comment here

.CondaPkg/env/lib/python3.11/site-packages/numpy/LICENSE.txt

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+Copyright (c) 2005-2022, 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: tempita
+Files: tools/npy_tempita/*
+License: MIT
+  For details, see tools/npy_tempita/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

.CondaPkg/env/lib/python3.11/site-packages/numpy/__config__.py

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+# This file is generated by numpy's setup.py
+# It contains system_info results at the time of building this package.
+__all__ = ["get_info","show"]
+
+
+import os
+import sys
+
+extra_dll_dir = os.path.join(os.path.dirname(__file__), '.libs')
+
+if sys.platform == 'win32' and os.path.isdir(extra_dll_dir):
+    os.add_dll_directory(extra_dll_dir)
+
+blas_info={'libraries': ['cblas', 'blas', 'cblas', 'blas'], 'library_dirs': ['/home/ton/.julia/dev/ImageUtils/.CondaPkg/env/lib'], 'include_dirs': ['/home/ton/.julia/dev/ImageUtils/.CondaPkg/env/include'], 'language': 'c', 'define_macros': [('HAVE_CBLAS', None)]}
+blas_opt_info={'define_macros': [('NO_ATLAS_INFO', 1), ('HAVE_CBLAS', None)], 'libraries': ['cblas', 'blas', 'cblas', 'blas'], 'library_dirs': ['/home/ton/.julia/dev/ImageUtils/.CondaPkg/env/lib'], 'include_dirs': ['/home/ton/.julia/dev/ImageUtils/.CondaPkg/env/include'], 'language': 'c'}
+lapack_info={'libraries': ['lapack', 'blas', 'lapack', 'blas'], 'library_dirs': ['/home/ton/.julia/dev/ImageUtils/.CondaPkg/env/lib'], 'language': 'f77'}
+lapack_opt_info={'libraries': ['lapack', 'blas', 'lapack', 'blas', 'cblas', 'blas', 'cblas', 'blas'], 'library_dirs': ['/home/ton/.julia/dev/ImageUtils/.CondaPkg/env/lib'], 'language': 'c', 'define_macros': [('NO_ATLAS_INFO', 1), ('HAVE_CBLAS', None)], 'include_dirs': ['/home/ton/.julia/dev/ImageUtils/.CondaPkg/env/include']}
+
+def get_info(name):
+    g = globals()
+    return g.get(name, g.get(name + "_info", {}))
+
+def show():
+    """
+    Show libraries in the system on which NumPy was built.
+
+    Print information about various resources (libraries, library
+    directories, include directories, etc.) in the system on which
+    NumPy was built.
+
+    See Also
+    --------
+    get_include : Returns the directory containing NumPy C
+                  header files.
+
+    Notes
+    -----
+    1. Classes specifying the information to be printed are defined
+       in the `numpy.distutils.system_info` module.
+
+       Information may include:
+
+       * ``language``: language used to write the libraries (mostly
+         C or f77)
+       * ``libraries``: names of libraries found in the system
+       * ``library_dirs``: directories containing the libraries
+       * ``include_dirs``: directories containing library header files
+       * ``src_dirs``: directories containing library source files
+       * ``define_macros``: preprocessor macros used by
+         ``distutils.setup``
+       * ``baseline``: minimum CPU features required
+       * ``found``: dispatched features supported in the system
+       * ``not found``: dispatched features that are not supported
+         in the system
+
+    2. NumPy BLAS/LAPACK Installation Notes
+
+       Installing a numpy wheel (``pip install numpy`` or force it
+       via ``pip install numpy --only-binary :numpy: numpy``) includes
+       an OpenBLAS implementation of the BLAS and LAPACK linear algebra
+       APIs. In this case, ``library_dirs`` reports the original build
+       time configuration as compiled with gcc/gfortran; at run time
+       the OpenBLAS library is in
+       ``site-packages/numpy.libs/`` (linux), or
+       ``site-packages/numpy/.dylibs/`` (macOS), or
+       ``site-packages/numpy/.libs/`` (windows).
+
+       Installing numpy from source
+       (``pip install numpy --no-binary numpy``) searches for BLAS and
+       LAPACK dynamic link libraries at build time as influenced by
+       environment variables NPY_BLAS_LIBS, NPY_CBLAS_LIBS, and
+       NPY_LAPACK_LIBS; or NPY_BLAS_ORDER and NPY_LAPACK_ORDER;
+       or the optional file ``~/.numpy-site.cfg``.
+       NumPy remembers those locations and expects to load the same
+       libraries at run-time.
+       In NumPy 1.21+ on macOS, 'accelerate' (Apple's Accelerate BLAS
+       library) is in the default build-time search order after
+       'openblas'.
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> np.show_config()
+    blas_opt_info:
+        language = c
+        define_macros = [('HAVE_CBLAS', None)]
+        libraries = ['openblas', 'openblas']
+        library_dirs = ['/usr/local/lib']
+    """
+    from numpy.core._multiarray_umath import (
+        __cpu_features__, __cpu_baseline__, __cpu_dispatch__
+    )
+    for name,info_dict in globals().items():
+        if name[0] == "_" or type(info_dict) is not type({}): continue
+        print(name + ":")
+        if not info_dict:
+            print("  NOT AVAILABLE")
+        for k,v in info_dict.items():
+            v = str(v)
+            if k == "sources" and len(v) > 200:
+                v = v[:60] + " ...\n... " + v[-60:]
+            print("    %s = %s" % (k,v))
+
+    features_found, features_not_found = [], []
+    for feature in __cpu_dispatch__:
+        if __cpu_features__[feature]:
+            features_found.append(feature)
+        else:
+            features_not_found.append(feature)
+
+    print("Supported SIMD extensions in this NumPy install:")
+    print("    baseline = %s" % (','.join(__cpu_baseline__)))
+    print("    found = %s" % (','.join(features_found)))
+    print("    not found = %s" % (','.join(features_not_found)))
+

.CondaPkg/env/lib/python3.11/site-packages/numpy/__init__.py

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+"""
+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.
+
+To search for documents containing a keyword, do::
+
+  >>> np.lookfor('keyword')
+  ... # doctest: +SKIP
+
+General-purpose documents like a glossary and help on the basic concepts
+of numpy are available under the ``doc`` sub-module::
+
+  >>> from numpy import doc
+  >>> help(doc)
+  ... # doctest: +SKIP
+
+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.
+
+Utilities
+---------
+test
+    Run numpy unittests
+show_config
+    Show numpy build configuration
+dual
+    Overwrite certain functions with high-performance SciPy tools.
+    Note: `numpy.dual` is deprecated.  Use the functions from NumPy or Scipy
+    directly instead of importing them from `numpy.dual`.
+matlib
+    Make everything matrices.
+__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.
+
+"""
+import sys
+import warnings
+
+from ._globals import (
+    ModuleDeprecationWarning, VisibleDeprecationWarning,
+    _NoValue, _CopyMode
+)
+
+# 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:
+    try:
+        from numpy.__config__ import show as 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
+
+    __all__ = ['ModuleDeprecationWarning',
+               'VisibleDeprecationWarning']
+
+    # mapping of {name: (value, deprecation_msg)}
+    __deprecated_attrs__ = {}
+
+    # Allow distributors to run custom init code
+    from . import _distributor_init
+
+    from . import core
+    from .core import *
+    from . import compat
+    from . import lib
+    # NOTE: to be revisited following future namespace cleanup.
+    # See gh-14454 and gh-15672 for discussion.
+    from .lib import *
+
+    from . import linalg
+    from . import fft
+    from . import polynomial
+    from . import random
+    from . import ctypeslib
+    from . import ma
+    from . import matrixlib as _mat
+    from .matrixlib import *
+
+    # Deprecations introduced in NumPy 1.20.0, 2020-06-06
+    import builtins as _builtins
+
+    _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.
+        ("bool", _specific_msg.format("bool_")),
+        ("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
+     }
+
+    # Future warning introduced in NumPy 1.24.0, 2022-11-17
+    _msg = (
+        "`np.{n}` is a deprecated alias for `{an}`.  (Deprecated NumPy 1.24)")
+
+    # Some of these are awkward (since `np.str` may be preferable in the long
+    # term), but overall the names ending in 0 seem undesireable
+    _type_info = [
+        ("bool8", bool_, "np.bool_"),
+        ("int0", intp, "np.intp"),
+        ("uint0", uintp, "np.uintp"),
+        ("str0", str_, "np.str_"),
+        ("bytes0", bytes_, "np.bytes_"),
+        ("void0", void, "np.void"),
+        ("object0", object_,
+            "`np.object0` is a deprecated alias for `np.object_`. "
+            "`object` can be used instead.  (Deprecated NumPy 1.24)")]
+
+    # 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__ = {"bool", "long", "ulong", "str", "bytes", "object"}
+
+    __deprecated_attrs__.update({
+        n: (alias, _msg.format(n=n, an=an)) for n, alias, an in _type_info})
+
+    del _msg, _type_info
+
+    from .core import round, abs, max, min
+    # now that numpy modules are imported, can initialize limits
+    core.getlimits._register_known_types()
+
+    __all__.extend(['__version__', 'show_config'])
+    __all__.extend(core.__all__)
+    __all__.extend(_mat.__all__)
+    __all__.extend(lib.__all__)
+    __all__.extend(['linalg', 'fft', 'random', 'ctypeslib', 'ma'])
+
+    # Remove one of the two occurrences of `issubdtype`, which is exposed as
+    # both `numpy.core.issubdtype` and `numpy.lib.issubdtype`.
+    __all__.remove('issubdtype')
+
+    # These are exported by np.core, but are replaced by the builtins below
+    # remove them to ensure that we don't end up with `np.long == np.int_`,
+    # which would be a breaking change.
+    del long, unicode
+    __all__.remove('long')
+    __all__.remove('unicode')
+
+    # Remove things that are in the numpy.lib but not in the numpy namespace
+    # Note that there is a test (numpy/tests/test_public_api.py:test_numpy_namespace)
+    # that prevents adding more things to the main namespace by accident.
+    # The list below will grow until the `from .lib import *` fixme above is
+    # taken care of
+    __all__.remove('Arrayterator')
+    del Arrayterator
+
+    # These names were removed in NumPy 1.20.  For at least one release,
+    # attempts to access these names in the numpy namespace will trigger
+    # a warning, and calling the function will raise an exception.
+    _financial_names = ['fv', 'ipmt', 'irr', 'mirr', 'nper', 'npv', 'pmt',
+                        'ppmt', 'pv', 'rate']
+    __expired_functions__ = {
+        name: (f'In accordance with NEP 32, the function {name} was removed '
+               'from NumPy version 1.20.  A replacement for this function '
+               'is available in the numpy_financial library: '
+               'https://pypi.org/project/numpy-financial')
+        for name in _financial_names}
+
+    # 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")
+
+    # oldnumeric and numarray were removed in 1.9. In case some packages import
+    # but do not use them, we define them here for backward compatibility.
+    oldnumeric = 'removed'
+    numarray = 'removed'
+
+    def __getattr__(attr):
+        # Warn for expired attributes, and return a dummy function
+        # that always raises an exception.
+        import warnings
+        try:
+            msg = __expired_functions__[attr]
+        except KeyError:
+            pass
+        else:
+            warnings.warn(msg, DeprecationWarning, stacklevel=2)
+
+            def _expired(*args, **kwds):
+                raise RuntimeError(msg)
+
+            return _expired
+
+        # Emit warnings for deprecated attributes
+        try:
+            val, msg = __deprecated_attrs__[attr]
+        except KeyError:
+            pass
+        else:
+            warnings.warn(msg, DeprecationWarning, stacklevel=2)
+            return val
+
+        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])
+
+        # Importing Tester requires importing all of UnitTest which is not a
+        # cheap import Since it is mainly used in test suits, we lazy import it
+        # here to save on the order of 10 ms of import time for most users
+        #
+        # The previous way Tester was imported also had a side effect of adding
+        # the full `numpy.testing` namespace
+        if attr == 'testing':
+            import numpy.testing as testing
+            return testing
+        elif attr == 'Tester':
+            from .testing import Tester
+            return Tester
+
+        raise AttributeError("module {!r} has no attribute "
+                             "{!r}".format(__name__, attr))
+
+    def __dir__():
+        public_symbols = globals().keys() | {'Tester', 'testing'}
+        public_symbols -= {
+            "core", "matrixlib",
+        }
+        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":
+        with warnings.catch_warnings(record=True) as w:
+            _mac_os_check()
+            # Throw runtime error, if the test failed Check for warning and error_message
+            error_message = ""
+            if len(w) > 0:
+                error_message = "{}: {}".format(w[-1].category.__name__, str(w[-1].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/doc/stable/user/building.html#accelerated-blas-lapack-libraries"
+                    "\nOtherwise report this to the vendor "
+                    "that provided NumPy.\n{}\n".format(error_message))
+                raise RuntimeError(msg)
+    del _mac_os_check
+
+    # 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
+    import os
+    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_hugepages 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_hugepages = 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)
+
+    # Note that this will currently only make a difference on Linux
+    core.multiarray._set_madvise_hugepage(use_hugepage)
+
+    # 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()
+
+    core._set_promotion_state(os.environ.get("NPY_PROMOTION_STATE", "legacy"))
+
+    # 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
+
+
+# get the version using versioneer
+from .version import __version__, git_revision as __git_version__
+
+# Remove symbols imported for internal use
+del sys, warnings

.CondaPkg/env/lib/python3.11/site-packages/numpy/_distributor_init.py

@@ -0,0 +1,10 @@
+""" Distributor init file
+
+Distributors: you can add custom code here to support particular distributions
+of numpy.
+
+For example, this is a good place to put any checks for hardware requirements.
+
+The numpy standard source distribution will not put code in this file, so you
+can safely replace this file with your own version.
+"""

.CondaPkg/env/lib/python3.11/site-packages/numpy/_globals.py

@@ -0,0 +1,125 @@
+"""
+Module defining global singleton classes.
+
+This module raises a RuntimeError if an attempt to reload it is made. In that
+way the identities of the classes defined here are fixed and will remain so
+even if numpy itself is reloaded. In particular, a function like the following
+will still work correctly after numpy is reloaded::
+
+    def foo(arg=np._NoValue):
+        if arg is np._NoValue:
+            ...
+
+That was not the case when the singleton classes were defined in the numpy
+``__init__.py`` file. See gh-7844 for a discussion of the reload problem that
+motivated this module.
+
+"""
+import enum
+
+__ALL__ = [
+    'ModuleDeprecationWarning', 'VisibleDeprecationWarning',
+    '_NoValue', '_CopyMode'
+    ]
+
+
+# Disallow reloading this module so as to preserve the identities of the
+# classes defined here.
+if '_is_loaded' in globals():
+    raise RuntimeError('Reloading numpy._globals is not allowed')
+_is_loaded = True
+
+
+class ModuleDeprecationWarning(DeprecationWarning):
+    """Module deprecation warning.
+
+    The nose tester turns ordinary Deprecation warnings into test failures.
+    That makes it hard to deprecate whole modules, because they get
+    imported by default. So this is a special Deprecation warning that the
+    nose tester will let pass without making tests fail.
+
+    """
+
+
+ModuleDeprecationWarning.__module__ = 'numpy'
+
+
+class VisibleDeprecationWarning(UserWarning):
+    """Visible deprecation warning.
+
+    By default, python will not show deprecation warnings, so this class
+    can be used when a very visible warning is helpful, for example because
+    the usage is most likely a user bug.
+
+    """
+
+
+VisibleDeprecationWarning.__module__ = 'numpy'
+
+
+class _NoValueType:
+    """Special keyword value.
+
+    The instance of this class may be used as the default value assigned to a
+    keyword if no other obvious default (e.g., `None`) is suitable,
+
+    Common reasons for using this keyword are:
+
+    - A new keyword is added to a function, and that function forwards its
+      inputs to another function or method which can be defined outside of
+      NumPy. For example, ``np.std(x)`` calls ``x.std``, so when a ``keepdims``
+      keyword was added that could only be forwarded if the user explicitly
+      specified ``keepdims``; downstream array libraries may not have added
+      the same keyword, so adding ``x.std(..., keepdims=keepdims)``
+      unconditionally could have broken previously working code.
+    - A keyword is being deprecated, and a deprecation warning must only be
+      emitted when the keyword is used.
+
+    """
+    __instance = None
+    def __new__(cls):
+        # ensure that only one instance exists
+        if not cls.__instance:
+            cls.__instance = super().__new__(cls)
+        return cls.__instance
+
+    def __repr__(self):
+        return "<no value>"
+
+
+_NoValue = _NoValueType()
+
+
+class _CopyMode(enum.Enum):
+    """
+    An enumeration for the copy modes supported
+    by numpy.copy() and numpy.array(). The following three modes are supported,
+
+    - ALWAYS: This means that a deep copy of the input
+              array will always be taken.
+    - IF_NEEDED: This means that a deep copy of the input
+                 array will be taken only if necessary.
+    - NEVER: This means that the deep copy will never be taken.
+             If a copy cannot be avoided then a `ValueError` will be
+             raised.
+
+    Note that the buffer-protocol could in theory do copies.  NumPy currently
+    assumes an object exporting the buffer protocol will never do this.
+    """
+
+    ALWAYS = True
+    IF_NEEDED = False
+    NEVER = 2
+
+    def __bool__(self):
+        # For backwards compatibility
+        if self == _CopyMode.ALWAYS:
+            return True
+
+        if self == _CopyMode.IF_NEEDED:
+            return False
+
+        raise ValueError(f"{self} is neither True nor False.")
+
+
+_CopyMode.__module__ = 'numpy'

.CondaPkg/env/lib/python3.11/site-packages/numpy/_pyinstaller/hook-numpy.py

@@ -0,0 +1,40 @@
+"""This hook should collect all binary files and any hidden modules that numpy
+needs.
+
+Our (some-what inadequate) docs for writing PyInstaller hooks are kept here:
+https://pyinstaller.readthedocs.io/en/stable/hooks.html
+
+"""
+from PyInstaller.compat import is_conda, is_pure_conda
+from PyInstaller.utils.hooks import collect_dynamic_libs, is_module_satisfies
+
+# Collect all DLLs inside numpy's installation folder, dump them into built
+# app's root.
+binaries = collect_dynamic_libs("numpy", ".")
+
+# If using Conda without any non-conda virtual environment manager:
+if is_pure_conda:
+    # Assume running the NumPy from Conda-forge and collect it's DLLs from the
+    # communal Conda bin directory. DLLs from NumPy's dependencies must also be
+    # collected to capture MKL, OpenBlas, OpenMP, etc.
+    from PyInstaller.utils.hooks import conda_support
+    datas = conda_support.collect_dynamic_libs("numpy", dependencies=True)
+
+# Submodules PyInstaller cannot detect (probably because they are only imported
+# by extension modules, which PyInstaller cannot read).
+hiddenimports = ['numpy.core._dtype_ctypes']
+if is_conda:
+    hiddenimports.append("six")
+
+# Remove testing and building code and packages that are referenced throughout
+# NumPy but are not really dependencies.
+excludedimports = [
+    "scipy",
+    "pytest",
+    "nose",
+    "f2py",
+    "setuptools",
+    "numpy.f2py",
+    "distutils",
+    "numpy.distutils",
+]

.CondaPkg/env/lib/python3.11/site-packages/numpy/_pyinstaller/pyinstaller-smoke.py

@@ -0,0 +1,32 @@
+"""A crude *bit of everything* smoke test to verify PyInstaller compatibility.
+
+PyInstaller typically goes wrong by forgetting to package modules, extension
+modules or shared libraries. This script should aim to touch as many of those
+as possible in an attempt to trip a ModuleNotFoundError or a DLL load failure
+due to an uncollected resource. Missing resources are unlikely to lead to
+arithmetic errors so there's generally no need to verify any calculation's
+output - merely that it made it to the end OK. This script should not
+explicitly import any of numpy's submodules as that gives PyInstaller undue
+hints that those submodules exist and should be collected (accessing implicitly
+loaded submodules is OK).
+
+"""
+import numpy as np
+
+a = np.arange(1., 10.).reshape((3, 3)) % 5
+np.linalg.det(a)
+a @ a
+a @ a.T
+np.linalg.inv(a)
+np.sin(np.exp(a))
+np.linalg.svd(a)
+np.linalg.eigh(a)
+
+np.unique(np.random.randint(0, 10, 100))
+np.sort(np.random.uniform(0, 10, 100))
+
+np.fft.fft(np.exp(2j * np.pi * np.arange(8) / 8))
+np.ma.masked_array(np.arange(10), np.random.rand(10) < .5).sum()
+np.polynomial.Legendre([7, 8, 9]).roots()
+
+print("I made it!")

.CondaPkg/env/lib/python3.11/site-packages/numpy/_pyinstaller/test_pyinstaller.py

@@ -0,0 +1,35 @@
+import subprocess
+from pathlib import Path
+
+import pytest
+
+
+# PyInstaller has been very unproactive about replacing 'imp' with 'importlib'.
+@pytest.mark.filterwarnings('ignore::DeprecationWarning')
+# It also leaks io.BytesIO()s.
+@pytest.mark.filterwarnings('ignore::ResourceWarning')
+@pytest.mark.parametrize("mode", ["--onedir", "--onefile"])
+@pytest.mark.slow
+def test_pyinstaller(mode, tmp_path):
+    """Compile and run pyinstaller-smoke.py using PyInstaller."""
+
+    pyinstaller_cli = pytest.importorskip("PyInstaller.__main__").run
+
+    source = Path(__file__).with_name("pyinstaller-smoke.py").resolve()
+    args = [
+        # Place all generated files in ``tmp_path``.
+        '--workpath', str(tmp_path / "build"),
+        '--distpath', str(tmp_path / "dist"),
+        '--specpath', str(tmp_path),
+        mode,
+        str(source),
+    ]
+    pyinstaller_cli(args)
+
+    if mode == "--onefile":
+        exe = tmp_path / "dist" / source.stem
+    else:
+        exe = tmp_path / "dist" / source.stem / source.stem
+
+    p = subprocess.run([str(exe)], check=True, stdout=subprocess.PIPE)
+    assert p.stdout.strip() == b"I made it!"

.CondaPkg/env/lib/python3.11/site-packages/numpy/_pytesttester.py

@@ -0,0 +1,206 @@
+"""
+Pytest test running.
+
+This module implements the ``test()`` function for NumPy modules. The usual
+boiler plate for doing that is to put the following in the module
+``__init__.py`` file::
+
+    from numpy._pytesttester import PytestTester
+    test = PytestTester(__name__)
+    del PytestTester
+
+
+Warnings filtering and other runtime settings should be dealt with in the
+``pytest.ini`` file in the numpy repo root. The behavior of the test depends on
+whether or not that file is found as follows:
+
+* ``pytest.ini`` is present (develop mode)
+    All warnings except those explicitly filtered out are raised as error.
+* ``pytest.ini`` is absent (release mode)
+    DeprecationWarnings and PendingDeprecationWarnings are ignored, other
+    warnings are passed through.
+
+In practice, tests run from the numpy repo are run in develop mode. That
+includes the standard ``python runtests.py`` invocation.
+
+This module is imported by every numpy subpackage, so lies at the top level to
+simplify circular import issues. For the same reason, it contains no numpy
+imports at module scope, instead importing numpy within function calls.
+"""
+import sys
+import os
+
+__all__ = ['PytestTester']
+
+
+def _show_numpy_info():
+    import numpy as np
+
+    print("NumPy version %s" % np.__version__)
+    relaxed_strides = np.ones((10, 1), order="C").flags.f_contiguous
+    print("NumPy relaxed strides checking option:", relaxed_strides)
+    info = np.lib.utils._opt_info()
+    print("NumPy CPU features: ", (info if info else 'nothing enabled'))
+
+
+class PytestTester:
+    """
+    Pytest test runner.
+
+    A test function is typically added to a package's __init__.py like so::
+
+      from numpy._pytesttester import PytestTester
+      test = PytestTester(__name__).test
+      del PytestTester
+
+    Calling this test function finds and runs all tests associated with the
+    module and all its sub-modules.
+
+    Attributes
+    ----------
+    module_name : str
+        Full path to the package to test.
+
+    Parameters
+    ----------
+    module_name : module name
+        The name of the module to test.
+
+    Notes
+    -----
+    Unlike the previous ``nose``-based implementation, this class is not
+    publicly exposed as it performs some ``numpy``-specific warning
+    suppression.
+
+    """
+    def __init__(self, module_name):
+        self.module_name = module_name
+
+    def __call__(self, label='fast', verbose=1, extra_argv=None,
+                 doctests=False, coverage=False, durations=-1, tests=None):
+        """
+        Run tests for module using pytest.
+
+        Parameters
+        ----------
+        label : {'fast', 'full'}, optional
+            Identifies the tests to run. When set to 'fast', tests decorated
+            with `pytest.mark.slow` are skipped, when 'full', the slow marker
+            is ignored.
+        verbose : int, optional
+            Verbosity value for test outputs, in the range 1-3. Default is 1.
+        extra_argv : list, optional
+            List with any extra arguments to pass to pytests.
+        doctests : bool, optional
+            .. note:: Not supported
+        coverage : bool, optional
+            If True, report coverage of NumPy code. Default is False.
+            Requires installation of (pip) pytest-cov.
+        durations : int, optional
+            If < 0, do nothing, If 0, report time of all tests, if > 0,
+            report the time of the slowest `timer` tests. Default is -1.
+        tests : test or list of tests
+            Tests to be executed with pytest '--pyargs'
+
+        Returns
+        -------
+        result : bool
+            Return True on success, false otherwise.
+
+        Notes
+        -----
+        Each NumPy module exposes `test` in its namespace to run all tests for
+        it. For example, to run all tests for numpy.lib:
+
+        >>> np.lib.test() #doctest: +SKIP
+
+        Examples
+        --------
+        >>> result = np.lib.test() #doctest: +SKIP
+        ...
+        1023 passed, 2 skipped, 6 deselected, 1 xfailed in 10.39 seconds
+        >>> result
+        True
+
+        """
+        import pytest
+        import warnings
+
+        module = sys.modules[self.module_name]
+        module_path = os.path.abspath(module.__path__[0])
+
+        # setup the pytest arguments
+        pytest_args = ["-l"]
+
+        # offset verbosity. The "-q" cancels a "-v".
+        pytest_args += ["-q"]
+
+        with warnings.catch_warnings():
+            warnings.simplefilter("always")
+            # Filter out distutils cpu warnings (could be localized to
+            # distutils tests). ASV has problems with top level import,
+            # so fetch module for suppression here.
+            from numpy.distutils import cpuinfo
+
+        with warnings.catch_warnings(record=True):
+            # Ignore the warning from importing the array_api submodule. This
+            # warning is done on import, so it would break pytest collection,
+            # but importing it early here prevents the warning from being
+            # issued when it imported again.
+            import numpy.array_api
+
+        # Filter out annoying import messages. Want these in both develop and
+        # release mode.
+        pytest_args += [
+            "-W ignore:Not importing directory",
+            "-W ignore:numpy.dtype size changed",
+            "-W ignore:numpy.ufunc size changed",
+            "-W ignore::UserWarning:cpuinfo",
+            ]
+
+        # When testing matrices, ignore their PendingDeprecationWarnings
+        pytest_args += [
+            "-W ignore:the matrix subclass is not",
+            "-W ignore:Importing from numpy.matlib is",
+            ]
+
+        if doctests:
+            pytest_args += ["--doctest-modules"]
+
+        if extra_argv:
+            pytest_args += list(extra_argv)
+
+        if verbose > 1:
+            pytest_args += ["-" + "v"*(verbose - 1)]
+
+        if coverage:
+            pytest_args += ["--cov=" + module_path]
+
+        if label == "fast":
+            # not importing at the top level to avoid circular import of module
+            from numpy.testing import IS_PYPY
+            if IS_PYPY:
+                pytest_args += ["-m", "not slow and not slow_pypy"]
+            else:
+                pytest_args += ["-m", "not slow"]
+
+        elif label != "full":
+            pytest_args += ["-m", label]
+
+        if durations >= 0:
+            pytest_args += ["--durations=%s" % durations]
+
+        if tests is None:
+            tests = [self.module_name]
+
+        pytest_args += ["--pyargs"] + list(tests)
+
+        # run tests.
+        _show_numpy_info()
+
+        try:
+            code = pytest.main(pytest_args)
+        except SystemExit as exc:
+            code = exc.code
+
+        return code == 0

.CondaPkg/env/lib/python3.11/site-packages/numpy/_pytesttester.pyi

@@ -0,0 +1,18 @@
+from collections.abc import Iterable
+from typing import Literal as L
+
+__all__: list[str]
+
+class PytestTester:
+    module_name: str
+    def __init__(self, module_name: str) -> None: ...
+    def __call__(
+        self,
+        label: L["fast", "full"] = ...,
+        verbose: int = ...,
+        extra_argv: None | Iterable[str] = ...,
+        doctests: L[False] = ...,
+        coverage: bool = ...,
+        durations: int = ...,
+        tests: None | Iterable[str] = ...,
+    ) -> bool: ...

.CondaPkg/env/lib/python3.11/site-packages/numpy/_typing/__init__.py

@@ -0,0 +1,225 @@
+"""Private counterpart of ``numpy.typing``."""
+
+from __future__ import annotations
+
+from numpy import ufunc
+from numpy.core.overrides import set_module
+from typing import TYPE_CHECKING, final
+
+
+@final  # Disallow the creation of arbitrary `NBitBase` subclasses
+@set_module("numpy.typing")
+class NBitBase:
+    """
+    A type representing `numpy.number` precision during static type checking.
+
+    Used exclusively for the purpose static type checking, `NBitBase`
+    represents the base of a hierarchical set of subclasses.
+    Each subsequent subclass is herein used for representing a lower level
+    of precision, *e.g.* ``64Bit > 32Bit > 16Bit``.
+
+    .. versionadded:: 1.20
+
+    Examples
+    --------
+    Below is a typical usage example: `NBitBase` is herein used for annotating
+    a function that takes a float and integer of arbitrary precision
+    as arguments and returns a new float of whichever precision is largest
+    (*e.g.* ``np.float16 + np.int64 -> np.float64``).
+
+    .. code-block:: python
+
+        >>> from __future__ import annotations
+        >>> from typing import TypeVar, TYPE_CHECKING
+        >>> import numpy as np
+        >>> import numpy.typing as npt
+
+        >>> T1 = TypeVar("T1", bound=npt.NBitBase)
+        >>> T2 = TypeVar("T2", bound=npt.NBitBase)
+
+        >>> def add(a: np.floating[T1], b: np.integer[T2]) -> np.floating[T1 | T2]:
+        ...     return a + b
+
+        >>> a = np.float16()
+        >>> b = np.int64()
+        >>> out = add(a, b)
+
+        >>> if TYPE_CHECKING:
+        ...     reveal_locals()
+        ...     # note: Revealed local types are:
+        ...     # note:     a: numpy.floating[numpy.typing._16Bit*]
+        ...     # note:     b: numpy.signedinteger[numpy.typing._64Bit*]
+        ...     # note:     out: numpy.floating[numpy.typing._64Bit*]
+
+    """
+
+    def __init_subclass__(cls) -> None:
+        allowed_names = {
+            "NBitBase", "_256Bit", "_128Bit", "_96Bit", "_80Bit",
+            "_64Bit", "_32Bit", "_16Bit", "_8Bit",
+        }
+        if cls.__name__ not in allowed_names:
+            raise TypeError('cannot inherit from final class "NBitBase"')
+        super().__init_subclass__()
+
+
+# Silence errors about subclassing a `@final`-decorated class
+class _256Bit(NBitBase):  # type: ignore[misc]
+    pass
+
+class _128Bit(_256Bit):  # type: ignore[misc]
+    pass
+
+class _96Bit(_128Bit):  # type: ignore[misc]
+    pass
+
+class _80Bit(_96Bit):  # type: ignore[misc]
+    pass
+
+class _64Bit(_80Bit):  # type: ignore[misc]
+    pass
+
+class _32Bit(_64Bit):  # type: ignore[misc]
+    pass
+
+class _16Bit(_32Bit):  # type: ignore[misc]
+    pass
+
+class _8Bit(_16Bit):  # type: ignore[misc]
+    pass
+
+
+from ._nested_sequence import (
+    _NestedSequence as _NestedSequence,
+)
+from ._nbit import (
+    _NBitByte as _NBitByte,
+    _NBitShort as _NBitShort,
+    _NBitIntC as _NBitIntC,
+    _NBitIntP as _NBitIntP,
+    _NBitInt as _NBitInt,
+    _NBitLongLong as _NBitLongLong,
+    _NBitHalf as _NBitHalf,
+    _NBitSingle as _NBitSingle,
+    _NBitDouble as _NBitDouble,
+    _NBitLongDouble as _NBitLongDouble,
+)
+from ._char_codes import (
+    _BoolCodes as _BoolCodes,
+    _UInt8Codes as _UInt8Codes,
+    _UInt16Codes as _UInt16Codes,
+    _UInt32Codes as _UInt32Codes,
+    _UInt64Codes as _UInt64Codes,
+    _Int8Codes as _Int8Codes,
+    _Int16Codes as _Int16Codes,
+    _Int32Codes as _Int32Codes,
+    _Int64Codes as _Int64Codes,
+    _Float16Codes as _Float16Codes,
+    _Float32Codes as _Float32Codes,
+    _Float64Codes as _Float64Codes,
+    _Complex64Codes as _Complex64Codes,
+    _Complex128Codes as _Complex128Codes,
+    _ByteCodes as _ByteCodes,
+    _ShortCodes as _ShortCodes,
+    _IntCCodes as _IntCCodes,
+    _IntPCodes as _IntPCodes,
+    _IntCodes as _IntCodes,
+    _LongLongCodes as _LongLongCodes,
+    _UByteCodes as _UByteCodes,
+    _UShortCodes as _UShortCodes,
+    _UIntCCodes as _UIntCCodes,
+    _UIntPCodes as _UIntPCodes,
+    _UIntCodes as _UIntCodes,
+    _ULongLongCodes as _ULongLongCodes,
+    _HalfCodes as _HalfCodes,
+    _SingleCodes as _SingleCodes,
+    _DoubleCodes as _DoubleCodes,
+    _LongDoubleCodes as _LongDoubleCodes,
+    _CSingleCodes as _CSingleCodes,
+    _CDoubleCodes as _CDoubleCodes,
+    _CLongDoubleCodes as _CLongDoubleCodes,
+    _DT64Codes as _DT64Codes,
+    _TD64Codes as _TD64Codes,
+    _StrCodes as _StrCodes,
+    _BytesCodes as _BytesCodes,
+    _VoidCodes as _VoidCodes,
+    _ObjectCodes as _ObjectCodes,
+)
+from ._scalars import (
+    _CharLike_co as _CharLike_co,
+    _BoolLike_co as _BoolLike_co,
+    _UIntLike_co as _UIntLike_co,
+    _IntLike_co as _IntLike_co,
+    _FloatLike_co as _FloatLike_co,
+    _ComplexLike_co as _ComplexLike_co,
+    _TD64Like_co as _TD64Like_co,
+    _NumberLike_co as _NumberLike_co,
+    _ScalarLike_co as _ScalarLike_co,
+    _VoidLike_co as _VoidLike_co,
+)
+from ._shape import (
+    _Shape as _Shape,
+    _ShapeLike as _ShapeLike,
+)
+from ._dtype_like import (
+    DTypeLike as DTypeLike,
+    _DTypeLike as _DTypeLike,
+    _SupportsDType as _SupportsDType,
+    _VoidDTypeLike as _VoidDTypeLike,
+    _DTypeLikeBool as _DTypeLikeBool,
+    _DTypeLikeUInt as _DTypeLikeUInt,
+    _DTypeLikeInt as _DTypeLikeInt,
+    _DTypeLikeFloat as _DTypeLikeFloat,
+    _DTypeLikeComplex as _DTypeLikeComplex,
+    _DTypeLikeTD64 as _DTypeLikeTD64,
+    _DTypeLikeDT64 as _DTypeLikeDT64,
+    _DTypeLikeObject as _DTypeLikeObject,
+    _DTypeLikeVoid as _DTypeLikeVoid,
+    _DTypeLikeStr as _DTypeLikeStr,
+    _DTypeLikeBytes as _DTypeLikeBytes,
+    _DTypeLikeComplex_co as _DTypeLikeComplex_co,
+)
+from ._array_like import (
+    ArrayLike as ArrayLike,
+    _ArrayLike as _ArrayLike,
+    _FiniteNestedSequence as _FiniteNestedSequence,
+    _SupportsArray as _SupportsArray,
+    _SupportsArrayFunc as _SupportsArrayFunc,
+    _ArrayLikeInt as _ArrayLikeInt,
+    _ArrayLikeBool_co as _ArrayLikeBool_co,
+    _ArrayLikeUInt_co as _ArrayLikeUInt_co,
+    _ArrayLikeInt_co as _ArrayLikeInt_co,
+    _ArrayLikeFloat_co as _ArrayLikeFloat_co,
+    _ArrayLikeComplex_co as _ArrayLikeComplex_co,
+    _ArrayLikeNumber_co as _ArrayLikeNumber_co,
+    _ArrayLikeTD64_co as _ArrayLikeTD64_co,
+    _ArrayLikeDT64_co as _ArrayLikeDT64_co,
+    _ArrayLikeObject_co as _ArrayLikeObject_co,
+    _ArrayLikeVoid_co as _ArrayLikeVoid_co,
+    _ArrayLikeStr_co as _ArrayLikeStr_co,
+    _ArrayLikeBytes_co as _ArrayLikeBytes_co,
+    _ArrayLikeUnknown as _ArrayLikeUnknown,
+    _UnknownType as _UnknownType,
+)
+from ._generic_alias import (
+    NDArray as NDArray,
+    _DType as _DType,
+    _GenericAlias as _GenericAlias,
+)
+
+if TYPE_CHECKING:
+    from ._ufunc import (
+        _UFunc_Nin1_Nout1 as _UFunc_Nin1_Nout1,
+        _UFunc_Nin2_Nout1 as _UFunc_Nin2_Nout1,
+        _UFunc_Nin1_Nout2 as _UFunc_Nin1_Nout2,
+        _UFunc_Nin2_Nout2 as _UFunc_Nin2_Nout2,
+        _GUFunc_Nin2_Nout1 as _GUFunc_Nin2_Nout1,
+    )
+else:
+    # Declare the (type-check-only) ufunc subclasses as ufunc aliases during
+    # runtime; this helps autocompletion tools such as Jedi (numpy/numpy#19834)
+    _UFunc_Nin1_Nout1 = ufunc
+    _UFunc_Nin2_Nout1 = ufunc
+    _UFunc_Nin1_Nout2 = ufunc
+    _UFunc_Nin2_Nout2 = ufunc
+    _GUFunc_Nin2_Nout1 = ufunc

.CondaPkg/env/lib/python3.11/site-packages/numpy/_typing/_add_docstring.py

@@ -0,0 +1,152 @@
+"""A module for creating docstrings for sphinx ``data`` domains."""
+
+import re
+import textwrap
+
+from ._generic_alias import NDArray
+
+_docstrings_list = []
+
+
+def add_newdoc(name: str, value: str, doc: str) -> None:
+    """Append ``_docstrings_list`` with a docstring for `name`.
+
+    Parameters
+    ----------
+    name : str
+        The name of the object.
+    value : str
+        A string-representation of the object.
+    doc : str
+        The docstring of the object.
+
+    """
+    _docstrings_list.append((name, value, doc))
+
+
+def _parse_docstrings() -> str:
+    """Convert all docstrings in ``_docstrings_list`` into a single
+    sphinx-legible text block.
+
+    """
+    type_list_ret = []
+    for name, value, doc in _docstrings_list:
+        s = textwrap.dedent(doc).replace("\n", "\n    ")
+
+        # Replace sections by rubrics
+        lines = s.split("\n")
+        new_lines = []
+        indent = ""
+        for line in lines:
+            m = re.match(r'^(\s+)[-=]+\s*$', line)
+            if m and new_lines:
+                prev = textwrap.dedent(new_lines.pop())
+                if prev == "Examples":
+                    indent = ""
+                    new_lines.append(f'{m.group(1)}.. rubric:: {prev}')
+                else:
+                    indent = 4 * " "
+                    new_lines.append(f'{m.group(1)}.. admonition:: {prev}')
+                new_lines.append("")
+            else:
+                new_lines.append(f"{indent}{line}")
+
+        s = "\n".join(new_lines)
+        s_block = f""".. data:: {name}\n    :value: {value}\n    {s}"""
+        type_list_ret.append(s_block)
+    return "\n".join(type_list_ret)
+
+
+add_newdoc('ArrayLike', 'typing.Union[...]',
+    """
+    A `~typing.Union` representing objects that can be coerced
+    into an `~numpy.ndarray`.
+
+    Among others this includes the likes of:
+
+    * Scalars.
+    * (Nested) sequences.
+    * Objects implementing the `~class.__array__` protocol.
+
+    .. versionadded:: 1.20
+
+    See Also
+    --------
+    :term:`array_like`:
+        Any scalar or sequence that can be interpreted as an ndarray.
+
+    Examples
+    --------
+    .. code-block:: python
+
+        >>> import numpy as np
+        >>> import numpy.typing as npt
+
+        >>> def as_array(a: npt.ArrayLike) -> np.ndarray:
+        ...     return np.array(a)
+
+    """)
+
+add_newdoc('DTypeLike', 'typing.Union[...]',
+    """
+    A `~typing.Union` representing objects that can be coerced
+    into a `~numpy.dtype`.
+
+    Among others this includes the likes of:
+
+    * :class:`type` objects.
+    * Character codes or the names of :class:`type` objects.
+    * Objects with the ``.dtype`` attribute.
+
+    .. versionadded:: 1.20
+
+    See Also
+    --------
+    :ref:`Specifying and constructing data types <arrays.dtypes.constructing>`
+        A comprehensive overview of all objects that can be coerced
+        into data types.
+
+    Examples
+    --------
+    .. code-block:: python
+
+        >>> import numpy as np
+        >>> import numpy.typing as npt
+
+        >>> def as_dtype(d: npt.DTypeLike) -> np.dtype:
+        ...     return np.dtype(d)
+
+    """)
+
+add_newdoc('NDArray', repr(NDArray),
+    """
+    A :term:`generic <generic type>` version of
+    `np.ndarray[Any, np.dtype[+ScalarType]] <numpy.ndarray>`.
+
+    Can be used during runtime for typing arrays with a given dtype
+    and unspecified shape.
+
+    .. versionadded:: 1.21
+
+    Examples
+    --------
+    .. code-block:: python
+
+        >>> import numpy as np
+        >>> import numpy.typing as npt
+
+        >>> print(npt.NDArray)
+        numpy.ndarray[typing.Any, numpy.dtype[+ScalarType]]
+
+        >>> print(npt.NDArray[np.float64])
+        numpy.ndarray[typing.Any, numpy.dtype[numpy.float64]]
+
+        >>> NDArrayInt = npt.NDArray[np.int_]
+        >>> a: NDArrayInt = np.arange(10)
+
+        >>> def func(a: npt.ArrayLike) -> npt.NDArray[Any]:
+        ...     return np.array(a)
+
+    """)
+
+_docstrings = _parse_docstrings()

.CondaPkg/env/lib/python3.11/site-packages/numpy/_typing/_array_like.py

@@ -0,0 +1,165 @@
+from __future__ import annotations
+
+# NOTE: Import `Sequence` from `typing` as we it is needed for a type-alias,
+# not an annotation
+import sys
+from collections.abc import Collection, Callable
+from typing import Any, Sequence, Protocol, Union, TypeVar, runtime_checkable
+from numpy import (
+    ndarray,
+    dtype,
+    generic,
+    bool_,
+    unsignedinteger,
+    integer,
+    floating,
+    complexfloating,
+    number,
+    timedelta64,
+    datetime64,
+    object_,
+    void,
+    str_,
+    bytes_,
+)
+from ._nested_sequence import _NestedSequence
+
+_T = TypeVar("_T")
+_ScalarType = TypeVar("_ScalarType", bound=generic)
+_DType = TypeVar("_DType", bound="dtype[Any]")
+_DType_co = TypeVar("_DType_co", covariant=True, bound="dtype[Any]")
+
+# The `_SupportsArray` protocol only cares about the default dtype
+# (i.e. `dtype=None` or no `dtype` parameter at all) of the to-be returned
+# array.
+# Concrete implementations of the protocol are responsible for adding
+# any and all remaining overloads
+@runtime_checkable
+class _SupportsArray(Protocol[_DType_co]):
+    def __array__(self) -> ndarray[Any, _DType_co]: ...
+
+
+@runtime_checkable
+class _SupportsArrayFunc(Protocol):
+    """A protocol class representing `~class.__array_function__`."""
+    def __array_function__(
+        self,
+        func: Callable[..., Any],
+        types: Collection[type[Any]],
+        args: tuple[Any, ...],
+        kwargs: dict[str, Any],
+    ) -> object: ...
+
+
+# TODO: Wait until mypy supports recursive objects in combination with typevars
+_FiniteNestedSequence = Union[
+    _T,
+    Sequence[_T],
+    Sequence[Sequence[_T]],
+    Sequence[Sequence[Sequence[_T]]],
+    Sequence[Sequence[Sequence[Sequence[_T]]]],
+]
+
+# A subset of `npt.ArrayLike` that can be parametrized w.r.t. `np.generic`
+_ArrayLike = Union[
+    _SupportsArray["dtype[_ScalarType]"],
+    _NestedSequence[_SupportsArray["dtype[_ScalarType]"]],
+]
+
+# A union representing array-like objects; consists of two typevars:
+# One representing types that can be parametrized w.r.t. `np.dtype`
+# and another one for the rest
+_DualArrayLike = Union[
+    _SupportsArray[_DType],
+    _NestedSequence[_SupportsArray[_DType]],
+    _T,
+    _NestedSequence[_T],
+]
+
+# TODO: support buffer protocols once
+#
+# https://bugs.python.org/issue27501
+#
+# is resolved. See also the mypy issue:
+#
+# https://github.com/python/typing/issues/593
+if sys.version_info[:2] < (3, 9):
+    ArrayLike = _DualArrayLike[
+        dtype,
+        Union[bool, int, float, complex, str, bytes],
+    ]
+else:
+    ArrayLike = _DualArrayLike[
+        dtype[Any],
+        Union[bool, int, float, complex, str, bytes],
+    ]
+
+# `ArrayLike<X>_co`: array-like objects that can be coerced into `X`
+# given the casting rules `same_kind`
+_ArrayLikeBool_co = _DualArrayLike[
+    "dtype[bool_]",
+    bool,
+]
+_ArrayLikeUInt_co = _DualArrayLike[
+    "dtype[Union[bool_, unsignedinteger[Any]]]",
+    bool,
+]
+_ArrayLikeInt_co = _DualArrayLike[
+    "dtype[Union[bool_, integer[Any]]]",
+    Union[bool, int],
+]
+_ArrayLikeFloat_co = _DualArrayLike[
+    "dtype[Union[bool_, integer[Any], floating[Any]]]",
+    Union[bool, int, float],
+]
+_ArrayLikeComplex_co = _DualArrayLike[
+    "dtype[Union[bool_, integer[Any], floating[Any], complexfloating[Any, Any]]]",
+    Union[bool, int, float, complex],
+]
+_ArrayLikeNumber_co = _DualArrayLike[
+    "dtype[Union[bool_, number[Any]]]",
+    Union[bool, int, float, complex],
+]
+_ArrayLikeTD64_co = _DualArrayLike[
+    "dtype[Union[bool_, integer[Any], timedelta64]]",
+    Union[bool, int],
+]
+_ArrayLikeDT64_co = Union[
+    _SupportsArray["dtype[datetime64]"],
+    _NestedSequence[_SupportsArray["dtype[datetime64]"]],
+]
+_ArrayLikeObject_co = Union[
+    _SupportsArray["dtype[object_]"],
+    _NestedSequence[_SupportsArray["dtype[object_]"]],
+]
+
+_ArrayLikeVoid_co = Union[
+    _SupportsArray["dtype[void]"],
+    _NestedSequence[_SupportsArray["dtype[void]"]],
+]
+_ArrayLikeStr_co = _DualArrayLike[
+    "dtype[str_]",
+    str,
+]
+_ArrayLikeBytes_co = _DualArrayLike[
+    "dtype[bytes_]",
+    bytes,
+]
+
+_ArrayLikeInt = _DualArrayLike[
+    "dtype[integer[Any]]",
+    int,
+]
+
+# Extra ArrayLike type so that pyright can deal with NDArray[Any]
+# Used as the first overload, should only match NDArray[Any],
+# not any actual types.
+# https://github.com/numpy/numpy/pull/22193
+class _UnknownType:
+    ...
+
+
+_ArrayLikeUnknown = _DualArrayLike[
+    "dtype[_UnknownType]",
+    _UnknownType,
+]

.CondaPkg/env/lib/python3.11/site-packages/numpy/_typing/_callable.pyi

@@ -0,0 +1,338 @@
+"""
+A module with various ``typing.Protocol`` subclasses that implement
+the ``__call__`` magic method.
+
+See the `Mypy documentation`_ on protocols for more details.
+
+.. _`Mypy documentation`: https://mypy.readthedocs.io/en/stable/protocols.html#callback-protocols
+
+"""
+
+from __future__ import annotations
+
+from typing import (
+    TypeVar,
+    overload,
+    Any,
+    NoReturn,
+    Protocol,
+)
+
+from numpy import (
+    ndarray,
+    dtype,
+    generic,
+    bool_,
+    timedelta64,
+    number,
+    integer,
+    unsignedinteger,
+    signedinteger,
+    int8,
+    int_,
+    floating,
+    float64,
+    complexfloating,
+    complex128,
+)
+from ._nbit import _NBitInt, _NBitDouble
+from ._scalars import (
+    _BoolLike_co,
+    _IntLike_co,
+    _FloatLike_co,
+    _NumberLike_co,
+)
+from . import NBitBase
+from ._generic_alias import NDArray
+from ._nested_sequence import _NestedSequence
+
+_T1 = TypeVar("_T1")
+_T2 = TypeVar("_T2")
+_T1_contra = TypeVar("_T1_contra", contravariant=True)
+_T2_contra = TypeVar("_T2_contra", contravariant=True)
+_2Tuple = tuple[_T1, _T1]
+
+_NBit1 = TypeVar("_NBit1", bound=NBitBase)
+_NBit2 = TypeVar("_NBit2", bound=NBitBase)
+
+_IntType = TypeVar("_IntType", bound=integer)
+_FloatType = TypeVar("_FloatType", bound=floating)
+_NumberType = TypeVar("_NumberType", bound=number)
+_NumberType_co = TypeVar("_NumberType_co", covariant=True, bound=number)
+_GenericType_co = TypeVar("_GenericType_co", covariant=True, bound=generic)
+
+class _BoolOp(Protocol[_GenericType_co]):
+    @overload
+    def __call__(self, other: _BoolLike_co, /) -> _GenericType_co: ...
+    @overload  # platform dependent
+    def __call__(self, other: int, /) -> int_: ...
+    @overload
+    def __call__(self, other: float, /) -> float64: ...
+    @overload
+    def __call__(self, other: complex, /) -> complex128: ...
+    @overload
+    def __call__(self, other: _NumberType, /) -> _NumberType: ...
+
+class _BoolBitOp(Protocol[_GenericType_co]):
+    @overload
+    def __call__(self, other: _BoolLike_co, /) -> _GenericType_co: ...
+    @overload  # platform dependent
+    def __call__(self, other: int, /) -> int_: ...
+    @overload
+    def __call__(self, other: _IntType, /) -> _IntType: ...
+
+class _BoolSub(Protocol):
+    # Note that `other: bool_` is absent here
+    @overload
+    def __call__(self, other: bool, /) -> NoReturn: ...
+    @overload  # platform dependent
+    def __call__(self, other: int, /) -> int_: ...
+    @overload
+    def __call__(self, other: float, /) -> float64: ...
+    @overload
+    def __call__(self, other: complex, /) -> complex128: ...
+    @overload
+    def __call__(self, other: _NumberType, /) -> _NumberType: ...
+
+class _BoolTrueDiv(Protocol):
+    @overload
+    def __call__(self, other: float | _IntLike_co, /) -> float64: ...
+    @overload
+    def __call__(self, other: complex, /) -> complex128: ...
+    @overload
+    def __call__(self, other: _NumberType, /) -> _NumberType: ...
+
+class _BoolMod(Protocol):
+    @overload
+    def __call__(self, other: _BoolLike_co, /) -> int8: ...
+    @overload  # platform dependent
+    def __call__(self, other: int, /) -> int_: ...
+    @overload
+    def __call__(self, other: float, /) -> float64: ...
+    @overload
+    def __call__(self, other: _IntType, /) -> _IntType: ...
+    @overload
+    def __call__(self, other: _FloatType, /) -> _FloatType: ...
+
+class _BoolDivMod(Protocol):
+    @overload
+    def __call__(self, other: _BoolLike_co, /) -> _2Tuple[int8]: ...
+    @overload  # platform dependent
+    def __call__(self, other: int, /) -> _2Tuple[int_]: ...
+    @overload
+    def __call__(self, other: float, /) -> _2Tuple[floating[_NBit1 | _NBitDouble]]: ...
+    @overload
+    def __call__(self, other: _IntType, /) -> _2Tuple[_IntType]: ...
+    @overload
+    def __call__(self, other: _FloatType, /) -> _2Tuple[_FloatType]: ...
+
+class _TD64Div(Protocol[_NumberType_co]):
+    @overload
+    def __call__(self, other: timedelta64, /) -> _NumberType_co: ...
+    @overload
+    def __call__(self, other: _BoolLike_co, /) -> NoReturn: ...
+    @overload
+    def __call__(self, other: _FloatLike_co, /) -> timedelta64: ...
+
+class _IntTrueDiv(Protocol[_NBit1]):
+    @overload
+    def __call__(self, other: bool, /) -> floating[_NBit1]: ...
+    @overload
+    def __call__(self, other: int, /) -> floating[_NBit1 | _NBitInt]: ...
+    @overload
+    def __call__(self, other: float, /) -> floating[_NBit1 | _NBitDouble]: ...
+    @overload
+    def __call__(
+        self, other: complex, /,
+    ) -> complexfloating[_NBit1 | _NBitDouble, _NBit1 | _NBitDouble]: ...
+    @overload
+    def __call__(self, other: integer[_NBit2], /) -> floating[_NBit1 | _NBit2]: ...
+
+class _UnsignedIntOp(Protocol[_NBit1]):
+    # NOTE: `uint64 + signedinteger -> float64`
+    @overload
+    def __call__(self, other: bool, /) -> unsignedinteger[_NBit1]: ...
+    @overload
+    def __call__(
+        self, other: int | signedinteger[Any], /
+    ) -> Any: ...
+    @overload
+    def __call__(self, other: float, /) -> floating[_NBit1 | _NBitDouble]: ...
+    @overload
+    def __call__(
+        self, other: complex, /,
+    ) -> complexfloating[_NBit1 | _NBitDouble, _NBit1 | _NBitDouble]: ...
+    @overload
+    def __call__(
+        self, other: unsignedinteger[_NBit2], /
+    ) -> unsignedinteger[_NBit1 | _NBit2]: ...
+
+class _UnsignedIntBitOp(Protocol[_NBit1]):
+    @overload
+    def __call__(self, other: bool, /) -> unsignedinteger[_NBit1]: ...
+    @overload
+    def __call__(self, other: int, /) -> signedinteger[Any]: ...
+    @overload
+    def __call__(self, other: signedinteger[Any], /) -> signedinteger[Any]: ...
+    @overload
+    def __call__(
+        self, other: unsignedinteger[_NBit2], /
+    ) -> unsignedinteger[_NBit1 | _NBit2]: ...
+
+class _UnsignedIntMod(Protocol[_NBit1]):
+    @overload
+    def __call__(self, other: bool, /) -> unsignedinteger[_NBit1]: ...
+    @overload
+    def __call__(
+        self, other: int | signedinteger[Any], /
+    ) -> Any: ...
+    @overload
+    def __call__(self, other: float, /) -> floating[_NBit1 | _NBitDouble]: ...
+    @overload
+    def __call__(
+        self, other: unsignedinteger[_NBit2], /
+    ) -> unsignedinteger[_NBit1 | _NBit2]: ...
+
+class _UnsignedIntDivMod(Protocol[_NBit1]):
+    @overload
+    def __call__(self, other: bool, /) -> _2Tuple[signedinteger[_NBit1]]: ...
+    @overload
+    def __call__(
+        self, other: int | signedinteger[Any], /
+    ) -> _2Tuple[Any]: ...
+    @overload
+    def __call__(self, other: float, /) -> _2Tuple[floating[_NBit1 | _NBitDouble]]: ...
+    @overload
+    def __call__(
+        self, other: unsignedinteger[_NBit2], /
+    ) -> _2Tuple[unsignedinteger[_NBit1 | _NBit2]]: ...
+
+class _SignedIntOp(Protocol[_NBit1]):
+    @overload
+    def __call__(self, other: bool, /) -> signedinteger[_NBit1]: ...
+    @overload
+    def __call__(self, other: int, /) -> signedinteger[_NBit1 | _NBitInt]: ...
+    @overload
+    def __call__(self, other: float, /) -> floating[_NBit1 | _NBitDouble]: ...
+    @overload
+    def __call__(
+        self, other: complex, /,
+    ) -> complexfloating[_NBit1 | _NBitDouble, _NBit1 | _NBitDouble]: ...
+    @overload
+    def __call__(
+        self, other: signedinteger[_NBit2], /,
+    ) -> signedinteger[_NBit1 | _NBit2]: ...
+
+class _SignedIntBitOp(Protocol[_NBit1]):
+    @overload
+    def __call__(self, other: bool, /) -> signedinteger[_NBit1]: ...
+    @overload
+    def __call__(self, other: int, /) -> signedinteger[_NBit1 | _NBitInt]: ...
+    @overload
+    def __call__(
+        self, other: signedinteger[_NBit2], /,
+    ) -> signedinteger[_NBit1 | _NBit2]: ...
+
+class _SignedIntMod(Protocol[_NBit1]):
+    @overload
+    def __call__(self, other: bool, /) -> signedinteger[_NBit1]: ...
+    @overload
+    def __call__(self, other: int, /) -> signedinteger[_NBit1 | _NBitInt]: ...
+    @overload
+    def __call__(self, other: float, /) -> floating[_NBit1 | _NBitDouble]: ...
+    @overload
+    def __call__(
+        self, other: signedinteger[_NBit2], /,
+    ) -> signedinteger[_NBit1 | _NBit2]: ...
+
+class _SignedIntDivMod(Protocol[_NBit1]):
+    @overload
+    def __call__(self, other: bool, /) -> _2Tuple[signedinteger[_NBit1]]: ...
+    @overload
+    def __call__(self, other: int, /) -> _2Tuple[signedinteger[_NBit1 | _NBitInt]]: ...
+    @overload
+    def __call__(self, other: float, /) -> _2Tuple[floating[_NBit1 | _NBitDouble]]: ...
+    @overload
+    def __call__(
+        self, other: signedinteger[_NBit2], /,
+    ) -> _2Tuple[signedinteger[_NBit1 | _NBit2]]: ...
+
+class _FloatOp(Protocol[_NBit1]):
+    @overload
+    def __call__(self, other: bool, /) -> floating[_NBit1]: ...
+    @overload
+    def __call__(self, other: int, /) -> floating[_NBit1 | _NBitInt]: ...
+    @overload
+    def __call__(self, other: float, /) -> floating[_NBit1 | _NBitDouble]: ...
+    @overload
+    def __call__(
+        self, other: complex, /,
+    ) -> complexfloating[_NBit1 | _NBitDouble, _NBit1 | _NBitDouble]: ...
+    @overload
+    def __call__(
+        self, other: integer[_NBit2] | floating[_NBit2], /
+    ) -> floating[_NBit1 | _NBit2]: ...
+
+class _FloatMod(Protocol[_NBit1]):
+    @overload
+    def __call__(self, other: bool, /) -> floating[_NBit1]: ...
+    @overload
+    def __call__(self, other: int, /) -> floating[_NBit1 | _NBitInt]: ...
+    @overload
+    def __call__(self, other: float, /) -> floating[_NBit1 | _NBitDouble]: ...
+    @overload
+    def __call__(
+        self, other: integer[_NBit2] | floating[_NBit2], /
+    ) -> floating[_NBit1 | _NBit2]: ...
+
+class _FloatDivMod(Protocol[_NBit1]):
+    @overload
+    def __call__(self, other: bool, /) -> _2Tuple[floating[_NBit1]]: ...
+    @overload
+    def __call__(self, other: int, /) -> _2Tuple[floating[_NBit1 | _NBitInt]]: ...
+    @overload
+    def __call__(self, other: float, /) -> _2Tuple[floating[_NBit1 | _NBitDouble]]: ...
+    @overload
+    def __call__(
+        self, other: integer[_NBit2] | floating[_NBit2], /
+    ) -> _2Tuple[floating[_NBit1 | _NBit2]]: ...
+
+class _ComplexOp(Protocol[_NBit1]):
+    @overload
+    def __call__(self, other: bool, /) -> complexfloating[_NBit1, _NBit1]: ...
+    @overload
+    def __call__(self, other: int, /) -> complexfloating[_NBit1 | _NBitInt, _NBit1 | _NBitInt]: ...
+    @overload
+    def __call__(
+        self, other: complex, /,
+    ) -> complexfloating[_NBit1 | _NBitDouble, _NBit1 | _NBitDouble]: ...
+    @overload
+    def __call__(
+        self,
+        other: (
+            integer[_NBit2]
+            | floating[_NBit2]
+            | complexfloating[_NBit2, _NBit2]
+        ), /,
+    ) -> complexfloating[_NBit1 | _NBit2, _NBit1 | _NBit2]: ...
+
+class _NumberOp(Protocol):
+    def __call__(self, other: _NumberLike_co, /) -> Any: ...
+
+class _SupportsLT(Protocol):
+    def __lt__(self, other: Any, /) -> object: ...
+
+class _SupportsGT(Protocol):
+    def __gt__(self, other: Any, /) -> object: ...
+
+class _ComparisonOp(Protocol[_T1_contra, _T2_contra]):
+    @overload
+    def __call__(self, other: _T1_contra, /) -> bool_: ...
+    @overload
+    def __call__(self, other: _T2_contra, /) -> NDArray[bool_]: ...
+    @overload
+    def __call__(
+        self,
+        other: _SupportsLT | _SupportsGT | _NestedSequence[_SupportsLT | _SupportsGT],
+        /,
+    ) -> Any: ...

.CondaPkg/env/lib/python3.11/site-packages/numpy/_typing/_char_codes.py

@@ -0,0 +1,111 @@
+from typing import Literal
+
+_BoolCodes = Literal["?", "=?", "<?", ">?", "bool", "bool_", "bool8"]
+
+_UInt8Codes = Literal["uint8", "u1", "=u1", "<u1", ">u1"]
+_UInt16Codes = Literal["uint16", "u2", "=u2", "<u2", ">u2"]
+_UInt32Codes = Literal["uint32", "u4", "=u4", "<u4", ">u4"]
+_UInt64Codes = Literal["uint64", "u8", "=u8", "<u8", ">u8"]
+
+_Int8Codes = Literal["int8", "i1", "=i1", "<i1", ">i1"]
+_Int16Codes = Literal["int16", "i2", "=i2", "<i2", ">i2"]
+_Int32Codes = Literal["int32", "i4", "=i4", "<i4", ">i4"]
+_Int64Codes = Literal["int64", "i8", "=i8", "<i8", ">i8"]
+
+_Float16Codes = Literal["float16", "f2", "=f2", "<f2", ">f2"]
+_Float32Codes = Literal["float32", "f4", "=f4", "<f4", ">f4"]
+_Float64Codes = Literal["float64", "f8", "=f8", "<f8", ">f8"]
+
+_Complex64Codes = Literal["complex64", "c8", "=c8", "<c8", ">c8"]
+_Complex128Codes = Literal["complex128", "c16", "=c16", "<c16", ">c16"]
+
+_ByteCodes = Literal["byte", "b", "=b", "<b", ">b"]
+_ShortCodes = Literal["short", "h", "=h", "<h", ">h"]
+_IntCCodes = Literal["intc", "i", "=i", "<i", ">i"]
+_IntPCodes = Literal["intp", "int0", "p", "=p", "<p", ">p"]
+_IntCodes = Literal["long", "int", "int_", "l", "=l", "<l", ">l"]
+_LongLongCodes = Literal["longlong", "q", "=q", "<q", ">q"]
+
+_UByteCodes = Literal["ubyte", "B", "=B", "<B", ">B"]
+_UShortCodes = Literal["ushort", "H", "=H", "<H", ">H"]
+_UIntCCodes = Literal["uintc", "I", "=I", "<I", ">I"]
+_UIntPCodes = Literal["uintp", "uint0", "P", "=P", "<P", ">P"]
+_UIntCodes = Literal["ulong", "uint", "L", "=L", "<L", ">L"]
+_ULongLongCodes = Literal["ulonglong", "Q", "=Q", "<Q", ">Q"]
+
+_HalfCodes = Literal["half", "e", "=e", "<e", ">e"]
+_SingleCodes = Literal["single", "f", "=f", "<f", ">f"]
+_DoubleCodes = Literal["double", "float", "float_", "d", "=d", "<d", ">d"]
+_LongDoubleCodes = Literal["longdouble", "longfloat", "g", "=g", "<g", ">g"]
+
+_CSingleCodes = Literal["csingle", "singlecomplex", "F", "=F", "<F", ">F"]
+_CDoubleCodes = Literal["cdouble", "complex", "complex_", "cfloat", "D", "=D", "<D", ">D"]
+_CLongDoubleCodes = Literal["clongdouble", "clongfloat", "longcomplex", "G", "=G", "<G", ">G"]
+
+_StrCodes = Literal["str", "str_", "str0", "unicode", "unicode_", "U", "=U", "<U", ">U"]
+_BytesCodes = Literal["bytes", "bytes_", "bytes0", "S", "=S", "<S", ">S"]
+_VoidCodes = Literal["void", "void0", "V", "=V", "<V", ">V"]
+_ObjectCodes = Literal["object", "object_", "O", "=O", "<O", ">O"]
+
+_DT64Codes = Literal[
+    "datetime64", "=datetime64", "<datetime64", ">datetime64",
+    "datetime64[Y]", "=datetime64[Y]", "<datetime64[Y]", ">datetime64[Y]",
+    "datetime64[M]", "=datetime64[M]", "<datetime64[M]", ">datetime64[M]",
+    "datetime64[W]", "=datetime64[W]", "<datetime64[W]", ">datetime64[W]",
+    "datetime64[D]", "=datetime64[D]", "<datetime64[D]", ">datetime64[D]",
+    "datetime64[h]", "=datetime64[h]", "<datetime64[h]", ">datetime64[h]",
+    "datetime64[m]", "=datetime64[m]", "<datetime64[m]", ">datetime64[m]",
+    "datetime64[s]", "=datetime64[s]", "<datetime64[s]", ">datetime64[s]",
+    "datetime64[ms]", "=datetime64[ms]", "<datetime64[ms]", ">datetime64[ms]",
+    "datetime64[us]", "=datetime64[us]", "<datetime64[us]", ">datetime64[us]",
+    "datetime64[ns]", "=datetime64[ns]", "<datetime64[ns]", ">datetime64[ns]",
+    "datetime64[ps]", "=datetime64[ps]", "<datetime64[ps]", ">datetime64[ps]",
+    "datetime64[fs]", "=datetime64[fs]", "<datetime64[fs]", ">datetime64[fs]",
+    "datetime64[as]", "=datetime64[as]", "<datetime64[as]", ">datetime64[as]",
+    "M", "=M", "<M", ">M",
+    "M8", "=M8", "<M8", ">M8",
+    "M8[Y]", "=M8[Y]", "<M8[Y]", ">M8[Y]",
+    "M8[M]", "=M8[M]", "<M8[M]", ">M8[M]",
+    "M8[W]", "=M8[W]", "<M8[W]", ">M8[W]",
+    "M8[D]", "=M8[D]", "<M8[D]", ">M8[D]",
+    "M8[h]", "=M8[h]", "<M8[h]", ">M8[h]",
+    "M8[m]", "=M8[m]", "<M8[m]", ">M8[m]",
+    "M8[s]", "=M8[s]", "<M8[s]", ">M8[s]",
+    "M8[ms]", "=M8[ms]", "<M8[ms]", ">M8[ms]",
+    "M8[us]", "=M8[us]", "<M8[us]", ">M8[us]",
+    "M8[ns]", "=M8[ns]", "<M8[ns]", ">M8[ns]",
+    "M8[ps]", "=M8[ps]", "<M8[ps]", ">M8[ps]",
+    "M8[fs]", "=M8[fs]", "<M8[fs]", ">M8[fs]",
+    "M8[as]", "=M8[as]", "<M8[as]", ">M8[as]",
+]
+_TD64Codes = Literal[
+    "timedelta64", "=timedelta64", "<timedelta64", ">timedelta64",
+    "timedelta64[Y]", "=timedelta64[Y]", "<timedelta64[Y]", ">timedelta64[Y]",
+    "timedelta64[M]", "=timedelta64[M]", "<timedelta64[M]", ">timedelta64[M]",
+    "timedelta64[W]", "=timedelta64[W]", "<timedelta64[W]", ">timedelta64[W]",
+    "timedelta64[D]", "=timedelta64[D]", "<timedelta64[D]", ">timedelta64[D]",
+    "timedelta64[h]", "=timedelta64[h]", "<timedelta64[h]", ">timedelta64[h]",
+    "timedelta64[m]", "=timedelta64[m]", "<timedelta64[m]", ">timedelta64[m]",
+    "timedelta64[s]", "=timedelta64[s]", "<timedelta64[s]", ">timedelta64[s]",
+    "timedelta64[ms]", "=timedelta64[ms]", "<timedelta64[ms]", ">timedelta64[ms]",
+    "timedelta64[us]", "=timedelta64[us]", "<timedelta64[us]", ">timedelta64[us]",
+    "timedelta64[ns]", "=timedelta64[ns]", "<timedelta64[ns]", ">timedelta64[ns]",
+    "timedelta64[ps]", "=timedelta64[ps]", "<timedelta64[ps]", ">timedelta64[ps]",
+    "timedelta64[fs]", "=timedelta64[fs]", "<timedelta64[fs]", ">timedelta64[fs]",
+    "timedelta64[as]", "=timedelta64[as]", "<timedelta64[as]", ">timedelta64[as]",
+    "m", "=m", "<m", ">m",
+    "m8", "=m8", "<m8", ">m8",
+    "m8[Y]", "=m8[Y]", "<m8[Y]", ">m8[Y]",
+    "m8[M]", "=m8[M]", "<m8[M]", ">m8[M]",
+    "m8[W]", "=m8[W]", "<m8[W]", ">m8[W]",
+    "m8[D]", "=m8[D]", "<m8[D]", ">m8[D]",
+    "m8[h]", "=m8[h]", "<m8[h]", ">m8[h]",
+    "m8[m]", "=m8[m]", "<m8[m]", ">m8[m]",
+    "m8[s]", "=m8[s]", "<m8[s]", ">m8[s]",
+    "m8[ms]", "=m8[ms]", "<m8[ms]", ">m8[ms]",
+    "m8[us]", "=m8[us]", "<m8[us]", ">m8[us]",
+    "m8[ns]", "=m8[ns]", "<m8[ns]", ">m8[ns]",
+    "m8[ps]", "=m8[ps]", "<m8[ps]", ">m8[ps]",
+    "m8[fs]", "=m8[fs]", "<m8[fs]", ">m8[fs]",
+    "m8[as]", "=m8[as]", "<m8[as]", ">m8[as]",
+]

.CondaPkg/env/lib/python3.11/site-packages/numpy/_typing/_dtype_like.py

@@ -0,0 +1,249 @@
+from typing import (
+    Any,
+    List,
+    Sequence,
+    Tuple,
+    Union,
+    Type,
+    TypeVar,
+    Protocol,
+    TypedDict,
+    runtime_checkable,
+)
+
+import numpy as np
+
+from ._shape import _ShapeLike
+from ._generic_alias import _DType as DType
+
+from ._char_codes import (
+    _BoolCodes,
+    _UInt8Codes,
+    _UInt16Codes,
+    _UInt32Codes,
+    _UInt64Codes,
+    _Int8Codes,
+    _Int16Codes,
+    _Int32Codes,
+    _Int64Codes,
+    _Float16Codes,
+    _Float32Codes,
+    _Float64Codes,
+    _Complex64Codes,
+    _Complex128Codes,
+    _ByteCodes,
+    _ShortCodes,
+    _IntCCodes,
+    _IntPCodes,
+    _IntCodes,
+    _LongLongCodes,
+    _UByteCodes,
+    _UShortCodes,
+    _UIntCCodes,
+    _UIntPCodes,
+    _UIntCodes,
+    _ULongLongCodes,
+    _HalfCodes,
+    _SingleCodes,
+    _DoubleCodes,
+    _LongDoubleCodes,
+    _CSingleCodes,
+    _CDoubleCodes,
+    _CLongDoubleCodes,
+    _DT64Codes,
+    _TD64Codes,
+    _StrCodes,
+    _BytesCodes,
+    _VoidCodes,
+    _ObjectCodes,
+)
+
+_SCT = TypeVar("_SCT", bound=np.generic)
+_DType_co = TypeVar("_DType_co", covariant=True, bound=DType[Any])
+
+_DTypeLikeNested = Any  # TODO: wait for support for recursive types
+
+
+# Mandatory keys
+class _DTypeDictBase(TypedDict):
+    names: Sequence[str]
+    formats: Sequence[_DTypeLikeNested]
+
+
+# Mandatory + optional keys
+class _DTypeDict(_DTypeDictBase, total=False):
+    # Only `str` elements are usable as indexing aliases,
+    # but `titles` can in principle accept any object
+    offsets: Sequence[int]
+    titles: Sequence[Any]
+    itemsize: int
+    aligned: bool
+
+
+# A protocol for anything with the dtype attribute
+@runtime_checkable
+class _SupportsDType(Protocol[_DType_co]):
+    @property
+    def dtype(self) -> _DType_co: ...
+
+
+# A subset of `npt.DTypeLike` that can be parametrized w.r.t. `np.generic`
+_DTypeLike = Union[
+    "np.dtype[_SCT]",
+    Type[_SCT],
+    _SupportsDType["np.dtype[_SCT]"],
+]
+
+
+# Would create a dtype[np.void]
+_VoidDTypeLike = Union[
+    # (flexible_dtype, itemsize)
+    Tuple[_DTypeLikeNested, int],
+    # (fixed_dtype, shape)
+    Tuple[_DTypeLikeNested, _ShapeLike],
+    # [(field_name, field_dtype, field_shape), ...]
+    #
+    # The type here is quite broad because NumPy accepts quite a wide
+    # range of inputs inside the list; see the tests for some
+    # examples.
+    List[Any],
+    # {'names': ..., 'formats': ..., 'offsets': ..., 'titles': ...,
+    #  'itemsize': ...}
+    _DTypeDict,
+    # (base_dtype, new_dtype)
+    Tuple[_DTypeLikeNested, _DTypeLikeNested],
+]
+
+# Anything that can be coerced into numpy.dtype.
+# Reference: https://docs.scipy.org/doc/numpy/reference/arrays.dtypes.html
+DTypeLike = Union[
+    DType[Any],
+    # default data type (float64)
+    None,
+    # array-scalar types and generic types
+    Type[Any],  # NOTE: We're stuck with `Type[Any]` due to object dtypes
+    # anything with a dtype attribute
+    _SupportsDType[DType[Any]],
+    # character codes, type strings or comma-separated fields, e.g., 'float64'
+    str,
+    _VoidDTypeLike,
+]
+
+# NOTE: while it is possible to provide the dtype as a dict of
+# dtype-like objects (e.g. `{'field1': ..., 'field2': ..., ...}`),
+# this syntax is officially discourged and
+# therefore not included in the Union defining `DTypeLike`.
+#
+# See https://github.com/numpy/numpy/issues/16891 for more details.
+
+# Aliases for commonly used dtype-like objects.
+# Note that the precision of `np.number` subclasses is ignored herein.
+_DTypeLikeBool = Union[
+    Type[bool],
+    Type[np.bool_],
+    DType[np.bool_],
+    _SupportsDType[DType[np.bool_]],
+    _BoolCodes,
+]
+_DTypeLikeUInt = Union[
+    Type[np.unsignedinteger],
+    DType[np.unsignedinteger],
+    _SupportsDType[DType[np.unsignedinteger]],
+    _UInt8Codes,
+    _UInt16Codes,
+    _UInt32Codes,
+    _UInt64Codes,
+    _UByteCodes,
+    _UShortCodes,
+    _UIntCCodes,
+    _UIntPCodes,
+    _UIntCodes,
+    _ULongLongCodes,
+]
+_DTypeLikeInt = Union[
+    Type[int],
+    Type[np.signedinteger],
+    DType[np.signedinteger],
+    _SupportsDType[DType[np.signedinteger]],
+    _Int8Codes,
+    _Int16Codes,
+    _Int32Codes,
+    _Int64Codes,
+    _ByteCodes,
+    _ShortCodes,
+    _IntCCodes,
+    _IntPCodes,
+    _IntCodes,
+    _LongLongCodes,
+]
+_DTypeLikeFloat = Union[
+    Type[float],
+    Type[np.floating],
+    DType[np.floating],
+    _SupportsDType[DType[np.floating]],
+    _Float16Codes,
+    _Float32Codes,
+    _Float64Codes,
+    _HalfCodes,
+    _SingleCodes,
+    _DoubleCodes,
+    _LongDoubleCodes,
+]
+_DTypeLikeComplex = Union[
+    Type[complex],
+    Type[np.complexfloating],
+    DType[np.complexfloating],
+    _SupportsDType[DType[np.complexfloating]],
+    _Complex64Codes,
+    _Complex128Codes,
+    _CSingleCodes,
+    _CDoubleCodes,
+    _CLongDoubleCodes,
+]
+_DTypeLikeDT64 = Union[
+    Type[np.timedelta64],
+    DType[np.timedelta64],
+    _SupportsDType[DType[np.timedelta64]],
+    _TD64Codes,
+]
+_DTypeLikeTD64 = Union[
+    Type[np.datetime64],
+    DType[np.datetime64],
+    _SupportsDType[DType[np.datetime64]],
+    _DT64Codes,
+]
+_DTypeLikeStr = Union[
+    Type[str],
+    Type[np.str_],
+    DType[np.str_],
+    _SupportsDType[DType[np.str_]],
+    _StrCodes,
+]
+_DTypeLikeBytes = Union[
+    Type[bytes],
+    Type[np.bytes_],
+    DType[np.bytes_],
+    _SupportsDType[DType[np.bytes_]],
+    _BytesCodes,
+]
+_DTypeLikeVoid = Union[
+    Type[np.void],
+    DType[np.void],
+    _SupportsDType[DType[np.void]],
+    _VoidCodes,
+    _VoidDTypeLike,
+]
+_DTypeLikeObject = Union[
+    type,
+    DType[np.object_],
+    _SupportsDType[DType[np.object_]],
+    _ObjectCodes,
+]
+
+_DTypeLikeComplex_co = Union[
+    _DTypeLikeBool,
+    _DTypeLikeUInt,
+    _DTypeLikeInt,
+    _DTypeLikeFloat,
+    _DTypeLikeComplex,
+]

.CondaPkg/env/lib/python3.11/site-packages/numpy/_typing/_extended_precision.py

@@ -0,0 +1,43 @@
+"""A module with platform-specific extended precision
+`numpy.number` subclasses.
+
+The subclasses are defined here (instead of ``__init__.pyi``) such
+that they can be imported conditionally via the numpy's mypy plugin.
+"""
+
+from typing import TYPE_CHECKING
+
+import numpy as np
+from . import (
+    _80Bit,
+    _96Bit,
+    _128Bit,
+    _256Bit,
+)
+
+if TYPE_CHECKING:
+    uint128 = np.unsignedinteger[_128Bit]
+    uint256 = np.unsignedinteger[_256Bit]
+    int128 = np.signedinteger[_128Bit]
+    int256 = np.signedinteger[_256Bit]
+    float80 = np.floating[_80Bit]
+    float96 = np.floating[_96Bit]
+    float128 = np.floating[_128Bit]
+    float256 = np.floating[_256Bit]
+    complex160 = np.complexfloating[_80Bit, _80Bit]
+    complex192 = np.complexfloating[_96Bit, _96Bit]
+    complex256 = np.complexfloating[_128Bit, _128Bit]
+    complex512 = np.complexfloating[_256Bit, _256Bit]
+else:
+    uint128 = Any
+    uint256 = Any
+    int128 = Any
+    int256 = Any
+    float80 = Any
+    float96 = Any
+    float128 = Any
+    float256 = Any
+    complex160 = Any
+    complex192 = Any
+    complex256 = Any
+    complex512 = Any

.CondaPkg/env/lib/python3.11/site-packages/numpy/_typing/_generic_alias.py

@@ -0,0 +1,245 @@
+from __future__ import annotations
+
+import sys
+import types
+from collections.abc import Generator, Iterable, Iterator
+from typing import (
+    Any,
+    ClassVar,
+    NoReturn,
+    TypeVar,
+    TYPE_CHECKING,
+)
+
+import numpy as np
+
+__all__ = ["_GenericAlias", "NDArray"]
+
+_T = TypeVar("_T", bound="_GenericAlias")
+
+
+def _to_str(obj: object) -> str:
+    """Helper function for `_GenericAlias.__repr__`."""
+    if obj is Ellipsis:
+        return '...'
+    elif isinstance(obj, type) and not isinstance(obj, _GENERIC_ALIAS_TYPE):
+        if obj.__module__ == 'builtins':
+            return obj.__qualname__
+        else:
+            return f'{obj.__module__}.{obj.__qualname__}'
+    else:
+        return repr(obj)
+
+
+def _parse_parameters(args: Iterable[Any]) -> Generator[TypeVar, None, None]:
+    """Search for all typevars and typevar-containing objects in `args`.
+
+    Helper function for `_GenericAlias.__init__`.
+
+    """
+    for i in args:
+        if hasattr(i, "__parameters__"):
+            yield from i.__parameters__
+        elif isinstance(i, TypeVar):
+            yield i
+
+
+def _reconstruct_alias(alias: _T, parameters: Iterator[TypeVar]) -> _T:
+    """Recursively replace all typevars with those from `parameters`.
+
+    Helper function for `_GenericAlias.__getitem__`.
+
+    """
+    args = []
+    for i in alias.__args__:
+        if isinstance(i, TypeVar):
+            value: Any = next(parameters)
+        elif isinstance(i, _GenericAlias):
+            value = _reconstruct_alias(i, parameters)
+        elif hasattr(i, "__parameters__"):
+            prm_tup = tuple(next(parameters) for _ in i.__parameters__)
+            value = i[prm_tup]
+        else:
+            value = i
+        args.append(value)
+
+    cls = type(alias)
+    return cls(alias.__origin__, tuple(args), alias.__unpacked__)
+
+
+class _GenericAlias:
+    """A python-based backport of the `types.GenericAlias` class.
+
+    E.g. for ``t = list[int]``, ``t.__origin__`` is ``list`` and
+    ``t.__args__`` is ``(int,)``.
+
+    See Also
+    --------
+    :pep:`585`
+        The PEP responsible for introducing `types.GenericAlias`.
+
+    """
+
+    __slots__ = (
+        "__weakref__",
+        "_origin",
+        "_args",
+        "_parameters",
+        "_hash",
+        "_starred",
+    )
+
+    @property
+    def __origin__(self) -> type:
+        return super().__getattribute__("_origin")
+
+    @property
+    def __args__(self) -> tuple[object, ...]:
+        return super().__getattribute__("_args")
+
+    @property
+    def __parameters__(self) -> tuple[TypeVar, ...]:
+        """Type variables in the ``GenericAlias``."""
+        return super().__getattribute__("_parameters")
+
+    @property
+    def __unpacked__(self) -> bool:
+        return super().__getattribute__("_starred")
+
+    @property
+    def __typing_unpacked_tuple_args__(self) -> tuple[object, ...] | None:
+        # NOTE: This should return `__args__` if `__origin__` is a tuple,
+        # which should never be the case with how `_GenericAlias` is used
+        # within numpy
+        return None
+
+    def __init__(
+        self,
+        origin: type,
+        args: object | tuple[object, ...],
+        starred: bool = False,
+    ) -> None:
+        self._origin = origin
+        self._args = args if isinstance(args, tuple) else (args,)
+        self._parameters = tuple(_parse_parameters(self.__args__))
+        self._starred = starred
+
+    @property
+    def __call__(self) -> type[Any]:
+        return self.__origin__
+
+    def __reduce__(self: _T) -> tuple[
+        type[_T],
+        tuple[type[Any], tuple[object, ...], bool],
+    ]:
+        cls = type(self)
+        return cls, (self.__origin__, self.__args__, self.__unpacked__)
+
+    def __mro_entries__(self, bases: Iterable[object]) -> tuple[type[Any]]:
+        return (self.__origin__,)
+
+    def __dir__(self) -> list[str]:
+        """Implement ``dir(self)``."""
+        cls = type(self)
+        dir_origin = set(dir(self.__origin__))
+        return sorted(cls._ATTR_EXCEPTIONS | dir_origin)
+
+    def __hash__(self) -> int:
+        """Return ``hash(self)``."""
+        # Attempt to use the cached hash
+        try:
+            return super().__getattribute__("_hash")
+        except AttributeError:
+            self._hash: int = (
+                hash(self.__origin__) ^
+                hash(self.__args__) ^
+                hash(self.__unpacked__)
+            )
+            return super().__getattribute__("_hash")
+
+    def __instancecheck__(self, obj: object) -> NoReturn:
+        """Check if an `obj` is an instance."""
+        raise TypeError("isinstance() argument 2 cannot be a "
+                        "parameterized generic")
+
+    def __subclasscheck__(self, cls: type) -> NoReturn:
+        """Check if a `cls` is a subclass."""
+        raise TypeError("issubclass() argument 2 cannot be a "
+                        "parameterized generic")
+
+    def __repr__(self) -> str:
+        """Return ``repr(self)``."""
+        args = ", ".join(_to_str(i) for i in self.__args__)
+        origin = _to_str(self.__origin__)
+        prefix = "*" if self.__unpacked__ else ""
+        return f"{prefix}{origin}[{args}]"
+
+    def __getitem__(self: _T, key: object | tuple[object, ...]) -> _T:
+        """Return ``self[key]``."""
+        key_tup = key if isinstance(key, tuple) else (key,)
+
+        if len(self.__parameters__) == 0:
+            raise TypeError(f"There are no type variables left in {self}")
+        elif len(key_tup) > len(self.__parameters__):
+            raise TypeError(f"Too many arguments for {self}")
+        elif len(key_tup) < len(self.__parameters__):
+            raise TypeError(f"Too few arguments for {self}")
+
+        key_iter = iter(key_tup)
+        return _reconstruct_alias(self, key_iter)
+
+    def __eq__(self, value: object) -> bool:
+        """Return ``self == value``."""
+        if not isinstance(value, _GENERIC_ALIAS_TYPE):
+            return NotImplemented
+        return (
+            self.__origin__ == value.__origin__ and
+            self.__args__ == value.__args__ and
+            self.__unpacked__ == getattr(
+                value, "__unpacked__", self.__unpacked__
+            )
+        )
+
+    def __iter__(self: _T) -> Generator[_T, None, None]:
+        """Return ``iter(self)``."""
+        cls = type(self)
+        yield cls(self.__origin__, self.__args__, True)
+
+    _ATTR_EXCEPTIONS: ClassVar[frozenset[str]] = frozenset({
+        "__origin__",
+        "__args__",
+        "__parameters__",
+        "__mro_entries__",
+        "__reduce__",
+        "__reduce_ex__",
+        "__copy__",
+        "__deepcopy__",
+        "__unpacked__",
+        "__typing_unpacked_tuple_args__",
+        "__class__",
+    })
+
+    def __getattribute__(self, name: str) -> Any:
+        """Return ``getattr(self, name)``."""
+        # Pull the attribute from `__origin__` unless its
+        # name is in `_ATTR_EXCEPTIONS`
+        cls = type(self)
+        if name in cls._ATTR_EXCEPTIONS:
+            return super().__getattribute__(name)
+        return getattr(self.__origin__, name)
+
+
+# See `_GenericAlias.__eq__`
+if sys.version_info >= (3, 9):
+    _GENERIC_ALIAS_TYPE = (_GenericAlias, types.GenericAlias)
+else:
+    _GENERIC_ALIAS_TYPE = (_GenericAlias,)
+
+ScalarType = TypeVar("ScalarType", bound=np.generic, covariant=True)
+
+if TYPE_CHECKING or sys.version_info >= (3, 9):
+    _DType = np.dtype[ScalarType]
+    NDArray = np.ndarray[Any, np.dtype[ScalarType]]
+else:
+    _DType = _GenericAlias(np.dtype, (ScalarType,))
+    NDArray = _GenericAlias(np.ndarray, (Any, _DType))

.CondaPkg/env/lib/python3.11/site-packages/numpy/_typing/_nbit.py

@@ -0,0 +1,16 @@
+"""A module with the precisions of platform-specific `~numpy.number`s."""
+
+from typing import Any
+
+# To-be replaced with a `npt.NBitBase` subclass by numpy's mypy plugin
+_NBitByte = Any
+_NBitShort = Any
+_NBitIntC = Any
+_NBitIntP = Any
+_NBitInt = Any
+_NBitLongLong = Any
+
+_NBitHalf = Any
+_NBitSingle = Any
+_NBitDouble = Any
+_NBitLongDouble = Any

.CondaPkg/env/lib/python3.11/site-packages/numpy/_typing/_nested_sequence.py

@@ -0,0 +1,92 @@
+"""A module containing the `_NestedSequence` protocol."""
+
+from __future__ import annotations
+
+from typing import (
+    Any,
+    Iterator,
+    overload,
+    TypeVar,
+    Protocol,
+    runtime_checkable,
+)
+
+__all__ = ["_NestedSequence"]
+
+_T_co = TypeVar("_T_co", covariant=True)
+
+
+@runtime_checkable
+class _NestedSequence(Protocol[_T_co]):
+    """A protocol for representing nested sequences.
+
+    Warning
+    -------
+    `_NestedSequence` currently does not work in combination with typevars,
+    *e.g.* ``def func(a: _NestedSequnce[T]) -> T: ...``.
+
+    See Also
+    --------
+    collections.abc.Sequence
+        ABCs for read-only and mutable :term:`sequences`.
+
+    Examples
+    --------
+    .. code-block:: python
+
+        >>> from __future__ import annotations
+
+        >>> from typing import TYPE_CHECKING
+        >>> import numpy as np
+        >>> from numpy._typing import _NestedSequence
+
+        >>> def get_dtype(seq: _NestedSequence[float]) -> np.dtype[np.float64]:
+        ...     return np.asarray(seq).dtype
+
+        >>> a = get_dtype([1.0])
+        >>> b = get_dtype([[1.0]])
+        >>> c = get_dtype([[[1.0]]])
+        >>> d = get_dtype([[[[1.0]]]])
+
+        >>> if TYPE_CHECKING:
+        ...     reveal_locals()
+        ...     # note: Revealed local types are:
+        ...     # note:     a: numpy.dtype[numpy.floating[numpy._typing._64Bit]]
+        ...     # note:     b: numpy.dtype[numpy.floating[numpy._typing._64Bit]]
+        ...     # note:     c: numpy.dtype[numpy.floating[numpy._typing._64Bit]]
+        ...     # note:     d: numpy.dtype[numpy.floating[numpy._typing._64Bit]]
+
+    """
+
+    def __len__(self, /) -> int:
+        """Implement ``len(self)``."""
+        raise NotImplementedError
+
+    @overload
+    def __getitem__(self, index: int, /) -> _T_co | _NestedSequence[_T_co]: ...
+    @overload
+    def __getitem__(self, index: slice, /) -> _NestedSequence[_T_co]: ...
+
+    def __getitem__(self, index, /):
+        """Implement ``self[x]``."""
+        raise NotImplementedError
+
+    def __contains__(self, x: object, /) -> bool:
+        """Implement ``x in self``."""
+        raise NotImplementedError
+
+    def __iter__(self, /) -> Iterator[_T_co | _NestedSequence[_T_co]]:
+        """Implement ``iter(self)``."""
+        raise NotImplementedError
+
+    def __reversed__(self, /) -> Iterator[_T_co | _NestedSequence[_T_co]]:
+        """Implement ``reversed(self)``."""
+        raise NotImplementedError
+
+    def count(self, value: Any, /) -> int:
+        """Return the number of occurrences of `value`."""
+        raise NotImplementedError
+
+    def index(self, value: Any, /) -> int:
+        """Return the first index of `value`."""
+        raise NotImplementedError

.CondaPkg/env/lib/python3.11/site-packages/numpy/_typing/_scalars.py

@@ -0,0 +1,30 @@
+from typing import Union, Tuple, Any
+
+import numpy as np
+
+# NOTE: `_StrLike_co` and `_BytesLike_co` are pointless, as `np.str_` and
+# `np.bytes_` are already subclasses of their builtin counterpart
+
+_CharLike_co = Union[str, bytes]
+
+# The 6 `<X>Like_co` type-aliases below represent all scalars that can be
+# coerced into `<X>` (with the casting rule `same_kind`)
+_BoolLike_co = Union[bool, np.bool_]
+_UIntLike_co = Union[_BoolLike_co, np.unsignedinteger]
+_IntLike_co = Union[_BoolLike_co, int, np.integer]
+_FloatLike_co = Union[_IntLike_co, float, np.floating]
+_ComplexLike_co = Union[_FloatLike_co, complex, np.complexfloating]
+_TD64Like_co = Union[_IntLike_co, np.timedelta64]
+
+_NumberLike_co = Union[int, float, complex, np.number, np.bool_]
+_ScalarLike_co = Union[
+    int,
+    float,
+    complex,
+    str,
+    bytes,
+    np.generic,
+]
+
+# `_VoidLike_co` is technically not a scalar, but it's close enough
+_VoidLike_co = Union[Tuple[Any, ...], np.void]

.CondaPkg/env/lib/python3.11/site-packages/numpy/_typing/_shape.py

@@ -0,0 +1,6 @@
+from typing import Sequence, Tuple, Union, SupportsIndex
+
+_Shape = Tuple[int, ...]
+
+# Anything that can be coerced to a shape tuple
+_ShapeLike = Union[SupportsIndex, Sequence[SupportsIndex]]

.CondaPkg/env/lib/python3.11/site-packages/numpy/_typing/_ufunc.pyi

@@ -0,0 +1,445 @@
+"""A module with private type-check-only `numpy.ufunc` subclasses.
+
+The signatures of the ufuncs are too varied to reasonably type
+with a single class. So instead, `ufunc` has been expanded into
+four private subclasses, one for each combination of
+`~ufunc.nin` and `~ufunc.nout`.
+
+"""
+
+from typing import (
+    Any,
+    Generic,
+    overload,
+    TypeVar,
+    Literal,
+    SupportsIndex,
+    Protocol,
+)
+
+from numpy import ufunc, _CastingKind, _OrderKACF
+from numpy.typing import NDArray
+
+from ._shape import _ShapeLike
+from ._scalars import _ScalarLike_co
+from ._array_like import ArrayLike, _ArrayLikeBool_co, _ArrayLikeInt_co
+from ._dtype_like import DTypeLike
+
+_T = TypeVar("_T")
+_2Tuple = tuple[_T, _T]
+_3Tuple = tuple[_T, _T, _T]
+_4Tuple = tuple[_T, _T, _T, _T]
+
+_NTypes = TypeVar("_NTypes", bound=int)
+_IDType = TypeVar("_IDType", bound=Any)
+_NameType = TypeVar("_NameType", bound=str)
+
+
+class _SupportsArrayUFunc(Protocol):
+    def __array_ufunc__(
+        self,
+        ufunc: ufunc,
+        method: Literal["__call__", "reduce", "reduceat", "accumulate", "outer", "inner"],
+        *inputs: Any,
+        **kwargs: Any,
+    ) -> Any: ...
+
+
+# NOTE: In reality `extobj` should be a length of list 3 containing an
+# int, an int, and a callable, but there's no way to properly express
+# non-homogenous lists.
+# Use `Any` over `Union` to avoid issues related to lists invariance.
+
+# NOTE: `reduce`, `accumulate`, `reduceat` and `outer` raise a ValueError for
+# ufuncs that don't accept two input arguments and return one output argument.
+# In such cases the respective methods are simply typed as `None`.
+
+# NOTE: Similarly, `at` won't be defined for ufuncs that return
+# multiple outputs; in such cases `at` is typed as `None`
+
+# NOTE: If 2 output types are returned then `out` must be a
+# 2-tuple of arrays. Otherwise `None` or a plain array are also acceptable
+
+class _UFunc_Nin1_Nout1(ufunc, Generic[_NameType, _NTypes, _IDType]):  # type: ignore[misc]
+    @property
+    def __name__(self) -> _NameType: ...
+    @property
+    def ntypes(self) -> _NTypes: ...
+    @property
+    def identity(self) -> _IDType: ...
+    @property
+    def nin(self) -> Literal[1]: ...
+    @property
+    def nout(self) -> Literal[1]: ...
+    @property
+    def nargs(self) -> Literal[2]: ...
+    @property
+    def signature(self) -> None: ...
+    @property
+    def reduce(self) -> None: ...
+    @property
+    def accumulate(self) -> None: ...
+    @property
+    def reduceat(self) -> None: ...
+    @property
+    def outer(self) -> None: ...
+
+    @overload
+    def __call__(
+        self,
+        __x1: _ScalarLike_co,
+        out: None = ...,
+        *,
+        where: None | _ArrayLikeBool_co = ...,
+        casting: _CastingKind = ...,
+        order: _OrderKACF = ...,
+        dtype: DTypeLike = ...,
+        subok: bool = ...,
+        signature: str | _2Tuple[None | str] = ...,
+        extobj: list[Any] = ...,
+    ) -> Any: ...
+    @overload
+    def __call__(
+        self,
+        __x1: ArrayLike,
+        out: None | NDArray[Any] | tuple[NDArray[Any]] = ...,
+        *,
+        where: None | _ArrayLikeBool_co = ...,
+        casting: _CastingKind = ...,
+        order: _OrderKACF = ...,
+        dtype: DTypeLike = ...,
+        subok: bool = ...,
+        signature: str | _2Tuple[None | str] = ...,
+        extobj: list[Any] = ...,
+    ) -> NDArray[Any]: ...
+    @overload
+    def __call__(
+        self,
+        __x1: _SupportsArrayUFunc,
+        out: None | NDArray[Any] | tuple[NDArray[Any]] = ...,
+        *,
+        where: None | _ArrayLikeBool_co = ...,
+        casting: _CastingKind = ...,
+        order: _OrderKACF = ...,
+        dtype: DTypeLike = ...,
+        subok: bool = ...,
+        signature: str | _2Tuple[None | str] = ...,
+        extobj: list[Any] = ...,
+    ) -> Any: ...
+
+    def at(
+        self,
+        a: _SupportsArrayUFunc,
+        indices: _ArrayLikeInt_co,
+        /,
+    ) -> None: ...
+
+class _UFunc_Nin2_Nout1(ufunc, Generic[_NameType, _NTypes, _IDType]):  # type: ignore[misc]
+    @property
+    def __name__(self) -> _NameType: ...
+    @property
+    def ntypes(self) -> _NTypes: ...
+    @property
+    def identity(self) -> _IDType: ...
+    @property
+    def nin(self) -> Literal[2]: ...
+    @property
+    def nout(self) -> Literal[1]: ...
+    @property
+    def nargs(self) -> Literal[3]: ...
+    @property
+    def signature(self) -> None: ...
+
+    @overload
+    def __call__(
+        self,
+        __x1: _ScalarLike_co,
+        __x2: _ScalarLike_co,
+        out: None = ...,
+        *,
+        where: None | _ArrayLikeBool_co = ...,
+        casting: _CastingKind = ...,
+        order: _OrderKACF = ...,
+        dtype: DTypeLike = ...,
+        subok: bool = ...,
+        signature: str | _3Tuple[None | str] = ...,
+        extobj: list[Any] = ...,
+    ) -> Any: ...
+    @overload
+    def __call__(
+        self,
+        __x1: ArrayLike,
+        __x2: ArrayLike,
+        out: None | NDArray[Any] | tuple[NDArray[Any]] = ...,
+        *,
+        where: None | _ArrayLikeBool_co = ...,
+        casting: _CastingKind = ...,
+        order: _OrderKACF = ...,
+        dtype: DTypeLike = ...,
+        subok: bool = ...,
+        signature: str | _3Tuple[None | str] = ...,
+        extobj: list[Any] = ...,
+    ) -> NDArray[Any]: ...
+
+    def at(
+        self,
+        a: NDArray[Any],
+        indices: _ArrayLikeInt_co,
+        b: ArrayLike,
+        /,
+    ) -> None: ...
+
+    def reduce(
+        self,
+        array: ArrayLike,
+        axis: None | _ShapeLike = ...,
+        dtype: DTypeLike = ...,
+        out: None | NDArray[Any] = ...,
+        keepdims: bool = ...,
+        initial: Any = ...,
+        where: _ArrayLikeBool_co = ...,
+    ) -> Any: ...
+
+    def accumulate(
+        self,
+        array: ArrayLike,
+        axis: SupportsIndex = ...,
+        dtype: DTypeLike = ...,
+        out: None | NDArray[Any] = ...,
+    ) -> NDArray[Any]: ...
+
+    def reduceat(
+        self,
+        array: ArrayLike,
+        indices: _ArrayLikeInt_co,
+        axis: SupportsIndex = ...,
+        dtype: DTypeLike = ...,
+        out: None | NDArray[Any] = ...,
+    ) -> NDArray[Any]: ...
+
+    # Expand `**kwargs` into explicit keyword-only arguments
+    @overload
+    def outer(
+        self,
+        A: _ScalarLike_co,
+        B: _ScalarLike_co,
+        /, *,
+        out: None = ...,
+        where: None | _ArrayLikeBool_co = ...,
+        casting: _CastingKind = ...,
+        order: _OrderKACF = ...,
+        dtype: DTypeLike = ...,
+        subok: bool = ...,
+        signature: str | _3Tuple[None | str] = ...,
+        extobj: list[Any] = ...,
+    ) -> Any: ...
+    @overload
+    def outer(  # type: ignore[misc]
+        self,
+        A: ArrayLike,
+        B: ArrayLike,
+        /, *,
+        out: None | NDArray[Any] | tuple[NDArray[Any]] = ...,
+        where: None | _ArrayLikeBool_co = ...,
+        casting: _CastingKind = ...,
+        order: _OrderKACF = ...,
+        dtype: DTypeLike = ...,
+        subok: bool = ...,
+        signature: str | _3Tuple[None | str] = ...,
+        extobj: list[Any] = ...,
+    ) -> NDArray[Any]: ...
+
+class _UFunc_Nin1_Nout2(ufunc, Generic[_NameType, _NTypes, _IDType]):  # type: ignore[misc]
+    @property
+    def __name__(self) -> _NameType: ...
+    @property
+    def ntypes(self) -> _NTypes: ...
+    @property
+    def identity(self) -> _IDType: ...
+    @property
+    def nin(self) -> Literal[1]: ...
+    @property
+    def nout(self) -> Literal[2]: ...
+    @property
+    def nargs(self) -> Literal[3]: ...
+    @property
+    def signature(self) -> None: ...
+    @property
+    def at(self) -> None: ...
+    @property
+    def reduce(self) -> None: ...
+    @property
+    def accumulate(self) -> None: ...
+    @property
+    def reduceat(self) -> None: ...
+    @property
+    def outer(self) -> None: ...
+
+    @overload
+    def __call__(
+        self,
+        __x1: _ScalarLike_co,
+        __out1: None = ...,
+        __out2: None = ...,
+        *,
+        where: None | _ArrayLikeBool_co = ...,
+        casting: _CastingKind = ...,
+        order: _OrderKACF = ...,
+        dtype: DTypeLike = ...,
+        subok: bool = ...,
+        signature: str | _3Tuple[None | str] = ...,
+        extobj: list[Any] = ...,
+    ) -> _2Tuple[Any]: ...
+    @overload
+    def __call__(
+        self,
+        __x1: ArrayLike,
+        __out1: None | NDArray[Any] = ...,
+        __out2: None | NDArray[Any] = ...,
+        *,
+        out: _2Tuple[NDArray[Any]] = ...,
+        where: None | _ArrayLikeBool_co = ...,
+        casting: _CastingKind = ...,
+        order: _OrderKACF = ...,
+        dtype: DTypeLike = ...,
+        subok: bool = ...,
+        signature: str | _3Tuple[None | str] = ...,
+        extobj: list[Any] = ...,
+    ) -> _2Tuple[NDArray[Any]]: ...
+    @overload
+    def __call__(
+        self,
+        __x1: _SupportsArrayUFunc,
+        __out1: None | NDArray[Any] = ...,
+        __out2: None | NDArray[Any] = ...,
+        *,
+        out: _2Tuple[NDArray[Any]] = ...,
+        where: None | _ArrayLikeBool_co = ...,
+        casting: _CastingKind = ...,
+        order: _OrderKACF = ...,
+        dtype: DTypeLike = ...,
+        subok: bool = ...,
+        signature: str | _3Tuple[None | str] = ...,
+        extobj: list[Any] = ...,
+    ) -> _2Tuple[Any]: ...
+
+class _UFunc_Nin2_Nout2(ufunc, Generic[_NameType, _NTypes, _IDType]):  # type: ignore[misc]
+    @property
+    def __name__(self) -> _NameType: ...
+    @property
+    def ntypes(self) -> _NTypes: ...
+    @property
+    def identity(self) -> _IDType: ...
+    @property
+    def nin(self) -> Literal[2]: ...
+    @property
+    def nout(self) -> Literal[2]: ...
+    @property
+    def nargs(self) -> Literal[4]: ...
+    @property
+    def signature(self) -> None: ...
+    @property
+    def at(self) -> None: ...
+    @property
+    def reduce(self) -> None: ...
+    @property
+    def accumulate(self) -> None: ...
+    @property
+    def reduceat(self) -> None: ...
+    @property
+    def outer(self) -> None: ...
+
+    @overload
+    def __call__(
+        self,
+        __x1: _ScalarLike_co,
+        __x2: _ScalarLike_co,
+        __out1: None = ...,
+        __out2: None = ...,
+        *,
+        where: None | _ArrayLikeBool_co = ...,
+        casting: _CastingKind = ...,
+        order: _OrderKACF = ...,
+        dtype: DTypeLike = ...,
+        subok: bool = ...,
+        signature: str | _4Tuple[None | str] = ...,
+        extobj: list[Any] = ...,
+    ) -> _2Tuple[Any]: ...
+    @overload
+    def __call__(
+        self,
+        __x1: ArrayLike,
+        __x2: ArrayLike,
+        __out1: None | NDArray[Any] = ...,
+        __out2: None | NDArray[Any] = ...,
+        *,
+        out: _2Tuple[NDArray[Any]] = ...,
+        where: None | _ArrayLikeBool_co = ...,
+        casting: _CastingKind = ...,
+        order: _OrderKACF = ...,
+        dtype: DTypeLike = ...,
+        subok: bool = ...,
+        signature: str | _4Tuple[None | str] = ...,
+        extobj: list[Any] = ...,
+    ) -> _2Tuple[NDArray[Any]]: ...
+
+class _GUFunc_Nin2_Nout1(ufunc, Generic[_NameType, _NTypes, _IDType]):  # type: ignore[misc]
+    @property
+    def __name__(self) -> _NameType: ...
+    @property
+    def ntypes(self) -> _NTypes: ...
+    @property
+    def identity(self) -> _IDType: ...
+    @property
+    def nin(self) -> Literal[2]: ...
+    @property
+    def nout(self) -> Literal[1]: ...
+    @property
+    def nargs(self) -> Literal[3]: ...
+
+    # NOTE: In practice the only gufunc in the main namespace is `matmul`,
+    # so we can use its signature here
+    @property
+    def signature(self) -> Literal["(n?,k),(k,m?)->(n?,m?)"]: ...
+    @property
+    def reduce(self) -> None: ...
+    @property
+    def accumulate(self) -> None: ...
+    @property
+    def reduceat(self) -> None: ...
+    @property
+    def outer(self) -> None: ...
+    @property
+    def at(self) -> None: ...
+
+    # Scalar for 1D array-likes; ndarray otherwise
+    @overload
+    def __call__(
+        self,
+        __x1: ArrayLike,
+        __x2: ArrayLike,
+        out: None = ...,
+        *,
+        casting: _CastingKind = ...,
+        order: _OrderKACF = ...,
+        dtype: DTypeLike = ...,
+        subok: bool = ...,
+        signature: str | _3Tuple[None | str] = ...,
+        extobj: list[Any] = ...,
+        axes: list[_2Tuple[SupportsIndex]] = ...,
+    ) -> Any: ...
+    @overload
+    def __call__(
+        self,
+        __x1: ArrayLike,
+        __x2: ArrayLike,
+        out: NDArray[Any] | tuple[NDArray[Any]],
+        *,
+        casting: _CastingKind = ...,
+        order: _OrderKACF = ...,
+        dtype: DTypeLike = ...,
+        subok: bool = ...,
+        signature: str | _3Tuple[None | str] = ...,
+        extobj: list[Any] = ...,
+        axes: list[_2Tuple[SupportsIndex]] = ...,
+    ) -> NDArray[Any]: ...

.CondaPkg/env/lib/python3.11/site-packages/numpy/_typing/setup.py

@@ -0,0 +1,10 @@
+def configuration(parent_package='', top_path=None):
+    from numpy.distutils.misc_util import Configuration
+    config = Configuration('_typing', parent_package, top_path)
+    config.add_data_files('*.pyi')
+    return config
+
+
+if __name__ == '__main__':
+    from numpy.distutils.core import setup
+    setup(configuration=configuration)

.CondaPkg/env/lib/python3.11/site-packages/numpy/_version.py

@@ -0,0 +1,21 @@
+
+# This file was generated by 'versioneer.py' (0.26) from
+# revision-control system data, or from the parent directory name of an
+# unpacked source archive. Distribution tarballs contain a pre-generated copy
+# of this file.
+
+import json
+
+version_json = '''
+{
+ "date": "2023-02-05T11:25:52-0500",
+ "dirty": false,
+ "error": null,
+ "full-revisionid": "85f38ab180ece5290f64e8ddbd9cf06ad8fa4a5e",
+ "version": "1.24.2"
+}
+'''  # END VERSION_JSON
+
+
+def get_versions():
+    return json.loads(version_json)

.CondaPkg/env/lib/python3.11/site-packages/numpy/array_api/__init__.py

@@ -0,0 +1,377 @@
+"""
+A NumPy sub-namespace that conforms to the Python array API standard.
+
+This submodule accompanies NEP 47, which proposes its inclusion in NumPy. It
+is still considered experimental, and will issue a warning when imported.
+
+This is a proof-of-concept namespace that wraps the corresponding NumPy
+functions to give a conforming implementation of the Python array API standard
+(https://data-apis.github.io/array-api/latest/). The standard is currently in
+an RFC phase and comments on it are both welcome and encouraged. Comments
+should be made either at https://github.com/data-apis/array-api or at
+https://github.com/data-apis/consortium-feedback/discussions.
+
+NumPy already follows the proposed spec for the most part, so this module
+serves mostly as a thin wrapper around it. However, NumPy also implements a
+lot of behavior that is not included in the spec, so this serves as a
+restricted subset of the API. Only those functions that are part of the spec
+are included in this namespace, and all functions are given with the exact
+signature given in the spec, including the use of position-only arguments, and
+omitting any extra keyword arguments implemented by NumPy but not part of the
+spec. The behavior of some functions is also modified from the NumPy behavior
+to conform to the standard. Note that the underlying array object itself is
+wrapped in a wrapper Array() class, but is otherwise unchanged. This submodule
+is implemented in pure Python with no C extensions.
+
+The array API spec is designed as a "minimal API subset" and explicitly allows
+libraries to include behaviors not specified by it. But users of this module
+that intend to write portable code should be aware that only those behaviors
+that are listed in the spec are guaranteed to be implemented across libraries.
+Consequently, the NumPy implementation was chosen to be both conforming and
+minimal, so that users can use this implementation of the array API namespace
+and be sure that behaviors that it defines will be available in conforming
+namespaces from other libraries.
+
+A few notes about the current state of this submodule:
+
+- There is a test suite that tests modules against the array API standard at
+  https://github.com/data-apis/array-api-tests. The test suite is still a work
+  in progress, but the existing tests pass on this module, with a few
+  exceptions:
+
+  - DLPack support (see https://github.com/data-apis/array-api/pull/106) is
+    not included here, as it requires a full implementation in NumPy proper
+    first.
+
+  The test suite is not yet complete, and even the tests that exist are not
+  guaranteed to give a comprehensive coverage of the spec. Therefore, when
+  reviewing and using this submodule, you should refer to the standard
+  documents themselves. There are some tests in numpy.array_api.tests, but
+  they primarily focus on things that are not tested by the official array API
+  test suite.
+
+- There is a custom array object, numpy.array_api.Array, which is returned by
+  all functions in this module. All functions in the array API namespace
+  implicitly assume that they will only receive this object as input. The only
+  way to create instances of this object is to use one of the array creation
+  functions. It does not have a public constructor on the object itself. The
+  object is a small wrapper class around numpy.ndarray. The main purpose of it
+  is to restrict the namespace of the array object to only those dtypes and
+  only those methods that are required by the spec, as well as to limit/change
+  certain behavior that differs in the spec. In particular:
+
+  - The array API namespace does not have scalar objects, only 0-D arrays.
+    Operations on Array that would create a scalar in NumPy create a 0-D
+    array.
+
+  - Indexing: Only a subset of indices supported by NumPy are required by the
+    spec. The Array object restricts indexing to only allow those types of
+    indices that are required by the spec. See the docstring of the
+    numpy.array_api.Array._validate_indices helper function for more
+    information.
+
+  - Type promotion: Some type promotion rules are different in the spec. In
+    particular, the spec does not have any value-based casting. The spec also
+    does not require cross-kind casting, like integer -> floating-point. Only
+    those promotions that are explicitly required by the array API
+    specification are allowed in this module. See NEP 47 for more info.
+
+  - Functions do not automatically call asarray() on their input, and will not
+    work if the input type is not Array. The exception is array creation
+    functions, and Python operators on the Array object, which accept Python
+    scalars of the same type as the array dtype.
+
+- All functions include type annotations, corresponding to those given in the
+  spec (see _typing.py for definitions of some custom types). These do not
+  currently fully pass mypy due to some limitations in mypy.
+
+- Dtype objects are just the NumPy dtype objects, e.g., float64 =
+  np.dtype('float64'). The spec does not require any behavior on these dtype
+  objects other than that they be accessible by name and be comparable by
+  equality, but it was considered too much extra complexity to create custom
+  objects to represent dtypes.
+
+- All places where the implementations in this submodule are known to deviate
+  from their corresponding functions in NumPy are marked with "# Note:"
+  comments.
+
+Still TODO in this module are:
+
+- DLPack support for numpy.ndarray is still in progress. See
+  https://github.com/numpy/numpy/pull/19083.
+
+- The copy=False keyword argument to asarray() is not yet implemented. This
+  requires support in numpy.asarray() first.
+
+- Some functions are not yet fully tested in the array API test suite, and may
+  require updates that are not yet known until the tests are written.
+
+- The spec is still in an RFC phase and may still have minor updates, which
+  will need to be reflected here.
+
+- Complex number support in array API spec is planned but not yet finalized,
+  as are the fft extension and certain linear algebra functions such as eig
+  that require complex dtypes.
+
+"""
+
+import warnings
+
+warnings.warn(
+    "The numpy.array_api submodule is still experimental. See NEP 47.", stacklevel=2
+)
+
+__array_api_version__ = "2021.12"
+
+__all__ = ["__array_api_version__"]
+
+from ._constants import e, inf, nan, pi
+
+__all__ += ["e", "inf", "nan", "pi"]
+
+from ._creation_functions import (
+    asarray,
+    arange,
+    empty,
+    empty_like,
+    eye,
+    from_dlpack,
+    full,
+    full_like,
+    linspace,
+    meshgrid,
+    ones,
+    ones_like,
+    tril,
+    triu,
+    zeros,
+    zeros_like,
+)
+
+__all__ += [
+    "asarray",
+    "arange",
+    "empty",
+    "empty_like",
+    "eye",
+    "from_dlpack",
+    "full",
+    "full_like",
+    "linspace",
+    "meshgrid",
+    "ones",
+    "ones_like",
+    "tril",
+    "triu",
+    "zeros",
+    "zeros_like",
+]
+
+from ._data_type_functions import (
+    astype,
+    broadcast_arrays,
+    broadcast_to,
+    can_cast,
+    finfo,
+    iinfo,
+    result_type,
+)
+
+__all__ += [
+    "astype",
+    "broadcast_arrays",
+    "broadcast_to",
+    "can_cast",
+    "finfo",
+    "iinfo",
+    "result_type",
+]
+
+from ._dtypes import (
+    int8,
+    int16,
+    int32,
+    int64,
+    uint8,
+    uint16,
+    uint32,
+    uint64,
+    float32,
+    float64,
+    bool,
+)
+
+__all__ += [
+    "int8",
+    "int16",
+    "int32",
+    "int64",
+    "uint8",
+    "uint16",
+    "uint32",
+    "uint64",
+    "float32",
+    "float64",
+    "bool",
+]
+
+from ._elementwise_functions import (
+    abs,
+    acos,
+    acosh,
+    add,
+    asin,
+    asinh,
+    atan,
+    atan2,
+    atanh,
+    bitwise_and,
+    bitwise_left_shift,
+    bitwise_invert,
+    bitwise_or,
+    bitwise_right_shift,
+    bitwise_xor,
+    ceil,
+    cos,
+    cosh,
+    divide,
+    equal,
+    exp,
+    expm1,
+    floor,
+    floor_divide,
+    greater,
+    greater_equal,
+    isfinite,
+    isinf,
+    isnan,
+    less,
+    less_equal,
+    log,
+    log1p,
+    log2,
+    log10,
+    logaddexp,
+    logical_and,
+    logical_not,
+    logical_or,
+    logical_xor,
+    multiply,
+    negative,
+    not_equal,
+    positive,
+    pow,
+    remainder,
+    round,
+    sign,
+    sin,
+    sinh,
+    square,
+    sqrt,
+    subtract,
+    tan,
+    tanh,
+    trunc,
+)
+
+__all__ += [
+    "abs",
+    "acos",
+    "acosh",
+    "add",
+    "asin",
+    "asinh",
+    "atan",
+    "atan2",
+    "atanh",
+    "bitwise_and",
+    "bitwise_left_shift",
+    "bitwise_invert",
+    "bitwise_or",
+    "bitwise_right_shift",
+    "bitwise_xor",
+    "ceil",
+    "cos",
+    "cosh",
+    "divide",
+    "equal",
+    "exp",
+    "expm1",
+    "floor",
+    "floor_divide",
+    "greater",
+    "greater_equal",
+    "isfinite",
+    "isinf",
+    "isnan",
+    "less",
+    "less_equal",
+    "log",
+    "log1p",
+    "log2",
+    "log10",
+    "logaddexp",
+    "logical_and",
+    "logical_not",
+    "logical_or",
+    "logical_xor",
+    "multiply",
+    "negative",
+    "not_equal",
+    "positive",
+    "pow",
+    "remainder",
+    "round",
+    "sign",
+    "sin",
+    "sinh",
+    "square",
+    "sqrt",
+    "subtract",
+    "tan",
+    "tanh",
+    "trunc",
+]
+
+# linalg is an extension in the array API spec, which is a sub-namespace. Only
+# a subset of functions in it are imported into the top-level namespace.
+from . import linalg
+
+__all__ += ["linalg"]
+
+from .linalg import matmul, tensordot, matrix_transpose, vecdot
+
+__all__ += ["matmul", "tensordot", "matrix_transpose", "vecdot"]
+
+from ._manipulation_functions import (
+    concat,
+    expand_dims,
+    flip,
+    permute_dims,
+    reshape,
+    roll,
+    squeeze,
+    stack,
+)
+
+__all__ += ["concat", "expand_dims", "flip", "permute_dims", "reshape", "roll", "squeeze", "stack"]
+
+from ._searching_functions import argmax, argmin, nonzero, where
+
+__all__ += ["argmax", "argmin", "nonzero", "where"]
+
+from ._set_functions import unique_all, unique_counts, unique_inverse, unique_values
+
+__all__ += ["unique_all", "unique_counts", "unique_inverse", "unique_values"]
+
+from ._sorting_functions import argsort, sort
+
+__all__ += ["argsort", "sort"]
+
+from ._statistical_functions import max, mean, min, prod, std, sum, var
+
+__all__ += ["max", "mean", "min", "prod", "std", "sum", "var"]
+
+from ._utility_functions import all, any
+
+__all__ += ["all", "any"]

.CondaPkg/env/lib/python3.11/site-packages/numpy/array_api/_constants.py

@@ -0,0 +1,6 @@
+import numpy as np
+
+e = np.e
+inf = np.inf
+nan = np.nan
+pi = np.pi

.CondaPkg/env/lib/python3.11/site-packages/numpy/array_api/_creation_functions.py

@@ -0,0 +1,351 @@
+from __future__ import annotations
+
+
+from typing import TYPE_CHECKING, List, Optional, Tuple, Union
+
+if TYPE_CHECKING:
+    from ._typing import (
+        Array,
+        Device,
+        Dtype,
+        NestedSequence,
+        SupportsBufferProtocol,
+    )
+    from collections.abc import Sequence
+from ._dtypes import _all_dtypes
+
+import numpy as np
+
+
+def _check_valid_dtype(dtype):
+    # Note: Only spelling dtypes as the dtype objects is supported.
+
+    # We use this instead of "dtype in _all_dtypes" because the dtype objects
+    # define equality with the sorts of things we want to disallow.
+    for d in (None,) + _all_dtypes:
+        if dtype is d:
+            return
+    raise ValueError("dtype must be one of the supported dtypes")
+
+
+def asarray(
+    obj: Union[
+        Array,
+        bool,
+        int,
+        float,
+        NestedSequence[bool | int | float],
+        SupportsBufferProtocol,
+    ],
+    /,
+    *,
+    dtype: Optional[Dtype] = None,
+    device: Optional[Device] = None,
+    copy: Optional[Union[bool, np._CopyMode]] = None,
+) -> Array:
+    """
+    Array API compatible wrapper for :py:func:`np.asarray <numpy.asarray>`.
+
+    See its docstring for more information.
+    """
+    # _array_object imports in this file are inside the functions to avoid
+    # circular imports
+    from ._array_object import Array
+
+    _check_valid_dtype(dtype)
+    if device not in ["cpu", None]:
+        raise ValueError(f"Unsupported device {device!r}")
+    if copy in (False, np._CopyMode.IF_NEEDED):
+        # Note: copy=False is not yet implemented in np.asarray
+        raise NotImplementedError("copy=False is not yet implemented")
+    if isinstance(obj, Array):
+        if dtype is not None and obj.dtype != dtype:
+            copy = True
+        if copy in (True, np._CopyMode.ALWAYS):
+            return Array._new(np.array(obj._array, copy=True, dtype=dtype))
+        return obj
+    if dtype is None and isinstance(obj, int) and (obj > 2 ** 64 or obj < -(2 ** 63)):
+        # Give a better error message in this case. NumPy would convert this
+        # to an object array. TODO: This won't handle large integers in lists.
+        raise OverflowError("Integer out of bounds for array dtypes")
+    res = np.asarray(obj, dtype=dtype)
+    return Array._new(res)
+
+
+def arange(
+    start: Union[int, float],
+    /,
+    stop: Optional[Union[int, float]] = None,
+    step: Union[int, float] = 1,
+    *,
+    dtype: Optional[Dtype] = None,
+    device: Optional[Device] = None,
+) -> Array:
+    """
+    Array API compatible wrapper for :py:func:`np.arange <numpy.arange>`.
+
+    See its docstring for more information.
+    """
+    from ._array_object import Array
+
+    _check_valid_dtype(dtype)
+    if device not in ["cpu", None]:
+        raise ValueError(f"Unsupported device {device!r}")
+    return Array._new(np.arange(start, stop=stop, step=step, dtype=dtype))
+
+
+def empty(
+    shape: Union[int, Tuple[int, ...]],
+    *,
+    dtype: Optional[Dtype] = None,
+    device: Optional[Device] = None,
+) -> Array:
+    """
+    Array API compatible wrapper for :py:func:`np.empty <numpy.empty>`.
+
+    See its docstring for more information.
+    """
+    from ._array_object import Array
+
+    _check_valid_dtype(dtype)
+    if device not in ["cpu", None]:
+        raise ValueError(f"Unsupported device {device!r}")
+    return Array._new(np.empty(shape, dtype=dtype))
+
+
+def empty_like(
+    x: Array, /, *, dtype: Optional[Dtype] = None, device: Optional[Device] = None
+) -> Array:
+    """
+    Array API compatible wrapper for :py:func:`np.empty_like <numpy.empty_like>`.
+
+    See its docstring for more information.
+    """
+    from ._array_object import Array
+
+    _check_valid_dtype(dtype)
+    if device not in ["cpu", None]:
+        raise ValueError(f"Unsupported device {device!r}")
+    return Array._new(np.empty_like(x._array, dtype=dtype))
+
+
+def eye(
+    n_rows: int,
+    n_cols: Optional[int] = None,
+    /,
+    *,
+    k: int = 0,
+    dtype: Optional[Dtype] = None,
+    device: Optional[Device] = None,
+) -> Array:
+    """
+    Array API compatible wrapper for :py:func:`np.eye <numpy.eye>`.
+
+    See its docstring for more information.
+    """
+    from ._array_object import Array
+
+    _check_valid_dtype(dtype)
+    if device not in ["cpu", None]:
+        raise ValueError(f"Unsupported device {device!r}")
+    return Array._new(np.eye(n_rows, M=n_cols, k=k, dtype=dtype))
+
+
+def from_dlpack(x: object, /) -> Array:
+    from ._array_object import Array
+
+    return Array._new(np.from_dlpack(x))
+
+
+def full(
+    shape: Union[int, Tuple[int, ...]],
+    fill_value: Union[int, float],
+    *,
+    dtype: Optional[Dtype] = None,
+    device: Optional[Device] = None,
+) -> Array:
+    """
+    Array API compatible wrapper for :py:func:`np.full <numpy.full>`.
+
+    See its docstring for more information.
+    """
+    from ._array_object import Array
+
+    _check_valid_dtype(dtype)
+    if device not in ["cpu", None]:
+        raise ValueError(f"Unsupported device {device!r}")
+    if isinstance(fill_value, Array) and fill_value.ndim == 0:
+        fill_value = fill_value._array
+    res = np.full(shape, fill_value, dtype=dtype)
+    if res.dtype not in _all_dtypes:
+        # This will happen if the fill value is not something that NumPy
+        # coerces to one of the acceptable dtypes.
+        raise TypeError("Invalid input to full")
+    return Array._new(res)
+
+
+def full_like(
+    x: Array,
+    /,
+    fill_value: Union[int, float],
+    *,
+    dtype: Optional[Dtype] = None,
+    device: Optional[Device] = None,
+) -> Array:
+    """
+    Array API compatible wrapper for :py:func:`np.full_like <numpy.full_like>`.
+
+    See its docstring for more information.
+    """
+    from ._array_object import Array
+
+    _check_valid_dtype(dtype)
+    if device not in ["cpu", None]:
+        raise ValueError(f"Unsupported device {device!r}")
+    res = np.full_like(x._array, fill_value, dtype=dtype)
+    if res.dtype not in _all_dtypes:
+        # This will happen if the fill value is not something that NumPy
+        # coerces to one of the acceptable dtypes.
+        raise TypeError("Invalid input to full_like")
+    return Array._new(res)
+
+
+def linspace(
+    start: Union[int, float],
+    stop: Union[int, float],
+    /,
+    num: int,
+    *,
+    dtype: Optional[Dtype] = None,
+    device: Optional[Device] = None,
+    endpoint: bool = True,
+) -> Array:
+    """
+    Array API compatible wrapper for :py:func:`np.linspace <numpy.linspace>`.
+
+    See its docstring for more information.
+    """
+    from ._array_object import Array
+
+    _check_valid_dtype(dtype)
+    if device not in ["cpu", None]:
+        raise ValueError(f"Unsupported device {device!r}")
+    return Array._new(np.linspace(start, stop, num, dtype=dtype, endpoint=endpoint))
+
+
+def meshgrid(*arrays: Array, indexing: str = "xy") -> List[Array]:
+    """
+    Array API compatible wrapper for :py:func:`np.meshgrid <numpy.meshgrid>`.
+
+    See its docstring for more information.
+    """
+    from ._array_object import Array
+
+    # Note: unlike np.meshgrid, only inputs with all the same dtype are
+    # allowed
+
+    if len({a.dtype for a in arrays}) > 1:
+        raise ValueError("meshgrid inputs must all have the same dtype")
+
+    return [
+        Array._new(array)
+        for array in np.meshgrid(*[a._array for a in arrays], indexing=indexing)
+    ]
+
+
+def ones(
+    shape: Union[int, Tuple[int, ...]],
+    *,
+    dtype: Optional[Dtype] = None,
+    device: Optional[Device] = None,
+) -> Array:
+    """
+    Array API compatible wrapper for :py:func:`np.ones <numpy.ones>`.
+
+    See its docstring for more information.
+    """
+    from ._array_object import Array
+
+    _check_valid_dtype(dtype)
+    if device not in ["cpu", None]:
+        raise ValueError(f"Unsupported device {device!r}")
+    return Array._new(np.ones(shape, dtype=dtype))
+
+
+def ones_like(
+    x: Array, /, *, dtype: Optional[Dtype] = None, device: Optional[Device] = None
+) -> Array:
+    """
+    Array API compatible wrapper for :py:func:`np.ones_like <numpy.ones_like>`.
+
+    See its docstring for more information.
+    """
+    from ._array_object import Array
+
+    _check_valid_dtype(dtype)
+    if device not in ["cpu", None]:
+        raise ValueError(f"Unsupported device {device!r}")
+    return Array._new(np.ones_like(x._array, dtype=dtype))
+
+
+def tril(x: Array, /, *, k: int = 0) -> Array:
+    """
+    Array API compatible wrapper for :py:func:`np.tril <numpy.tril>`.
+
+    See its docstring for more information.
+    """
+    from ._array_object import Array
+
+    if x.ndim < 2:
+        # Note: Unlike np.tril, x must be at least 2-D
+        raise ValueError("x must be at least 2-dimensional for tril")
+    return Array._new(np.tril(x._array, k=k))
+
+
+def triu(x: Array, /, *, k: int = 0) -> Array:
+    """
+    Array API compatible wrapper for :py:func:`np.triu <numpy.triu>`.
+
+    See its docstring for more information.
+    """
+    from ._array_object import Array
+
+    if x.ndim < 2:
+        # Note: Unlike np.triu, x must be at least 2-D
+        raise ValueError("x must be at least 2-dimensional for triu")
+    return Array._new(np.triu(x._array, k=k))
+
+
+def zeros(
+    shape: Union[int, Tuple[int, ...]],
+    *,
+    dtype: Optional[Dtype] = None,
+    device: Optional[Device] = None,
+) -> Array:
+    """
+    Array API compatible wrapper for :py:func:`np.zeros <numpy.zeros>`.
+
+    See its docstring for more information.
+    """
+    from ._array_object import Array
+
+    _check_valid_dtype(dtype)
+    if device not in ["cpu", None]:
+        raise ValueError(f"Unsupported device {device!r}")
+    return Array._new(np.zeros(shape, dtype=dtype))
+
+
+def zeros_like(
+    x: Array, /, *, dtype: Optional[Dtype] = None, device: Optional[Device] = None
+) -> Array:
+    """
+    Array API compatible wrapper for :py:func:`np.zeros_like <numpy.zeros_like>`.
+
+    See its docstring for more information.
+    """
+    from ._array_object import Array
+
+    _check_valid_dtype(dtype)
+    if device not in ["cpu", None]:
+        raise ValueError(f"Unsupported device {device!r}")
+    return Array._new(np.zeros_like(x._array, dtype=dtype))

.CondaPkg/env/lib/python3.11/site-packages/numpy/array_api/_data_type_functions.py

@@ -0,0 +1,146 @@
+from __future__ import annotations
+
+from ._array_object import Array
+from ._dtypes import _all_dtypes, _result_type
+
+from dataclasses import dataclass
+from typing import TYPE_CHECKING, List, Tuple, Union
+
+if TYPE_CHECKING:
+    from ._typing import Dtype
+    from collections.abc import Sequence
+
+import numpy as np
+
+
+# Note: astype is a function, not an array method as in NumPy.
+def astype(x: Array, dtype: Dtype, /, *, copy: bool = True) -> Array:
+    if not copy and dtype == x.dtype:
+        return x
+    return Array._new(x._array.astype(dtype=dtype, copy=copy))
+
+
+def broadcast_arrays(*arrays: Array) -> List[Array]:
+    """
+    Array API compatible wrapper for :py:func:`np.broadcast_arrays <numpy.broadcast_arrays>`.
+
+    See its docstring for more information.
+    """
+    from ._array_object import Array
+
+    return [
+        Array._new(array) for array in np.broadcast_arrays(*[a._array for a in arrays])
+    ]
+
+
+def broadcast_to(x: Array, /, shape: Tuple[int, ...]) -> Array:
+    """
+    Array API compatible wrapper for :py:func:`np.broadcast_to <numpy.broadcast_to>`.
+
+    See its docstring for more information.
+    """
+    from ._array_object import Array
+
+    return Array._new(np.broadcast_to(x._array, shape))
+
+
+def can_cast(from_: Union[Dtype, Array], to: Dtype, /) -> bool:
+    """
+    Array API compatible wrapper for :py:func:`np.can_cast <numpy.can_cast>`.
+
+    See its docstring for more information.
+    """
+    if isinstance(from_, Array):
+        from_ = from_.dtype
+    elif from_ not in _all_dtypes:
+        raise TypeError(f"{from_=}, but should be an array_api array or dtype")
+    if to not in _all_dtypes:
+        raise TypeError(f"{to=}, but should be a dtype")
+    # Note: We avoid np.can_cast() as it has discrepancies with the array API,
+    # since NumPy allows cross-kind casting (e.g., NumPy allows bool -> int8).
+    # See https://github.com/numpy/numpy/issues/20870
+    try:
+        # We promote `from_` and `to` together. We then check if the promoted
+        # dtype is `to`, which indicates if `from_` can (up)cast to `to`.
+        dtype = _result_type(from_, to)
+        return to == dtype
+    except TypeError:
+        # _result_type() raises if the dtypes don't promote together
+        return False
+
+
+# These are internal objects for the return types of finfo and iinfo, since
+# the NumPy versions contain extra data that isn't part of the spec.
+@dataclass
+class finfo_object:
+    bits: int
+    # Note: The types of the float data here are float, whereas in NumPy they
+    # are scalars of the corresponding float dtype.
+    eps: float
+    max: float
+    min: float
+    smallest_normal: float
+
+
+@dataclass
+class iinfo_object:
+    bits: int
+    max: int
+    min: int
+
+
+def finfo(type: Union[Dtype, Array], /) -> finfo_object:
+    """
+    Array API compatible wrapper for :py:func:`np.finfo <numpy.finfo>`.
+
+    See its docstring for more information.
+    """
+    fi = np.finfo(type)
+    # Note: The types of the float data here are float, whereas in NumPy they
+    # are scalars of the corresponding float dtype.
+    return finfo_object(
+        fi.bits,
+        float(fi.eps),
+        float(fi.max),
+        float(fi.min),
+        float(fi.smallest_normal),
+    )
+
+
+def iinfo(type: Union[Dtype, Array], /) -> iinfo_object:
+    """
+    Array API compatible wrapper for :py:func:`np.iinfo <numpy.iinfo>`.
+
+    See its docstring for more information.
+    """
+    ii = np.iinfo(type)
+    return iinfo_object(ii.bits, ii.max, ii.min)
+
+
+def result_type(*arrays_and_dtypes: Union[Array, Dtype]) -> Dtype:
+    """
+    Array API compatible wrapper for :py:func:`np.result_type <numpy.result_type>`.
+
+    See its docstring for more information.
+    """
+    # Note: we use a custom implementation that gives only the type promotions
+    # required by the spec rather than using np.result_type. NumPy implements
+    # too many extra type promotions like int64 + uint64 -> float64, and does
+    # value-based casting on scalar arrays.
+    A = []
+    for a in arrays_and_dtypes:
+        if isinstance(a, Array):
+            a = a.dtype
+        elif isinstance(a, np.ndarray) or a not in _all_dtypes:
+            raise TypeError("result_type() inputs must be array_api arrays or dtypes")
+        A.append(a)
+
+    if len(A) == 0:
+        raise ValueError("at least one array or dtype is required")
+    elif len(A) == 1:
+        return A[0]
+    else:
+        t = A[0]
+        for t2 in A[1:]:
+            t = _result_type(t, t2)
+        return t

.CondaPkg/env/lib/python3.11/site-packages/numpy/array_api/_dtypes.py

@@ -0,0 +1,143 @@
+import numpy as np
+
+# Note: we use dtype objects instead of dtype classes. The spec does not
+# require any behavior on dtypes other than equality.
+int8 = np.dtype("int8")
+int16 = np.dtype("int16")
+int32 = np.dtype("int32")
+int64 = np.dtype("int64")
+uint8 = np.dtype("uint8")
+uint16 = np.dtype("uint16")
+uint32 = np.dtype("uint32")
+uint64 = np.dtype("uint64")
+float32 = np.dtype("float32")
+float64 = np.dtype("float64")
+# Note: This name is changed
+bool = np.dtype("bool")
+
+_all_dtypes = (
+    int8,
+    int16,
+    int32,
+    int64,
+    uint8,
+    uint16,
+    uint32,
+    uint64,
+    float32,
+    float64,
+    bool,
+)
+_boolean_dtypes = (bool,)
+_floating_dtypes = (float32, float64)
+_integer_dtypes = (int8, int16, int32, int64, uint8, uint16, uint32, uint64)
+_integer_or_boolean_dtypes = (
+    bool,
+    int8,
+    int16,
+    int32,
+    int64,
+    uint8,
+    uint16,
+    uint32,
+    uint64,
+)
+_numeric_dtypes = (
+    float32,
+    float64,
+    int8,
+    int16,
+    int32,
+    int64,
+    uint8,
+    uint16,
+    uint32,
+    uint64,
+)
+
+_dtype_categories = {
+    "all": _all_dtypes,
+    "numeric": _numeric_dtypes,
+    "integer": _integer_dtypes,
+    "integer or boolean": _integer_or_boolean_dtypes,
+    "boolean": _boolean_dtypes,
+    "floating-point": _floating_dtypes,
+}
+
+
+# Note: the spec defines a restricted type promotion table compared to NumPy.
+# In particular, cross-kind promotions like integer + float or boolean +
+# integer are not allowed, even for functions that accept both kinds.
+# Additionally, NumPy promotes signed integer + uint64 to float64, but this
+# promotion is not allowed here. To be clear, Python scalar int objects are
+# allowed to promote to floating-point dtypes, but only in array operators
+# (see Array._promote_scalar) method in _array_object.py.
+_promotion_table = {
+    (int8, int8): int8,
+    (int8, int16): int16,
+    (int8, int32): int32,
+    (int8, int64): int64,
+    (int16, int8): int16,
+    (int16, int16): int16,
+    (int16, int32): int32,
+    (int16, int64): int64,
+    (int32, int8): int32,
+    (int32, int16): int32,
+    (int32, int32): int32,
+    (int32, int64): int64,
+    (int64, int8): int64,
+    (int64, int16): int64,
+    (int64, int32): int64,
+    (int64, int64): int64,
+    (uint8, uint8): uint8,
+    (uint8, uint16): uint16,
+    (uint8, uint32): uint32,
+    (uint8, uint64): uint64,
+    (uint16, uint8): uint16,
+    (uint16, uint16): uint16,
+    (uint16, uint32): uint32,
+    (uint16, uint64): uint64,
+    (uint32, uint8): uint32,
+    (uint32, uint16): uint32,
+    (uint32, uint32): uint32,
+    (uint32, uint64): uint64,
+    (uint64, uint8): uint64,
+    (uint64, uint16): uint64,
+    (uint64, uint32): uint64,
+    (uint64, uint64): uint64,
+    (int8, uint8): int16,
+    (int8, uint16): int32,
+    (int8, uint32): int64,
+    (int16, uint8): int16,
+    (int16, uint16): int32,
+    (int16, uint32): int64,
+    (int32, uint8): int32,
+    (int32, uint16): int32,
+    (int32, uint32): int64,
+    (int64, uint8): int64,
+    (int64, uint16): int64,
+    (int64, uint32): int64,
+    (uint8, int8): int16,
+    (uint16, int8): int32,
+    (uint32, int8): int64,
+    (uint8, int16): int16,
+    (uint16, int16): int32,
+    (uint32, int16): int64,
+    (uint8, int32): int32,
+    (uint16, int32): int32,
+    (uint32, int32): int64,
+    (uint8, int64): int64,
+    (uint16, int64): int64,
+    (uint32, int64): int64,
+    (float32, float32): float32,
+    (float32, float64): float64,
+    (float64, float32): float64,
+    (float64, float64): float64,
+    (bool, bool): bool,
+}
+
+
+def _result_type(type1, type2):
+    if (type1, type2) in _promotion_table:
+        return _promotion_table[type1, type2]
+    raise TypeError(f"{type1} and {type2} cannot be type promoted together")

.CondaPkg/env/lib/python3.11/site-packages/numpy/array_api/_elementwise_functions.py

@@ -0,0 +1,729 @@
+from __future__ import annotations
+
+from ._dtypes import (
+    _boolean_dtypes,
+    _floating_dtypes,
+    _integer_dtypes,
+    _integer_or_boolean_dtypes,
+    _numeric_dtypes,
+    _result_type,
+)
+from ._array_object import Array
+
+import numpy as np
+
+
+def abs(x: Array, /) -> Array:
+    """
+    Array API compatible wrapper for :py:func:`np.abs <numpy.abs>`.
+
+    See its docstring for more information.
+    """
+    if x.dtype not in _numeric_dtypes:
+        raise TypeError("Only numeric dtypes are allowed in abs")
+    return Array._new(np.abs(x._array))
+
+
+# Note: the function name is different here
+def acos(x: Array, /) -> Array:
+    """
+    Array API compatible wrapper for :py:func:`np.arccos <numpy.arccos>`.
+
+    See its docstring for more information.
+    """
+    if x.dtype not in _floating_dtypes:
+        raise TypeError("Only floating-point dtypes are allowed in acos")
+    return Array._new(np.arccos(x._array))
+
+
+# Note: the function name is different here
+def acosh(x: Array, /) -> Array:
+    """
+    Array API compatible wrapper for :py:func:`np.arccosh <numpy.arccosh>`.
+
+    See its docstring for more information.
+    """
+    if x.dtype not in _floating_dtypes:
+        raise TypeError("Only floating-point dtypes are allowed in acosh")
+    return Array._new(np.arccosh(x._array))
+
+
+def add(x1: Array, x2: Array, /) -> Array:
+    """
+    Array API compatible wrapper for :py:func:`np.add <numpy.add>`.
+
+    See its docstring for more information.
+    """
+    if x1.dtype not in _numeric_dtypes or x2.dtype not in _numeric_dtypes:
+        raise TypeError("Only numeric dtypes are allowed in add")
+    # Call result type here just to raise on disallowed type combinations
+    _result_type(x1.dtype, x2.dtype)
+    x1, x2 = Array._normalize_two_args(x1, x2)
+    return Array._new(np.add(x1._array, x2._array))
+
+
+# Note: the function name is different here
+def asin(x: Array, /) -> Array:
+    """
+    Array API compatible wrapper for :py:func:`np.arcsin <numpy.arcsin>`.
+
+    See its docstring for more information.
+    """
+    if x.dtype not in _floating_dtypes:
+        raise TypeError("Only floating-point dtypes are allowed in asin")
+    return Array._new(np.arcsin(x._array))
+
+
+# Note: the function name is different here
+def asinh(x: Array, /) -> Array:
+    """
+    Array API compatible wrapper for :py:func:`np.arcsinh <numpy.arcsinh>`.
+
+    See its docstring for more information.
+    """
+    if x.dtype not in _floating_dtypes:
+        raise TypeError("Only floating-point dtypes are allowed in asinh")
+    return Array._new(np.arcsinh(x._array))
+
+
+# Note: the function name is different here
+def atan(x: Array, /) -> Array:
+    """
+    Array API compatible wrapper for :py:func:`np.arctan <numpy.arctan>`.
+
+    See its docstring for more information.
+    """
+    if x.dtype not in _floating_dtypes:
+        raise TypeError("Only floating-point dtypes are allowed in atan")
+    return Array._new(np.arctan(x._array))
+
+
+# Note: the function name is different here
+def atan2(x1: Array, x2: Array, /) -> Array:
+    """
+    Array API compatible wrapper for :py:func:`np.arctan2 <numpy.arctan2>`.
+
+    See its docstring for more information.
+    """
+    if x1.dtype not in _floating_dtypes or x2.dtype not in _floating_dtypes:
+        raise TypeError("Only floating-point dtypes are allowed in atan2")
+    # Call result type here just to raise on disallowed type combinations
+    _result_type(x1.dtype, x2.dtype)
+    x1, x2 = Array._normalize_two_args(x1, x2)
+    return Array._new(np.arctan2(x1._array, x2._array))
+
+
+# Note: the function name is different here
+def atanh(x: Array, /) -> Array:
+    """
+    Array API compatible wrapper for :py:func:`np.arctanh <numpy.arctanh>`.
+
+    See its docstring for more information.
+    """
+    if x.dtype not in _floating_dtypes:
+        raise TypeError("Only floating-point dtypes are allowed in atanh")
+    return Array._new(np.arctanh(x._array))
+
+
+def bitwise_and(x1: Array, x2: Array, /) -> Array:
+    """
+    Array API compatible wrapper for :py:func:`np.bitwise_and <numpy.bitwise_and>`.
+
+    See its docstring for more information.
+    """
+    if (
+        x1.dtype not in _integer_or_boolean_dtypes
+        or x2.dtype not in _integer_or_boolean_dtypes
+    ):
+        raise TypeError("Only integer or boolean dtypes are allowed in bitwise_and")
+    # Call result type here just to raise on disallowed type combinations
+    _result_type(x1.dtype, x2.dtype)
+    x1, x2 = Array._normalize_two_args(x1, x2)
+    return Array._new(np.bitwise_and(x1._array, x2._array))
+
+
+# Note: the function name is different here
+def bitwise_left_shift(x1: Array, x2: Array, /) -> Array:
+    """
+    Array API compatible wrapper for :py:func:`np.left_shift <numpy.left_shift>`.
+
+    See its docstring for more information.
+    """
+    if x1.dtype not in _integer_dtypes or x2.dtype not in _integer_dtypes:
+        raise TypeError("Only integer dtypes are allowed in bitwise_left_shift")
+    # Call result type here just to raise on disallowed type combinations
+    _result_type(x1.dtype, x2.dtype)
+    x1, x2 = Array._normalize_two_args(x1, x2)
+    # Note: bitwise_left_shift is only defined for x2 nonnegative.
+    if np.any(x2._array < 0):
+        raise ValueError("bitwise_left_shift(x1, x2) is only defined for x2 >= 0")
+    return Array._new(np.left_shift(x1._array, x2._array))
+
+
+# Note: the function name is different here
+def bitwise_invert(x: Array, /) -> Array:
+    """
+    Array API compatible wrapper for :py:func:`np.invert <numpy.invert>`.
+
+    See its docstring for more information.
+    """
+    if x.dtype not in _integer_or_boolean_dtypes:
+        raise TypeError("Only integer or boolean dtypes are allowed in bitwise_invert")
+    return Array._new(np.invert(x._array))
+
+
+def bitwise_or(x1: Array, x2: Array, /) -> Array:
+    """
+    Array API compatible wrapper for :py:func:`np.bitwise_or <numpy.bitwise_or>`.
+
+    See its docstring for more information.
+    """
+    if (
+        x1.dtype not in _integer_or_boolean_dtypes
+        or x2.dtype not in _integer_or_boolean_dtypes
+    ):
+        raise TypeError("Only integer or boolean dtypes are allowed in bitwise_or")
+    # Call result type here just to raise on disallowed type combinations
+    _result_type(x1.dtype, x2.dtype)
+    x1, x2 = Array._normalize_two_args(x1, x2)
+    return Array._new(np.bitwise_or(x1._array, x2._array))
+
+
+# Note: the function name is different here
+def bitwise_right_shift(x1: Array, x2: Array, /) -> Array:
+    """
+    Array API compatible wrapper for :py:func:`np.right_shift <numpy.right_shift>`.
+
+    See its docstring for more information.
+    """
+    if x1.dtype not in _integer_dtypes or x2.dtype not in _integer_dtypes:
+        raise TypeError("Only integer dtypes are allowed in bitwise_right_shift")
+    # Call result type here just to raise on disallowed type combinations
+    _result_type(x1.dtype, x2.dtype)
+    x1, x2 = Array._normalize_two_args(x1, x2)
+    # Note: bitwise_right_shift is only defined for x2 nonnegative.
+    if np.any(x2._array < 0):
+        raise ValueError("bitwise_right_shift(x1, x2) is only defined for x2 >= 0")
+    return Array._new(np.right_shift(x1._array, x2._array))
+
+
+def bitwise_xor(x1: Array, x2: Array, /) -> Array:
+    """
+    Array API compatible wrapper for :py:func:`np.bitwise_xor <numpy.bitwise_xor>`.
+
+    See its docstring for more information.
+    """
+    if (
+        x1.dtype not in _integer_or_boolean_dtypes
+        or x2.dtype not in _integer_or_boolean_dtypes
+    ):
+        raise TypeError("Only integer or boolean dtypes are allowed in bitwise_xor")
+    # Call result type here just to raise on disallowed type combinations
+    _result_type(x1.dtype, x2.dtype)
+    x1, x2 = Array._normalize_two_args(x1, x2)
+    return Array._new(np.bitwise_xor(x1._array, x2._array))
+
+
+def ceil(x: Array, /) -> Array:
+    """
+    Array API compatible wrapper for :py:func:`np.ceil <numpy.ceil>`.
+
+    See its docstring for more information.
+    """
+    if x.dtype not in _numeric_dtypes:
+        raise TypeError("Only numeric dtypes are allowed in ceil")
+    if x.dtype in _integer_dtypes:
+        # Note: The return dtype of ceil is the same as the input
+        return x
+    return Array._new(np.ceil(x._array))
+
+
+def cos(x: Array, /) -> Array:
+    """
+    Array API compatible wrapper for :py:func:`np.cos <numpy.cos>`.
+
+    See its docstring for more information.
+    """
+    if x.dtype not in _floating_dtypes:
+        raise TypeError("Only floating-point dtypes are allowed in cos")
+    return Array._new(np.cos(x._array))
+
+
+def cosh(x: Array, /) -> Array:
+    """
+    Array API compatible wrapper for :py:func:`np.cosh <numpy.cosh>`.
+
+    See its docstring for more information.
+    """
+    if x.dtype not in _floating_dtypes:
+        raise TypeError("Only floating-point dtypes are allowed in cosh")
+    return Array._new(np.cosh(x._array))
+
+
+def divide(x1: Array, x2: Array, /) -> Array:
+    """
+    Array API compatible wrapper for :py:func:`np.divide <numpy.divide>`.
+
+    See its docstring for more information.
+    """
+    if x1.dtype not in _floating_dtypes or x2.dtype not in _floating_dtypes:
+        raise TypeError("Only floating-point dtypes are allowed in divide")
+    # Call result type here just to raise on disallowed type combinations
+    _result_type(x1.dtype, x2.dtype)
+    x1, x2 = Array._normalize_two_args(x1, x2)
+    return Array._new(np.divide(x1._array, x2._array))
+
+
+def equal(x1: Array, x2: Array, /) -> Array:
+    """
+    Array API compatible wrapper for :py:func:`np.equal <numpy.equal>`.
+
+    See its docstring for more information.
+    """
+    # Call result type here just to raise on disallowed type combinations
+    _result_type(x1.dtype, x2.dtype)
+    x1, x2 = Array._normalize_two_args(x1, x2)
+    return Array._new(np.equal(x1._array, x2._array))
+
+
+def exp(x: Array, /) -> Array:
+    """
+    Array API compatible wrapper for :py:func:`np.exp <numpy.exp>`.
+
+    See its docstring for more information.
+    """
+    if x.dtype not in _floating_dtypes:
+        raise TypeError("Only floating-point dtypes are allowed in exp")
+    return Array._new(np.exp(x._array))
+
+
+def expm1(x: Array, /) -> Array:
+    """
+    Array API compatible wrapper for :py:func:`np.expm1 <numpy.expm1>`.
+
+    See its docstring for more information.
+    """
+    if x.dtype not in _floating_dtypes:
+        raise TypeError("Only floating-point dtypes are allowed in expm1")
+    return Array._new(np.expm1(x._array))
+
+
+def floor(x: Array, /) -> Array:
+    """
+    Array API compatible wrapper for :py:func:`np.floor <numpy.floor>`.
+
+    See its docstring for more information.
+    """
+    if x.dtype not in _numeric_dtypes:
+        raise TypeError("Only numeric dtypes are allowed in floor")
+    if x.dtype in _integer_dtypes:
+        # Note: The return dtype of floor is the same as the input
+        return x
+    return Array._new(np.floor(x._array))
+
+
+def floor_divide(x1: Array, x2: Array, /) -> Array:
+    """
+    Array API compatible wrapper for :py:func:`np.floor_divide <numpy.floor_divide>`.
+
+    See its docstring for more information.
+    """
+    if x1.dtype not in _numeric_dtypes or x2.dtype not in _numeric_dtypes:
+        raise TypeError("Only numeric dtypes are allowed in floor_divide")
+    # Call result type here just to raise on disallowed type combinations
+    _result_type(x1.dtype, x2.dtype)
+    x1, x2 = Array._normalize_two_args(x1, x2)
+    return Array._new(np.floor_divide(x1._array, x2._array))
+
+
+def greater(x1: Array, x2: Array, /) -> Array:
+    """
+    Array API compatible wrapper for :py:func:`np.greater <numpy.greater>`.
+
+    See its docstring for more information.
+    """
+    if x1.dtype not in _numeric_dtypes or x2.dtype not in _numeric_dtypes:
+        raise TypeError("Only numeric dtypes are allowed in greater")
+    # Call result type here just to raise on disallowed type combinations
+    _result_type(x1.dtype, x2.dtype)
+    x1, x2 = Array._normalize_two_args(x1, x2)
+    return Array._new(np.greater(x1._array, x2._array))
+
+
+def greater_equal(x1: Array, x2: Array, /) -> Array:
+    """
+    Array API compatible wrapper for :py:func:`np.greater_equal <numpy.greater_equal>`.
+
+    See its docstring for more information.
+    """
+    if x1.dtype not in _numeric_dtypes or x2.dtype not in _numeric_dtypes:
+        raise TypeError("Only numeric dtypes are allowed in greater_equal")
+    # Call result type here just to raise on disallowed type combinations
+    _result_type(x1.dtype, x2.dtype)
+    x1, x2 = Array._normalize_two_args(x1, x2)
+    return Array._new(np.greater_equal(x1._array, x2._array))
+
+
+def isfinite(x: Array, /) -> Array:
+    """
+    Array API compatible wrapper for :py:func:`np.isfinite <numpy.isfinite>`.
+
+    See its docstring for more information.
+    """
+    if x.dtype not in _numeric_dtypes:
+        raise TypeError("Only numeric dtypes are allowed in isfinite")
+    return Array._new(np.isfinite(x._array))
+
+
+def isinf(x: Array, /) -> Array:
+    """
+    Array API compatible wrapper for :py:func:`np.isinf <numpy.isinf>`.
+
+    See its docstring for more information.
+    """
+    if x.dtype not in _numeric_dtypes:
+        raise TypeError("Only numeric dtypes are allowed in isinf")
+    return Array._new(np.isinf(x._array))
+
+
+def isnan(x: Array, /) -> Array:
+    """
+    Array API compatible wrapper for :py:func:`np.isnan <numpy.isnan>`.
+
+    See its docstring for more information.
+    """
+    if x.dtype not in _numeric_dtypes:
+        raise TypeError("Only numeric dtypes are allowed in isnan")
+    return Array._new(np.isnan(x._array))
+
+
+def less(x1: Array, x2: Array, /) -> Array:
+    """
+    Array API compatible wrapper for :py:func:`np.less <numpy.less>`.
+
+    See its docstring for more information.
+    """
+    if x1.dtype not in _numeric_dtypes or x2.dtype not in _numeric_dtypes:
+        raise TypeError("Only numeric dtypes are allowed in less")
+    # Call result type here just to raise on disallowed type combinations
+    _result_type(x1.dtype, x2.dtype)
+    x1, x2 = Array._normalize_two_args(x1, x2)
+    return Array._new(np.less(x1._array, x2._array))
+
+
+def less_equal(x1: Array, x2: Array, /) -> Array:
+    """
+    Array API compatible wrapper for :py:func:`np.less_equal <numpy.less_equal>`.
+
+    See its docstring for more information.
+    """
+    if x1.dtype not in _numeric_dtypes or x2.dtype not in _numeric_dtypes:
+        raise TypeError("Only numeric dtypes are allowed in less_equal")
+    # Call result type here just to raise on disallowed type combinations
+    _result_type(x1.dtype, x2.dtype)
+    x1, x2 = Array._normalize_two_args(x1, x2)
+    return Array._new(np.less_equal(x1._array, x2._array))
+
+
+def log(x: Array, /) -> Array:
+    """
+    Array API compatible wrapper for :py:func:`np.log <numpy.log>`.
+
+    See its docstring for more information.
+    """
+    if x.dtype not in _floating_dtypes:
+        raise TypeError("Only floating-point dtypes are allowed in log")
+    return Array._new(np.log(x._array))
+
+
+def log1p(x: Array, /) -> Array:
+    """
+    Array API compatible wrapper for :py:func:`np.log1p <numpy.log1p>`.
+
+    See its docstring for more information.
+    """
+    if x.dtype not in _floating_dtypes:
+        raise TypeError("Only floating-point dtypes are allowed in log1p")
+    return Array._new(np.log1p(x._array))
+
+
+def log2(x: Array, /) -> Array:
+    """
+    Array API compatible wrapper for :py:func:`np.log2 <numpy.log2>`.
+
+    See its docstring for more information.
+    """
+    if x.dtype not in _floating_dtypes:
+        raise TypeError("Only floating-point dtypes are allowed in log2")
+    return Array._new(np.log2(x._array))
+
+
+def log10(x: Array, /) -> Array:
+    """
+    Array API compatible wrapper for :py:func:`np.log10 <numpy.log10>`.
+
+    See its docstring for more information.
+    """
+    if x.dtype not in _floating_dtypes:
+        raise TypeError("Only floating-point dtypes are allowed in log10")
+    return Array._new(np.log10(x._array))
+
+
+def logaddexp(x1: Array, x2: Array) -> Array:
+    """
+    Array API compatible wrapper for :py:func:`np.logaddexp <numpy.logaddexp>`.
+
+    See its docstring for more information.
+    """
+    if x1.dtype not in _floating_dtypes or x2.dtype not in _floating_dtypes:
+        raise TypeError("Only floating-point dtypes are allowed in logaddexp")
+    # Call result type here just to raise on disallowed type combinations
+    _result_type(x1.dtype, x2.dtype)
+    x1, x2 = Array._normalize_two_args(x1, x2)
+    return Array._new(np.logaddexp(x1._array, x2._array))
+
+
+def logical_and(x1: Array, x2: Array, /) -> Array:
+    """
+    Array API compatible wrapper for :py:func:`np.logical_and <numpy.logical_and>`.
+
+    See its docstring for more information.
+    """
+    if x1.dtype not in _boolean_dtypes or x2.dtype not in _boolean_dtypes:
+        raise TypeError("Only boolean dtypes are allowed in logical_and")
+    # Call result type here just to raise on disallowed type combinations
+    _result_type(x1.dtype, x2.dtype)
+    x1, x2 = Array._normalize_two_args(x1, x2)
+    return Array._new(np.logical_and(x1._array, x2._array))
+
+
+def logical_not(x: Array, /) -> Array:
+    """
+    Array API compatible wrapper for :py:func:`np.logical_not <numpy.logical_not>`.
+
+    See its docstring for more information.
+    """
+    if x.dtype not in _boolean_dtypes:
+        raise TypeError("Only boolean dtypes are allowed in logical_not")
+    return Array._new(np.logical_not(x._array))
+
+
+def logical_or(x1: Array, x2: Array, /) -> Array:
+    """
+    Array API compatible wrapper for :py:func:`np.logical_or <numpy.logical_or>`.
+
+    See its docstring for more information.
+    """
+    if x1.dtype not in _boolean_dtypes or x2.dtype not in _boolean_dtypes:
+        raise TypeError("Only boolean dtypes are allowed in logical_or")
+    # Call result type here just to raise on disallowed type combinations
+    _result_type(x1.dtype, x2.dtype)
+    x1, x2 = Array._normalize_two_args(x1, x2)
+    return Array._new(np.logical_or(x1._array, x2._array))
+
+
+def logical_xor(x1: Array, x2: Array, /) -> Array:
+    """
+    Array API compatible wrapper for :py:func:`np.logical_xor <numpy.logical_xor>`.
+
+    See its docstring for more information.
+    """
+    if x1.dtype not in _boolean_dtypes or x2.dtype not in _boolean_dtypes:
+        raise TypeError("Only boolean dtypes are allowed in logical_xor")
+    # Call result type here just to raise on disallowed type combinations
+    _result_type(x1.dtype, x2.dtype)
+    x1, x2 = Array._normalize_two_args(x1, x2)
+    return Array._new(np.logical_xor(x1._array, x2._array))
+
+
+def multiply(x1: Array, x2: Array, /) -> Array:
+    """
+    Array API compatible wrapper for :py:func:`np.multiply <numpy.multiply>`.
+
+    See its docstring for more information.
+    """
+    if x1.dtype not in _numeric_dtypes or x2.dtype not in _numeric_dtypes:
+        raise TypeError("Only numeric dtypes are allowed in multiply")
+    # Call result type here just to raise on disallowed type combinations
+    _result_type(x1.dtype, x2.dtype)
+    x1, x2 = Array._normalize_two_args(x1, x2)
+    return Array._new(np.multiply(x1._array, x2._array))
+
+
+def negative(x: Array, /) -> Array:
+    """
+    Array API compatible wrapper for :py:func:`np.negative <numpy.negative>`.
+
+    See its docstring for more information.
+    """
+    if x.dtype not in _numeric_dtypes:
+        raise TypeError("Only numeric dtypes are allowed in negative")
+    return Array._new(np.negative(x._array))
+
+
+def not_equal(x1: Array, x2: Array, /) -> Array:
+    """
+    Array API compatible wrapper for :py:func:`np.not_equal <numpy.not_equal>`.
+
+    See its docstring for more information.
+    """
+    # Call result type here just to raise on disallowed type combinations
+    _result_type(x1.dtype, x2.dtype)
+    x1, x2 = Array._normalize_two_args(x1, x2)
+    return Array._new(np.not_equal(x1._array, x2._array))
+
+
+def positive(x: Array, /) -> Array:
+    """
+    Array API compatible wrapper for :py:func:`np.positive <numpy.positive>`.
+
+    See its docstring for more information.
+    """
+    if x.dtype not in _numeric_dtypes:
+        raise TypeError("Only numeric dtypes are allowed in positive")
+    return Array._new(np.positive(x._array))
+
+
+# Note: the function name is different here
+def pow(x1: Array, x2: Array, /) -> Array:
+    """
+    Array API compatible wrapper for :py:func:`np.power <numpy.power>`.
+
+    See its docstring for more information.
+    """
+    if x1.dtype not in _numeric_dtypes or x2.dtype not in _numeric_dtypes:
+        raise TypeError("Only numeric dtypes are allowed in pow")
+    # Call result type here just to raise on disallowed type combinations
+    _result_type(x1.dtype, x2.dtype)
+    x1, x2 = Array._normalize_two_args(x1, x2)
+    return Array._new(np.power(x1._array, x2._array))
+
+
+def remainder(x1: Array, x2: Array, /) -> Array:
+    """
+    Array API compatible wrapper for :py:func:`np.remainder <numpy.remainder>`.
+
+    See its docstring for more information.
+    """
+    if x1.dtype not in _numeric_dtypes or x2.dtype not in _numeric_dtypes:
+        raise TypeError("Only numeric dtypes are allowed in remainder")
+    # Call result type here just to raise on disallowed type combinations
+    _result_type(x1.dtype, x2.dtype)
+    x1, x2 = Array._normalize_two_args(x1, x2)
+    return Array._new(np.remainder(x1._array, x2._array))
+
+
+def round(x: Array, /) -> Array:
+    """
+    Array API compatible wrapper for :py:func:`np.round <numpy.round>`.
+
+    See its docstring for more information.
+    """
+    if x.dtype not in _numeric_dtypes:
+        raise TypeError("Only numeric dtypes are allowed in round")
+    return Array._new(np.round(x._array))
+
+
+def sign(x: Array, /) -> Array:
+    """
+    Array API compatible wrapper for :py:func:`np.sign <numpy.sign>`.
+
+    See its docstring for more information.
+    """
+    if x.dtype not in _numeric_dtypes:
+        raise TypeError("Only numeric dtypes are allowed in sign")
+    return Array._new(np.sign(x._array))
+
+
+def sin(x: Array, /) -> Array:
+    """
+    Array API compatible wrapper for :py:func:`np.sin <numpy.sin>`.
+
+    See its docstring for more information.
+    """
+    if x.dtype not in _floating_dtypes:
+        raise TypeError("Only floating-point dtypes are allowed in sin")
+    return Array._new(np.sin(x._array))
+
+
+def sinh(x: Array, /) -> Array:
+    """
+    Array API compatible wrapper for :py:func:`np.sinh <numpy.sinh>`.
+
+    See its docstring for more information.
+    """
+    if x.dtype not in _floating_dtypes:
+        raise TypeError("Only floating-point dtypes are allowed in sinh")
+    return Array._new(np.sinh(x._array))
+
+
+def square(x: Array, /) -> Array:
+    """
+    Array API compatible wrapper for :py:func:`np.square <numpy.square>`.
+
+    See its docstring for more information.
+    """
+    if x.dtype not in _numeric_dtypes:
+        raise TypeError("Only numeric dtypes are allowed in square")
+    return Array._new(np.square(x._array))
+
+
+def sqrt(x: Array, /) -> Array:
+    """
+    Array API compatible wrapper for :py:func:`np.sqrt <numpy.sqrt>`.
+
+    See its docstring for more information.
+    """
+    if x.dtype not in _floating_dtypes:
+        raise TypeError("Only floating-point dtypes are allowed in sqrt")
+    return Array._new(np.sqrt(x._array))
+
+
+def subtract(x1: Array, x2: Array, /) -> Array:
+    """
+    Array API compatible wrapper for :py:func:`np.subtract <numpy.subtract>`.
+
+    See its docstring for more information.
+    """
+    if x1.dtype not in _numeric_dtypes or x2.dtype not in _numeric_dtypes:
+        raise TypeError("Only numeric dtypes are allowed in subtract")
+    # Call result type here just to raise on disallowed type combinations
+    _result_type(x1.dtype, x2.dtype)
+    x1, x2 = Array._normalize_two_args(x1, x2)
+    return Array._new(np.subtract(x1._array, x2._array))
+
+
+def tan(x: Array, /) -> Array:
+    """
+    Array API compatible wrapper for :py:func:`np.tan <numpy.tan>`.
+
+    See its docstring for more information.
+    """
+    if x.dtype not in _floating_dtypes:
+        raise TypeError("Only floating-point dtypes are allowed in tan")
+    return Array._new(np.tan(x._array))
+
+
+def tanh(x: Array, /) -> Array:
+    """
+    Array API compatible wrapper for :py:func:`np.tanh <numpy.tanh>`.
+
+    See its docstring for more information.
+    """
+    if x.dtype not in _floating_dtypes:
+        raise TypeError("Only floating-point dtypes are allowed in tanh")
+    return Array._new(np.tanh(x._array))
+
+
+def trunc(x: Array, /) -> Array:
+    """
+    Array API compatible wrapper for :py:func:`np.trunc <numpy.trunc>`.
+
+    See its docstring for more information.
+    """
+    if x.dtype not in _numeric_dtypes:
+        raise TypeError("Only numeric dtypes are allowed in trunc")
+    if x.dtype in _integer_dtypes:
+        # Note: The return dtype of trunc is the same as the input
+        return x
+    return Array._new(np.trunc(x._array))

.CondaPkg/env/lib/python3.11/site-packages/numpy/array_api/_manipulation_functions.py

@@ -0,0 +1,98 @@
+from __future__ import annotations
+
+from ._array_object import Array
+from ._data_type_functions import result_type
+
+from typing import List, Optional, Tuple, Union
+
+import numpy as np
+
+# Note: the function name is different here
+def concat(
+    arrays: Union[Tuple[Array, ...], List[Array]], /, *, axis: Optional[int] = 0
+) -> Array:
+    """
+    Array API compatible wrapper for :py:func:`np.concatenate <numpy.concatenate>`.
+
+    See its docstring for more information.
+    """
+    # Note: Casting rules here are different from the np.concatenate default
+    # (no for scalars with axis=None, no cross-kind casting)
+    dtype = result_type(*arrays)
+    arrays = tuple(a._array for a in arrays)
+    return Array._new(np.concatenate(arrays, axis=axis, dtype=dtype))
+
+
+def expand_dims(x: Array, /, *, axis: int) -> Array:
+    """
+    Array API compatible wrapper for :py:func:`np.expand_dims <numpy.expand_dims>`.
+
+    See its docstring for more information.
+    """
+    return Array._new(np.expand_dims(x._array, axis))
+
+
+def flip(x: Array, /, *, axis: Optional[Union[int, Tuple[int, ...]]] = None) -> Array:
+    """
+    Array API compatible wrapper for :py:func:`np.flip <numpy.flip>`.
+
+    See its docstring for more information.
+    """
+    return Array._new(np.flip(x._array, axis=axis))
+
+
+# Note: The function name is different here (see also matrix_transpose).
+# Unlike transpose(), the axes argument is required.
+def permute_dims(x: Array, /, axes: Tuple[int, ...]) -> Array:
+    """
+    Array API compatible wrapper for :py:func:`np.transpose <numpy.transpose>`.
+
+    See its docstring for more information.
+    """
+    return Array._new(np.transpose(x._array, axes))
+
+
+# Note: the optional argument is called 'shape', not 'newshape'
+def reshape(x: Array, /, shape: Tuple[int, ...]) -> Array:
+    """
+    Array API compatible wrapper for :py:func:`np.reshape <numpy.reshape>`.
+
+    See its docstring for more information.
+    """
+    return Array._new(np.reshape(x._array, shape))
+
+
+def roll(
+    x: Array,
+    /,
+    shift: Union[int, Tuple[int, ...]],
+    *,
+    axis: Optional[Union[int, Tuple[int, ...]]] = None,
+) -> Array:
+    """
+    Array API compatible wrapper for :py:func:`np.roll <numpy.roll>`.
+
+    See its docstring for more information.
+    """
+    return Array._new(np.roll(x._array, shift, axis=axis))
+
+
+def squeeze(x: Array, /, axis: Union[int, Tuple[int, ...]]) -> Array:
+    """
+    Array API compatible wrapper for :py:func:`np.squeeze <numpy.squeeze>`.
+
+    See its docstring for more information.
+    """
+    return Array._new(np.squeeze(x._array, axis=axis))
+
+
+def stack(arrays: Union[Tuple[Array, ...], List[Array]], /, *, axis: int = 0) -> Array:
+    """
+    Array API compatible wrapper for :py:func:`np.stack <numpy.stack>`.
+
+    See its docstring for more information.
+    """
+    # Call result type here just to raise on disallowed type combinations
+    result_type(*arrays)
+    arrays = tuple(a._array for a in arrays)
+    return Array._new(np.stack(arrays, axis=axis))

.CondaPkg/env/lib/python3.11/site-packages/numpy/array_api/_searching_functions.py

@@ -0,0 +1,47 @@
+from __future__ import annotations
+
+from ._array_object import Array
+from ._dtypes import _result_type
+
+from typing import Optional, Tuple
+
+import numpy as np
+
+
+def argmax(x: Array, /, *, axis: Optional[int] = None, keepdims: bool = False) -> Array:
+    """
+    Array API compatible wrapper for :py:func:`np.argmax <numpy.argmax>`.
+
+    See its docstring for more information.
+    """
+    return Array._new(np.asarray(np.argmax(x._array, axis=axis, keepdims=keepdims)))
+
+
+def argmin(x: Array, /, *, axis: Optional[int] = None, keepdims: bool = False) -> Array:
+    """
+    Array API compatible wrapper for :py:func:`np.argmin <numpy.argmin>`.
+
+    See its docstring for more information.
+    """
+    return Array._new(np.asarray(np.argmin(x._array, axis=axis, keepdims=keepdims)))
+
+
+def nonzero(x: Array, /) -> Tuple[Array, ...]:
+    """
+    Array API compatible wrapper for :py:func:`np.nonzero <numpy.nonzero>`.
+
+    See its docstring for more information.
+    """
+    return tuple(Array._new(i) for i in np.nonzero(x._array))
+
+
+def where(condition: Array, x1: Array, x2: Array, /) -> Array:
+    """
+    Array API compatible wrapper for :py:func:`np.where <numpy.where>`.
+
+    See its docstring for more information.
+    """
+    # Call result type here just to raise on disallowed type combinations
+    _result_type(x1.dtype, x2.dtype)
+    x1, x2 = Array._normalize_two_args(x1, x2)
+    return Array._new(np.where(condition._array, x1._array, x2._array))

.CondaPkg/env/lib/python3.11/site-packages/numpy/array_api/_set_functions.py

@@ -0,0 +1,106 @@
+from __future__ import annotations
+
+from ._array_object import Array
+
+from typing import NamedTuple
+
+import numpy as np
+
+# Note: np.unique() is split into four functions in the array API:
+# unique_all, unique_counts, unique_inverse, and unique_values (this is done
+# to remove polymorphic return types).
+
+# Note: The various unique() functions are supposed to return multiple NaNs.
+# This does not match the NumPy behavior, however, this is currently left as a
+# TODO in this implementation as this behavior may be reverted in np.unique().
+# See https://github.com/numpy/numpy/issues/20326.
+
+# Note: The functions here return a namedtuple (np.unique() returns a normal
+# tuple).
+
+class UniqueAllResult(NamedTuple):
+    values: Array
+    indices: Array
+    inverse_indices: Array
+    counts: Array
+
+
+class UniqueCountsResult(NamedTuple):
+    values: Array
+    counts: Array
+
+
+class UniqueInverseResult(NamedTuple):
+    values: Array
+    inverse_indices: Array
+
+
+def unique_all(x: Array, /) -> UniqueAllResult:
+    """
+    Array API compatible wrapper for :py:func:`np.unique <numpy.unique>`.
+
+    See its docstring for more information.
+    """
+    values, indices, inverse_indices, counts = np.unique(
+        x._array,
+        return_counts=True,
+        return_index=True,
+        return_inverse=True,
+        equal_nan=False,
+    )
+    # np.unique() flattens inverse indices, but they need to share x's shape
+    # See https://github.com/numpy/numpy/issues/20638
+    inverse_indices = inverse_indices.reshape(x.shape)
+    return UniqueAllResult(
+        Array._new(values),
+        Array._new(indices),
+        Array._new(inverse_indices),
+        Array._new(counts),
+    )
+
+
+def unique_counts(x: Array, /) -> UniqueCountsResult:
+    res = np.unique(
+        x._array,
+        return_counts=True,
+        return_index=False,
+        return_inverse=False,
+        equal_nan=False,
+    )
+
+    return UniqueCountsResult(*[Array._new(i) for i in res])
+
+
+def unique_inverse(x: Array, /) -> UniqueInverseResult:
+    """
+    Array API compatible wrapper for :py:func:`np.unique <numpy.unique>`.
+
+    See its docstring for more information.
+    """
+    values, inverse_indices = np.unique(
+        x._array,
+        return_counts=False,
+        return_index=False,
+        return_inverse=True,
+        equal_nan=False,
+    )
+    # np.unique() flattens inverse indices, but they need to share x's shape
+    # See https://github.com/numpy/numpy/issues/20638
+    inverse_indices = inverse_indices.reshape(x.shape)
+    return UniqueInverseResult(Array._new(values), Array._new(inverse_indices))
+
+
+def unique_values(x: Array, /) -> Array:
+    """
+    Array API compatible wrapper for :py:func:`np.unique <numpy.unique>`.
+
+    See its docstring for more information.
+    """
+    res = np.unique(
+        x._array,
+        return_counts=False,
+        return_index=False,
+        return_inverse=False,
+        equal_nan=False,
+    )
+    return Array._new(res)

.CondaPkg/env/lib/python3.11/site-packages/numpy/array_api/_sorting_functions.py

@@ -0,0 +1,49 @@
+from __future__ import annotations
+
+from ._array_object import Array
+
+import numpy as np
+
+
+# Note: the descending keyword argument is new in this function
+def argsort(
+    x: Array, /, *, axis: int = -1, descending: bool = False, stable: bool = True
+) -> Array:
+    """
+    Array API compatible wrapper for :py:func:`np.argsort <numpy.argsort>`.
+
+    See its docstring for more information.
+    """
+    # Note: this keyword argument is different, and the default is different.
+    kind = "stable" if stable else "quicksort"
+    if not descending:
+        res = np.argsort(x._array, axis=axis, kind=kind)
+    else:
+        # As NumPy has no native descending sort, we imitate it here. Note that
+        # simply flipping the results of np.argsort(x._array, ...) would not
+        # respect the relative order like it would in native descending sorts.
+        res = np.flip(
+            np.argsort(np.flip(x._array, axis=axis), axis=axis, kind=kind),
+            axis=axis,
+        )
+        # Rely on flip()/argsort() to validate axis
+        normalised_axis = axis if axis >= 0 else x.ndim + axis
+        max_i = x.shape[normalised_axis] - 1
+        res = max_i - res
+    return Array._new(res)
+
+# Note: the descending keyword argument is new in this function
+def sort(
+    x: Array, /, *, axis: int = -1, descending: bool = False, stable: bool = True
+) -> Array:
+    """
+    Array API compatible wrapper for :py:func:`np.sort <numpy.sort>`.
+
+    See its docstring for more information.
+    """
+    # Note: this keyword argument is different, and the default is different.
+    kind = "stable" if stable else "quicksort"
+    res = np.sort(x._array, axis=axis, kind=kind)
+    if descending:
+        res = np.flip(res, axis=axis)
+    return Array._new(res)

.CondaPkg/env/lib/python3.11/site-packages/numpy/array_api/_statistical_functions.py

@@ -0,0 +1,115 @@
+from __future__ import annotations
+
+from ._dtypes import (
+    _floating_dtypes,
+    _numeric_dtypes,
+)
+from ._array_object import Array
+from ._creation_functions import asarray
+from ._dtypes import float32, float64
+
+from typing import TYPE_CHECKING, Optional, Tuple, Union
+
+if TYPE_CHECKING:
+    from ._typing import Dtype
+
+import numpy as np
+
+
+def max(
+    x: Array,
+    /,
+    *,
+    axis: Optional[Union[int, Tuple[int, ...]]] = None,
+    keepdims: bool = False,
+) -> Array:
+    if x.dtype not in _numeric_dtypes:
+        raise TypeError("Only numeric dtypes are allowed in max")
+    return Array._new(np.max(x._array, axis=axis, keepdims=keepdims))
+
+
+def mean(
+    x: Array,
+    /,
+    *,
+    axis: Optional[Union[int, Tuple[int, ...]]] = None,
+    keepdims: bool = False,
+) -> Array:
+    if x.dtype not in _floating_dtypes:
+        raise TypeError("Only floating-point dtypes are allowed in mean")
+    return Array._new(np.mean(x._array, axis=axis, keepdims=keepdims))
+
+
+def min(
+    x: Array,
+    /,
+    *,
+    axis: Optional[Union[int, Tuple[int, ...]]] = None,
+    keepdims: bool = False,
+) -> Array:
+    if x.dtype not in _numeric_dtypes:
+        raise TypeError("Only numeric dtypes are allowed in min")
+    return Array._new(np.min(x._array, axis=axis, keepdims=keepdims))
+
+
+def prod(
+    x: Array,
+    /,
+    *,
+    axis: Optional[Union[int, Tuple[int, ...]]] = None,
+    dtype: Optional[Dtype] = None,
+    keepdims: bool = False,
+) -> Array:
+    if x.dtype not in _numeric_dtypes:
+        raise TypeError("Only numeric dtypes are allowed in prod")
+    # Note: sum() and prod() always upcast float32 to float64 for dtype=None
+    # We need to do so here before computing the product to avoid overflow
+    if dtype is None and x.dtype == float32:
+        dtype = float64
+    return Array._new(np.prod(x._array, dtype=dtype, axis=axis, keepdims=keepdims))
+
+
+def std(
+    x: Array,
+    /,
+    *,
+    axis: Optional[Union[int, Tuple[int, ...]]] = None,
+    correction: Union[int, float] = 0.0,
+    keepdims: bool = False,
+) -> Array:
+    # Note: the keyword argument correction is different here
+    if x.dtype not in _floating_dtypes:
+        raise TypeError("Only floating-point dtypes are allowed in std")
+    return Array._new(np.std(x._array, axis=axis, ddof=correction, keepdims=keepdims))
+
+
+def sum(
+    x: Array,
+    /,
+    *,
+    axis: Optional[Union[int, Tuple[int, ...]]] = None,
+    dtype: Optional[Dtype] = None,
+    keepdims: bool = False,
+) -> Array:
+    if x.dtype not in _numeric_dtypes:
+        raise TypeError("Only numeric dtypes are allowed in sum")
+    # Note: sum() and prod() always upcast integers to (u)int64 and float32 to
+    # float64 for dtype=None. `np.sum` does that too for integers, but not for
+    # float32, so we need to special-case it here
+    if dtype is None and x.dtype == float32:
+        dtype = float64
+    return Array._new(np.sum(x._array, axis=axis, dtype=dtype, keepdims=keepdims))
+
+
+def var(
+    x: Array,
+    /,
+    *,
+    axis: Optional[Union[int, Tuple[int, ...]]] = None,
+    correction: Union[int, float] = 0.0,
+    keepdims: bool = False,
+) -> Array:
+    # Note: the keyword argument correction is different here
+    if x.dtype not in _floating_dtypes:
+        raise TypeError("Only floating-point dtypes are allowed in var")
+    return Array._new(np.var(x._array, axis=axis, ddof=correction, keepdims=keepdims))

.CondaPkg/env/lib/python3.11/site-packages/numpy/array_api/_typing.py

@@ -0,0 +1,74 @@
+"""
+This file defines the types for type annotations.
+
+These names aren't part of the module namespace, but they are used in the
+annotations in the function signatures. The functions in the module are only
+valid for inputs that match the given type annotations.
+"""
+
+from __future__ import annotations
+
+__all__ = [
+    "Array",
+    "Device",
+    "Dtype",
+    "SupportsDLPack",
+    "SupportsBufferProtocol",
+    "PyCapsule",
+]
+
+import sys
+from typing import (
+    Any,
+    Literal,
+    Sequence,
+    Type,
+    Union,
+    TYPE_CHECKING,
+    TypeVar,
+    Protocol,
+)
+
+from ._array_object import Array
+from numpy import (
+    dtype,
+    int8,
+    int16,
+    int32,
+    int64,
+    uint8,
+    uint16,
+    uint32,
+    uint64,
+    float32,
+    float64,
+)
+
+_T_co = TypeVar("_T_co", covariant=True)
+
+class NestedSequence(Protocol[_T_co]):
+    def __getitem__(self, key: int, /) -> _T_co | NestedSequence[_T_co]: ...
+    def __len__(self, /) -> int: ...
+
+Device = Literal["cpu"]
+if TYPE_CHECKING or sys.version_info >= (3, 9):
+    Dtype = dtype[Union[
+        int8,
+        int16,
+        int32,
+        int64,
+        uint8,
+        uint16,
+        uint32,
+        uint64,
+        float32,
+        float64,
+    ]]
+else:
+    Dtype = dtype
+
+SupportsBufferProtocol = Any
+PyCapsule = Any
+
+class SupportsDLPack(Protocol):
+    def __dlpack__(self, /, *, stream: None = ...) -> PyCapsule: ...

.CondaPkg/env/lib/python3.11/site-packages/numpy/array_api/_utility_functions.py

@@ -0,0 +1,37 @@
+from __future__ import annotations
+
+from ._array_object import Array
+
+from typing import Optional, Tuple, Union
+
+import numpy as np
+
+
+def all(
+    x: Array,
+    /,
+    *,
+    axis: Optional[Union[int, Tuple[int, ...]]] = None,
+    keepdims: bool = False,
+) -> Array:
+    """
+    Array API compatible wrapper for :py:func:`np.all <numpy.all>`.
+
+    See its docstring for more information.
+    """
+    return Array._new(np.asarray(np.all(x._array, axis=axis, keepdims=keepdims)))
+
+
+def any(
+    x: Array,
+    /,
+    *,
+    axis: Optional[Union[int, Tuple[int, ...]]] = None,
+    keepdims: bool = False,
+) -> Array:
+    """
+    Array API compatible wrapper for :py:func:`np.any <numpy.any>`.
+
+    See its docstring for more information.
+    """
+    return Array._new(np.asarray(np.any(x._array, axis=axis, keepdims=keepdims)))

.CondaPkg/env/lib/python3.11/site-packages/numpy/array_api/linalg.py

@@ -0,0 +1,446 @@
+from __future__ import annotations
+
+from ._dtypes import _floating_dtypes, _numeric_dtypes
+from ._manipulation_functions import reshape
+from ._array_object import Array
+
+from ..core.numeric import normalize_axis_tuple
+
+from typing import TYPE_CHECKING
+if TYPE_CHECKING:
+    from ._typing import Literal, Optional, Sequence, Tuple, Union
+
+from typing import NamedTuple
+
+import numpy.linalg
+import numpy as np
+
+class EighResult(NamedTuple):
+    eigenvalues: Array
+    eigenvectors: Array
+
+class QRResult(NamedTuple):
+    Q: Array
+    R: Array
+
+class SlogdetResult(NamedTuple):
+    sign: Array
+    logabsdet: Array
+
+class SVDResult(NamedTuple):
+    U: Array
+    S: Array
+    Vh: Array
+
+# Note: the inclusion of the upper keyword is different from
+# np.linalg.cholesky, which does not have it.
+def cholesky(x: Array, /, *, upper: bool = False) -> Array:
+    """
+    Array API compatible wrapper for :py:func:`np.linalg.cholesky <numpy.linalg.cholesky>`.
+
+    See its docstring for more information.
+    """
+    # Note: the restriction to floating-point dtypes only is different from
+    # np.linalg.cholesky.
+    if x.dtype not in _floating_dtypes:
+        raise TypeError('Only floating-point dtypes are allowed in cholesky')
+    L = np.linalg.cholesky(x._array)
+    if upper:
+        return Array._new(L).mT
+    return Array._new(L)
+
+# Note: cross is the numpy top-level namespace, not np.linalg
+def cross(x1: Array, x2: Array, /, *, axis: int = -1) -> Array:
+    """
+    Array API compatible wrapper for :py:func:`np.cross <numpy.cross>`.
+
+    See its docstring for more information.
+    """
+    if x1.dtype not in _numeric_dtypes or x2.dtype not in _numeric_dtypes:
+        raise TypeError('Only numeric dtypes are allowed in cross')
+    # Note: this is different from np.cross(), which broadcasts
+    if x1.shape != x2.shape:
+        raise ValueError('x1 and x2 must have the same shape')
+    if x1.ndim == 0:
+        raise ValueError('cross() requires arrays of dimension at least 1')
+    # Note: this is different from np.cross(), which allows dimension 2
+    if x1.shape[axis] != 3:
+        raise ValueError('cross() dimension must equal 3')
+    return Array._new(np.cross(x1._array, x2._array, axis=axis))
+
+def det(x: Array, /) -> Array:
+    """
+    Array API compatible wrapper for :py:func:`np.linalg.det <numpy.linalg.det>`.
+
+    See its docstring for more information.
+    """
+    # Note: the restriction to floating-point dtypes only is different from
+    # np.linalg.det.
+    if x.dtype not in _floating_dtypes:
+        raise TypeError('Only floating-point dtypes are allowed in det')
+    return Array._new(np.linalg.det(x._array))
+
+# Note: diagonal is the numpy top-level namespace, not np.linalg
+def diagonal(x: Array, /, *, offset: int = 0) -> Array:
+    """
+    Array API compatible wrapper for :py:func:`np.diagonal <numpy.diagonal>`.
+
+    See its docstring for more information.
+    """
+    # Note: diagonal always operates on the last two axes, whereas np.diagonal
+    # operates on the first two axes by default
+    return Array._new(np.diagonal(x._array, offset=offset, axis1=-2, axis2=-1))
+
+
+def eigh(x: Array, /) -> EighResult:
+    """
+    Array API compatible wrapper for :py:func:`np.linalg.eigh <numpy.linalg.eigh>`.
+
+    See its docstring for more information.
+    """
+    # Note: the restriction to floating-point dtypes only is different from
+    # np.linalg.eigh.
+    if x.dtype not in _floating_dtypes:
+        raise TypeError('Only floating-point dtypes are allowed in eigh')
+
+    # Note: the return type here is a namedtuple, which is different from
+    # np.eigh, which only returns a tuple.
+    return EighResult(*map(Array._new, np.linalg.eigh(x._array)))
+
+
+def eigvalsh(x: Array, /) -> Array:
+    """
+    Array API compatible wrapper for :py:func:`np.linalg.eigvalsh <numpy.linalg.eigvalsh>`.
+
+    See its docstring for more information.
+    """
+    # Note: the restriction to floating-point dtypes only is different from
+    # np.linalg.eigvalsh.
+    if x.dtype not in _floating_dtypes:
+        raise TypeError('Only floating-point dtypes are allowed in eigvalsh')
+
+    return Array._new(np.linalg.eigvalsh(x._array))
+
+def inv(x: Array, /) -> Array:
+    """
+    Array API compatible wrapper for :py:func:`np.linalg.inv <numpy.linalg.inv>`.
+
+    See its docstring for more information.
+    """
+    # Note: the restriction to floating-point dtypes only is different from
+    # np.linalg.inv.
+    if x.dtype not in _floating_dtypes:
+        raise TypeError('Only floating-point dtypes are allowed in inv')
+
+    return Array._new(np.linalg.inv(x._array))
+
+
+# Note: matmul is the numpy top-level namespace but not in np.linalg
+def matmul(x1: Array, x2: Array, /) -> Array:
+    """
+    Array API compatible wrapper for :py:func:`np.matmul <numpy.matmul>`.
+
+    See its docstring for more information.
+    """
+    # Note: the restriction to numeric dtypes only is different from
+    # np.matmul.
+    if x1.dtype not in _numeric_dtypes or x2.dtype not in _numeric_dtypes:
+        raise TypeError('Only numeric dtypes are allowed in matmul')
+
+    return Array._new(np.matmul(x1._array, x2._array))
+
+
+# Note: the name here is different from norm(). The array API norm is split
+# into matrix_norm and vector_norm().
+
+# The type for ord should be Optional[Union[int, float, Literal[np.inf,
+# -np.inf, 'fro', 'nuc']]], but Literal does not support floating-point
+# literals.
+def matrix_norm(x: Array, /, *, keepdims: bool = False, ord: Optional[Union[int, float, Literal['fro', 'nuc']]] = 'fro') -> Array:
+    """
+    Array API compatible wrapper for :py:func:`np.linalg.norm <numpy.linalg.norm>`.
+
+    See its docstring for more information.
+    """
+    # Note: the restriction to floating-point dtypes only is different from
+    # np.linalg.norm.
+    if x.dtype not in _floating_dtypes:
+        raise TypeError('Only floating-point dtypes are allowed in matrix_norm')
+
+    return Array._new(np.linalg.norm(x._array, axis=(-2, -1), keepdims=keepdims, ord=ord))
+
+
+def matrix_power(x: Array, n: int, /) -> Array:
+    """
+    Array API compatible wrapper for :py:func:`np.matrix_power <numpy.matrix_power>`.
+
+    See its docstring for more information.
+    """
+    # Note: the restriction to floating-point dtypes only is different from
+    # np.linalg.matrix_power.
+    if x.dtype not in _floating_dtypes:
+        raise TypeError('Only floating-point dtypes are allowed for the first argument of matrix_power')
+
+    # np.matrix_power already checks if n is an integer
+    return Array._new(np.linalg.matrix_power(x._array, n))
+
+# Note: the keyword argument name rtol is different from np.linalg.matrix_rank
+def matrix_rank(x: Array, /, *, rtol: Optional[Union[float, Array]] = None) -> Array:
+    """
+    Array API compatible wrapper for :py:func:`np.matrix_rank <numpy.matrix_rank>`.
+
+    See its docstring for more information.
+    """
+    # Note: this is different from np.linalg.matrix_rank, which supports 1
+    # dimensional arrays.
+    if x.ndim < 2:
+        raise np.linalg.LinAlgError("1-dimensional array given. Array must be at least two-dimensional")
+    S = np.linalg.svd(x._array, compute_uv=False)
+    if rtol is None:
+        tol = S.max(axis=-1, keepdims=True) * max(x.shape[-2:]) * np.finfo(S.dtype).eps
+    else:
+        if isinstance(rtol, Array):
+            rtol = rtol._array
+        # Note: this is different from np.linalg.matrix_rank, which does not multiply
+        # the tolerance by the largest singular value.
+        tol = S.max(axis=-1, keepdims=True)*np.asarray(rtol)[..., np.newaxis]
+    return Array._new(np.count_nonzero(S > tol, axis=-1))
+
+
+# Note: this function is new in the array API spec. Unlike transpose, it only
+# transposes the last two axes.
+def matrix_transpose(x: Array, /) -> Array:
+    if x.ndim < 2:
+        raise ValueError("x must be at least 2-dimensional for matrix_transpose")
+    return Array._new(np.swapaxes(x._array, -1, -2))
+
+# Note: outer is the numpy top-level namespace, not np.linalg
+def outer(x1: Array, x2: Array, /) -> Array:
+    """
+    Array API compatible wrapper for :py:func:`np.outer <numpy.outer>`.
+
+    See its docstring for more information.
+    """
+    # Note: the restriction to numeric dtypes only is different from
+    # np.outer.
+    if x1.dtype not in _numeric_dtypes or x2.dtype not in _numeric_dtypes:
+        raise TypeError('Only numeric dtypes are allowed in outer')
+
+    # Note: the restriction to only 1-dim arrays is different from np.outer
+    if x1.ndim != 1 or x2.ndim != 1:
+        raise ValueError('The input arrays to outer must be 1-dimensional')
+
+    return Array._new(np.outer(x1._array, x2._array))
+
+# Note: the keyword argument name rtol is different from np.linalg.pinv
+def pinv(x: Array, /, *, rtol: Optional[Union[float, Array]] = None) -> Array:
+    """
+    Array API compatible wrapper for :py:func:`np.linalg.pinv <numpy.linalg.pinv>`.
+
+    See its docstring for more information.
+    """
+    # Note: the restriction to floating-point dtypes only is different from
+    # np.linalg.pinv.
+    if x.dtype not in _floating_dtypes:
+        raise TypeError('Only floating-point dtypes are allowed in pinv')
+
+    # Note: this is different from np.linalg.pinv, which does not multiply the
+    # default tolerance by max(M, N).
+    if rtol is None:
+        rtol = max(x.shape[-2:]) * np.finfo(x.dtype).eps
+    return Array._new(np.linalg.pinv(x._array, rcond=rtol))
+
+def qr(x: Array, /, *, mode: Literal['reduced', 'complete'] = 'reduced') -> QRResult:
+    """
+    Array API compatible wrapper for :py:func:`np.linalg.qr <numpy.linalg.qr>`.
+
+    See its docstring for more information.
+    """
+    # Note: the restriction to floating-point dtypes only is different from
+    # np.linalg.qr.
+    if x.dtype not in _floating_dtypes:
+        raise TypeError('Only floating-point dtypes are allowed in qr')
+
+    # Note: the return type here is a namedtuple, which is different from
+    # np.linalg.qr, which only returns a tuple.
+    return QRResult(*map(Array._new, np.linalg.qr(x._array, mode=mode)))
+
+def slogdet(x: Array, /) -> SlogdetResult:
+    """
+    Array API compatible wrapper for :py:func:`np.linalg.slogdet <numpy.linalg.slogdet>`.
+
+    See its docstring for more information.
+    """
+    # Note: the restriction to floating-point dtypes only is different from
+    # np.linalg.slogdet.
+    if x.dtype not in _floating_dtypes:
+        raise TypeError('Only floating-point dtypes are allowed in slogdet')
+
+    # Note: the return type here is a namedtuple, which is different from
+    # np.linalg.slogdet, which only returns a tuple.
+    return SlogdetResult(*map(Array._new, np.linalg.slogdet(x._array)))
+
+# Note: unlike np.linalg.solve, the array API solve() only accepts x2 as a
+# vector when it is exactly 1-dimensional. All other cases treat x2 as a stack
+# of matrices. The np.linalg.solve behavior of allowing stacks of both
+# matrices and vectors is ambiguous c.f.
+# https://github.com/numpy/numpy/issues/15349 and
+# https://github.com/data-apis/array-api/issues/285.
+
+# To workaround this, the below is the code from np.linalg.solve except
+# only calling solve1 in the exactly 1D case.
+def _solve(a, b):
+    from ..linalg.linalg import (_makearray, _assert_stacked_2d,
+                                 _assert_stacked_square, _commonType,
+                                 isComplexType, get_linalg_error_extobj,
+                                 _raise_linalgerror_singular)
+    from ..linalg import _umath_linalg
+
+    a, _ = _makearray(a)
+    _assert_stacked_2d(a)
+    _assert_stacked_square(a)
+    b, wrap = _makearray(b)
+    t, result_t = _commonType(a, b)
+
+    # This part is different from np.linalg.solve
+    if b.ndim == 1:
+        gufunc = _umath_linalg.solve1
+    else:
+        gufunc = _umath_linalg.solve
+
+    # This does nothing currently but is left in because it will be relevant
+    # when complex dtype support is added to the spec in 2022.
+    signature = 'DD->D' if isComplexType(t) else 'dd->d'
+    extobj = get_linalg_error_extobj(_raise_linalgerror_singular)
+    r = gufunc(a, b, signature=signature, extobj=extobj)
+
+    return wrap(r.astype(result_t, copy=False))
+
+def solve(x1: Array, x2: Array, /) -> Array:
+    """
+    Array API compatible wrapper for :py:func:`np.linalg.solve <numpy.linalg.solve>`.
+
+    See its docstring for more information.
+    """
+    # Note: the restriction to floating-point dtypes only is different from
+    # np.linalg.solve.
+    if x1.dtype not in _floating_dtypes or x2.dtype not in _floating_dtypes:
+        raise TypeError('Only floating-point dtypes are allowed in solve')
+
+    return Array._new(_solve(x1._array, x2._array))
+
+def svd(x: Array, /, *, full_matrices: bool = True) -> SVDResult:
+    """
+    Array API compatible wrapper for :py:func:`np.linalg.svd <numpy.linalg.svd>`.
+
+    See its docstring for more information.
+    """
+    # Note: the restriction to floating-point dtypes only is different from
+    # np.linalg.svd.
+    if x.dtype not in _floating_dtypes:
+        raise TypeError('Only floating-point dtypes are allowed in svd')
+
+    # Note: the return type here is a namedtuple, which is different from
+    # np.svd, which only returns a tuple.
+    return SVDResult(*map(Array._new, np.linalg.svd(x._array, full_matrices=full_matrices)))
+
+# Note: svdvals is not in NumPy (but it is in SciPy). It is equivalent to
+# np.linalg.svd(compute_uv=False).
+def svdvals(x: Array, /) -> Union[Array, Tuple[Array, ...]]:
+    if x.dtype not in _floating_dtypes:
+        raise TypeError('Only floating-point dtypes are allowed in svdvals')
+    return Array._new(np.linalg.svd(x._array, compute_uv=False))
+
+# Note: tensordot is the numpy top-level namespace but not in np.linalg
+
+# Note: axes must be a tuple, unlike np.tensordot where it can be an array or array-like.
+def tensordot(x1: Array, x2: Array, /, *, axes: Union[int, Tuple[Sequence[int], Sequence[int]]] = 2) -> Array:
+    # Note: the restriction to numeric dtypes only is different from
+    # np.tensordot.
+    if x1.dtype not in _numeric_dtypes or x2.dtype not in _numeric_dtypes:
+        raise TypeError('Only numeric dtypes are allowed in tensordot')
+
+    return Array._new(np.tensordot(x1._array, x2._array, axes=axes))
+
+# Note: trace is the numpy top-level namespace, not np.linalg
+def trace(x: Array, /, *, offset: int = 0) -> Array:
+    """
+    Array API compatible wrapper for :py:func:`np.trace <numpy.trace>`.
+
+    See its docstring for more information.
+    """
+    if x.dtype not in _numeric_dtypes:
+        raise TypeError('Only numeric dtypes are allowed in trace')
+    # Note: trace always operates on the last two axes, whereas np.trace
+    # operates on the first two axes by default
+    return Array._new(np.asarray(np.trace(x._array, offset=offset, axis1=-2, axis2=-1)))
+
+# Note: vecdot is not in NumPy
+def vecdot(x1: Array, x2: Array, /, *, axis: int = -1) -> Array:
+    if x1.dtype not in _numeric_dtypes or x2.dtype not in _numeric_dtypes:
+        raise TypeError('Only numeric dtypes are allowed in vecdot')
+    ndim = max(x1.ndim, x2.ndim)
+    x1_shape = (1,)*(ndim - x1.ndim) + tuple(x1.shape)
+    x2_shape = (1,)*(ndim - x2.ndim) + tuple(x2.shape)
+    if x1_shape[axis] != x2_shape[axis]:
+        raise ValueError("x1 and x2 must have the same size along the given axis")
+
+    x1_, x2_ = np.broadcast_arrays(x1._array, x2._array)
+    x1_ = np.moveaxis(x1_, axis, -1)
+    x2_ = np.moveaxis(x2_, axis, -1)
+
+    res = x1_[..., None, :] @ x2_[..., None]
+    return Array._new(res[..., 0, 0])
+
+
+# Note: the name here is different from norm(). The array API norm is split
+# into matrix_norm and vector_norm().
+
+# The type for ord should be Optional[Union[int, float, Literal[np.inf,
+# -np.inf]]] but Literal does not support floating-point literals.
+def vector_norm(x: Array, /, *, axis: Optional[Union[int, Tuple[int, ...]]] = None, keepdims: bool = False, ord: Optional[Union[int, float]] = 2) -> Array:
+    """
+    Array API compatible wrapper for :py:func:`np.linalg.norm <numpy.linalg.norm>`.
+
+    See its docstring for more information.
+    """
+    # Note: the restriction to floating-point dtypes only is different from
+    # np.linalg.norm.
+    if x.dtype not in _floating_dtypes:
+        raise TypeError('Only floating-point dtypes are allowed in norm')
+
+    # np.linalg.norm tries to do a matrix norm whenever axis is a 2-tuple or
+    # when axis=None and the input is 2-D, so to force a vector norm, we make
+    # it so the input is 1-D (for axis=None), or reshape so that norm is done
+    # on a single dimension.
+    a = x._array
+    if axis is None:
+        # Note: np.linalg.norm() doesn't handle 0-D arrays
+        a = a.ravel()
+        _axis = 0
+    elif isinstance(axis, tuple):
+        # Note: The axis argument supports any number of axes, whereas
+        # np.linalg.norm() only supports a single axis for vector norm.
+        normalized_axis = normalize_axis_tuple(axis, x.ndim)
+        rest = tuple(i for i in range(a.ndim) if i not in normalized_axis)
+        newshape = axis + rest
+        a = np.transpose(a, newshape).reshape(
+            (np.prod([a.shape[i] for i in axis], dtype=int), *[a.shape[i] for i in rest]))
+        _axis = 0
+    else:
+        _axis = axis
+
+    res = Array._new(np.linalg.norm(a, axis=_axis, ord=ord))
+
+    if keepdims:
+        # We can't reuse np.linalg.norm(keepdims) because of the reshape hacks
+        # above to avoid matrix norm logic.
+        shape = list(x.shape)
+        _axis = normalize_axis_tuple(range(x.ndim) if axis is None else axis, x.ndim)
+        for i in _axis:
+            shape[i] = 1
+        res = reshape(res, tuple(shape))
+
+    return res
+
+__all__ = ['cholesky', 'cross', 'det', 'diagonal', 'eigh', 'eigvalsh', 'inv', 'matmul', 'matrix_norm', 'matrix_power', 'matrix_rank', 'matrix_transpose', 'outer', 'pinv', 'qr', 'slogdet', 'solve', 'svd', 'svdvals', 'tensordot', 'trace', 'vecdot', 'vector_norm']

.CondaPkg/env/lib/python3.11/site-packages/numpy/array_api/setup.py

@@ -0,0 +1,12 @@
+def configuration(parent_package="", top_path=None):
+    from numpy.distutils.misc_util import Configuration
+
+    config = Configuration("array_api", parent_package, top_path)
+    config.add_subpackage("tests")
+    return config
+
+
+if __name__ == "__main__":
+    from numpy.distutils.core import setup
+
+    setup(configuration=configuration)

.CondaPkg/env/lib/python3.11/site-packages/numpy/array_api/tests/__init__.py

@@ -0,0 +1,7 @@
+"""
+Tests for the array API namespace.
+
+Note, full compliance with the array API can be tested with the official array API test
+suite https://github.com/data-apis/array-api-tests. This test suite primarily
+focuses on those things that are not tested by the official test suite.
+"""

.CondaPkg/env/lib/python3.11/site-packages/numpy/array_api/tests/test_array_object.py

@@ -0,0 +1,375 @@
+import operator
+
+from numpy.testing import assert_raises
+import numpy as np
+import pytest
+
+from .. import ones, asarray, reshape, result_type, all, equal
+from .._array_object import Array
+from .._dtypes import (
+    _all_dtypes,
+    _boolean_dtypes,
+    _floating_dtypes,
+    _integer_dtypes,
+    _integer_or_boolean_dtypes,
+    _numeric_dtypes,
+    int8,
+    int16,
+    int32,
+    int64,
+    uint64,
+    bool as bool_,
+)
+
+
+def test_validate_index():
+    # The indexing tests in the official array API test suite test that the
+    # array object correctly handles the subset of indices that are required
+    # by the spec. But the NumPy array API implementation specifically
+    # disallows any index not required by the spec, via Array._validate_index.
+    # This test focuses on testing that non-valid indices are correctly
+    # rejected. See
+    # https://data-apis.org/array-api/latest/API_specification/indexing.html
+    # and the docstring of Array._validate_index for the exact indexing
+    # behavior that should be allowed. This does not test indices that are
+    # already invalid in NumPy itself because Array will generally just pass
+    # such indices directly to the underlying np.ndarray.
+
+    a = ones((3, 4))
+
+    # Out of bounds slices are not allowed
+    assert_raises(IndexError, lambda: a[:4])
+    assert_raises(IndexError, lambda: a[:-4])
+    assert_raises(IndexError, lambda: a[:3:-1])
+    assert_raises(IndexError, lambda: a[:-5:-1])
+    assert_raises(IndexError, lambda: a[4:])
+    assert_raises(IndexError, lambda: a[-4:])
+    assert_raises(IndexError, lambda: a[4::-1])
+    assert_raises(IndexError, lambda: a[-4::-1])
+
+    assert_raises(IndexError, lambda: a[...,:5])
+    assert_raises(IndexError, lambda: a[...,:-5])
+    assert_raises(IndexError, lambda: a[...,:5:-1])
+    assert_raises(IndexError, lambda: a[...,:-6:-1])
+    assert_raises(IndexError, lambda: a[...,5:])
+    assert_raises(IndexError, lambda: a[...,-5:])
+    assert_raises(IndexError, lambda: a[...,5::-1])
+    assert_raises(IndexError, lambda: a[...,-5::-1])
+
+    # Boolean indices cannot be part of a larger tuple index
+    assert_raises(IndexError, lambda: a[a[:,0]==1,0])
+    assert_raises(IndexError, lambda: a[a[:,0]==1,...])
+    assert_raises(IndexError, lambda: a[..., a[0]==1])
+    assert_raises(IndexError, lambda: a[[True, True, True]])
+    assert_raises(IndexError, lambda: a[(True, True, True),])
+
+    # Integer array indices are not allowed (except for 0-D)
+    idx = asarray([[0, 1]])
+    assert_raises(IndexError, lambda: a[idx])
+    assert_raises(IndexError, lambda: a[idx,])
+    assert_raises(IndexError, lambda: a[[0, 1]])
+    assert_raises(IndexError, lambda: a[(0, 1), (0, 1)])
+    assert_raises(IndexError, lambda: a[[0, 1]])
+    assert_raises(IndexError, lambda: a[np.array([[0, 1]])])
+
+    # Multiaxis indices must contain exactly as many indices as dimensions
+    assert_raises(IndexError, lambda: a[()])
+    assert_raises(IndexError, lambda: a[0,])
+    assert_raises(IndexError, lambda: a[0])
+    assert_raises(IndexError, lambda: a[:])
+
+def test_operators():
+    # For every operator, we test that it works for the required type
+    # combinations and raises TypeError otherwise
+    binary_op_dtypes = {
+        "__add__": "numeric",
+        "__and__": "integer_or_boolean",
+        "__eq__": "all",
+        "__floordiv__": "numeric",
+        "__ge__": "numeric",
+        "__gt__": "numeric",
+        "__le__": "numeric",
+        "__lshift__": "integer",
+        "__lt__": "numeric",
+        "__mod__": "numeric",
+        "__mul__": "numeric",
+        "__ne__": "all",
+        "__or__": "integer_or_boolean",
+        "__pow__": "numeric",
+        "__rshift__": "integer",
+        "__sub__": "numeric",
+        "__truediv__": "floating",
+        "__xor__": "integer_or_boolean",
+    }
+
+    # Recompute each time because of in-place ops
+    def _array_vals():
+        for d in _integer_dtypes:
+            yield asarray(1, dtype=d)
+        for d in _boolean_dtypes:
+            yield asarray(False, dtype=d)
+        for d in _floating_dtypes:
+            yield asarray(1.0, dtype=d)
+
+    for op, dtypes in binary_op_dtypes.items():
+        ops = [op]
+        if op not in ["__eq__", "__ne__", "__le__", "__ge__", "__lt__", "__gt__"]:
+            rop = "__r" + op[2:]
+            iop = "__i" + op[2:]
+            ops += [rop, iop]
+        for s in [1, 1.0, False]:
+            for _op in ops:
+                for a in _array_vals():
+                    # Test array op scalar. From the spec, the following combinations
+                    # are supported:
+
+                    # - Python bool for a bool array dtype,
+                    # - a Python int within the bounds of the given dtype for integer array dtypes,
+                    # - a Python int or float for floating-point array dtypes
+
+                    # We do not do bounds checking for int scalars, but rather use the default
+                    # NumPy behavior for casting in that case.
+
+                    if ((dtypes == "all"
+                         or dtypes == "numeric" and a.dtype in _numeric_dtypes
+                         or dtypes == "integer" and a.dtype in _integer_dtypes
+                         or dtypes == "integer_or_boolean" and a.dtype in _integer_or_boolean_dtypes
+                         or dtypes == "boolean" and a.dtype in _boolean_dtypes
+                         or dtypes == "floating" and a.dtype in _floating_dtypes
+                        )
+                        # bool is a subtype of int, which is why we avoid
+                        # isinstance here.
+                        and (a.dtype in _boolean_dtypes and type(s) == bool
+                             or a.dtype in _integer_dtypes and type(s) == int
+                             or a.dtype in _floating_dtypes and type(s) in [float, int]
+                        )):
+                        # Only test for no error
+                        getattr(a, _op)(s)
+                    else:
+                        assert_raises(TypeError, lambda: getattr(a, _op)(s))
+
+                # Test array op array.
+                for _op in ops:
+                    for x in _array_vals():
+                        for y in _array_vals():
+                            # See the promotion table in NEP 47 or the array
+                            # API spec page on type promotion. Mixed kind
+                            # promotion is not defined.
+                            if (x.dtype == uint64 and y.dtype in [int8, int16, int32, int64]
+                                or y.dtype == uint64 and x.dtype in [int8, int16, int32, int64]
+                                or x.dtype in _integer_dtypes and y.dtype not in _integer_dtypes
+                                or y.dtype in _integer_dtypes and x.dtype not in _integer_dtypes
+                                or x.dtype in _boolean_dtypes and y.dtype not in _boolean_dtypes
+                                or y.dtype in _boolean_dtypes and x.dtype not in _boolean_dtypes
+                                or x.dtype in _floating_dtypes and y.dtype not in _floating_dtypes
+                                or y.dtype in _floating_dtypes and x.dtype not in _floating_dtypes
+                                ):
+                                assert_raises(TypeError, lambda: getattr(x, _op)(y))
+                            # Ensure in-place operators only promote to the same dtype as the left operand.
+                            elif (
+                                _op.startswith("__i")
+                                and result_type(x.dtype, y.dtype) != x.dtype
+                            ):
+                                assert_raises(TypeError, lambda: getattr(x, _op)(y))
+                            # Ensure only those dtypes that are required for every operator are allowed.
+                            elif (dtypes == "all" and (x.dtype in _boolean_dtypes and y.dtype in _boolean_dtypes
+                                                      or x.dtype in _numeric_dtypes and y.dtype in _numeric_dtypes)
+                                or (dtypes == "numeric" and x.dtype in _numeric_dtypes and y.dtype in _numeric_dtypes)
+                                or dtypes == "integer" and x.dtype in _integer_dtypes and y.dtype in _numeric_dtypes
+                                or dtypes == "integer_or_boolean" and (x.dtype in _integer_dtypes and y.dtype in _integer_dtypes
+                                                                       or x.dtype in _boolean_dtypes and y.dtype in _boolean_dtypes)
+                                or dtypes == "boolean" and x.dtype in _boolean_dtypes and y.dtype in _boolean_dtypes
+                                or dtypes == "floating" and x.dtype in _floating_dtypes and y.dtype in _floating_dtypes
+                            ):
+                                getattr(x, _op)(y)
+                            else:
+                                assert_raises(TypeError, lambda: getattr(x, _op)(y))
+
+    unary_op_dtypes = {
+        "__abs__": "numeric",
+        "__invert__": "integer_or_boolean",
+        "__neg__": "numeric",
+        "__pos__": "numeric",
+    }
+    for op, dtypes in unary_op_dtypes.items():
+        for a in _array_vals():
+            if (
+                dtypes == "numeric"
+                and a.dtype in _numeric_dtypes
+                or dtypes == "integer_or_boolean"
+                and a.dtype in _integer_or_boolean_dtypes
+            ):
+                # Only test for no error
+                getattr(a, op)()
+            else:
+                assert_raises(TypeError, lambda: getattr(a, op)())
+
+    # Finally, matmul() must be tested separately, because it works a bit
+    # different from the other operations.
+    def _matmul_array_vals():
+        for a in _array_vals():
+            yield a
+        for d in _all_dtypes:
+            yield ones((3, 4), dtype=d)
+            yield ones((4, 2), dtype=d)
+            yield ones((4, 4), dtype=d)
+
+    # Scalars always error
+    for _op in ["__matmul__", "__rmatmul__", "__imatmul__"]:
+        for s in [1, 1.0, False]:
+            for a in _matmul_array_vals():
+                if (type(s) in [float, int] and a.dtype in _floating_dtypes
+                    or type(s) == int and a.dtype in _integer_dtypes):
+                    # Type promotion is valid, but @ is not allowed on 0-D
+                    # inputs, so the error is a ValueError
+                    assert_raises(ValueError, lambda: getattr(a, _op)(s))
+                else:
+                    assert_raises(TypeError, lambda: getattr(a, _op)(s))
+
+    for x in _matmul_array_vals():
+        for y in _matmul_array_vals():
+            if (x.dtype == uint64 and y.dtype in [int8, int16, int32, int64]
+                or y.dtype == uint64 and x.dtype in [int8, int16, int32, int64]
+                or x.dtype in _integer_dtypes and y.dtype not in _integer_dtypes
+                or y.dtype in _integer_dtypes and x.dtype not in _integer_dtypes
+                or x.dtype in _floating_dtypes and y.dtype not in _floating_dtypes
+                or y.dtype in _floating_dtypes and x.dtype not in _floating_dtypes
+                or x.dtype in _boolean_dtypes
+                or y.dtype in _boolean_dtypes
+                ):
+                assert_raises(TypeError, lambda: x.__matmul__(y))
+                assert_raises(TypeError, lambda: y.__rmatmul__(x))
+                assert_raises(TypeError, lambda: x.__imatmul__(y))
+            elif x.shape == () or y.shape == () or x.shape[1] != y.shape[0]:
+                assert_raises(ValueError, lambda: x.__matmul__(y))
+                assert_raises(ValueError, lambda: y.__rmatmul__(x))
+                if result_type(x.dtype, y.dtype) != x.dtype:
+                    assert_raises(TypeError, lambda: x.__imatmul__(y))
+                else:
+                    assert_raises(ValueError, lambda: x.__imatmul__(y))
+            else:
+                x.__matmul__(y)
+                y.__rmatmul__(x)
+                if result_type(x.dtype, y.dtype) != x.dtype:
+                    assert_raises(TypeError, lambda: x.__imatmul__(y))
+                elif y.shape[0] != y.shape[1]:
+                    # This one fails because x @ y has a different shape from x
+                    assert_raises(ValueError, lambda: x.__imatmul__(y))
+                else:
+                    x.__imatmul__(y)
+
+
+def test_python_scalar_construtors():
+    b = asarray(False)
+    i = asarray(0)
+    f = asarray(0.0)
+
+    assert bool(b) == False
+    assert int(i) == 0
+    assert float(f) == 0.0
+    assert operator.index(i) == 0
+
+    # bool/int/float should only be allowed on 0-D arrays.
+    assert_raises(TypeError, lambda: bool(asarray([False])))
+    assert_raises(TypeError, lambda: int(asarray([0])))
+    assert_raises(TypeError, lambda: float(asarray([0.0])))
+    assert_raises(TypeError, lambda: operator.index(asarray([0])))
+
+    # bool/int/float should only be allowed on arrays of the corresponding
+    # dtype
+    assert_raises(ValueError, lambda: bool(i))
+    assert_raises(ValueError, lambda: bool(f))
+
+    assert_raises(ValueError, lambda: int(b))
+    assert_raises(ValueError, lambda: int(f))
+
+    assert_raises(ValueError, lambda: float(b))
+    assert_raises(ValueError, lambda: float(i))
+
+    assert_raises(TypeError, lambda: operator.index(b))
+    assert_raises(TypeError, lambda: operator.index(f))
+
+
+def test_device_property():
+    a = ones((3, 4))
+    assert a.device == 'cpu'
+
+    assert all(equal(a.to_device('cpu'), a))
+    assert_raises(ValueError, lambda: a.to_device('gpu'))
+
+    assert all(equal(asarray(a, device='cpu'), a))
+    assert_raises(ValueError, lambda: asarray(a, device='gpu'))
+
+def test_array_properties():
+    a = ones((1, 2, 3))
+    b = ones((2, 3))
+    assert_raises(ValueError, lambda: a.T)
+
+    assert isinstance(b.T, Array)
+    assert b.T.shape == (3, 2)
+
+    assert isinstance(a.mT, Array)
+    assert a.mT.shape == (1, 3, 2)
+    assert isinstance(b.mT, Array)
+    assert b.mT.shape == (3, 2)
+
+def test___array__():
+    a = ones((2, 3), dtype=int16)
+    assert np.asarray(a) is a._array
+    b = np.asarray(a, dtype=np.float64)
+    assert np.all(np.equal(b, np.ones((2, 3), dtype=np.float64)))
+    assert b.dtype == np.float64
+
+def test_allow_newaxis():
+    a = ones(5)
+    indexed_a = a[None, :]
+    assert indexed_a.shape == (1, 5)
+
+def test_disallow_flat_indexing_with_newaxis():
+    a = ones((3, 3, 3))
+    with pytest.raises(IndexError):
+        a[None, 0, 0]
+
+def test_disallow_mask_with_newaxis():
+    a = ones((3, 3, 3))
+    with pytest.raises(IndexError):
+        a[None, asarray(True)]
+
+@pytest.mark.parametrize("shape", [(), (5,), (3, 3, 3)])
+@pytest.mark.parametrize("index", ["string", False, True])
+def test_error_on_invalid_index(shape, index):
+    a = ones(shape)
+    with pytest.raises(IndexError):
+        a[index]
+
+def test_mask_0d_array_without_errors():
+    a = ones(())
+    a[asarray(True)]
+
+@pytest.mark.parametrize(
+    "i", [slice(5), slice(5, 0), asarray(True), asarray([0, 1])]
+)
+def test_error_on_invalid_index_with_ellipsis(i):
+    a = ones((3, 3, 3))
+    with pytest.raises(IndexError):
+        a[..., i]
+    with pytest.raises(IndexError):
+        a[i, ...]
+
+def test_array_keys_use_private_array():
+    """
+    Indexing operations convert array keys before indexing the internal array
+
+    Fails when array_api array keys are not converted into NumPy-proper arrays
+    in __getitem__(). This is achieved by passing array_api arrays with 0-sized
+    dimensions, which NumPy-proper treats erroneously - not sure why!
+
+    TODO: Find and use appropriate __setitem__() case.
+    """
+    a = ones((0, 0), dtype=bool_)
+    assert a[a].shape == (0,)
+
+    a = ones((0,), dtype=bool_)
+    key = ones((0, 0), dtype=bool_)
+    with pytest.raises(IndexError):
+        a[key]

.CondaPkg/env/lib/python3.11/site-packages/numpy/array_api/tests/test_creation_functions.py

@@ -0,0 +1,142 @@
+from numpy.testing import assert_raises
+import numpy as np
+
+from .. import all
+from .._creation_functions import (
+    asarray,
+    arange,
+    empty,
+    empty_like,
+    eye,
+    full,
+    full_like,
+    linspace,
+    meshgrid,
+    ones,
+    ones_like,
+    zeros,
+    zeros_like,
+)
+from .._dtypes import float32, float64
+from .._array_object import Array
+
+
+def test_asarray_errors():
+    # Test various protections against incorrect usage
+    assert_raises(TypeError, lambda: Array([1]))
+    assert_raises(TypeError, lambda: asarray(["a"]))
+    assert_raises(ValueError, lambda: asarray([1.0], dtype=np.float16))
+    assert_raises(OverflowError, lambda: asarray(2**100))
+    # Preferably this would be OverflowError
+    # assert_raises(OverflowError, lambda: asarray([2**100]))
+    assert_raises(TypeError, lambda: asarray([2**100]))
+    asarray([1], device="cpu")  # Doesn't error
+    assert_raises(ValueError, lambda: asarray([1], device="gpu"))
+
+    assert_raises(ValueError, lambda: asarray([1], dtype=int))
+    assert_raises(ValueError, lambda: asarray([1], dtype="i"))
+
+
+def test_asarray_copy():
+    a = asarray([1])
+    b = asarray(a, copy=True)
+    a[0] = 0
+    assert all(b[0] == 1)
+    assert all(a[0] == 0)
+    a = asarray([1])
+    b = asarray(a, copy=np._CopyMode.ALWAYS)
+    a[0] = 0
+    assert all(b[0] == 1)
+    assert all(a[0] == 0)
+    a = asarray([1])
+    b = asarray(a, copy=np._CopyMode.NEVER)
+    a[0] = 0
+    assert all(b[0] == 0)
+    assert_raises(NotImplementedError, lambda: asarray(a, copy=False))
+    assert_raises(NotImplementedError,
+                  lambda: asarray(a, copy=np._CopyMode.IF_NEEDED))
+
+
+def test_arange_errors():
+    arange(1, device="cpu")  # Doesn't error
+    assert_raises(ValueError, lambda: arange(1, device="gpu"))
+    assert_raises(ValueError, lambda: arange(1, dtype=int))
+    assert_raises(ValueError, lambda: arange(1, dtype="i"))
+
+
+def test_empty_errors():
+    empty((1,), device="cpu")  # Doesn't error
+    assert_raises(ValueError, lambda: empty((1,), device="gpu"))
+    assert_raises(ValueError, lambda: empty((1,), dtype=int))
+    assert_raises(ValueError, lambda: empty((1,), dtype="i"))
+
+
+def test_empty_like_errors():
+    empty_like(asarray(1), device="cpu")  # Doesn't error
+    assert_raises(ValueError, lambda: empty_like(asarray(1), device="gpu"))
+    assert_raises(ValueError, lambda: empty_like(asarray(1), dtype=int))
+    assert_raises(ValueError, lambda: empty_like(asarray(1), dtype="i"))
+
+
+def test_eye_errors():
+    eye(1, device="cpu")  # Doesn't error
+    assert_raises(ValueError, lambda: eye(1, device="gpu"))
+    assert_raises(ValueError, lambda: eye(1, dtype=int))
+    assert_raises(ValueError, lambda: eye(1, dtype="i"))
+
+
+def test_full_errors():
+    full((1,), 0, device="cpu")  # Doesn't error
+    assert_raises(ValueError, lambda: full((1,), 0, device="gpu"))
+    assert_raises(ValueError, lambda: full((1,), 0, dtype=int))
+    assert_raises(ValueError, lambda: full((1,), 0, dtype="i"))
+
+
+def test_full_like_errors():
+    full_like(asarray(1), 0, device="cpu")  # Doesn't error
+    assert_raises(ValueError, lambda: full_like(asarray(1), 0, device="gpu"))
+    assert_raises(ValueError, lambda: full_like(asarray(1), 0, dtype=int))
+    assert_raises(ValueError, lambda: full_like(asarray(1), 0, dtype="i"))
+
+
+def test_linspace_errors():
+    linspace(0, 1, 10, device="cpu")  # Doesn't error
+    assert_raises(ValueError, lambda: linspace(0, 1, 10, device="gpu"))
+    assert_raises(ValueError, lambda: linspace(0, 1, 10, dtype=float))
+    assert_raises(ValueError, lambda: linspace(0, 1, 10, dtype="f"))
+
+
+def test_ones_errors():
+    ones((1,), device="cpu")  # Doesn't error
+    assert_raises(ValueError, lambda: ones((1,), device="gpu"))
+    assert_raises(ValueError, lambda: ones((1,), dtype=int))
+    assert_raises(ValueError, lambda: ones((1,), dtype="i"))
+
+
+def test_ones_like_errors():
+    ones_like(asarray(1), device="cpu")  # Doesn't error
+    assert_raises(ValueError, lambda: ones_like(asarray(1), device="gpu"))
+    assert_raises(ValueError, lambda: ones_like(asarray(1), dtype=int))
+    assert_raises(ValueError, lambda: ones_like(asarray(1), dtype="i"))
+
+
+def test_zeros_errors():
+    zeros((1,), device="cpu")  # Doesn't error
+    assert_raises(ValueError, lambda: zeros((1,), device="gpu"))
+    assert_raises(ValueError, lambda: zeros((1,), dtype=int))
+    assert_raises(ValueError, lambda: zeros((1,), dtype="i"))
+
+
+def test_zeros_like_errors():
+    zeros_like(asarray(1), device="cpu")  # Doesn't error
+    assert_raises(ValueError, lambda: zeros_like(asarray(1), device="gpu"))
+    assert_raises(ValueError, lambda: zeros_like(asarray(1), dtype=int))
+    assert_raises(ValueError, lambda: zeros_like(asarray(1), dtype="i"))
+
+def test_meshgrid_dtype_errors():
+    # Doesn't raise
+    meshgrid()
+    meshgrid(asarray([1.], dtype=float32))
+    meshgrid(asarray([1.], dtype=float32), asarray([1.], dtype=float32))
+
+    assert_raises(ValueError, lambda: meshgrid(asarray([1.], dtype=float32), asarray([1.], dtype=float64)))