite arguments, found "{a}" and "{b}"')
+    while b:
+        a, b = b, a % b
+    return a
+
+def _lcm(a, b):
+    return a // _gcd(a, b) * b
+
+def array_ufunc_errmsg_formatter(dummy, ufunc, method, *inputs, **kwargs):
+    """ Format the error message for when __array_ufunc__ gives up. """
+    args_string = ', '.join(['{!r}'.format(arg) for arg in inputs] +
+                            ['{}={!r}'.format(k, v)
+                             for k, v in kwargs.items()])
+    args = inputs + kwargs.get('out', ())
+    types_string = ', '.join(repr(type(arg).__name__) for arg in args)
+    return ('operand type(s) all returned NotImplemented from '
+            '__array_ufunc__({!r}, {!r}, {}): {}'
+            .format(ufunc, method, args_string, types_string))
+
+
+def array_function_errmsg_formatter(public_api, types):
+    """ Format the error message for when __array_ufunc__ gives up. """
+    func_name = '{}.{}'.format(public_api.__module__, public_api.__name__)
+    return ("no implementation found for '{}' on types that implement "
+            '__array_function__: {}'.format(func_name, list(types)))
+
+
+def _ufunc_doc_signature_formatter(ufunc):
+    """
+    Builds a signature string which resembles PEP 457
+
+    This is used to construct the first line of the docstring
+    """
+
+    # input arguments are simple
+    if ufunc.nin == 1:
+        in_args = 'x'
+    else:
+        in_args = ', '.join(f'x{i+1}' for i in range(ufunc.nin))
+
+    # output arguments are both keyword or positional
+    if ufunc.nout == 0:
+        out_args = ', /, out=()'
+    elif ufunc.nout == 1:
+        out_args = ', /, out=None'
+    else:
+        out_args = '[, {positional}], / [, out={default}]'.format(
+            positional=', '.join(
+                'out{}'.format(i+1) for i in range(ufunc.nout)),
+            default=repr((None,)*ufunc.nout)
+        )
+
+    # keyword only args depend on whether this is a gufunc
+    kwargs = (
+        ", casting='same_kind'"
+        ", order='K'"
+        ", dtype=None"
+        ", subok=True"
+    )
+
+    # NOTE: gufuncs may or may not support the `axis` parameter
+    if ufunc.signature is None:
+        kwargs = f", where=True{kwargs}[, signature]"
+    else:
+        kwargs += "[, signature, axes, axis]"
+
+    # join all the parts together
+    return '{name}({in_args}{out_args}, *{kwargs})'.format(
+        name=ufunc.__name__,
+        in_args=in_args,
+        out_args=out_args,
+        kwargs=kwargs
+    )
+
+
+def npy_ctypes_check(cls):
+    # determine if a class comes from ctypes, in order to work around
+    # a bug in the buffer protocol for those objects, bpo-10746
+    try:
+        # ctypes class are new-style, so have an __mro__. This probably fails
+        # for ctypes classes with multiple inheritance.
+        if IS_PYPY:
+            # (..., _ctypes.basics._CData, Bufferable, object)
+            ctype_base = cls.__mro__[-3]
+        else:
+            # # (..., _ctypes._CData, object)
+            ctype_base = cls.__mro__[-2]
+        # right now, they're part of the _ctypes module
+        return '_ctypes' in ctype_base.__module__
+    except Exception:
+        return False
+
+# used to handle the _NoValue default argument for na_object
+# in the C implementation of the __reduce__ method for stringdtype
+def _convert_to_stringdtype_kwargs(coerce, na_object=_NoValue):
+    if na_object is _NoValue:
+        return StringDType(coerce=coerce)
+    return StringDType(coerce=coerce, na_object=na_object)

env/Lib/site-packages/numpy/_core/_internal.pyi

@@ -0,0 +1,72 @@
+import ctypes as ct
+import re
+from collections.abc import Callable, Iterable
+from typing import Any, Final, Generic, overload
+
+from typing_extensions import Self, TypeVar, deprecated
+
+import numpy as np
+import numpy.typing as npt
+from numpy.ctypeslib import c_intp
+
+_CastT = TypeVar("_CastT", bound=ct._CanCastTo)
+_T_co = TypeVar("_T_co", covariant=True)
+_CT = TypeVar("_CT", bound=ct._CData)
+_PT_co = TypeVar("_PT_co", bound=int | None, default=None, covariant=True)
+
+###
+
+IS_PYPY: Final[bool] = ...
+
+format_re: Final[re.Pattern[str]] = ...
+sep_re: Final[re.Pattern[str]] = ...
+space_re: Final[re.Pattern[str]] = ...
+
+###
+
+# TODO: Let the likes of `shape_as` and `strides_as` return `None`
+# for 0D arrays once we've got shape-support
+
+class _ctypes(Generic[_PT_co]):
+    @overload
+    def __init__(self: _ctypes[None], /, array: npt.NDArray[Any], ptr: None = None) -> None: ...
+    @overload
+    def __init__(self, /, array: npt.NDArray[Any], ptr: _PT_co) -> None: ...
+
+    #
+    @property
+    def data(self) -> _PT_co: ...
+    @property
+    def shape(self) -> ct.Array[c_intp]: ...
+    @property
+    def strides(self) -> ct.Array[c_intp]: ...
+    @property
+    def _as_parameter_(self) -> ct.c_void_p: ...
+
+    #
+    def data_as(self, /, obj: type[_CastT]) -> _CastT: ...
+    def shape_as(self, /, obj: type[_CT]) -> ct.Array[_CT]: ...
+    def strides_as(self, /, obj: type[_CT]) -> ct.Array[_CT]: ...
+
+    #
+    @deprecated('"get_data" is deprecated. Use "data" instead')
+    def get_data(self, /) -> _PT_co: ...
+    @deprecated('"get_shape" is deprecated. Use "shape" instead')
+    def get_shape(self, /) -> ct.Array[c_intp]: ...
+    @deprecated('"get_strides" is deprecated. Use "strides" instead')
+    def get_strides(self, /) -> ct.Array[c_intp]: ...
+    @deprecated('"get_as_parameter" is deprecated. Use "_as_parameter_" instead')
+    def get_as_parameter(self, /) -> ct.c_void_p: ...
+
+class dummy_ctype(Generic[_T_co]):
+    _cls: type[_T_co]
+
+    def __init__(self, /, cls: type[_T_co]) -> None: ...
+    def __eq__(self, other: Self, /) -> bool: ...  # type: ignore[override]  # pyright: ignore[reportIncompatibleMethodOverride]
+    def __ne__(self, other: Self, /) -> bool: ...  # type: ignore[override]  # pyright: ignore[reportIncompatibleMethodOverride]
+    def __mul__(self, other: object, /) -> Self: ...
+    def __call__(self, /, *other: object) -> _T_co: ...
+
+def array_ufunc_errmsg_formatter(dummy: object, ufunc: np.ufunc, method: str, *inputs: object, **kwargs: object) -> str: ...
+def array_function_errmsg_formatter(public_api: Callable[..., object], types: Iterable[str]) -> str: ...
+def npy_ctypes_check(cls: type) -> bool: ...

env/Lib/site-packages/numpy/_core/_machar.py

@@ -0,0 +1,356 @@
+"""
+Machine arithmetic - determine the parameters of the
+floating-point arithmetic system
+
+Author: Pearu Peterson, September 2003
+
+"""
+__all__ = ['MachAr']
+
+from .fromnumeric import any
+from ._ufunc_config import errstate
+from .._utils import set_module
+
+# Need to speed this up...especially for longdouble
+
+# Deprecated 2021-10-20, NumPy 1.22
+class MachAr:
+    """
+    Diagnosing machine parameters.
+
+    Attributes
+    ----------
+    ibeta : int
+        Radix in which numbers are represented.
+    it : int
+        Number of base-`ibeta` digits in the floating point mantissa M.
+    machep : int
+        Exponent of the smallest (most negative) power of `ibeta` that,
+        added to 1.0, gives something different from 1.0
+    eps : float
+        Floating-point number ``beta**machep`` (floating point precision)
+    negep : int
+        Exponent of the smallest power of `ibeta` that, subtracted
+        from 1.0, gives something different from 1.0.
+    epsneg : float
+        Floating-point number ``beta**negep``.
+    iexp : int
+        Number of bits in the exponent (including its sign and bias).
+    minexp : int
+        Smallest (most negative) power of `ibeta` consistent with there
+        being no leading zeros in the mantissa.
+    xmin : float
+        Floating-point number ``beta**minexp`` (the smallest [in
+        magnitude] positive floating point number with full precision).
+    maxexp : int
+        Smallest (positive) power of `ibeta` that causes overflow.
+    xmax : float
+        ``(1-epsneg) * beta**maxexp`` (the largest [in magnitude]
+        usable floating value).
+    irnd : int
+        In ``range(6)``, information on what kind of rounding is done
+        in addition, and on how underflow is handled.
+    ngrd : int
+        Number of 'guard digits' used when truncating the product
+        of two mantissas to fit the representation.
+    epsilon : float
+        Same as `eps`.
+    tiny : float
+        An alias for `smallest_normal`, kept for backwards compatibility.
+    huge : float
+        Same as `xmax`.
+    precision : float
+        ``- int(-log10(eps))``
+    resolution : float
+        ``- 10**(-precision)``
+    smallest_normal : float
+        The smallest positive floating point number with 1 as leading bit in
+        the mantissa following IEEE-754. Same as `xmin`.
+    smallest_subnormal : float
+        The smallest positive floating point number with 0 as leading bit in
+        the mantissa following IEEE-754.
+
+    Parameters
+    ----------
+    float_conv : function, optional
+        Function that converts an integer or integer array to a float
+        or float array. Default is `float`.
+    int_conv : function, optional
+        Function that converts a float or float array to an integer or
+        integer array. Default is `int`.
+    float_to_float : function, optional
+        Function that converts a float array to float. Default is `float`.
+        Note that this does not seem to do anything useful in the current
+        implementation.
+    float_to_str : function, optional
+        Function that converts a single float to a string. Default is
+        ``lambda v:'%24.16e' %v``.
+    title : str, optional
+        Title that is printed in the string representation of `MachAr`.
+
+    See Also
+    --------
+    finfo : Machine limits for floating point types.
+    iinfo : Machine limits for integer types.
+
+    References
+    ----------
+    .. [1] Press, Teukolsky, Vetterling and Flannery,
+           "Numerical Recipes in C++," 2nd ed,
+           Cambridge University Press, 2002, p. 31.
+
+    """
+
+    def __init__(self, float_conv=float,int_conv=int,
+                 float_to_float=float,
+                 float_to_str=lambda v:'%24.16e' % v,
+                 title='Python floating point number'):
+        """
+
+        float_conv - convert integer to float (array)
+        int_conv   - convert float (array) to integer
+        float_to_float - convert float array to float
+        float_to_str - convert array float to str
+        title        - description of used floating point numbers
+
+        """
+        # We ignore all errors here because we are purposely triggering
+        # underflow to detect the properties of the running arch.
+        with errstate(under='ignore'):
+            self._do_init(float_conv, int_conv, float_to_float, float_to_str, title)
+
+    def _do_init(self, float_conv, int_conv, float_to_float, float_to_str, title):
+        max_iterN = 10000
+        msg = "Did not converge after %d tries with %s"
+        one = float_conv(1)
+        two = one + one
+        zero = one - one
+
+        # Do we really need to do this?  Aren't they 2 and 2.0?
+        # Determine ibeta and beta
+        a = one
+        for _ in range(max_iterN):
+            a = a + a
+            temp = a + one
+            temp1 = temp - a
+            if any(temp1 - one != zero):
+                break
+        else:
+            raise RuntimeError(msg % (_, one.dtype))
+        b = one
+        for _ in range(max_iterN):
+            b = b + b
+            temp = a + b
+            itemp = int_conv(temp-a)
+            if any(itemp != 0):
+                break
+        else:
+            raise RuntimeError(msg % (_, one.dtype))
+        ibeta = itemp
+        beta = float_conv(ibeta)
+
+        # Determine it and irnd
+        it = -1
+        b = one
+        for _ in range(max_iterN):
+            it = it + 1
+            b = b * beta
+            temp = b + one
+            temp1 = temp - b
+            if any(temp1 - one != zero):
+                break
+        else:
+            raise RuntimeError(msg % (_, one.dtype))
+
+        betah = beta / two
+        a = one
+        for _ in range(max_iterN):
+            a = a + a
+            temp = a + one
+            temp1 = temp - a
+            if any(temp1 - one != zero):
+                break
+        else:
+            raise RuntimeError(msg % (_, one.dtype))
+        temp = a + betah
+        irnd = 0
+        if any(temp-a != zero):
+            irnd = 1
+        tempa = a + beta
+        temp = tempa + betah
+        if irnd == 0 and any(temp-tempa != zero):
+            irnd = 2
+
+        # Determine negep and epsneg
+        negep = it + 3
+        betain = one / beta
+        a = one
+        for i in range(negep):
+            a = a * betain
+        b = a
+        for _ in range(max_iterN):
+            temp = one - a
+            if any(temp-one != zero):
+                break
+            a = a * beta
+            negep = negep - 1
+            # Prevent infinite loop on PPC with gcc 4.0:
+            if negep < 0:
+                raise RuntimeError("could not determine machine tolerance "
+                                   "for 'negep', locals() -> %s" % (locals()))
+        else:
+            raise RuntimeError(msg % (_, one.dtype))
+        negep = -negep
+        epsneg = a
+
+        # Determine machep and eps
+        machep = - it - 3
+        a = b
+
+        for _ in range(max_iterN):
+            temp = one + a
+            if any(temp-one != zero):
+                break
+            a = a * beta
+            machep = machep + 1
+        else:
+            raise RuntimeError(msg % (_, one.dtype))
+        eps = a
+
+        # Determine ngrd
+        ngrd = 0
+        temp = one + eps
+        if irnd == 0 and any(temp*one - one != zero):
+            ngrd = 1
+
+        # Determine iexp
+        i = 0
+        k = 1
+        z = betain
+        t = one + eps
+        nxres = 0
+        for _ in range(max_iterN):
+            y = z
+            z = y*y
+            a = z*one  # Check here for underflow
+            temp = z*t
+            if any(a+a == zero) or any(abs(z) >= y):
+                break
+            temp1 = temp * betain
+            if any(temp1*beta == z):
+                break
+            i = i + 1
+            k = k + k
+        else:
+            raise RuntimeError(msg % (_, one.dtype))
+        if ibeta != 10:
+            iexp = i + 1
+            mx = k + k
+        else:
+            iexp = 2
+            iz = ibeta
+            while k >= iz:
+                iz = iz * ibeta
+                iexp = iexp + 1
+            mx = iz + iz - 1
+
+        # Determine minexp and xmin
+        for _ in range(max_iterN):
+            xmin = y
+            y = y * betain
+            a = y * one
+            temp = y * t
+            if any((a + a) != zero) and any(abs(y) < xmin):
+                k = k + 1
+                temp1 = temp * betain
+                if any(temp1*beta == y) and any(temp != y):
+                    nxres = 3
+                    xmin = y
+                    break
+            else:
+                break
+        else:
+            raise RuntimeError(msg % (_, one.dtype))
+        minexp = -k
+
+        # Determine maxexp, xmax
+        if mx <= k + k - 3 and ibeta != 10:
+            mx = mx + mx
+            iexp = iexp + 1
+        maxexp = mx + minexp
+        irnd = irnd + nxres
+        if irnd >= 2:
+            maxexp = maxexp - 2
+        i = maxexp + minexp
+        if ibeta == 2 and not i:
+            maxexp = maxexp - 1
+        if i > 20:
+            maxexp = maxexp - 1
+        if any(a != y):
+            maxexp = maxexp - 2
+        xmax = one - epsneg
+        if any(xmax*one != xmax):
+            xmax = one - beta*epsneg
+        xmax = xmax / (xmin*beta*beta*beta)
+        i = maxexp + minexp + 3
+        for j in range(i):
+            if ibeta == 2:
+                xmax = xmax + xmax
+            else:
+                xmax = xmax * beta
+
+        smallest_subnormal = abs(xmin / beta ** (it))
+
+        self.ibeta = ibeta
+        self.it = it
+        self.negep = negep
+        self.epsneg = float_to_float(epsneg)
+        self._str_epsneg = float_to_str(epsneg)
+        self.machep = machep
+        self.eps = float_to_float(eps)
+        self._str_eps = float_to_str(eps)
+        self.ngrd = ngrd
+        self.iexp = iexp
+        self.minexp = minexp
+        self.xmin = float_to_float(xmin)
+        self._str_xmin = float_to_str(xmin)
+        self.maxexp = maxexp
+        self.xmax = float_to_float(xmax)
+        self._str_xmax = float_to_str(xmax)
+        self.irnd = irnd
+
+        self.title = title
+        # Commonly used parameters
+        self.epsilon = self.eps
+        self.tiny = self.xmin
+        self.huge = self.xmax
+        self.smallest_normal = self.xmin
+        self._str_smallest_normal = float_to_str(self.xmin)
+        self.smallest_subnormal = float_to_float(smallest_subnormal)
+        self._str_smallest_subnormal = float_to_str(smallest_subnormal)
+
+        import math
+        self.precision = int(-math.log10(float_to_float(self.eps)))
+        ten = two + two + two + two + two
+        resolution = ten ** (-self.precision)
+        self.resolution = float_to_float(resolution)
+        self._str_resolution = float_to_str(resolution)
+
+    def __str__(self):
+        fmt = (
+           'Machine parameters for %(title)s\n'
+           '---------------------------------------------------------------------\n'
+           'ibeta=%(ibeta)s it=%(it)s iexp=%(iexp)s ngrd=%(ngrd)s irnd=%(irnd)s\n'
+           'machep=%(machep)s     eps=%(_str_eps)s (beta**machep == epsilon)\n'
+           'negep =%(negep)s  epsneg=%(_str_epsneg)s (beta**epsneg)\n'
+           'minexp=%(minexp)s   xmin=%(_str_xmin)s (beta**minexp == tiny)\n'
+           'maxexp=%(maxexp)s    xmax=%(_str_xmax)s ((1-epsneg)*beta**maxexp == huge)\n'
+           'smallest_normal=%(smallest_normal)s    '
+           'smallest_subnormal=%(smallest_subnormal)s\n'
+           '---------------------------------------------------------------------\n'
+           )
+        return fmt % self.__dict__
+
+
+if __name__ == '__main__':
+    print(MachAr())

env/Lib/site-packages/numpy/_core/_machar.pyi

@@ -0,0 +1,73 @@
+from collections.abc import Iterable
+from typing import Any, Final, overload
+
+from typing_extensions import TypeVar, Unpack
+
+import numpy as np
+from numpy import _CastingKind
+from numpy._utils import set_module as set_module
+
+###
+
+_T = TypeVar("_T")
+_TupleT = TypeVar("_TupleT", bound=tuple[()] | tuple[Any, Any, Unpack[tuple[Any, ...]]])
+_ExceptionT = TypeVar("_ExceptionT", bound=Exception)
+
+###
+
+class UFuncTypeError(TypeError):
+    ufunc: Final[np.ufunc]
+    def __init__(self, /, ufunc: np.ufunc) -> None: ...
+
+class _UFuncNoLoopError(UFuncTypeError):
+    dtypes: tuple[np.dtype[Any], ...]
+    def __init__(self, /, ufunc: np.ufunc, dtypes: Iterable[np.dtype[Any]]) -> None: ...
+
+class _UFuncBinaryResolutionError(_UFuncNoLoopError):
+    dtypes: tuple[np.dtype[Any], np.dtype[Any]]
+    def __init__(self, /, ufunc: np.ufunc, dtypes: Iterable[np.dtype[Any]]) -> None: ...
+
+class _UFuncCastingError(UFuncTypeError):
+    casting: Final[_CastingKind]
+    from_: Final[np.dtype[Any]]
+    to: Final[np.dtype[Any]]
+    def __init__(self, /, ufunc: np.ufunc, casting: _CastingKind, from_: np.dtype[Any], to: np.dtype[Any]) -> None: ...
+
+class _UFuncInputCastingError(_UFuncCastingError):
+    in_i: Final[int]
+    def __init__(
+        self,
+        /,
+        ufunc: np.ufunc,
+        casting: _CastingKind,
+        from_: np.dtype[Any],
+        to: np.dtype[Any],
+        i: int,
+    ) -> None: ...
+
+class _UFuncOutputCastingError(_UFuncCastingError):
+    out_i: Final[int]
+    def __init__(
+        self,
+        /,
+        ufunc: np.ufunc,
+        casting: _CastingKind,
+        from_: np.dtype[Any],
+        to: np.dtype[Any],
+        i: int,
+    ) -> None: ...
+
+class _ArrayMemoryError(MemoryError):
+    shape: tuple[int, ...]
+    dtype: np.dtype[Any]
+    def __init__(self, /, shape: tuple[int, ...], dtype: np.dtype[Any]) -> None: ...
+    @property
+    def _total_size(self) -> int: ...
+    @staticmethod
+    def _size_to_string(num_bytes: int) -> str: ...
+
+@overload
+def _unpack_tuple(tup: tuple[_T]) -> _T: ...
+@overload
+def _unpack_tuple(tup: _TupleT) -> _TupleT: ...
+def _display_as_base(cls: type[_ExceptionT]) -> type[_ExceptionT]: ...

env/Lib/site-packages/numpy/_core/_methods.py

@@ -0,0 +1,256 @@
+"""
+Array methods which are called by both the C-code for the method
+and the Python code for the NumPy-namespace function
+
+"""
+import os
+import pickle
+import warnings
+from contextlib import nullcontext
+
+import numpy as np
+from numpy._core import multiarray as mu
+from numpy._core import umath as um
+from numpy._core.multiarray import asanyarray
+from numpy._core import numerictypes as nt
+from numpy._core import _exceptions
+from numpy._globals import _NoValue
+
+# save those O(100) nanoseconds!
+bool_dt = mu.dtype("bool")
+umr_maximum = um.maximum.reduce
+umr_minimum = um.minimum.reduce
+umr_sum = um.add.reduce
+umr_prod = um.multiply.reduce
+umr_bitwise_count = um.bitwise_count
+umr_any = um.logical_or.reduce
+umr_all = um.logical_and.reduce
+
+# Complex types to -> (2,)float view for fast-path computation in _var()
+_complex_to_float = {
+    nt.dtype(nt.csingle) : nt.dtype(nt.single),
+    nt.dtype(nt.cdouble) : nt.dtype(nt.double),
+}
+# Special case for windows: ensure double takes precedence
+if nt.dtype(nt.longdouble) != nt.dtype(nt.double):
+    _complex_to_float.update({
+        nt.dtype(nt.clongdouble) : nt.dtype(nt.longdouble),
+    })
+
+# avoid keyword arguments to speed up parsing, saves about 15%-20% for very
+# small reductions
+def _amax(a, axis=None, out=None, keepdims=False,
+          initial=_NoValue, where=True):
+    return umr_maximum(a, axis, None, out, keepdims, initial, where)
+
+def _amin(a, axis=None, out=None, keepdims=False,
+          initial=_NoValue, where=True):
+    return umr_minimum(a, axis, None, out, keepdims, initial, where)
+
+def _sum(a, axis=None, dtype=None, out=None, keepdims=False,
+         initial=_NoValue, where=True):
+    return umr_sum(a, axis, dtype, out, keepdims, initial, where)
+
+def _prod(a, axis=None, dtype=None, out=None, keepdims=False,
+          initial=_NoValue, where=True):
+    return umr_prod(a, axis, dtype, out, keepdims, initial, where)
+
+def _any(a, axis=None, dtype=None, out=None, keepdims=False, *, where=True):
+    # By default, return a boolean for any and all
+    if dtype is None:
+        dtype = bool_dt
+    # Parsing keyword arguments is currently fairly slow, so avoid it for now
+    if where is True:
+        return umr_any(a, axis, dtype, out, keepdims)
+    return umr_any(a, axis, dtype, out, keepdims, where=where)
+
+def _all(a, axis=None, dtype=None, out=None, keepdims=False, *, where=True):
+    # By default, return a boolean for any and all
+    if dtype is None:
+        dtype = bool_dt
+    # Parsing keyword arguments is currently fairly slow, so avoid it for now
+    if where is True:
+        return umr_all(a, axis, dtype, out, keepdims)
+    return umr_all(a, axis, dtype, out, keepdims, where=where)
+
+def _count_reduce_items(arr, axis, keepdims=False, where=True):
+    # fast-path for the default case
+    if where is True:
+        # no boolean mask given, calculate items according to axis
+        if axis is None:
+            axis = tuple(range(arr.ndim))
+        elif not isinstance(axis, tuple):
+            axis = (axis,)
+        items = 1
+        for ax in axis:
+            items *= arr.shape[mu.normalize_axis_index(ax, arr.ndim)]
+        items = nt.intp(items)
+    else:
+        # TODO: Optimize case when `where` is broadcast along a non-reduction
+        # axis and full sum is more excessive than needed.
+
+        # guarded to protect circular imports
+        from numpy.lib._stride_tricks_impl import broadcast_to
+        # count True values in (potentially broadcasted) boolean mask
+        items = umr_sum(broadcast_to(where, arr.shape), axis, nt.intp, None,
+                        keepdims)
+    return items
+
+def _clip(a, min=None, max=None, out=None, **kwargs):
+    if a.dtype.kind in "iu":
+        # If min/max is a Python integer, deal with out-of-bound values here.
+        # (This enforces NEP 50 rules as no value based promotion is done.)
+        if type(min) is int and min <= np.iinfo(a.dtype).min:
+            min = None
+        if type(max) is int and max >= np.iinfo(a.dtype).max:
+            max = None
+
+    if min is None and max is None:
+        # return identity
+        return um.positive(a, out=out, **kwargs)
+    elif min is None:
+        return um.minimum(a, max, out=out, **kwargs)
+    elif max is None:
+        return um.maximum(a, min, out=out, **kwargs)
+    else:
+        return um.clip(a, min, max, out=out, **kwargs)
+
+def _mean(a, axis=None, dtype=None, out=None, keepdims=False, *, where=True):
+    arr = asanyarray(a)
+
+    is_float16_result = False
+
+    rcount = _count_reduce_items(arr, axis, keepdims=keepdims, where=where)
+    if rcount == 0 if where is True else umr_any(rcount == 0, axis=None):
+        warnings.warn("Mean of empty slice.", RuntimeWarning, stacklevel=2)
+
+    # Cast bool, unsigned int, and int to float64 by default
+    if dtype is None:
+        if issubclass(arr.dtype.type, (nt.integer, nt.bool)):
+            dtype = mu.dtype('f8')
+        elif issubclass(arr.dtype.type, nt.float16):
+            dtype = mu.dtype('f4')
+            is_float16_result = True
+
+    ret = umr_sum(arr, axis, dtype, out, keepdims, where=where)
+    if isinstance(ret, mu.ndarray):
+        ret = um.true_divide(
+                ret, rcount, out=ret, casting='unsafe', subok=False)
+        if is_float16_result and out is None:
+            ret = arr.dtype.type(ret)
+    elif hasattr(ret, 'dtype'):
+        if is_float16_result:
+            ret = arr.dtype.type(ret / rcount)
+        else:
+            ret = ret.dtype.type(ret / rcount)
+    else:
+        ret = ret / rcount
+
+    return ret
+
+def _var(a, axis=None, dtype=None, out=None, ddof=0, keepdims=False, *,
+         where=True, mean=None):
+    arr = asanyarray(a)
+
+    rcount = _count_reduce_items(arr, axis, keepdims=keepdims, where=where)
+    # Make this warning show up on top.
+    if ddof >= rcount if where is True else umr_any(ddof >= rcount, axis=None):
+        warnings.warn("Degrees of freedom <= 0 for slice", RuntimeWarning,
+                      stacklevel=2)
+
+    # Cast bool, unsigned int, and int to float64 by default
+    if dtype is None and issubclass(arr.dtype.type, (nt.integer, nt.bool)):
+        dtype = mu.dtype('f8')
+
+    if mean is not None:
+        arrmean = mean
+    else:
+        # Compute the mean.
+        # Note that if dtype is not of inexact type then arraymean will
+        # not be either.
+        arrmean = umr_sum(arr, axis, dtype, keepdims=True, where=where)
+        # The shape of rcount has to match arrmean to not change the shape of
+        # out in broadcasting. Otherwise, it cannot be stored back to arrmean.
+        if rcount.ndim == 0:
+            # fast-path for default case when where is True
+            div = rcount
+        else:
+            # matching rcount to arrmean when where is specified as array
+            div = rcount.reshape(arrmean.shape)
+        if isinstance(arrmean, mu.ndarray):
+            arrmean = um.true_divide(arrmean, div, out=arrmean,
+                                     casting='unsafe', subok=False)
+        elif hasattr(arrmean, "dtype"):
+            arrmean = arrmean.dtype.type(arrmean / rcount)
+        else:
+            arrmean = arrmean / rcount
+
+    # Compute sum of squared deviations from mean
+    # Note that x may not be inexact and that we need it to be an array,
+    # not a scalar.
+    x = asanyarray(arr - arrmean)
+
+    if issubclass(arr.dtype.type, (nt.floating, nt.integer)):
+        x = um.multiply(x, x, out=x)
+    # Fast-paths for built-in complex types
+    elif x.dtype in _complex_to_float:
+        xv = x.view(dtype=(_complex_to_float[x.dtype], (2,)))
+        um.multiply(xv, xv, out=xv)
+        x = um.add(xv[..., 0], xv[..., 1], out=x.real).real
+    # Most general case; includes handling object arrays containing imaginary
+    # numbers and complex types with non-native byteorder
+    else:
+        x = um.multiply(x, um.conjugate(x), out=x).real
+
+    ret = umr_sum(x, axis, dtype, out, keepdims=keepdims, where=where)
+
+    # Compute degrees of freedom and make sure it is not negative.
+    rcount = um.maximum(rcount - ddof, 0)
+
+    # divide by degrees of freedom
+    if isinstance(ret, mu.ndarray):
+        ret = um.true_divide(
+                ret, rcount, out=ret, casting='unsafe', subok=False)
+    elif hasattr(ret, 'dtype'):
+        ret = ret.dtype.type(ret / rcount)
+    else:
+        ret = ret / rcount
+
+    return ret
+
+def _std(a, axis=None, dtype=None, out=None, ddof=0, keepdims=False, *,
+         where=True, mean=None):
+    ret = _var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,
+               keepdims=keepdims, where=where, mean=mean)
+
+    if isinstance(ret, mu.ndarray):
+        ret = um.sqrt(ret, out=ret)
+    elif hasattr(ret, 'dtype'):
+        ret = ret.dtype.type(um.sqrt(ret))
+    else:
+        ret = um.sqrt(ret)
+
+    return ret
+
+def _ptp(a, axis=None, out=None, keepdims=False):
+    return um.subtract(
+        umr_maximum(a, axis, None, out, keepdims),
+        umr_minimum(a, axis, None, None, keepdims),
+        out
+    )
+
+def _dump(self, file, protocol=2):
+    if hasattr(file, 'write'):
+        ctx = nullcontext(file)
+    else:
+        ctx = open(os.fspath(file), "wb")
+    with ctx as f:
+        pickle.dump(self, f, protocol=protocol)
+
+def _dumps(self, protocol=2):
+    return pickle.dumps(self, protocol=protocol)
+
+def _bitwise_count(a, out=None, *, where=True, casting='same_kind',
+          order='K', dtype=None, subok=True):
+    return umr_bitwise_count(a, out, where=where, casting=casting,
+            order=order, dtype=dtype, subok=subok)

env/Lib/site-packages/numpy/_core/_methods.pyi

@@ -0,0 +1,24 @@
+from collections.abc import Callable
+from typing import Any, TypeAlias
+
+from typing_extensions import Concatenate
+
+import numpy as np
+
+from . import _exceptions as _exceptions
+
+###
+
+_Reduce2: TypeAlias = Callable[Concatenate[object, ...], Any]
+
+###
+
+bool_dt: np.dtype[np.bool] = ...
+umr_maximum: _Reduce2 = ...
+umr_minimum: _Reduce2 = ...
+umr_sum: _Reduce2 = ...
+umr_prod: _Reduce2 = ...
+umr_bitwise_count = np.bitwise_count
+umr_any: _Reduce2 = ...
+umr_all: _Reduce2 = ...
+_complex_to_float: dict[np.dtype[np.complexfloating], np.dtype[np.floating]] = ...