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Prism/LLaDA/LLaDA_Prism/.venv/lib/python3.12/site-packages/charset_normalizer/cli/__init__.py

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+from __future__ import annotations
+
+from .__main__ import cli_detect, query_yes_no
+
+__all__ = (
+    "cli_detect",
+    "query_yes_no",
+)

Prism/LLaDA/LLaDA_Prism/.venv/lib/python3.12/site-packages/charset_normalizer/cli/__main__.py

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+from __future__ import annotations
+
+import argparse
+import sys
+from json import dumps
+from os.path import abspath, basename, dirname, join, realpath
+from platform import python_version
+from unicodedata import unidata_version
+
+import charset_normalizer.md as md_module
+from charset_normalizer import from_fp
+from charset_normalizer.models import CliDetectionResult
+from charset_normalizer.version import __version__
+
+
+def query_yes_no(question: str, default: str = "yes") -> bool:
+    """Ask a yes/no question via input() and return their answer.
+
+    "question" is a string that is presented to the user.
+    "default" is the presumed answer if the user just hits <Enter>.
+        It must be "yes" (the default), "no" or None (meaning
+        an answer is required of the user).
+
+    The "answer" return value is True for "yes" or False for "no".
+
+    Credit goes to (c) https://stackoverflow.com/questions/3041986/apt-command-line-interface-like-yes-no-input
+    """
+    valid = {"yes": True, "y": True, "ye": True, "no": False, "n": False}
+    if default is None:
+        prompt = " [y/n] "
+    elif default == "yes":
+        prompt = " [Y/n] "
+    elif default == "no":
+        prompt = " [y/N] "
+    else:
+        raise ValueError("invalid default answer: '%s'" % default)
+
+    while True:
+        sys.stdout.write(question + prompt)
+        choice = input().lower()
+        if default is not None and choice == "":
+            return valid[default]
+        elif choice in valid:
+            return valid[choice]
+        else:
+            sys.stdout.write("Please respond with 'yes' or 'no' " "(or 'y' or 'n').\n")
+
+
+def cli_detect(argv: list[str] | None = None) -> int:
+    """
+    CLI assistant using ARGV and ArgumentParser
+    :param argv:
+    :return: 0 if everything is fine, anything else equal trouble
+    """
+    parser = argparse.ArgumentParser(
+        description="The Real First Universal Charset Detector. "
+        "Discover originating encoding used on text file. "
+        "Normalize text to unicode."
+    )
+
+    parser.add_argument(
+        "files", type=argparse.FileType("rb"), nargs="+", help="File(s) to be analysed"
+    )
+    parser.add_argument(
+        "-v",
+        "--verbose",
+        action="store_true",
+        default=False,
+        dest="verbose",
+        help="Display complementary information about file if any. "
+        "Stdout will contain logs about the detection process.",
+    )
+    parser.add_argument(
+        "-a",
+        "--with-alternative",
+        action="store_true",
+        default=False,
+        dest="alternatives",
+        help="Output complementary possibilities if any. Top-level JSON WILL be a list.",
+    )
+    parser.add_argument(
+        "-n",
+        "--normalize",
+        action="store_true",
+        default=False,
+        dest="normalize",
+        help="Permit to normalize input file. If not set, program does not write anything.",
+    )
+    parser.add_argument(
+        "-m",
+        "--minimal",
+        action="store_true",
+        default=False,
+        dest="minimal",
+        help="Only output the charset detected to STDOUT. Disabling JSON output.",
+    )
+    parser.add_argument(
+        "-r",
+        "--replace",
+        action="store_true",
+        default=False,
+        dest="replace",
+        help="Replace file when trying to normalize it instead of creating a new one.",
+    )
+    parser.add_argument(
+        "-f",
+        "--force",
+        action="store_true",
+        default=False,
+        dest="force",
+        help="Replace file without asking if you are sure, use this flag with caution.",
+    )
+    parser.add_argument(
+        "-i",
+        "--no-preemptive",
+        action="store_true",
+        default=False,
+        dest="no_preemptive",
+        help="Disable looking at a charset declaration to hint the detector.",
+    )
+    parser.add_argument(
+        "-t",
+        "--threshold",
+        action="store",
+        default=0.2,
+        type=float,
+        dest="threshold",
+        help="Define a custom maximum amount of noise allowed in decoded content. 0. <= noise <= 1.",
+    )
+    parser.add_argument(
+        "--version",
+        action="version",
+        version="Charset-Normalizer {} - Python {} - Unicode {} - SpeedUp {}".format(
+            __version__,
+            python_version(),
+            unidata_version,
+            "OFF" if md_module.__file__.lower().endswith(".py") else "ON",
+        ),
+        help="Show version information and exit.",
+    )
+
+    args = parser.parse_args(argv)
+
+    if args.replace is True and args.normalize is False:
+        if args.files:
+            for my_file in args.files:
+                my_file.close()
+        print("Use --replace in addition of --normalize only.", file=sys.stderr)
+        return 1
+
+    if args.force is True and args.replace is False:
+        if args.files:
+            for my_file in args.files:
+                my_file.close()
+        print("Use --force in addition of --replace only.", file=sys.stderr)
+        return 1
+
+    if args.threshold < 0.0 or args.threshold > 1.0:
+        if args.files:
+            for my_file in args.files:
+                my_file.close()
+        print("--threshold VALUE should be between 0. AND 1.", file=sys.stderr)
+        return 1
+
+    x_ = []
+
+    for my_file in args.files:
+        matches = from_fp(
+            my_file,
+            threshold=args.threshold,
+            explain=args.verbose,
+            preemptive_behaviour=args.no_preemptive is False,
+        )
+
+        best_guess = matches.best()
+
+        if best_guess is None:
+            print(
+                'Unable to identify originating encoding for "{}". {}'.format(
+                    my_file.name,
+                    (
+                        "Maybe try increasing maximum amount of chaos."
+                        if args.threshold < 1.0
+                        else ""
+                    ),
+                ),
+                file=sys.stderr,
+            )
+            x_.append(
+                CliDetectionResult(
+                    abspath(my_file.name),
+                    None,
+                    [],
+                    [],
+                    "Unknown",
+                    [],
+                    False,
+                    1.0,
+                    0.0,
+                    None,
+                    True,
+                )
+            )
+        else:
+            x_.append(
+                CliDetectionResult(
+                    abspath(my_file.name),
+                    best_guess.encoding,
+                    best_guess.encoding_aliases,
+                    [
+                        cp
+                        for cp in best_guess.could_be_from_charset
+                        if cp != best_guess.encoding
+                    ],
+                    best_guess.language,
+                    best_guess.alphabets,
+                    best_guess.bom,
+                    best_guess.percent_chaos,
+                    best_guess.percent_coherence,
+                    None,
+                    True,
+                )
+            )
+
+            if len(matches) > 1 and args.alternatives:
+                for el in matches:
+                    if el != best_guess:
+                        x_.append(
+                            CliDetectionResult(
+                                abspath(my_file.name),
+                                el.encoding,
+                                el.encoding_aliases,
+                                [
+                                    cp
+                                    for cp in el.could_be_from_charset
+                                    if cp != el.encoding
+                                ],
+                                el.language,
+                                el.alphabets,
+                                el.bom,
+                                el.percent_chaos,
+                                el.percent_coherence,
+                                None,
+                                False,
+                            )
+                        )
+
+            if args.normalize is True:
+                if best_guess.encoding.startswith("utf") is True:
+                    print(
+                        '"{}" file does not need to be normalized, as it already came from unicode.'.format(
+                            my_file.name
+                        ),
+                        file=sys.stderr,
+                    )
+                    if my_file.closed is False:
+                        my_file.close()
+                    continue
+
+                dir_path = dirname(realpath(my_file.name))
+                file_name = basename(realpath(my_file.name))
+
+                o_: list[str] = file_name.split(".")
+
+                if args.replace is False:
+                    o_.insert(-1, best_guess.encoding)
+                    if my_file.closed is False:
+                        my_file.close()
+                elif (
+                    args.force is False
+                    and query_yes_no(
+                        'Are you sure to normalize "{}" by replacing it ?'.format(
+                            my_file.name
+                        ),
+                        "no",
+                    )
+                    is False
+                ):
+                    if my_file.closed is False:
+                        my_file.close()
+                    continue
+
+                try:
+                    x_[0].unicode_path = join(dir_path, ".".join(o_))
+
+                    with open(x_[0].unicode_path, "wb") as fp:
+                        fp.write(best_guess.output())
+                except OSError as e:
+                    print(str(e), file=sys.stderr)
+                    if my_file.closed is False:
+                        my_file.close()
+                    return 2
+
+        if my_file.closed is False:
+            my_file.close()
+
+    if args.minimal is False:
+        print(
+            dumps(
+                [el.__dict__ for el in x_] if len(x_) > 1 else x_[0].__dict__,
+                ensure_ascii=True,
+                indent=4,
+            )
+        )
+    else:
+        for my_file in args.files:
+            print(
+                ", ".join(
+                    [
+                        el.encoding or "undefined"
+                        for el in x_
+                        if el.path == abspath(my_file.name)
+                    ]
+                )
+            )
+
+    return 0
+
+
+if __name__ == "__main__":
+    cli_detect()

Prism/LLaDA/LLaDA_Prism/.venv/lib/python3.12/site-packages/numpy/linalg/tests/test_deprecations.py

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+"""Test deprecation and future warnings.
+
+"""
+import numpy as np
+from numpy.testing import assert_warns
+
+
+def test_qr_mode_full_future_warning():
+    """Check mode='full' FutureWarning.
+
+    In numpy 1.8 the mode options 'full' and 'economic' in linalg.qr were
+    deprecated. The release date will probably be sometime in the summer
+    of 2013.
+
+    """
+    a = np.eye(2)
+    assert_warns(DeprecationWarning, np.linalg.qr, a, mode='full')
+    assert_warns(DeprecationWarning, np.linalg.qr, a, mode='f')
+    assert_warns(DeprecationWarning, np.linalg.qr, a, mode='economic')
+    assert_warns(DeprecationWarning, np.linalg.qr, a, mode='e')

Prism/LLaDA/LLaDA_Prism/.venv/lib/python3.12/site-packages/numpy/linalg/tests/test_linalg.py

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+""" Test functions for linalg module
+
+"""
+import os
+import sys
+import itertools
+import traceback
+import textwrap
+import subprocess
+import pytest
+
+import numpy as np
+from numpy import array, single, double, csingle, cdouble, dot, identity, matmul
+from numpy.core import swapaxes
+from numpy import multiply, atleast_2d, inf, asarray
+from numpy import linalg
+from numpy.linalg import matrix_power, norm, matrix_rank, multi_dot, LinAlgError
+from numpy.linalg.linalg import _multi_dot_matrix_chain_order
+from numpy.testing import (
+    assert_, assert_equal, assert_raises, assert_array_equal,
+    assert_almost_equal, assert_allclose, suppress_warnings,
+    assert_raises_regex, HAS_LAPACK64, IS_WASM
+    )
+try:
+    import numpy.linalg.lapack_lite
+except ImportError:
+    # May be broken when numpy was built without BLAS/LAPACK present
+    # If so, ensure we don't break the whole test suite - the `lapack_lite`
+    # submodule should be removed, it's only used in two tests in this file.
+    pass
+
+
+def consistent_subclass(out, in_):
+    # For ndarray subclass input, our output should have the same subclass
+    # (non-ndarray input gets converted to ndarray).
+    return type(out) is (type(in_) if isinstance(in_, np.ndarray)
+                         else np.ndarray)
+
+
+old_assert_almost_equal = assert_almost_equal
+
+
+def assert_almost_equal(a, b, single_decimal=6, double_decimal=12, **kw):
+    if asarray(a).dtype.type in (single, csingle):
+        decimal = single_decimal
+    else:
+        decimal = double_decimal
+    old_assert_almost_equal(a, b, decimal=decimal, **kw)
+
+
+def get_real_dtype(dtype):
+    return {single: single, double: double,
+            csingle: single, cdouble: double}[dtype]
+
+
+def get_complex_dtype(dtype):
+    return {single: csingle, double: cdouble,
+            csingle: csingle, cdouble: cdouble}[dtype]
+
+
+def get_rtol(dtype):
+    # Choose a safe rtol
+    if dtype in (single, csingle):
+        return 1e-5
+    else:
+        return 1e-11
+
+
+# used to categorize tests
+all_tags = {
+  'square', 'nonsquare', 'hermitian',  # mutually exclusive
+  'generalized', 'size-0', 'strided' # optional additions
+}
+
+
+class LinalgCase:
+    def __init__(self, name, a, b, tags=set()):
+        """
+        A bundle of arguments to be passed to a test case, with an identifying
+        name, the operands a and b, and a set of tags to filter the tests
+        """
+        assert_(isinstance(name, str))
+        self.name = name
+        self.a = a
+        self.b = b
+        self.tags = frozenset(tags)  # prevent shared tags
+
+    def check(self, do):
+        """
+        Run the function `do` on this test case, expanding arguments
+        """
+        do(self.a, self.b, tags=self.tags)
+
+    def __repr__(self):
+        return f'<LinalgCase: {self.name}>'
+
+
+def apply_tag(tag, cases):
+    """
+    Add the given tag (a string) to each of the cases (a list of LinalgCase
+    objects)
+    """
+    assert tag in all_tags, "Invalid tag"
+    for case in cases:
+        case.tags = case.tags | {tag}
+    return cases
+
+
+#
+# Base test cases
+#
+
+np.random.seed(1234)
+
+CASES = []
+
+# square test cases
+CASES += apply_tag('square', [
+    LinalgCase("single",
+               array([[1., 2.], [3., 4.]], dtype=single),
+               array([2., 1.], dtype=single)),
+    LinalgCase("double",
+               array([[1., 2.], [3., 4.]], dtype=double),
+               array([2., 1.], dtype=double)),
+    LinalgCase("double_2",
+               array([[1., 2.], [3., 4.]], dtype=double),
+               array([[2., 1., 4.], [3., 4., 6.]], dtype=double)),
+    LinalgCase("csingle",
+               array([[1. + 2j, 2 + 3j], [3 + 4j, 4 + 5j]], dtype=csingle),
+               array([2. + 1j, 1. + 2j], dtype=csingle)),
+    LinalgCase("cdouble",
+               array([[1. + 2j, 2 + 3j], [3 + 4j, 4 + 5j]], dtype=cdouble),
+               array([2. + 1j, 1. + 2j], dtype=cdouble)),
+    LinalgCase("cdouble_2",
+               array([[1. + 2j, 2 + 3j], [3 + 4j, 4 + 5j]], dtype=cdouble),
+               array([[2. + 1j, 1. + 2j, 1 + 3j], [1 - 2j, 1 - 3j, 1 - 6j]], dtype=cdouble)),
+    LinalgCase("0x0",
+               np.empty((0, 0), dtype=double),
+               np.empty((0,), dtype=double),
+               tags={'size-0'}),
+    LinalgCase("8x8",
+               np.random.rand(8, 8),
+               np.random.rand(8)),
+    LinalgCase("1x1",
+               np.random.rand(1, 1),
+               np.random.rand(1)),
+    LinalgCase("nonarray",
+               [[1, 2], [3, 4]],
+               [2, 1]),
+])
+
+# non-square test-cases
+CASES += apply_tag('nonsquare', [
+    LinalgCase("single_nsq_1",
+               array([[1., 2., 3.], [3., 4., 6.]], dtype=single),
+               array([2., 1.], dtype=single)),
+    LinalgCase("single_nsq_2",
+               array([[1., 2.], [3., 4.], [5., 6.]], dtype=single),
+               array([2., 1., 3.], dtype=single)),
+    LinalgCase("double_nsq_1",
+               array([[1., 2., 3.], [3., 4., 6.]], dtype=double),
+               array([2., 1.], dtype=double)),
+    LinalgCase("double_nsq_2",
+               array([[1., 2.], [3., 4.], [5., 6.]], dtype=double),
+               array([2., 1., 3.], dtype=double)),
+    LinalgCase("csingle_nsq_1",
+               array(
+                   [[1. + 1j, 2. + 2j, 3. - 3j], [3. - 5j, 4. + 9j, 6. + 2j]], dtype=csingle),
+               array([2. + 1j, 1. + 2j], dtype=csingle)),
+    LinalgCase("csingle_nsq_2",
+               array(
+                   [[1. + 1j, 2. + 2j], [3. - 3j, 4. - 9j], [5. - 4j, 6. + 8j]], dtype=csingle),
+               array([2. + 1j, 1. + 2j, 3. - 3j], dtype=csingle)),
+    LinalgCase("cdouble_nsq_1",
+               array(
+                   [[1. + 1j, 2. + 2j, 3. - 3j], [3. - 5j, 4. + 9j, 6. + 2j]], dtype=cdouble),
+               array([2. + 1j, 1. + 2j], dtype=cdouble)),
+    LinalgCase("cdouble_nsq_2",
+               array(
+                   [[1. + 1j, 2. + 2j], [3. - 3j, 4. - 9j], [5. - 4j, 6. + 8j]], dtype=cdouble),
+               array([2. + 1j, 1. + 2j, 3. - 3j], dtype=cdouble)),
+    LinalgCase("cdouble_nsq_1_2",
+               array(
+                   [[1. + 1j, 2. + 2j, 3. - 3j], [3. - 5j, 4. + 9j, 6. + 2j]], dtype=cdouble),
+               array([[2. + 1j, 1. + 2j], [1 - 1j, 2 - 2j]], dtype=cdouble)),
+    LinalgCase("cdouble_nsq_2_2",
+               array(
+                   [[1. + 1j, 2. + 2j], [3. - 3j, 4. - 9j], [5. - 4j, 6. + 8j]], dtype=cdouble),
+               array([[2. + 1j, 1. + 2j], [1 - 1j, 2 - 2j], [1 - 1j, 2 - 2j]], dtype=cdouble)),
+    LinalgCase("8x11",
+               np.random.rand(8, 11),
+               np.random.rand(8)),
+    LinalgCase("1x5",
+               np.random.rand(1, 5),
+               np.random.rand(1)),
+    LinalgCase("5x1",
+               np.random.rand(5, 1),
+               np.random.rand(5)),
+    LinalgCase("0x4",
+               np.random.rand(0, 4),
+               np.random.rand(0),
+               tags={'size-0'}),
+    LinalgCase("4x0",
+               np.random.rand(4, 0),
+               np.random.rand(4),
+               tags={'size-0'}),
+])
+
+# hermitian test-cases
+CASES += apply_tag('hermitian', [
+    LinalgCase("hsingle",
+               array([[1., 2.], [2., 1.]], dtype=single),
+               None),
+    LinalgCase("hdouble",
+               array([[1., 2.], [2., 1.]], dtype=double),
+               None),
+    LinalgCase("hcsingle",
+               array([[1., 2 + 3j], [2 - 3j, 1]], dtype=csingle),
+               None),
+    LinalgCase("hcdouble",
+               array([[1., 2 + 3j], [2 - 3j, 1]], dtype=cdouble),
+               None),
+    LinalgCase("hempty",
+               np.empty((0, 0), dtype=double),
+               None,
+               tags={'size-0'}),
+    LinalgCase("hnonarray",
+               [[1, 2], [2, 1]],
+               None),
+    LinalgCase("matrix_b_only",
+               array([[1., 2.], [2., 1.]]),
+               None),
+    LinalgCase("hmatrix_1x1",
+               np.random.rand(1, 1),
+               None),
+])
+
+
+#
+# Gufunc test cases
+#
+def _make_generalized_cases():
+    new_cases = []
+
+    for case in CASES:
+        if not isinstance(case.a, np.ndarray):
+            continue
+
+        a = np.array([case.a, 2 * case.a, 3 * case.a])
+        if case.b is None:
+            b = None
+        else:
+            b = np.array([case.b, 7 * case.b, 6 * case.b])
+        new_case = LinalgCase(case.name + "_tile3", a, b,
+                              tags=case.tags | {'generalized'})
+        new_cases.append(new_case)
+
+        a = np.array([case.a] * 2 * 3).reshape((3, 2) + case.a.shape)
+        if case.b is None:
+            b = None
+        else:
+            b = np.array([case.b] * 2 * 3).reshape((3, 2) + case.b.shape)
+        new_case = LinalgCase(case.name + "_tile213", a, b,
+                              tags=case.tags | {'generalized'})
+        new_cases.append(new_case)
+
+    return new_cases
+
+
+CASES += _make_generalized_cases()
+
+
+#
+# Generate stride combination variations of the above
+#
+def _stride_comb_iter(x):
+    """
+    Generate cartesian product of strides for all axes
+    """
+
+    if not isinstance(x, np.ndarray):
+        yield x, "nop"
+        return
+
+    stride_set = [(1,)] * x.ndim
+    stride_set[-1] = (1, 3, -4)
+    if x.ndim > 1:
+        stride_set[-2] = (1, 3, -4)
+    if x.ndim > 2:
+        stride_set[-3] = (1, -4)
+
+    for repeats in itertools.product(*tuple(stride_set)):
+        new_shape = [abs(a * b) for a, b in zip(x.shape, repeats)]
+        slices = tuple([slice(None, None, repeat) for repeat in repeats])
+
+        # new array with different strides, but same data
+        xi = np.empty(new_shape, dtype=x.dtype)
+        xi.view(np.uint32).fill(0xdeadbeef)
+        xi = xi[slices]
+        xi[...] = x
+        xi = xi.view(x.__class__)
+        assert_(np.all(xi == x))
+        yield xi, "stride_" + "_".join(["%+d" % j for j in repeats])
+
+        # generate also zero strides if possible
+        if x.ndim >= 1 and x.shape[-1] == 1:
+            s = list(x.strides)
+            s[-1] = 0
+            xi = np.lib.stride_tricks.as_strided(x, strides=s)
+            yield xi, "stride_xxx_0"
+        if x.ndim >= 2 and x.shape[-2] == 1:
+            s = list(x.strides)
+            s[-2] = 0
+            xi = np.lib.stride_tricks.as_strided(x, strides=s)
+            yield xi, "stride_xxx_0_x"
+        if x.ndim >= 2 and x.shape[:-2] == (1, 1):
+            s = list(x.strides)
+            s[-1] = 0
+            s[-2] = 0
+            xi = np.lib.stride_tricks.as_strided(x, strides=s)
+            yield xi, "stride_xxx_0_0"
+
+
+def _make_strided_cases():
+    new_cases = []
+    for case in CASES:
+        for a, a_label in _stride_comb_iter(case.a):
+            for b, b_label in _stride_comb_iter(case.b):
+                new_case = LinalgCase(case.name + "_" + a_label + "_" + b_label, a, b,
+                                      tags=case.tags | {'strided'})
+                new_cases.append(new_case)
+    return new_cases
+
+
+CASES += _make_strided_cases()
+
+
+#
+# Test different routines against the above cases
+#
+class LinalgTestCase:
+    TEST_CASES = CASES
+
+    def check_cases(self, require=set(), exclude=set()):
+        """
+        Run func on each of the cases with all of the tags in require, and none
+        of the tags in exclude
+        """
+        for case in self.TEST_CASES:
+            # filter by require and exclude
+            if case.tags & require != require:
+                continue
+            if case.tags & exclude:
+                continue
+
+            try:
+                case.check(self.do)
+            except Exception as e:
+                msg = f'In test case: {case!r}\n\n'
+                msg += traceback.format_exc()
+                raise AssertionError(msg) from e
+
+
+class LinalgSquareTestCase(LinalgTestCase):
+
+    def test_sq_cases(self):
+        self.check_cases(require={'square'},
+                         exclude={'generalized', 'size-0'})
+
+    def test_empty_sq_cases(self):
+        self.check_cases(require={'square', 'size-0'},
+                         exclude={'generalized'})
+
+
+class LinalgNonsquareTestCase(LinalgTestCase):
+
+    def test_nonsq_cases(self):
+        self.check_cases(require={'nonsquare'},
+                         exclude={'generalized', 'size-0'})
+
+    def test_empty_nonsq_cases(self):
+        self.check_cases(require={'nonsquare', 'size-0'},
+                         exclude={'generalized'})
+
+
+class HermitianTestCase(LinalgTestCase):
+
+    def test_herm_cases(self):
+        self.check_cases(require={'hermitian'},
+                         exclude={'generalized', 'size-0'})
+
+    def test_empty_herm_cases(self):
+        self.check_cases(require={'hermitian', 'size-0'},
+                         exclude={'generalized'})
+
+
+class LinalgGeneralizedSquareTestCase(LinalgTestCase):
+
+    @pytest.mark.slow
+    def test_generalized_sq_cases(self):
+        self.check_cases(require={'generalized', 'square'},
+                         exclude={'size-0'})
+
+    @pytest.mark.slow
+    def test_generalized_empty_sq_cases(self):
+        self.check_cases(require={'generalized', 'square', 'size-0'})
+
+
+class LinalgGeneralizedNonsquareTestCase(LinalgTestCase):
+
+    @pytest.mark.slow
+    def test_generalized_nonsq_cases(self):
+        self.check_cases(require={'generalized', 'nonsquare'},
+                         exclude={'size-0'})
+
+    @pytest.mark.slow
+    def test_generalized_empty_nonsq_cases(self):
+        self.check_cases(require={'generalized', 'nonsquare', 'size-0'})
+
+
+class HermitianGeneralizedTestCase(LinalgTestCase):
+
+    @pytest.mark.slow
+    def test_generalized_herm_cases(self):
+        self.check_cases(require={'generalized', 'hermitian'},
+                         exclude={'size-0'})
+
+    @pytest.mark.slow
+    def test_generalized_empty_herm_cases(self):
+        self.check_cases(require={'generalized', 'hermitian', 'size-0'},
+                         exclude={'none'})
+
+
+def dot_generalized(a, b):
+    a = asarray(a)
+    if a.ndim >= 3:
+        if a.ndim == b.ndim:
+            # matrix x matrix
+            new_shape = a.shape[:-1] + b.shape[-1:]
+        elif a.ndim == b.ndim + 1:
+            # matrix x vector
+            new_shape = a.shape[:-1]
+        else:
+            raise ValueError("Not implemented...")
+        r = np.empty(new_shape, dtype=np.common_type(a, b))
+        for c in itertools.product(*map(range, a.shape[:-2])):
+            r[c] = dot(a[c], b[c])
+        return r
+    else:
+        return dot(a, b)
+
+
+def identity_like_generalized(a):
+    a = asarray(a)
+    if a.ndim >= 3:
+        r = np.empty(a.shape, dtype=a.dtype)
+        r[...] = identity(a.shape[-2])
+        return r
+    else:
+        return identity(a.shape[0])
+
+
+class SolveCases(LinalgSquareTestCase, LinalgGeneralizedSquareTestCase):
+    # kept apart from TestSolve for use for testing with matrices.
+    def do(self, a, b, tags):
+        x = linalg.solve(a, b)
+        assert_almost_equal(b, dot_generalized(a, x))
+        assert_(consistent_subclass(x, b))
+
+
+class TestSolve(SolveCases):
+    @pytest.mark.parametrize('dtype', [single, double, csingle, cdouble])
+    def test_types(self, dtype):
+        x = np.array([[1, 0.5], [0.5, 1]], dtype=dtype)
+        assert_equal(linalg.solve(x, x).dtype, dtype)
+
+    def test_0_size(self):
+        class ArraySubclass(np.ndarray):
+            pass
+        # Test system of 0x0 matrices
+        a = np.arange(8).reshape(2, 2, 2)
+        b = np.arange(6).reshape(1, 2, 3).view(ArraySubclass)
+
+        expected = linalg.solve(a, b)[:, 0:0, :]
+        result = linalg.solve(a[:, 0:0, 0:0], b[:, 0:0, :])
+        assert_array_equal(result, expected)
+        assert_(isinstance(result, ArraySubclass))
+
+        # Test errors for non-square and only b's dimension being 0
+        assert_raises(linalg.LinAlgError, linalg.solve, a[:, 0:0, 0:1], b)
+        assert_raises(ValueError, linalg.solve, a, b[:, 0:0, :])
+
+        # Test broadcasting error
+        b = np.arange(6).reshape(1, 3, 2)  # broadcasting error
+        assert_raises(ValueError, linalg.solve, a, b)
+        assert_raises(ValueError, linalg.solve, a[0:0], b[0:0])
+
+        # Test zero "single equations" with 0x0 matrices.
+        b = np.arange(2).reshape(1, 2).view(ArraySubclass)
+        expected = linalg.solve(a, b)[:, 0:0]
+        result = linalg.solve(a[:, 0:0, 0:0], b[:, 0:0])
+        assert_array_equal(result, expected)
+        assert_(isinstance(result, ArraySubclass))
+
+        b = np.arange(3).reshape(1, 3)
+        assert_raises(ValueError, linalg.solve, a, b)
+        assert_raises(ValueError, linalg.solve, a[0:0], b[0:0])
+        assert_raises(ValueError, linalg.solve, a[:, 0:0, 0:0], b)
+
+    def test_0_size_k(self):
+        # test zero multiple equation (K=0) case.
+        class ArraySubclass(np.ndarray):
+            pass
+        a = np.arange(4).reshape(1, 2, 2)
+        b = np.arange(6).reshape(3, 2, 1).view(ArraySubclass)
+
+        expected = linalg.solve(a, b)[:, :, 0:0]
+        result = linalg.solve(a, b[:, :, 0:0])
+        assert_array_equal(result, expected)
+        assert_(isinstance(result, ArraySubclass))
+
+        # test both zero.
+        expected = linalg.solve(a, b)[:, 0:0, 0:0]
+        result = linalg.solve(a[:, 0:0, 0:0], b[:, 0:0, 0:0])
+        assert_array_equal(result, expected)
+        assert_(isinstance(result, ArraySubclass))
+
+
+class InvCases(LinalgSquareTestCase, LinalgGeneralizedSquareTestCase):
+
+    def do(self, a, b, tags):
+        a_inv = linalg.inv(a)
+        assert_almost_equal(dot_generalized(a, a_inv),
+                            identity_like_generalized(a))
+        assert_(consistent_subclass(a_inv, a))
+
+
+class TestInv(InvCases):
+    @pytest.mark.parametrize('dtype', [single, double, csingle, cdouble])
+    def test_types(self, dtype):
+        x = np.array([[1, 0.5], [0.5, 1]], dtype=dtype)
+        assert_equal(linalg.inv(x).dtype, dtype)
+
+    def test_0_size(self):
+        # Check that all kinds of 0-sized arrays work
+        class ArraySubclass(np.ndarray):
+            pass
+        a = np.zeros((0, 1, 1), dtype=np.int_).view(ArraySubclass)
+        res = linalg.inv(a)
+        assert_(res.dtype.type is np.float64)
+        assert_equal(a.shape, res.shape)
+        assert_(isinstance(res, ArraySubclass))
+
+        a = np.zeros((0, 0), dtype=np.complex64).view(ArraySubclass)
+        res = linalg.inv(a)
+        assert_(res.dtype.type is np.complex64)
+        assert_equal(a.shape, res.shape)
+        assert_(isinstance(res, ArraySubclass))
+
+
+class EigvalsCases(LinalgSquareTestCase, LinalgGeneralizedSquareTestCase):
+
+    def do(self, a, b, tags):
+        ev = linalg.eigvals(a)
+        evalues, evectors = linalg.eig(a)
+        assert_almost_equal(ev, evalues)
+
+
+class TestEigvals(EigvalsCases):
+    @pytest.mark.parametrize('dtype', [single, double, csingle, cdouble])
+    def test_types(self, dtype):
+        x = np.array([[1, 0.5], [0.5, 1]], dtype=dtype)
+        assert_equal(linalg.eigvals(x).dtype, dtype)
+        x = np.array([[1, 0.5], [-1, 1]], dtype=dtype)
+        assert_equal(linalg.eigvals(x).dtype, get_complex_dtype(dtype))
+
+    def test_0_size(self):
+        # Check that all kinds of 0-sized arrays work
+        class ArraySubclass(np.ndarray):
+            pass
+        a = np.zeros((0, 1, 1), dtype=np.int_).view(ArraySubclass)
+        res = linalg.eigvals(a)
+        assert_(res.dtype.type is np.float64)
+        assert_equal((0, 1), res.shape)
+        # This is just for documentation, it might make sense to change:
+        assert_(isinstance(res, np.ndarray))
+
+        a = np.zeros((0, 0), dtype=np.complex64).view(ArraySubclass)
+        res = linalg.eigvals(a)
+        assert_(res.dtype.type is np.complex64)
+        assert_equal((0,), res.shape)
+        # This is just for documentation, it might make sense to change:
+        assert_(isinstance(res, np.ndarray))
+
+
+class EigCases(LinalgSquareTestCase, LinalgGeneralizedSquareTestCase):
+
+    def do(self, a, b, tags):
+        res = linalg.eig(a)
+        eigenvalues, eigenvectors = res.eigenvalues, res.eigenvectors
+        assert_allclose(dot_generalized(a, eigenvectors),
+                        np.asarray(eigenvectors) * np.asarray(eigenvalues)[..., None, :],
+                        rtol=get_rtol(eigenvalues.dtype))
+        assert_(consistent_subclass(eigenvectors, a))
+
+
+class TestEig(EigCases):
+    @pytest.mark.parametrize('dtype', [single, double, csingle, cdouble])
+    def test_types(self, dtype):
+        x = np.array([[1, 0.5], [0.5, 1]], dtype=dtype)
+        w, v = np.linalg.eig(x)
+        assert_equal(w.dtype, dtype)
+        assert_equal(v.dtype, dtype)
+
+        x = np.array([[1, 0.5], [-1, 1]], dtype=dtype)
+        w, v = np.linalg.eig(x)
+        assert_equal(w.dtype, get_complex_dtype(dtype))
+        assert_equal(v.dtype, get_complex_dtype(dtype))
+
+    def test_0_size(self):
+        # Check that all kinds of 0-sized arrays work
+        class ArraySubclass(np.ndarray):
+            pass
+        a = np.zeros((0, 1, 1), dtype=np.int_).view(ArraySubclass)
+        res, res_v = linalg.eig(a)
+        assert_(res_v.dtype.type is np.float64)
+        assert_(res.dtype.type is np.float64)
+        assert_equal(a.shape, res_v.shape)
+        assert_equal((0, 1), res.shape)
+        # This is just for documentation, it might make sense to change:
+        assert_(isinstance(a, np.ndarray))
+
+        a = np.zeros((0, 0), dtype=np.complex64).view(ArraySubclass)
+        res, res_v = linalg.eig(a)
+        assert_(res_v.dtype.type is np.complex64)
+        assert_(res.dtype.type is np.complex64)
+        assert_equal(a.shape, res_v.shape)
+        assert_equal((0,), res.shape)
+        # This is just for documentation, it might make sense to change:
+        assert_(isinstance(a, np.ndarray))
+
+
+class SVDBaseTests:
+    hermitian = False
+
+    @pytest.mark.parametrize('dtype', [single, double, csingle, cdouble])
+    def test_types(self, dtype):
+        x = np.array([[1, 0.5], [0.5, 1]], dtype=dtype)
+        res = linalg.svd(x)
+        U, S, Vh = res.U, res.S, res.Vh
+        assert_equal(U.dtype, dtype)
+        assert_equal(S.dtype, get_real_dtype(dtype))
+        assert_equal(Vh.dtype, dtype)
+        s = linalg.svd(x, compute_uv=False, hermitian=self.hermitian)
+        assert_equal(s.dtype, get_real_dtype(dtype))
+
+
+class SVDCases(LinalgSquareTestCase, LinalgGeneralizedSquareTestCase):
+
+    def do(self, a, b, tags):
+        u, s, vt = linalg.svd(a, False)
+        assert_allclose(a, dot_generalized(np.asarray(u) * np.asarray(s)[..., None, :],
+                                           np.asarray(vt)),
+                        rtol=get_rtol(u.dtype))
+        assert_(consistent_subclass(u, a))
+        assert_(consistent_subclass(vt, a))
+
+
+class TestSVD(SVDCases, SVDBaseTests):
+    def test_empty_identity(self):
+        """ Empty input should put an identity matrix in u or vh """
+        x = np.empty((4, 0))
+        u, s, vh = linalg.svd(x, compute_uv=True, hermitian=self.hermitian)
+        assert_equal(u.shape, (4, 4))
+        assert_equal(vh.shape, (0, 0))
+        assert_equal(u, np.eye(4))
+
+        x = np.empty((0, 4))
+        u, s, vh = linalg.svd(x, compute_uv=True, hermitian=self.hermitian)
+        assert_equal(u.shape, (0, 0))
+        assert_equal(vh.shape, (4, 4))
+        assert_equal(vh, np.eye(4))
+
+
+class SVDHermitianCases(HermitianTestCase, HermitianGeneralizedTestCase):
+
+    def do(self, a, b, tags):
+        u, s, vt = linalg.svd(a, False, hermitian=True)
+        assert_allclose(a, dot_generalized(np.asarray(u) * np.asarray(s)[..., None, :],
+                                           np.asarray(vt)),
+                        rtol=get_rtol(u.dtype))
+        def hermitian(mat):
+            axes = list(range(mat.ndim))
+            axes[-1], axes[-2] = axes[-2], axes[-1]
+            return np.conj(np.transpose(mat, axes=axes))
+
+        assert_almost_equal(np.matmul(u, hermitian(u)), np.broadcast_to(np.eye(u.shape[-1]), u.shape))
+        assert_almost_equal(np.matmul(vt, hermitian(vt)), np.broadcast_to(np.eye(vt.shape[-1]), vt.shape))
+        assert_equal(np.sort(s)[..., ::-1], s)
+        assert_(consistent_subclass(u, a))
+        assert_(consistent_subclass(vt, a))
+
+
+class TestSVDHermitian(SVDHermitianCases, SVDBaseTests):
+    hermitian = True
+
+
+class CondCases(LinalgSquareTestCase, LinalgGeneralizedSquareTestCase):
+    # cond(x, p) for p in (None, 2, -2)
+
+    def do(self, a, b, tags):
+        c = asarray(a)  # a might be a matrix
+        if 'size-0' in tags:
+            assert_raises(LinAlgError, linalg.cond, c)
+            return
+
+        # +-2 norms
+        s = linalg.svd(c, compute_uv=False)
+        assert_almost_equal(
+            linalg.cond(a), s[..., 0] / s[..., -1],
+            single_decimal=5, double_decimal=11)
+        assert_almost_equal(
+            linalg.cond(a, 2), s[..., 0] / s[..., -1],
+            single_decimal=5, double_decimal=11)
+        assert_almost_equal(
+            linalg.cond(a, -2), s[..., -1] / s[..., 0],
+            single_decimal=5, double_decimal=11)
+
+        # Other norms
+        cinv = np.linalg.inv(c)
+        assert_almost_equal(
+            linalg.cond(a, 1),
+            abs(c).sum(-2).max(-1) * abs(cinv).sum(-2).max(-1),
+            single_decimal=5, double_decimal=11)
+        assert_almost_equal(
+            linalg.cond(a, -1),
+            abs(c).sum(-2).min(-1) * abs(cinv).sum(-2).min(-1),
+            single_decimal=5, double_decimal=11)
+        assert_almost_equal(
+            linalg.cond(a, np.inf),
+            abs(c).sum(-1).max(-1) * abs(cinv).sum(-1).max(-1),
+            single_decimal=5, double_decimal=11)
+        assert_almost_equal(
+            linalg.cond(a, -np.inf),
+            abs(c).sum(-1).min(-1) * abs(cinv).sum(-1).min(-1),
+            single_decimal=5, double_decimal=11)
+        assert_almost_equal(
+            linalg.cond(a, 'fro'),
+            np.sqrt((abs(c)**2).sum(-1).sum(-1)
+                    * (abs(cinv)**2).sum(-1).sum(-1)),
+            single_decimal=5, double_decimal=11)
+
+
+class TestCond(CondCases):
+    def test_basic_nonsvd(self):
+        # Smoketest the non-svd norms
+        A = array([[1., 0, 1], [0, -2., 0], [0, 0, 3.]])
+        assert_almost_equal(linalg.cond(A, inf), 4)
+        assert_almost_equal(linalg.cond(A, -inf), 2/3)
+        assert_almost_equal(linalg.cond(A, 1), 4)
+        assert_almost_equal(linalg.cond(A, -1), 0.5)
+        assert_almost_equal(linalg.cond(A, 'fro'), np.sqrt(265 / 12))
+
+    def test_singular(self):
+        # Singular matrices have infinite condition number for
+        # positive norms, and negative norms shouldn't raise
+        # exceptions
+        As = [np.zeros((2, 2)), np.ones((2, 2))]
+        p_pos = [None, 1, 2, 'fro']
+        p_neg = [-1, -2]
+        for A, p in itertools.product(As, p_pos):
+            # Inversion may not hit exact infinity, so just check the
+            # number is large
+            assert_(linalg.cond(A, p) > 1e15)
+        for A, p in itertools.product(As, p_neg):
+            linalg.cond(A, p)
+
+    @pytest.mark.xfail(True, run=False,
+                       reason="Platform/LAPACK-dependent failure, "
+                              "see gh-18914")
+    def test_nan(self):
+        # nans should be passed through, not converted to infs
+        ps = [None, 1, -1, 2, -2, 'fro']
+        p_pos = [None, 1, 2, 'fro']
+
+        A = np.ones((2, 2))
+        A[0,1] = np.nan
+        for p in ps:
+            c = linalg.cond(A, p)
+            assert_(isinstance(c, np.float_))
+            assert_(np.isnan(c))
+
+        A = np.ones((3, 2, 2))
+        A[1,0,1] = np.nan
+        for p in ps:
+            c = linalg.cond(A, p)
+            assert_(np.isnan(c[1]))
+            if p in p_pos:
+                assert_(c[0] > 1e15)
+                assert_(c[2] > 1e15)
+            else:
+                assert_(not np.isnan(c[0]))
+                assert_(not np.isnan(c[2]))
+
+    def test_stacked_singular(self):
+        # Check behavior when only some of the stacked matrices are
+        # singular
+        np.random.seed(1234)
+        A = np.random.rand(2, 2, 2, 2)
+        A[0,0] = 0
+        A[1,1] = 0
+
+        for p in (None, 1, 2, 'fro', -1, -2):
+            c = linalg.cond(A, p)
+            assert_equal(c[0,0], np.inf)
+            assert_equal(c[1,1], np.inf)
+            assert_(np.isfinite(c[0,1]))
+            assert_(np.isfinite(c[1,0]))
+
+
+class PinvCases(LinalgSquareTestCase,
+                LinalgNonsquareTestCase,
+                LinalgGeneralizedSquareTestCase,
+                LinalgGeneralizedNonsquareTestCase):
+
+    def do(self, a, b, tags):
+        a_ginv = linalg.pinv(a)
+        # `a @ a_ginv == I` does not hold if a is singular
+        dot = dot_generalized
+        assert_almost_equal(dot(dot(a, a_ginv), a), a, single_decimal=5, double_decimal=11)
+        assert_(consistent_subclass(a_ginv, a))
+
+
+class TestPinv(PinvCases):
+    pass
+
+
+class PinvHermitianCases(HermitianTestCase, HermitianGeneralizedTestCase):
+
+    def do(self, a, b, tags):
+        a_ginv = linalg.pinv(a, hermitian=True)
+        # `a @ a_ginv == I` does not hold if a is singular
+        dot = dot_generalized
+        assert_almost_equal(dot(dot(a, a_ginv), a), a, single_decimal=5, double_decimal=11)
+        assert_(consistent_subclass(a_ginv, a))
+
+
+class TestPinvHermitian(PinvHermitianCases):
+    pass
+
+
+class DetCases(LinalgSquareTestCase, LinalgGeneralizedSquareTestCase):
+
+    def do(self, a, b, tags):
+        d = linalg.det(a)
+        res = linalg.slogdet(a)
+        s, ld = res.sign, res.logabsdet
+        if asarray(a).dtype.type in (single, double):
+            ad = asarray(a).astype(double)
+        else:
+            ad = asarray(a).astype(cdouble)
+        ev = linalg.eigvals(ad)
+        assert_almost_equal(d, multiply.reduce(ev, axis=-1))
+        assert_almost_equal(s * np.exp(ld), multiply.reduce(ev, axis=-1))
+
+        s = np.atleast_1d(s)
+        ld = np.atleast_1d(ld)
+        m = (s != 0)
+        assert_almost_equal(np.abs(s[m]), 1)
+        assert_equal(ld[~m], -inf)
+
+
+class TestDet(DetCases):
+    def test_zero(self):
+        assert_equal(linalg.det([[0.0]]), 0.0)
+        assert_equal(type(linalg.det([[0.0]])), double)
+        assert_equal(linalg.det([[0.0j]]), 0.0)
+        assert_equal(type(linalg.det([[0.0j]])), cdouble)
+
+        assert_equal(linalg.slogdet([[0.0]]), (0.0, -inf))
+        assert_equal(type(linalg.slogdet([[0.0]])[0]), double)
+        assert_equal(type(linalg.slogdet([[0.0]])[1]), double)
+        assert_equal(linalg.slogdet([[0.0j]]), (0.0j, -inf))
+        assert_equal(type(linalg.slogdet([[0.0j]])[0]), cdouble)
+        assert_equal(type(linalg.slogdet([[0.0j]])[1]), double)
+
+    @pytest.mark.parametrize('dtype', [single, double, csingle, cdouble])
+    def test_types(self, dtype):
+        x = np.array([[1, 0.5], [0.5, 1]], dtype=dtype)
+        assert_equal(np.linalg.det(x).dtype, dtype)
+        ph, s = np.linalg.slogdet(x)
+        assert_equal(s.dtype, get_real_dtype(dtype))
+        assert_equal(ph.dtype, dtype)
+
+    def test_0_size(self):
+        a = np.zeros((0, 0), dtype=np.complex64)
+        res = linalg.det(a)
+        assert_equal(res, 1.)
+        assert_(res.dtype.type is np.complex64)
+        res = linalg.slogdet(a)
+        assert_equal(res, (1, 0))
+        assert_(res[0].dtype.type is np.complex64)
+        assert_(res[1].dtype.type is np.float32)
+
+        a = np.zeros((0, 0), dtype=np.float64)
+        res = linalg.det(a)
+        assert_equal(res, 1.)
+        assert_(res.dtype.type is np.float64)
+        res = linalg.slogdet(a)
+        assert_equal(res, (1, 0))
+        assert_(res[0].dtype.type is np.float64)
+        assert_(res[1].dtype.type is np.float64)
+
+
+class LstsqCases(LinalgSquareTestCase, LinalgNonsquareTestCase):
+
+    def do(self, a, b, tags):
+        arr = np.asarray(a)
+        m, n = arr.shape
+        u, s, vt = linalg.svd(a, False)
+        x, residuals, rank, sv = linalg.lstsq(a, b, rcond=-1)
+        if m == 0:
+            assert_((x == 0).all())
+        if m <= n:
+            assert_almost_equal(b, dot(a, x))
+            assert_equal(rank, m)
+        else:
+            assert_equal(rank, n)
+        assert_almost_equal(sv, sv.__array_wrap__(s))
+        if rank == n and m > n:
+            expect_resids = (
+                np.asarray(abs(np.dot(a, x) - b)) ** 2).sum(axis=0)
+            expect_resids = np.asarray(expect_resids)
+            if np.asarray(b).ndim == 1:
+                expect_resids.shape = (1,)
+                assert_equal(residuals.shape, expect_resids.shape)
+        else:
+            expect_resids = np.array([]).view(type(x))
+        assert_almost_equal(residuals, expect_resids)
+        assert_(np.issubdtype(residuals.dtype, np.floating))
+        assert_(consistent_subclass(x, b))
+        assert_(consistent_subclass(residuals, b))
+
+
+class TestLstsq(LstsqCases):
+    def test_future_rcond(self):
+        a = np.array([[0., 1.,  0.,  1.,  2.,  0.],
+                      [0., 2.,  0.,  0.,  1.,  0.],
+                      [1., 0.,  1.,  0.,  0.,  4.],
+                      [0., 0.,  0.,  2.,  3.,  0.]]).T
+
+        b = np.array([1, 0, 0, 0, 0, 0])
+        with suppress_warnings() as sup:
+            w = sup.record(FutureWarning, "`rcond` parameter will change")
+            x, residuals, rank, s = linalg.lstsq(a, b)
+            assert_(rank == 4)
+            x, residuals, rank, s = linalg.lstsq(a, b, rcond=-1)
+            assert_(rank == 4)
+            x, residuals, rank, s = linalg.lstsq(a, b, rcond=None)
+            assert_(rank == 3)
+            # Warning should be raised exactly once (first command)
+            assert_(len(w) == 1)
+
+    @pytest.mark.parametrize(["m", "n", "n_rhs"], [
+        (4, 2, 2),
+        (0, 4, 1),
+        (0, 4, 2),
+        (4, 0, 1),
+        (4, 0, 2),
+        (4, 2, 0),
+        (0, 0, 0)
+    ])
+    def test_empty_a_b(self, m, n, n_rhs):
+        a = np.arange(m * n).reshape(m, n)
+        b = np.ones((m, n_rhs))
+        x, residuals, rank, s = linalg.lstsq(a, b, rcond=None)
+        if m == 0:
+            assert_((x == 0).all())
+        assert_equal(x.shape, (n, n_rhs))
+        assert_equal(residuals.shape, ((n_rhs,) if m > n else (0,)))
+        if m > n and n_rhs > 0:
+            # residuals are exactly the squared norms of b's columns
+            r = b - np.dot(a, x)
+            assert_almost_equal(residuals, (r * r).sum(axis=-2))
+        assert_equal(rank, min(m, n))
+        assert_equal(s.shape, (min(m, n),))
+
+    def test_incompatible_dims(self):
+        # use modified version of docstring example
+        x = np.array([0, 1, 2, 3])
+        y = np.array([-1, 0.2, 0.9, 2.1, 3.3])
+        A = np.vstack([x, np.ones(len(x))]).T
+        with assert_raises_regex(LinAlgError, "Incompatible dimensions"):
+            linalg.lstsq(A, y, rcond=None)
+
+
+@pytest.mark.parametrize('dt', [np.dtype(c) for c in '?bBhHiIqQefdgFDGO'])
+class TestMatrixPower:
+
+    rshft_0 = np.eye(4)
+    rshft_1 = rshft_0[[3, 0, 1, 2]]
+    rshft_2 = rshft_0[[2, 3, 0, 1]]
+    rshft_3 = rshft_0[[1, 2, 3, 0]]
+    rshft_all = [rshft_0, rshft_1, rshft_2, rshft_3]
+    noninv = array([[1, 0], [0, 0]])
+    stacked = np.block([[[rshft_0]]]*2)
+    #FIXME the 'e' dtype might work in future
+    dtnoinv = [object, np.dtype('e'), np.dtype('g'), np.dtype('G')]
+
+    def test_large_power(self, dt):
+        rshft = self.rshft_1.astype(dt)
+        assert_equal(
+            matrix_power(rshft, 2**100 + 2**10 + 2**5 + 0), self.rshft_0)
+        assert_equal(
+            matrix_power(rshft, 2**100 + 2**10 + 2**5 + 1), self.rshft_1)
+        assert_equal(
+            matrix_power(rshft, 2**100 + 2**10 + 2**5 + 2), self.rshft_2)
+        assert_equal(
+            matrix_power(rshft, 2**100 + 2**10 + 2**5 + 3), self.rshft_3)
+
+    def test_power_is_zero(self, dt):
+        def tz(M):
+            mz = matrix_power(M, 0)
+            assert_equal(mz, identity_like_generalized(M))
+            assert_equal(mz.dtype, M.dtype)
+
+        for mat in self.rshft_all:
+            tz(mat.astype(dt))
+            if dt != object:
+                tz(self.stacked.astype(dt))
+
+    def test_power_is_one(self, dt):
+        def tz(mat):
+            mz = matrix_power(mat, 1)
+            assert_equal(mz, mat)
+            assert_equal(mz.dtype, mat.dtype)
+
+        for mat in self.rshft_all:
+            tz(mat.astype(dt))
+            if dt != object:
+                tz(self.stacked.astype(dt))
+
+    def test_power_is_two(self, dt):
+        def tz(mat):
+            mz = matrix_power(mat, 2)
+            mmul = matmul if mat.dtype != object else dot
+            assert_equal(mz, mmul(mat, mat))
+            assert_equal(mz.dtype, mat.dtype)
+
+        for mat in self.rshft_all:
+            tz(mat.astype(dt))
+            if dt != object:
+                tz(self.stacked.astype(dt))
+
+    def test_power_is_minus_one(self, dt):
+        def tz(mat):
+            invmat = matrix_power(mat, -1)
+            mmul = matmul if mat.dtype != object else dot
+            assert_almost_equal(
+                mmul(invmat, mat), identity_like_generalized(mat))
+
+        for mat in self.rshft_all:
+            if dt not in self.dtnoinv:
+                tz(mat.astype(dt))
+
+    def test_exceptions_bad_power(self, dt):
+        mat = self.rshft_0.astype(dt)
+        assert_raises(TypeError, matrix_power, mat, 1.5)
+        assert_raises(TypeError, matrix_power, mat, [1])
+
+    def test_exceptions_non_square(self, dt):
+        assert_raises(LinAlgError, matrix_power, np.array([1], dt), 1)
+        assert_raises(LinAlgError, matrix_power, np.array([[1], [2]], dt), 1)
+        assert_raises(LinAlgError, matrix_power, np.ones((4, 3, 2), dt), 1)
+
+    @pytest.mark.skipif(IS_WASM, reason="fp errors don't work in wasm")
+    def test_exceptions_not_invertible(self, dt):
+        if dt in self.dtnoinv:
+            return
+        mat = self.noninv.astype(dt)
+        assert_raises(LinAlgError, matrix_power, mat, -1)
+
+
+class TestEigvalshCases(HermitianTestCase, HermitianGeneralizedTestCase):
+
+    def do(self, a, b, tags):
+        # note that eigenvalue arrays returned by eig must be sorted since
+        # their order isn't guaranteed.
+        ev = linalg.eigvalsh(a, 'L')
+        evalues, evectors = linalg.eig(a)
+        evalues.sort(axis=-1)
+        assert_allclose(ev, evalues, rtol=get_rtol(ev.dtype))
+
+        ev2 = linalg.eigvalsh(a, 'U')
+        assert_allclose(ev2, evalues, rtol=get_rtol(ev.dtype))
+
+
+class TestEigvalsh:
+    @pytest.mark.parametrize('dtype', [single, double, csingle, cdouble])
+    def test_types(self, dtype):
+        x = np.array([[1, 0.5], [0.5, 1]], dtype=dtype)
+        w = np.linalg.eigvalsh(x)
+        assert_equal(w.dtype, get_real_dtype(dtype))
+
+    def test_invalid(self):
+        x = np.array([[1, 0.5], [0.5, 1]], dtype=np.float32)
+        assert_raises(ValueError, np.linalg.eigvalsh, x, UPLO="lrong")
+        assert_raises(ValueError, np.linalg.eigvalsh, x, "lower")
+        assert_raises(ValueError, np.linalg.eigvalsh, x, "upper")
+
+    def test_UPLO(self):
+        Klo = np.array([[0, 0], [1, 0]], dtype=np.double)
+        Kup = np.array([[0, 1], [0, 0]], dtype=np.double)
+        tgt = np.array([-1, 1], dtype=np.double)
+        rtol = get_rtol(np.double)
+
+        # Check default is 'L'
+        w = np.linalg.eigvalsh(Klo)
+        assert_allclose(w, tgt, rtol=rtol)
+        # Check 'L'
+        w = np.linalg.eigvalsh(Klo, UPLO='L')
+        assert_allclose(w, tgt, rtol=rtol)
+        # Check 'l'
+        w = np.linalg.eigvalsh(Klo, UPLO='l')
+        assert_allclose(w, tgt, rtol=rtol)
+        # Check 'U'
+        w = np.linalg.eigvalsh(Kup, UPLO='U')
+        assert_allclose(w, tgt, rtol=rtol)
+        # Check 'u'
+        w = np.linalg.eigvalsh(Kup, UPLO='u')
+        assert_allclose(w, tgt, rtol=rtol)
+
+    def test_0_size(self):
+        # Check that all kinds of 0-sized arrays work
+        class ArraySubclass(np.ndarray):
+            pass
+        a = np.zeros((0, 1, 1), dtype=np.int_).view(ArraySubclass)
+        res = linalg.eigvalsh(a)
+        assert_(res.dtype.type is np.float64)
+        assert_equal((0, 1), res.shape)
+        # This is just for documentation, it might make sense to change:
+        assert_(isinstance(res, np.ndarray))
+
+        a = np.zeros((0, 0), dtype=np.complex64).view(ArraySubclass)
+        res = linalg.eigvalsh(a)
+        assert_(res.dtype.type is np.float32)
+        assert_equal((0,), res.shape)
+        # This is just for documentation, it might make sense to change:
+        assert_(isinstance(res, np.ndarray))
+
+
+class TestEighCases(HermitianTestCase, HermitianGeneralizedTestCase):
+
+    def do(self, a, b, tags):
+        # note that eigenvalue arrays returned by eig must be sorted since
+        # their order isn't guaranteed.
+        res = linalg.eigh(a)
+        ev, evc = res.eigenvalues, res.eigenvectors
+        evalues, evectors = linalg.eig(a)
+        evalues.sort(axis=-1)
+        assert_almost_equal(ev, evalues)
+
+        assert_allclose(dot_generalized(a, evc),
+                        np.asarray(ev)[..., None, :] * np.asarray(evc),
+                        rtol=get_rtol(ev.dtype))
+
+        ev2, evc2 = linalg.eigh(a, 'U')
+        assert_almost_equal(ev2, evalues)
+
+        assert_allclose(dot_generalized(a, evc2),
+                        np.asarray(ev2)[..., None, :] * np.asarray(evc2),
+                        rtol=get_rtol(ev.dtype), err_msg=repr(a))
+
+
+class TestEigh:
+    @pytest.mark.parametrize('dtype', [single, double, csingle, cdouble])
+    def test_types(self, dtype):
+        x = np.array([[1, 0.5], [0.5, 1]], dtype=dtype)
+        w, v = np.linalg.eigh(x)
+        assert_equal(w.dtype, get_real_dtype(dtype))
+        assert_equal(v.dtype, dtype)
+
+    def test_invalid(self):
+        x = np.array([[1, 0.5], [0.5, 1]], dtype=np.float32)
+        assert_raises(ValueError, np.linalg.eigh, x, UPLO="lrong")
+        assert_raises(ValueError, np.linalg.eigh, x, "lower")
+        assert_raises(ValueError, np.linalg.eigh, x, "upper")
+
+    def test_UPLO(self):
+        Klo = np.array([[0, 0], [1, 0]], dtype=np.double)
+        Kup = np.array([[0, 1], [0, 0]], dtype=np.double)
+        tgt = np.array([-1, 1], dtype=np.double)
+        rtol = get_rtol(np.double)
+
+        # Check default is 'L'
+        w, v = np.linalg.eigh(Klo)
+        assert_allclose(w, tgt, rtol=rtol)
+        # Check 'L'
+        w, v = np.linalg.eigh(Klo, UPLO='L')
+        assert_allclose(w, tgt, rtol=rtol)
+        # Check 'l'
+        w, v = np.linalg.eigh(Klo, UPLO='l')
+        assert_allclose(w, tgt, rtol=rtol)
+        # Check 'U'
+        w, v = np.linalg.eigh(Kup, UPLO='U')
+        assert_allclose(w, tgt, rtol=rtol)
+        # Check 'u'
+        w, v = np.linalg.eigh(Kup, UPLO='u')
+        assert_allclose(w, tgt, rtol=rtol)
+
+    def test_0_size(self):
+        # Check that all kinds of 0-sized arrays work
+        class ArraySubclass(np.ndarray):
+            pass
+        a = np.zeros((0, 1, 1), dtype=np.int_).view(ArraySubclass)
+        res, res_v = linalg.eigh(a)
+        assert_(res_v.dtype.type is np.float64)
+        assert_(res.dtype.type is np.float64)
+        assert_equal(a.shape, res_v.shape)
+        assert_equal((0, 1), res.shape)
+        # This is just for documentation, it might make sense to change:
+        assert_(isinstance(a, np.ndarray))
+
+        a = np.zeros((0, 0), dtype=np.complex64).view(ArraySubclass)
+        res, res_v = linalg.eigh(a)
+        assert_(res_v.dtype.type is np.complex64)
+        assert_(res.dtype.type is np.float32)
+        assert_equal(a.shape, res_v.shape)
+        assert_equal((0,), res.shape)
+        # This is just for documentation, it might make sense to change:
+        assert_(isinstance(a, np.ndarray))
+
+
+class _TestNormBase:
+    dt = None
+    dec = None
+
+    @staticmethod
+    def check_dtype(x, res):
+        if issubclass(x.dtype.type, np.inexact):
+            assert_equal(res.dtype, x.real.dtype)
+        else:
+            # For integer input, don't have to test float precision of output.
+            assert_(issubclass(res.dtype.type, np.floating))
+
+
+class _TestNormGeneral(_TestNormBase):
+
+    def test_empty(self):
+        assert_equal(norm([]), 0.0)
+        assert_equal(norm(array([], dtype=self.dt)), 0.0)
+        assert_equal(norm(atleast_2d(array([], dtype=self.dt))), 0.0)
+
+    def test_vector_return_type(self):
+        a = np.array([1, 0, 1])
+
+        exact_types = np.typecodes['AllInteger']
+        inexact_types = np.typecodes['AllFloat']
+
+        all_types = exact_types + inexact_types
+
+        for each_type in all_types:
+            at = a.astype(each_type)
+
+            an = norm(at, -np.inf)
+            self.check_dtype(at, an)
+            assert_almost_equal(an, 0.0)
+
+            with suppress_warnings() as sup:
+                sup.filter(RuntimeWarning, "divide by zero encountered")
+                an = norm(at, -1)
+                self.check_dtype(at, an)
+                assert_almost_equal(an, 0.0)
+
+            an = norm(at, 0)
+            self.check_dtype(at, an)
+            assert_almost_equal(an, 2)
+
+            an = norm(at, 1)
+            self.check_dtype(at, an)
+            assert_almost_equal(an, 2.0)
+
+            an = norm(at, 2)
+            self.check_dtype(at, an)
+            assert_almost_equal(an, an.dtype.type(2.0)**an.dtype.type(1.0/2.0))
+
+            an = norm(at, 4)
+            self.check_dtype(at, an)
+            assert_almost_equal(an, an.dtype.type(2.0)**an.dtype.type(1.0/4.0))
+
+            an = norm(at, np.inf)
+            self.check_dtype(at, an)
+            assert_almost_equal(an, 1.0)
+
+    def test_vector(self):
+        a = [1, 2, 3, 4]
+        b = [-1, -2, -3, -4]
+        c = [-1, 2, -3, 4]
+
+        def _test(v):
+            np.testing.assert_almost_equal(norm(v), 30 ** 0.5,
+                                           decimal=self.dec)
+            np.testing.assert_almost_equal(norm(v, inf), 4.0,
+                                           decimal=self.dec)
+            np.testing.assert_almost_equal(norm(v, -inf), 1.0,
+                                           decimal=self.dec)
+            np.testing.assert_almost_equal(norm(v, 1), 10.0,
+                                           decimal=self.dec)
+            np.testing.assert_almost_equal(norm(v, -1), 12.0 / 25,
+                                           decimal=self.dec)
+            np.testing.assert_almost_equal(norm(v, 2), 30 ** 0.5,
+                                           decimal=self.dec)
+            np.testing.assert_almost_equal(norm(v, -2), ((205. / 144) ** -0.5),
+                                           decimal=self.dec)
+            np.testing.assert_almost_equal(norm(v, 0), 4,
+                                           decimal=self.dec)
+
+        for v in (a, b, c,):
+            _test(v)
+
+        for v in (array(a, dtype=self.dt), array(b, dtype=self.dt),
+                  array(c, dtype=self.dt)):
+            _test(v)
+
+    def test_axis(self):
+        # Vector norms.
+        # Compare the use of `axis` with computing the norm of each row
+        # or column separately.
+        A = array([[1, 2, 3], [4, 5, 6]], dtype=self.dt)
+        for order in [None, -1, 0, 1, 2, 3, np.Inf, -np.Inf]:
+            expected0 = [norm(A[:, k], ord=order) for k in range(A.shape[1])]
+            assert_almost_equal(norm(A, ord=order, axis=0), expected0)
+            expected1 = [norm(A[k, :], ord=order) for k in range(A.shape[0])]
+            assert_almost_equal(norm(A, ord=order, axis=1), expected1)
+
+        # Matrix norms.
+        B = np.arange(1, 25, dtype=self.dt).reshape(2, 3, 4)
+        nd = B.ndim
+        for order in [None, -2, 2, -1, 1, np.Inf, -np.Inf, 'fro']:
+            for axis in itertools.combinations(range(-nd, nd), 2):
+                row_axis, col_axis = axis
+                if row_axis < 0:
+                    row_axis += nd
+                if col_axis < 0:
+                    col_axis += nd
+                if row_axis == col_axis:
+                    assert_raises(ValueError, norm, B, ord=order, axis=axis)
+                else:
+                    n = norm(B, ord=order, axis=axis)
+
+                    # The logic using k_index only works for nd = 3.
+                    # This has to be changed if nd is increased.
+                    k_index = nd - (row_axis + col_axis)
+                    if row_axis < col_axis:
+                        expected = [norm(B[:].take(k, axis=k_index), ord=order)
+                                    for k in range(B.shape[k_index])]
+                    else:
+                        expected = [norm(B[:].take(k, axis=k_index).T, ord=order)
+                                    for k in range(B.shape[k_index])]
+                    assert_almost_equal(n, expected)
+
+    def test_keepdims(self):
+        A = np.arange(1, 25, dtype=self.dt).reshape(2, 3, 4)
+
+        allclose_err = 'order {0}, axis = {1}'
+        shape_err = 'Shape mismatch found {0}, expected {1}, order={2}, axis={3}'
+
+        # check the order=None, axis=None case
+        expected = norm(A, ord=None, axis=None)
+        found = norm(A, ord=None, axis=None, keepdims=True)
+        assert_allclose(np.squeeze(found), expected,
+                        err_msg=allclose_err.format(None, None))
+        expected_shape = (1, 1, 1)
+        assert_(found.shape == expected_shape,
+                shape_err.format(found.shape, expected_shape, None, None))
+
+        # Vector norms.
+        for order in [None, -1, 0, 1, 2, 3, np.Inf, -np.Inf]:
+            for k in range(A.ndim):
+                expected = norm(A, ord=order, axis=k)
+                found = norm(A, ord=order, axis=k, keepdims=True)
+                assert_allclose(np.squeeze(found), expected,
+                                err_msg=allclose_err.format(order, k))
+                expected_shape = list(A.shape)
+                expected_shape[k] = 1
+                expected_shape = tuple(expected_shape)
+                assert_(found.shape == expected_shape,
+                        shape_err.format(found.shape, expected_shape, order, k))
+
+        # Matrix norms.
+        for order in [None, -2, 2, -1, 1, np.Inf, -np.Inf, 'fro', 'nuc']:
+            for k in itertools.permutations(range(A.ndim), 2):
+                expected = norm(A, ord=order, axis=k)
+                found = norm(A, ord=order, axis=k, keepdims=True)
+                assert_allclose(np.squeeze(found), expected,
+                                err_msg=allclose_err.format(order, k))
+                expected_shape = list(A.shape)
+                expected_shape[k[0]] = 1
+                expected_shape[k[1]] = 1
+                expected_shape = tuple(expected_shape)
+                assert_(found.shape == expected_shape,
+                        shape_err.format(found.shape, expected_shape, order, k))
+
+
+class _TestNorm2D(_TestNormBase):
+    # Define the part for 2d arrays separately, so we can subclass this
+    # and run the tests using np.matrix in matrixlib.tests.test_matrix_linalg.
+    array = np.array
+
+    def test_matrix_empty(self):
+        assert_equal(norm(self.array([[]], dtype=self.dt)), 0.0)
+
+    def test_matrix_return_type(self):
+        a = self.array([[1, 0, 1], [0, 1, 1]])
+
+        exact_types = np.typecodes['AllInteger']
+
+        # float32, complex64, float64, complex128 types are the only types
+        # allowed by `linalg`, which performs the matrix operations used
+        # within `norm`.
+        inexact_types = 'fdFD'
+
+        all_types = exact_types + inexact_types
+
+        for each_type in all_types:
+            at = a.astype(each_type)
+
+            an = norm(at, -np.inf)
+            self.check_dtype(at, an)
+            assert_almost_equal(an, 2.0)
+
+            with suppress_warnings() as sup:
+                sup.filter(RuntimeWarning, "divide by zero encountered")
+                an = norm(at, -1)
+                self.check_dtype(at, an)
+                assert_almost_equal(an, 1.0)
+
+            an = norm(at, 1)
+            self.check_dtype(at, an)
+            assert_almost_equal(an, 2.0)
+
+            an = norm(at, 2)
+            self.check_dtype(at, an)
+            assert_almost_equal(an, 3.0**(1.0/2.0))
+
+            an = norm(at, -2)
+            self.check_dtype(at, an)
+            assert_almost_equal(an, 1.0)
+
+            an = norm(at, np.inf)
+            self.check_dtype(at, an)
+            assert_almost_equal(an, 2.0)
+
+            an = norm(at, 'fro')
+            self.check_dtype(at, an)
+            assert_almost_equal(an, 2.0)
+
+            an = norm(at, 'nuc')
+            self.check_dtype(at, an)
+            # Lower bar needed to support low precision floats.
+            # They end up being off by 1 in the 7th place.
+            np.testing.assert_almost_equal(an, 2.7320508075688772, decimal=6)
+
+    def test_matrix_2x2(self):
+        A = self.array([[1, 3], [5, 7]], dtype=self.dt)
+        assert_almost_equal(norm(A), 84 ** 0.5)
+        assert_almost_equal(norm(A, 'fro'), 84 ** 0.5)
+        assert_almost_equal(norm(A, 'nuc'), 10.0)
+        assert_almost_equal(norm(A, inf), 12.0)
+        assert_almost_equal(norm(A, -inf), 4.0)
+        assert_almost_equal(norm(A, 1), 10.0)
+        assert_almost_equal(norm(A, -1), 6.0)
+        assert_almost_equal(norm(A, 2), 9.1231056256176615)
+        assert_almost_equal(norm(A, -2), 0.87689437438234041)
+
+        assert_raises(ValueError, norm, A, 'nofro')
+        assert_raises(ValueError, norm, A, -3)
+        assert_raises(ValueError, norm, A, 0)
+
+    def test_matrix_3x3(self):
+        # This test has been added because the 2x2 example
+        # happened to have equal nuclear norm and induced 1-norm.
+        # The 1/10 scaling factor accommodates the absolute tolerance
+        # used in assert_almost_equal.
+        A = (1 / 10) * \
+            self.array([[1, 2, 3], [6, 0, 5], [3, 2, 1]], dtype=self.dt)
+        assert_almost_equal(norm(A), (1 / 10) * 89 ** 0.5)
+        assert_almost_equal(norm(A, 'fro'), (1 / 10) * 89 ** 0.5)
+        assert_almost_equal(norm(A, 'nuc'), 1.3366836911774836)
+        assert_almost_equal(norm(A, inf), 1.1)
+        assert_almost_equal(norm(A, -inf), 0.6)
+        assert_almost_equal(norm(A, 1), 1.0)
+        assert_almost_equal(norm(A, -1), 0.4)
+        assert_almost_equal(norm(A, 2), 0.88722940323461277)
+        assert_almost_equal(norm(A, -2), 0.19456584790481812)
+
+    def test_bad_args(self):
+        # Check that bad arguments raise the appropriate exceptions.
+
+        A = self.array([[1, 2, 3], [4, 5, 6]], dtype=self.dt)
+        B = np.arange(1, 25, dtype=self.dt).reshape(2, 3, 4)
+
+        # Using `axis=<integer>` or passing in a 1-D array implies vector
+        # norms are being computed, so also using `ord='fro'`
+        # or `ord='nuc'` or any other string raises a ValueError.
+        assert_raises(ValueError, norm, A, 'fro', 0)
+        assert_raises(ValueError, norm, A, 'nuc', 0)
+        assert_raises(ValueError, norm, [3, 4], 'fro', None)
+        assert_raises(ValueError, norm, [3, 4], 'nuc', None)
+        assert_raises(ValueError, norm, [3, 4], 'test', None)
+
+        # Similarly, norm should raise an exception when ord is any finite
+        # number other than 1, 2, -1 or -2 when computing matrix norms.
+        for order in [0, 3]:
+            assert_raises(ValueError, norm, A, order, None)
+            assert_raises(ValueError, norm, A, order, (0, 1))
+            assert_raises(ValueError, norm, B, order, (1, 2))
+
+        # Invalid axis
+        assert_raises(np.AxisError, norm, B, None, 3)
+        assert_raises(np.AxisError, norm, B, None, (2, 3))
+        assert_raises(ValueError, norm, B, None, (0, 1, 2))
+
+
+class _TestNorm(_TestNorm2D, _TestNormGeneral):
+    pass
+
+
+class TestNorm_NonSystematic:
+
+    def test_longdouble_norm(self):
+        # Non-regression test: p-norm of longdouble would previously raise
+        # UnboundLocalError.
+        x = np.arange(10, dtype=np.longdouble)
+        old_assert_almost_equal(norm(x, ord=3), 12.65, decimal=2)
+
+    def test_intmin(self):
+        # Non-regression test: p-norm of signed integer would previously do
+        # float cast and abs in the wrong order.
+        x = np.array([-2 ** 31], dtype=np.int32)
+        old_assert_almost_equal(norm(x, ord=3), 2 ** 31, decimal=5)
+
+    def test_complex_high_ord(self):
+        # gh-4156
+        d = np.empty((2,), dtype=np.clongdouble)
+        d[0] = 6 + 7j
+        d[1] = -6 + 7j
+        res = 11.615898132184
+        old_assert_almost_equal(np.linalg.norm(d, ord=3), res, decimal=10)
+        d = d.astype(np.complex128)
+        old_assert_almost_equal(np.linalg.norm(d, ord=3), res, decimal=9)
+        d = d.astype(np.complex64)
+        old_assert_almost_equal(np.linalg.norm(d, ord=3), res, decimal=5)
+
+
+# Separate definitions so we can use them for matrix tests.
+class _TestNormDoubleBase(_TestNormBase):
+    dt = np.double
+    dec = 12
+
+
+class _TestNormSingleBase(_TestNormBase):
+    dt = np.float32
+    dec = 6
+
+
+class _TestNormInt64Base(_TestNormBase):
+    dt = np.int64
+    dec = 12
+
+
+class TestNormDouble(_TestNorm, _TestNormDoubleBase):
+    pass
+
+
+class TestNormSingle(_TestNorm, _TestNormSingleBase):
+    pass
+
+
+class TestNormInt64(_TestNorm, _TestNormInt64Base):
+    pass
+
+
+class TestMatrixRank:
+
+    def test_matrix_rank(self):
+        # Full rank matrix
+        assert_equal(4, matrix_rank(np.eye(4)))
+        # rank deficient matrix
+        I = np.eye(4)
+        I[-1, -1] = 0.
+        assert_equal(matrix_rank(I), 3)
+        # All zeros - zero rank
+        assert_equal(matrix_rank(np.zeros((4, 4))), 0)
+        # 1 dimension - rank 1 unless all 0
+        assert_equal(matrix_rank([1, 0, 0, 0]), 1)
+        assert_equal(matrix_rank(np.zeros((4,))), 0)
+        # accepts array-like
+        assert_equal(matrix_rank([1]), 1)
+        # greater than 2 dimensions treated as stacked matrices
+        ms = np.array([I, np.eye(4), np.zeros((4,4))])
+        assert_equal(matrix_rank(ms), np.array([3, 4, 0]))
+        # works on scalar
+        assert_equal(matrix_rank(1), 1)
+
+    def test_symmetric_rank(self):
+        assert_equal(4, matrix_rank(np.eye(4), hermitian=True))
+        assert_equal(1, matrix_rank(np.ones((4, 4)), hermitian=True))
+        assert_equal(0, matrix_rank(np.zeros((4, 4)), hermitian=True))
+        # rank deficient matrix
+        I = np.eye(4)
+        I[-1, -1] = 0.
+        assert_equal(3, matrix_rank(I, hermitian=True))
+        # manually supplied tolerance
+        I[-1, -1] = 1e-8
+        assert_equal(4, matrix_rank(I, hermitian=True, tol=0.99e-8))
+        assert_equal(3, matrix_rank(I, hermitian=True, tol=1.01e-8))
+
+
+def test_reduced_rank():
+    # Test matrices with reduced rank
+    rng = np.random.RandomState(20120714)
+    for i in range(100):
+        # Make a rank deficient matrix
+        X = rng.normal(size=(40, 10))
+        X[:, 0] = X[:, 1] + X[:, 2]
+        # Assert that matrix_rank detected deficiency
+        assert_equal(matrix_rank(X), 9)
+        X[:, 3] = X[:, 4] + X[:, 5]
+        assert_equal(matrix_rank(X), 8)
+
+
+class TestQR:
+    # Define the array class here, so run this on matrices elsewhere.
+    array = np.array
+
+    def check_qr(self, a):
+        # This test expects the argument `a` to be an ndarray or
+        # a subclass of an ndarray of inexact type.
+        a_type = type(a)
+        a_dtype = a.dtype
+        m, n = a.shape
+        k = min(m, n)
+
+        # mode == 'complete'
+        res = linalg.qr(a, mode='complete')
+        Q, R = res.Q, res.R
+        assert_(Q.dtype == a_dtype)
+        assert_(R.dtype == a_dtype)
+        assert_(isinstance(Q, a_type))
+        assert_(isinstance(R, a_type))
+        assert_(Q.shape == (m, m))
+        assert_(R.shape == (m, n))
+        assert_almost_equal(dot(Q, R), a)
+        assert_almost_equal(dot(Q.T.conj(), Q), np.eye(m))
+        assert_almost_equal(np.triu(R), R)
+
+        # mode == 'reduced'
+        q1, r1 = linalg.qr(a, mode='reduced')
+        assert_(q1.dtype == a_dtype)
+        assert_(r1.dtype == a_dtype)
+        assert_(isinstance(q1, a_type))
+        assert_(isinstance(r1, a_type))
+        assert_(q1.shape == (m, k))
+        assert_(r1.shape == (k, n))
+        assert_almost_equal(dot(q1, r1), a)
+        assert_almost_equal(dot(q1.T.conj(), q1), np.eye(k))
+        assert_almost_equal(np.triu(r1), r1)
+
+        # mode == 'r'
+        r2 = linalg.qr(a, mode='r')
+        assert_(r2.dtype == a_dtype)
+        assert_(isinstance(r2, a_type))
+        assert_almost_equal(r2, r1)
+
+
+    @pytest.mark.parametrize(["m", "n"], [
+        (3, 0),
+        (0, 3),
+        (0, 0)
+    ])
+    def test_qr_empty(self, m, n):
+        k = min(m, n)
+        a = np.empty((m, n))
+
+        self.check_qr(a)
+
+        h, tau = np.linalg.qr(a, mode='raw')
+        assert_equal(h.dtype, np.double)
+        assert_equal(tau.dtype, np.double)
+        assert_equal(h.shape, (n, m))
+        assert_equal(tau.shape, (k,))
+
+    def test_mode_raw(self):
+        # The factorization is not unique and varies between libraries,
+        # so it is not possible to check against known values. Functional
+        # testing is a possibility, but awaits the exposure of more
+        # of the functions in lapack_lite. Consequently, this test is
+        # very limited in scope. Note that the results are in FORTRAN
+        # order, hence the h arrays are transposed.
+        a = self.array([[1, 2], [3, 4], [5, 6]], dtype=np.double)
+
+        # Test double
+        h, tau = linalg.qr(a, mode='raw')
+        assert_(h.dtype == np.double)
+        assert_(tau.dtype == np.double)
+        assert_(h.shape == (2, 3))
+        assert_(tau.shape == (2,))
+
+        h, tau = linalg.qr(a.T, mode='raw')
+        assert_(h.dtype == np.double)
+        assert_(tau.dtype == np.double)
+        assert_(h.shape == (3, 2))
+        assert_(tau.shape == (2,))
+
+    def test_mode_all_but_economic(self):
+        a = self.array([[1, 2], [3, 4]])
+        b = self.array([[1, 2], [3, 4], [5, 6]])
+        for dt in "fd":
+            m1 = a.astype(dt)
+            m2 = b.astype(dt)
+            self.check_qr(m1)
+            self.check_qr(m2)
+            self.check_qr(m2.T)
+
+        for dt in "fd":
+            m1 = 1 + 1j * a.astype(dt)
+            m2 = 1 + 1j * b.astype(dt)
+            self.check_qr(m1)
+            self.check_qr(m2)
+            self.check_qr(m2.T)
+
+    def check_qr_stacked(self, a):
+        # This test expects the argument `a` to be an ndarray or
+        # a subclass of an ndarray of inexact type.
+        a_type = type(a)
+        a_dtype = a.dtype
+        m, n = a.shape[-2:]
+        k = min(m, n)
+
+        # mode == 'complete'
+        q, r = linalg.qr(a, mode='complete')
+        assert_(q.dtype == a_dtype)
+        assert_(r.dtype == a_dtype)
+        assert_(isinstance(q, a_type))
+        assert_(isinstance(r, a_type))
+        assert_(q.shape[-2:] == (m, m))
+        assert_(r.shape[-2:] == (m, n))
+        assert_almost_equal(matmul(q, r), a)
+        I_mat = np.identity(q.shape[-1])
+        stack_I_mat = np.broadcast_to(I_mat,
+                        q.shape[:-2] + (q.shape[-1],)*2)
+        assert_almost_equal(matmul(swapaxes(q, -1, -2).conj(), q), stack_I_mat)
+        assert_almost_equal(np.triu(r[..., :, :]), r)
+
+        # mode == 'reduced'
+        q1, r1 = linalg.qr(a, mode='reduced')
+        assert_(q1.dtype == a_dtype)
+        assert_(r1.dtype == a_dtype)
+        assert_(isinstance(q1, a_type))
+        assert_(isinstance(r1, a_type))
+        assert_(q1.shape[-2:] == (m, k))
+        assert_(r1.shape[-2:] == (k, n))
+        assert_almost_equal(matmul(q1, r1), a)
+        I_mat = np.identity(q1.shape[-1])
+        stack_I_mat = np.broadcast_to(I_mat,
+                        q1.shape[:-2] + (q1.shape[-1],)*2)
+        assert_almost_equal(matmul(swapaxes(q1, -1, -2).conj(), q1),
+                            stack_I_mat)
+        assert_almost_equal(np.triu(r1[..., :, :]), r1)
+
+        # mode == 'r'
+        r2 = linalg.qr(a, mode='r')
+        assert_(r2.dtype == a_dtype)
+        assert_(isinstance(r2, a_type))
+        assert_almost_equal(r2, r1)
+
+    @pytest.mark.parametrize("size", [
+        (3, 4), (4, 3), (4, 4),
+        (3, 0), (0, 3)])
+    @pytest.mark.parametrize("outer_size", [
+        (2, 2), (2,), (2, 3, 4)])
+    @pytest.mark.parametrize("dt", [
+        np.single, np.double,
+        np.csingle, np.cdouble])
+    def test_stacked_inputs(self, outer_size, size, dt):
+
+        A = np.random.normal(size=outer_size + size).astype(dt)
+        B = np.random.normal(size=outer_size + size).astype(dt)
+        self.check_qr_stacked(A)
+        self.check_qr_stacked(A + 1.j*B)
+
+
+class TestCholesky:
+    # TODO: are there no other tests for cholesky?
+
+    @pytest.mark.parametrize(
+        'shape', [(1, 1), (2, 2), (3, 3), (50, 50), (3, 10, 10)]
+    )
+    @pytest.mark.parametrize(
+        'dtype', (np.float32, np.float64, np.complex64, np.complex128)
+    )
+    def test_basic_property(self, shape, dtype):
+        # Check A = L L^H
+        np.random.seed(1)
+        a = np.random.randn(*shape)
+        if np.issubdtype(dtype, np.complexfloating):
+            a = a + 1j*np.random.randn(*shape)
+
+        t = list(range(len(shape)))
+        t[-2:] = -1, -2
+
+        a = np.matmul(a.transpose(t).conj(), a)
+        a = np.asarray(a, dtype=dtype)
+
+        c = np.linalg.cholesky(a)
+
+        b = np.matmul(c, c.transpose(t).conj())
+        with np._no_nep50_warning():
+            atol = 500 * a.shape[0] * np.finfo(dtype).eps
+        assert_allclose(b, a, atol=atol, err_msg=f'{shape} {dtype}\n{a}\n{c}')
+
+    def test_0_size(self):
+        class ArraySubclass(np.ndarray):
+            pass
+        a = np.zeros((0, 1, 1), dtype=np.int_).view(ArraySubclass)
+        res = linalg.cholesky(a)
+        assert_equal(a.shape, res.shape)
+        assert_(res.dtype.type is np.float64)
+        # for documentation purpose:
+        assert_(isinstance(res, np.ndarray))
+
+        a = np.zeros((1, 0, 0), dtype=np.complex64).view(ArraySubclass)
+        res = linalg.cholesky(a)
+        assert_equal(a.shape, res.shape)
+        assert_(res.dtype.type is np.complex64)
+        assert_(isinstance(res, np.ndarray))
+
+
+def test_byteorder_check():
+    # Byte order check should pass for native order
+    if sys.byteorder == 'little':
+        native = '<'
+    else:
+        native = '>'
+
+    for dtt in (np.float32, np.float64):
+        arr = np.eye(4, dtype=dtt)
+        n_arr = arr.newbyteorder(native)
+        sw_arr = arr.newbyteorder('S').byteswap()
+        assert_equal(arr.dtype.byteorder, '=')
+        for routine in (linalg.inv, linalg.det, linalg.pinv):
+            # Normal call
+            res = routine(arr)
+            # Native but not '='
+            assert_array_equal(res, routine(n_arr))
+            # Swapped
+            assert_array_equal(res, routine(sw_arr))
+
+
+@pytest.mark.skipif(IS_WASM, reason="fp errors don't work in wasm")
+def test_generalized_raise_multiloop():
+    # It should raise an error even if the error doesn't occur in the
+    # last iteration of the ufunc inner loop
+
+    invertible = np.array([[1, 2], [3, 4]])
+    non_invertible = np.array([[1, 1], [1, 1]])
+
+    x = np.zeros([4, 4, 2, 2])[1::2]
+    x[...] = invertible
+    x[0, 0] = non_invertible
+
+    assert_raises(np.linalg.LinAlgError, np.linalg.inv, x)
+
+
+def test_xerbla_override():
+    # Check that our xerbla has been successfully linked in. If it is not,
+    # the default xerbla routine is called, which prints a message to stdout
+    # and may, or may not, abort the process depending on the LAPACK package.
+
+    XERBLA_OK = 255
+
+    try:
+        pid = os.fork()
+    except (OSError, AttributeError):
+        # fork failed, or not running on POSIX
+        pytest.skip("Not POSIX or fork failed.")
+
+    if pid == 0:
+        # child; close i/o file handles
+        os.close(1)
+        os.close(0)
+        # Avoid producing core files.
+        import resource
+        resource.setrlimit(resource.RLIMIT_CORE, (0, 0))
+        # These calls may abort.
+        try:
+            np.linalg.lapack_lite.xerbla()
+        except ValueError:
+            pass
+        except Exception:
+            os._exit(os.EX_CONFIG)
+
+        try:
+            a = np.array([[1.]])
+            np.linalg.lapack_lite.dorgqr(
+                1, 1, 1, a,
+                0,  # <- invalid value
+                a, a, 0, 0)
+        except ValueError as e:
+            if "DORGQR parameter number 5" in str(e):
+                # success, reuse error code to mark success as
+                # FORTRAN STOP returns as success.
+                os._exit(XERBLA_OK)
+
+        # Did not abort, but our xerbla was not linked in.
+        os._exit(os.EX_CONFIG)
+    else:
+        # parent
+        pid, status = os.wait()
+        if os.WEXITSTATUS(status) != XERBLA_OK:
+            pytest.skip('Numpy xerbla not linked in.')
+
+
+@pytest.mark.skipif(IS_WASM, reason="Cannot start subprocess")
+@pytest.mark.slow
+def test_sdot_bug_8577():
+    # Regression test that loading certain other libraries does not
+    # result to wrong results in float32 linear algebra.
+    #
+    # There's a bug gh-8577 on OSX that can trigger this, and perhaps
+    # there are also other situations in which it occurs.
+    #
+    # Do the check in a separate process.
+
+    bad_libs = ['PyQt5.QtWidgets', 'IPython']
+
+    template = textwrap.dedent("""
+    import sys
+    {before}
+    try:
+        import {bad_lib}
+    except ImportError:
+        sys.exit(0)
+    {after}
+    x = np.ones(2, dtype=np.float32)
+    sys.exit(0 if np.allclose(x.dot(x), 2.0) else 1)
+    """)
+
+    for bad_lib in bad_libs:
+        code = template.format(before="import numpy as np", after="",
+                               bad_lib=bad_lib)
+        subprocess.check_call([sys.executable, "-c", code])
+
+        # Swapped import order
+        code = template.format(after="import numpy as np", before="",
+                               bad_lib=bad_lib)
+        subprocess.check_call([sys.executable, "-c", code])
+
+
+class TestMultiDot:
+
+    def test_basic_function_with_three_arguments(self):
+        # multi_dot with three arguments uses a fast hand coded algorithm to
+        # determine the optimal order. Therefore test it separately.
+        A = np.random.random((6, 2))
+        B = np.random.random((2, 6))
+        C = np.random.random((6, 2))
+
+        assert_almost_equal(multi_dot([A, B, C]), A.dot(B).dot(C))
+        assert_almost_equal(multi_dot([A, B, C]), np.dot(A, np.dot(B, C)))
+
+    def test_basic_function_with_two_arguments(self):
+        # separate code path with two arguments
+        A = np.random.random((6, 2))
+        B = np.random.random((2, 6))
+
+        assert_almost_equal(multi_dot([A, B]), A.dot(B))
+        assert_almost_equal(multi_dot([A, B]), np.dot(A, B))
+
+    def test_basic_function_with_dynamic_programming_optimization(self):
+        # multi_dot with four or more arguments uses the dynamic programming
+        # optimization and therefore deserve a separate
+        A = np.random.random((6, 2))
+        B = np.random.random((2, 6))
+        C = np.random.random((6, 2))
+        D = np.random.random((2, 1))
+        assert_almost_equal(multi_dot([A, B, C, D]), A.dot(B).dot(C).dot(D))
+
+    def test_vector_as_first_argument(self):
+        # The first argument can be 1-D
+        A1d = np.random.random(2)  # 1-D
+        B = np.random.random((2, 6))
+        C = np.random.random((6, 2))
+        D = np.random.random((2, 2))
+
+        # the result should be 1-D
+        assert_equal(multi_dot([A1d, B, C, D]).shape, (2,))
+
+    def test_vector_as_last_argument(self):
+        # The last argument can be 1-D
+        A = np.random.random((6, 2))
+        B = np.random.random((2, 6))
+        C = np.random.random((6, 2))
+        D1d = np.random.random(2)  # 1-D
+
+        # the result should be 1-D
+        assert_equal(multi_dot([A, B, C, D1d]).shape, (6,))
+
+    def test_vector_as_first_and_last_argument(self):
+        # The first and last arguments can be 1-D
+        A1d = np.random.random(2)  # 1-D
+        B = np.random.random((2, 6))
+        C = np.random.random((6, 2))
+        D1d = np.random.random(2)  # 1-D
+
+        # the result should be a scalar
+        assert_equal(multi_dot([A1d, B, C, D1d]).shape, ())
+
+    def test_three_arguments_and_out(self):
+        # multi_dot with three arguments uses a fast hand coded algorithm to
+        # determine the optimal order. Therefore test it separately.
+        A = np.random.random((6, 2))
+        B = np.random.random((2, 6))
+        C = np.random.random((6, 2))
+
+        out = np.zeros((6, 2))
+        ret = multi_dot([A, B, C], out=out)
+        assert out is ret
+        assert_almost_equal(out, A.dot(B).dot(C))
+        assert_almost_equal(out, np.dot(A, np.dot(B, C)))
+
+    def test_two_arguments_and_out(self):
+        # separate code path with two arguments
+        A = np.random.random((6, 2))
+        B = np.random.random((2, 6))
+        out = np.zeros((6, 6))
+        ret = multi_dot([A, B], out=out)
+        assert out is ret
+        assert_almost_equal(out, A.dot(B))
+        assert_almost_equal(out, np.dot(A, B))
+
+    def test_dynamic_programming_optimization_and_out(self):
+        # multi_dot with four or more arguments uses the dynamic programming
+        # optimization and therefore deserve a separate test
+        A = np.random.random((6, 2))
+        B = np.random.random((2, 6))
+        C = np.random.random((6, 2))
+        D = np.random.random((2, 1))
+        out = np.zeros((6, 1))
+        ret = multi_dot([A, B, C, D], out=out)
+        assert out is ret
+        assert_almost_equal(out, A.dot(B).dot(C).dot(D))
+
+    def test_dynamic_programming_logic(self):
+        # Test for the dynamic programming part
+        # This test is directly taken from Cormen page 376.
+        arrays = [np.random.random((30, 35)),
+                  np.random.random((35, 15)),
+                  np.random.random((15, 5)),
+                  np.random.random((5, 10)),
+                  np.random.random((10, 20)),
+                  np.random.random((20, 25))]
+        m_expected = np.array([[0., 15750., 7875., 9375., 11875., 15125.],
+                               [0.,     0., 2625., 4375.,  7125., 10500.],
+                               [0.,     0.,    0.,  750.,  2500.,  5375.],
+                               [0.,     0.,    0.,    0.,  1000.,  3500.],
+                               [0.,     0.,    0.,    0.,     0.,  5000.],
+                               [0.,     0.,    0.,    0.,     0.,     0.]])
+        s_expected = np.array([[0,  1,  1,  3,  3,  3],
+                               [0,  0,  2,  3,  3,  3],
+                               [0,  0,  0,  3,  3,  3],
+                               [0,  0,  0,  0,  4,  5],
+                               [0,  0,  0,  0,  0,  5],
+                               [0,  0,  0,  0,  0,  0]], dtype=int)
+        s_expected -= 1  # Cormen uses 1-based index, python does not.
+
+        s, m = _multi_dot_matrix_chain_order(arrays, return_costs=True)
+
+        # Only the upper triangular part (without the diagonal) is interesting.
+        assert_almost_equal(np.triu(s[:-1, 1:]),
+                            np.triu(s_expected[:-1, 1:]))
+        assert_almost_equal(np.triu(m), np.triu(m_expected))
+
+    def test_too_few_input_arrays(self):
+        assert_raises(ValueError, multi_dot, [])
+        assert_raises(ValueError, multi_dot, [np.random.random((3, 3))])
+
+
+class TestTensorinv:
+
+    @pytest.mark.parametrize("arr, ind", [
+        (np.ones((4, 6, 8, 2)), 2),
+        (np.ones((3, 3, 2)), 1),
+        ])
+    def test_non_square_handling(self, arr, ind):
+        with assert_raises(LinAlgError):
+            linalg.tensorinv(arr, ind=ind)
+
+    @pytest.mark.parametrize("shape, ind", [
+        # examples from docstring
+        ((4, 6, 8, 3), 2),
+        ((24, 8, 3), 1),
+        ])
+    def test_tensorinv_shape(self, shape, ind):
+        a = np.eye(24)
+        a.shape = shape
+        ainv = linalg.tensorinv(a=a, ind=ind)
+        expected = a.shape[ind:] + a.shape[:ind]
+        actual = ainv.shape
+        assert_equal(actual, expected)
+
+    @pytest.mark.parametrize("ind", [
+        0, -2,
+        ])
+    def test_tensorinv_ind_limit(self, ind):
+        a = np.eye(24)
+        a.shape = (4, 6, 8, 3)
+        with assert_raises(ValueError):
+            linalg.tensorinv(a=a, ind=ind)
+
+    def test_tensorinv_result(self):
+        # mimic a docstring example
+        a = np.eye(24)
+        a.shape = (24, 8, 3)
+        ainv = linalg.tensorinv(a, ind=1)
+        b = np.ones(24)
+        assert_allclose(np.tensordot(ainv, b, 1), np.linalg.tensorsolve(a, b))
+
+
+class TestTensorsolve:
+
+    @pytest.mark.parametrize("a, axes", [
+        (np.ones((4, 6, 8, 2)), None),
+        (np.ones((3, 3, 2)), (0, 2)),
+        ])
+    def test_non_square_handling(self, a, axes):
+        with assert_raises(LinAlgError):
+            b = np.ones(a.shape[:2])
+            linalg.tensorsolve(a, b, axes=axes)
+
+    @pytest.mark.parametrize("shape",
+        [(2, 3, 6), (3, 4, 4, 3), (0, 3, 3, 0)],
+    )
+    def test_tensorsolve_result(self, shape):
+        a = np.random.randn(*shape)
+        b = np.ones(a.shape[:2])
+        x = np.linalg.tensorsolve(a, b)
+        assert_allclose(np.tensordot(a, x, axes=len(x.shape)), b)
+
+
+def test_unsupported_commontype():
+    # linalg gracefully handles unsupported type
+    arr = np.array([[1, -2], [2, 5]], dtype='float16')
+    with assert_raises_regex(TypeError, "unsupported in linalg"):
+        linalg.cholesky(arr)
+
+
+#@pytest.mark.slow
+#@pytest.mark.xfail(not HAS_LAPACK64, run=False,
+#                   reason="Numpy not compiled with 64-bit BLAS/LAPACK")
+#@requires_memory(free_bytes=16e9)
+@pytest.mark.skip(reason="Bad memory reports lead to OOM in ci testing")
+def test_blas64_dot():
+    n = 2**32
+    a = np.zeros([1, n], dtype=np.float32)
+    b = np.ones([1, 1], dtype=np.float32)
+    a[0,-1] = 1
+    c = np.dot(b, a)
+    assert_equal(c[0,-1], 1)
+
+
+@pytest.mark.xfail(not HAS_LAPACK64,
+                   reason="Numpy not compiled with 64-bit BLAS/LAPACK")
+def test_blas64_geqrf_lwork_smoketest():
+    # Smoke test LAPACK geqrf lwork call with 64-bit integers
+    dtype = np.float64
+    lapack_routine = np.linalg.lapack_lite.dgeqrf
+
+    m = 2**32 + 1
+    n = 2**32 + 1
+    lda = m
+
+    # Dummy arrays, not referenced by the lapack routine, so don't
+    # need to be of the right size
+    a = np.zeros([1, 1], dtype=dtype)
+    work = np.zeros([1], dtype=dtype)
+    tau = np.zeros([1], dtype=dtype)
+
+    # Size query
+    results = lapack_routine(m, n, a, lda, tau, work, -1, 0)
+    assert_equal(results['info'], 0)
+    assert_equal(results['m'], m)
+    assert_equal(results['n'], m)
+
+    # Should result to an integer of a reasonable size
+    lwork = int(work.item())
+    assert_(2**32 < lwork < 2**42)

Prism/LLaDA/LLaDA_Prism/.venv/lib/python3.12/site-packages/numpy/linalg/tests/test_regression.py

@@ -0,0 +1,145 @@
+""" Test functions for linalg module
+"""
+import warnings
+
+import numpy as np
+from numpy import linalg, arange, float64, array, dot, transpose
+from numpy.testing import (
+    assert_, assert_raises, assert_equal, assert_array_equal,
+    assert_array_almost_equal, assert_array_less
+)
+
+
+class TestRegression:
+
+    def test_eig_build(self):
+        # Ticket #652
+        rva = array([1.03221168e+02 + 0.j,
+                     -1.91843603e+01 + 0.j,
+                     -6.04004526e-01 + 15.84422474j,
+                     -6.04004526e-01 - 15.84422474j,
+                     -1.13692929e+01 + 0.j,
+                     -6.57612485e-01 + 10.41755503j,
+                     -6.57612485e-01 - 10.41755503j,
+                     1.82126812e+01 + 0.j,
+                     1.06011014e+01 + 0.j,
+                     7.80732773e+00 + 0.j,
+                     -7.65390898e-01 + 0.j,
+                     1.51971555e-15 + 0.j,
+                     -1.51308713e-15 + 0.j])
+        a = arange(13 * 13, dtype=float64)
+        a.shape = (13, 13)
+        a = a % 17
+        va, ve = linalg.eig(a)
+        va.sort()
+        rva.sort()
+        assert_array_almost_equal(va, rva)
+
+    def test_eigh_build(self):
+        # Ticket 662.
+        rvals = [68.60568999, 89.57756725, 106.67185574]
+
+        cov = array([[77.70273908,   3.51489954,  15.64602427],
+                     [3.51489954,  88.97013878,  -1.07431931],
+                     [15.64602427,  -1.07431931,  98.18223512]])
+
+        vals, vecs = linalg.eigh(cov)
+        assert_array_almost_equal(vals, rvals)
+
+    def test_svd_build(self):
+        # Ticket 627.
+        a = array([[0., 1.], [1., 1.], [2., 1.], [3., 1.]])
+        m, n = a.shape
+        u, s, vh = linalg.svd(a)
+
+        b = dot(transpose(u[:, n:]), a)
+
+        assert_array_almost_equal(b, np.zeros((2, 2)))
+
+    def test_norm_vector_badarg(self):
+        # Regression for #786: Frobenius norm for vectors raises
+        # ValueError.
+        assert_raises(ValueError, linalg.norm, array([1., 2., 3.]), 'fro')
+
+    def test_lapack_endian(self):
+        # For bug #1482
+        a = array([[5.7998084,  -2.1825367],
+                   [-2.1825367,   9.85910595]], dtype='>f8')
+        b = array(a, dtype='<f8')
+
+        ap = linalg.cholesky(a)
+        bp = linalg.cholesky(b)
+        assert_array_equal(ap, bp)
+
+    def test_large_svd_32bit(self):
+        # See gh-4442, 64bit would require very large/slow matrices.
+        x = np.eye(1000, 66)
+        np.linalg.svd(x)
+
+    def test_svd_no_uv(self):
+        # gh-4733
+        for shape in (3, 4), (4, 4), (4, 3):
+            for t in float, complex:
+                a = np.ones(shape, dtype=t)
+                w = linalg.svd(a, compute_uv=False)
+                c = np.count_nonzero(np.absolute(w) > 0.5)
+                assert_equal(c, 1)
+                assert_equal(np.linalg.matrix_rank(a), 1)
+                assert_array_less(1, np.linalg.norm(a, ord=2))
+
+    def test_norm_object_array(self):
+        # gh-7575
+        testvector = np.array([np.array([0, 1]), 0, 0], dtype=object)
+
+        norm = linalg.norm(testvector)
+        assert_array_equal(norm, [0, 1])
+        assert_(norm.dtype == np.dtype('float64'))
+
+        norm = linalg.norm(testvector, ord=1)
+        assert_array_equal(norm, [0, 1])
+        assert_(norm.dtype != np.dtype('float64'))
+
+        norm = linalg.norm(testvector, ord=2)
+        assert_array_equal(norm, [0, 1])
+        assert_(norm.dtype == np.dtype('float64'))
+
+        assert_raises(ValueError, linalg.norm, testvector, ord='fro')
+        assert_raises(ValueError, linalg.norm, testvector, ord='nuc')
+        assert_raises(ValueError, linalg.norm, testvector, ord=np.inf)
+        assert_raises(ValueError, linalg.norm, testvector, ord=-np.inf)
+        assert_raises(ValueError, linalg.norm, testvector, ord=0)
+        assert_raises(ValueError, linalg.norm, testvector, ord=-1)
+        assert_raises(ValueError, linalg.norm, testvector, ord=-2)
+
+        testmatrix = np.array([[np.array([0, 1]), 0, 0],
+                               [0,                0, 0]], dtype=object)
+
+        norm = linalg.norm(testmatrix)
+        assert_array_equal(norm, [0, 1])
+        assert_(norm.dtype == np.dtype('float64'))
+
+        norm = linalg.norm(testmatrix, ord='fro')
+        assert_array_equal(norm, [0, 1])
+        assert_(norm.dtype == np.dtype('float64'))
+
+        assert_raises(TypeError, linalg.norm, testmatrix, ord='nuc')
+        assert_raises(ValueError, linalg.norm, testmatrix, ord=np.inf)
+        assert_raises(ValueError, linalg.norm, testmatrix, ord=-np.inf)
+        assert_raises(ValueError, linalg.norm, testmatrix, ord=0)
+        assert_raises(ValueError, linalg.norm, testmatrix, ord=1)
+        assert_raises(ValueError, linalg.norm, testmatrix, ord=-1)
+        assert_raises(TypeError, linalg.norm, testmatrix, ord=2)
+        assert_raises(TypeError, linalg.norm, testmatrix, ord=-2)
+        assert_raises(ValueError, linalg.norm, testmatrix, ord=3)
+
+    def test_lstsq_complex_larger_rhs(self):
+        # gh-9891
+        size = 20
+        n_rhs = 70
+        G = np.random.randn(size, size) + 1j * np.random.randn(size, size)
+        u = np.random.randn(size, n_rhs) + 1j * np.random.randn(size, n_rhs)
+        b = G.dot(u)
+        # This should work without segmentation fault.
+        u_lstsq, res, rank, sv = linalg.lstsq(G, b, rcond=None)
+        # check results just in case
+        assert_array_almost_equal(u_lstsq, u)

Prism/LLaDA/LLaDA_Prism/.venv/lib/python3.12/site-packages/torch/include/ATen/ops/_convolution_double_backward.h

@@ -0,0 +1,53 @@
+#if !defined(TORCH_STABLE_ONLY) && !defined(TORCH_TARGET_VERSION)
+#pragma once
+
+// @generated by torchgen/gen.py from Function.h
+
+#include <ATen/Context.h>
+#include <ATen/DeviceGuard.h>
+#include <ATen/TensorUtils.h>
+#include <ATen/TracerMode.h>
+#include <ATen/core/Generator.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <optional>
+#include <string_view>
+
+
+
+#include <ATen/ops/_convolution_double_backward_ops.h>
+
+namespace at {
+
+
+// aten::_convolution_double_backward(Tensor? ggI, Tensor? ggW, Tensor? ggb, Tensor gO, Tensor weight, Tensor self, SymInt[] stride, SymInt[] padding, SymInt[] dilation, bool transposed, SymInt[] output_padding, SymInt groups, bool[3] output_mask) -> (Tensor, Tensor, Tensor)
+inline ::std::tuple<at::Tensor,at::Tensor,at::Tensor> _convolution_double_backward(const ::std::optional<at::Tensor> & ggI, const ::std::optional<at::Tensor> & ggW, const ::std::optional<at::Tensor> & ggb, const at::Tensor & gO, const at::Tensor & weight, const at::Tensor & self, at::IntArrayRef stride, at::IntArrayRef padding, at::IntArrayRef dilation, bool transposed, at::IntArrayRef output_padding, int64_t groups, ::std::array<bool,3> output_mask) {
+    return at::_ops::_convolution_double_backward::call(ggI, ggW, ggb, gO, weight, self, c10::fromIntArrayRefSlow(stride), c10::fromIntArrayRefSlow(padding), c10::fromIntArrayRefSlow(dilation), transposed, c10::fromIntArrayRefSlow(output_padding), groups, output_mask);
+}
+namespace symint {
+  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
+  ::std::tuple<at::Tensor,at::Tensor,at::Tensor> _convolution_double_backward(const ::std::optional<at::Tensor> & ggI, const ::std::optional<at::Tensor> & ggW, const ::std::optional<at::Tensor> & ggb, const at::Tensor & gO, const at::Tensor & weight, const at::Tensor & self, at::IntArrayRef stride, at::IntArrayRef padding, at::IntArrayRef dilation, bool transposed, at::IntArrayRef output_padding, int64_t groups, ::std::array<bool,3> output_mask) {
+    return at::_ops::_convolution_double_backward::call(ggI, ggW, ggb, gO, weight, self, c10::fromIntArrayRefSlow(stride), c10::fromIntArrayRefSlow(padding), c10::fromIntArrayRefSlow(dilation), transposed, c10::fromIntArrayRefSlow(output_padding), groups, output_mask);
+  }
+}
+
+// aten::_convolution_double_backward(Tensor? ggI, Tensor? ggW, Tensor? ggb, Tensor gO, Tensor weight, Tensor self, SymInt[] stride, SymInt[] padding, SymInt[] dilation, bool transposed, SymInt[] output_padding, SymInt groups, bool[3] output_mask) -> (Tensor, Tensor, Tensor)
+inline ::std::tuple<at::Tensor,at::Tensor,at::Tensor> _convolution_double_backward_symint(const ::std::optional<at::Tensor> & ggI, const ::std::optional<at::Tensor> & ggW, const ::std::optional<at::Tensor> & ggb, const at::Tensor & gO, const at::Tensor & weight, const at::Tensor & self, c10::SymIntArrayRef stride, c10::SymIntArrayRef padding, c10::SymIntArrayRef dilation, bool transposed, c10::SymIntArrayRef output_padding, c10::SymInt groups, ::std::array<bool,3> output_mask) {
+    return at::_ops::_convolution_double_backward::call(ggI, ggW, ggb, gO, weight, self, stride, padding, dilation, transposed, output_padding, groups, output_mask);
+}
+namespace symint {
+  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
+  ::std::tuple<at::Tensor,at::Tensor,at::Tensor> _convolution_double_backward(const ::std::optional<at::Tensor> & ggI, const ::std::optional<at::Tensor> & ggW, const ::std::optional<at::Tensor> & ggb, const at::Tensor & gO, const at::Tensor & weight, const at::Tensor & self, c10::SymIntArrayRef stride, c10::SymIntArrayRef padding, c10::SymIntArrayRef dilation, bool transposed, c10::SymIntArrayRef output_padding, c10::SymInt groups, ::std::array<bool,3> output_mask) {
+    return at::_ops::_convolution_double_backward::call(ggI, ggW, ggb, gO, weight, self, stride, padding, dilation, transposed, output_padding, groups, output_mask);
+  }
+}
+
+}
+
+#else
+#error "This file should not be included when either TORCH_STABLE_ONLY or TORCH_TARGET_VERSION is defined."
+#endif  // !defined(TORCH_STABLE_ONLY) && !defined(TORCH_TARGET_VERSION)

Prism/LLaDA/LLaDA_Prism/.venv/lib/python3.12/site-packages/torch/include/ATen/ops/cudnn_convolution_relu_ops.h

@@ -0,0 +1,45 @@
+#if !defined(TORCH_STABLE_ONLY) && !defined(TORCH_TARGET_VERSION)
+#pragma once
+
+// @generated by torchgen/gen.py from Operator.h
+
+#include <string_view>
+#include <tuple>
+#include <vector>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+namespace _ops {
+
+
+struct TORCH_API cudnn_convolution_relu {
+  using schema = at::Tensor (const at::Tensor &, const at::Tensor &, const ::std::optional<at::Tensor> &, c10::SymIntArrayRef, c10::SymIntArrayRef, c10::SymIntArrayRef, c10::SymInt);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::cudnn_convolution_relu";
+  static constexpr const char* overload_name = "";
+  static constexpr const char* schema_str = "cudnn_convolution_relu(Tensor self, Tensor weight, Tensor? bias, SymInt[] stride, SymInt[] padding, SymInt[] dilation, SymInt groups) -> Tensor";
+  static at::Tensor call(const at::Tensor & self, const at::Tensor & weight, const ::std::optional<at::Tensor> & bias, c10::SymIntArrayRef stride, c10::SymIntArrayRef padding, c10::SymIntArrayRef dilation, c10::SymInt groups);
+  static at::Tensor redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & weight, const ::std::optional<at::Tensor> & bias, c10::SymIntArrayRef stride, c10::SymIntArrayRef padding, c10::SymIntArrayRef dilation, c10::SymInt groups);
+};
+
+struct TORCH_API cudnn_convolution_relu_out {
+  using schema = at::Tensor & (const at::Tensor &, const at::Tensor &, const ::std::optional<at::Tensor> &, c10::SymIntArrayRef, c10::SymIntArrayRef, c10::SymIntArrayRef, c10::SymInt, at::Tensor &);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::cudnn_convolution_relu";
+  static constexpr const char* overload_name = "out";
+  static constexpr const char* schema_str = "cudnn_convolution_relu.out(Tensor self, Tensor weight, Tensor? bias, SymInt[] stride, SymInt[] padding, SymInt[] dilation, SymInt groups, *, Tensor(a!) out) -> Tensor(a!)";
+  static at::Tensor & call(const at::Tensor & self, const at::Tensor & weight, const ::std::optional<at::Tensor> & bias, c10::SymIntArrayRef stride, c10::SymIntArrayRef padding, c10::SymIntArrayRef dilation, c10::SymInt groups, at::Tensor & out);
+  static at::Tensor & redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & weight, const ::std::optional<at::Tensor> & bias, c10::SymIntArrayRef stride, c10::SymIntArrayRef padding, c10::SymIntArrayRef dilation, c10::SymInt groups, at::Tensor & out);
+};
+
+}} // namespace at::_ops
+
+#else
+#error "This file should not be included when either TORCH_STABLE_ONLY or TORCH_TARGET_VERSION is defined."
+#endif  // !defined(TORCH_STABLE_ONLY) && !defined(TORCH_TARGET_VERSION)

Prism/LLaDA/LLaDA_Prism/.venv/lib/python3.12/site-packages/torch/include/ATen/ops/mkldnn_adaptive_avg_pool2d_ops.h

@@ -0,0 +1,45 @@
+#if !defined(TORCH_STABLE_ONLY) && !defined(TORCH_TARGET_VERSION)
+#pragma once
+
+// @generated by torchgen/gen.py from Operator.h
+
+#include <string_view>
+#include <tuple>
+#include <vector>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+namespace _ops {
+
+
+struct TORCH_API mkldnn_adaptive_avg_pool2d {
+  using schema = at::Tensor (const at::Tensor &, at::IntArrayRef);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::mkldnn_adaptive_avg_pool2d";
+  static constexpr const char* overload_name = "";
+  static constexpr const char* schema_str = "mkldnn_adaptive_avg_pool2d(Tensor self, int[2] output_size) -> Tensor";
+  static at::Tensor call(const at::Tensor & self, at::IntArrayRef output_size);
+  static at::Tensor redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::IntArrayRef output_size);
+};
+
+struct TORCH_API mkldnn_adaptive_avg_pool2d_out {
+  using schema = at::Tensor & (const at::Tensor &, at::IntArrayRef, at::Tensor &);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::mkldnn_adaptive_avg_pool2d";
+  static constexpr const char* overload_name = "out";
+  static constexpr const char* schema_str = "mkldnn_adaptive_avg_pool2d.out(Tensor self, int[2] output_size, *, Tensor(a!) out) -> Tensor(a!)";
+  static at::Tensor & call(const at::Tensor & self, at::IntArrayRef output_size, at::Tensor & out);
+  static at::Tensor & redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::IntArrayRef output_size, at::Tensor & out);
+};
+
+}} // namespace at::_ops
+
+#else
+#error "This file should not be included when either TORCH_STABLE_ONLY or TORCH_TARGET_VERSION is defined."
+#endif  // !defined(TORCH_STABLE_ONLY) && !defined(TORCH_TARGET_VERSION)

Prism/LLaDA/LLaDA_Prism/.venv/lib/python3.12/site-packages/torch/include/ATen/ops/silu_meta.h

@@ -0,0 +1,32 @@
+#if !defined(TORCH_STABLE_ONLY) && !defined(TORCH_TARGET_VERSION)
+#pragma once
+
+// @generated by torchgen/gen.py from NativeMetaFunction.h
+
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <optional>
+#include <c10/core/QScheme.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/TensorIterator.h>
+#include <ATen/TensorMeta.h>
+#include <tuple>
+#include <vector>
+
+namespace at {
+namespace meta {
+
+struct TORCH_API structured_silu : public TensorIteratorBase {
+
+
+    void meta(const at::Tensor & self);
+};
+
+} // namespace native
+} // namespace at
+
+#else
+#error "This file should not be included when either TORCH_STABLE_ONLY or TORCH_TARGET_VERSION is defined."
+#endif  // !defined(TORCH_STABLE_ONLY) && !defined(TORCH_TARGET_VERSION)

Prism/LLaDA/LLaDA_Prism/.venv/lib/python3.12/site-packages/torch/include/ATen/ops/slow_conv_transpose2d_compositeexplicitautogradnonfunctional_dispatch.h

@@ -0,0 +1,29 @@
+#if !defined(TORCH_STABLE_ONLY) && !defined(TORCH_TARGET_VERSION)
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace compositeexplicitautogradnonfunctional {
+
+TORCH_API at::Tensor slow_conv_transpose2d(const at::Tensor & self, const at::Tensor & weight, at::IntArrayRef kernel_size, const ::std::optional<at::Tensor> & bias={}, at::IntArrayRef stride=1, at::IntArrayRef padding=0, at::IntArrayRef output_padding=0, at::IntArrayRef dilation=1);
+TORCH_API at::Tensor slow_conv_transpose2d_symint(const at::Tensor & self, const at::Tensor & weight, c10::SymIntArrayRef kernel_size, const ::std::optional<at::Tensor> & bias={}, c10::SymIntArrayRef stride=c10::SymInt(1), c10::SymIntArrayRef padding=c10::SymInt(0), c10::SymIntArrayRef output_padding=c10::SymInt(0), c10::SymIntArrayRef dilation=c10::SymInt(1));
+
+} // namespace compositeexplicitautogradnonfunctional
+} // namespace at
+
+#else
+#error "This file should not be included when either TORCH_STABLE_ONLY or TORCH_TARGET_VERSION is defined."
+#endif  // !defined(TORCH_STABLE_ONLY) && !defined(TORCH_TARGET_VERSION)

Prism/LLaDA/LLaDA_Prism/.venv/lib/python3.12/site-packages/torch/include/ATen/ops/upsample_bilinear2d_compositeexplicitautogradnonfunctional_dispatch.h

@@ -0,0 +1,29 @@
+#if !defined(TORCH_STABLE_ONLY) && !defined(TORCH_TARGET_VERSION)
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace compositeexplicitautogradnonfunctional {
+
+TORCH_API at::Tensor upsample_bilinear2d(const at::Tensor & self, at::IntArrayRef output_size, bool align_corners, ::std::optional<double> scales_h=::std::nullopt, ::std::optional<double> scales_w=::std::nullopt);
+TORCH_API at::Tensor upsample_bilinear2d_symint(const at::Tensor & self, c10::SymIntArrayRef output_size, bool align_corners, ::std::optional<double> scales_h=::std::nullopt, ::std::optional<double> scales_w=::std::nullopt);
+
+} // namespace compositeexplicitautogradnonfunctional
+} // namespace at
+
+#else
+#error "This file should not be included when either TORCH_STABLE_ONLY or TORCH_TARGET_VERSION is defined."
+#endif  // !defined(TORCH_STABLE_ONLY) && !defined(TORCH_TARGET_VERSION)

Prism/LLaDA/LLaDA_Prism/.venv/lib/python3.12/site-packages/torch/include/google/protobuf/any.h

@@ -0,0 +1,154 @@
+#if !defined(TORCH_STABLE_ONLY) && !defined(TORCH_TARGET_VERSION)
+// Protocol Buffers - Google's data interchange format
+// Copyright 2008 Google Inc.  All rights reserved.
+// https://developers.google.com/protocol-buffers/
+//
+// 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 Google Inc. nor the names of its
+// contributors may be used to endorse or promote products derived from
+// this software without specific prior written permission.
+//
+// THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
+// "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
+// LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
+// A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT
+// OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
+// SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT
+// LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
+// DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
+// THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+// (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+// OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+
+#ifndef GOOGLE_PROTOBUF_ANY_H__
+#define GOOGLE_PROTOBUF_ANY_H__
+
+#include <string>
+
+#include <google/protobuf/stubs/common.h>
+#include <google/protobuf/arenastring.h>
+#include <google/protobuf/message_lite.h>
+
+#include <google/protobuf/port_def.inc>
+
+namespace google {
+namespace protobuf {
+
+class FieldDescriptor;
+class Message;
+
+namespace internal {
+
+extern const char kAnyFullTypeName[];          // "google.protobuf.Any".
+extern const char kTypeGoogleApisComPrefix[];  // "type.googleapis.com/".
+extern const char kTypeGoogleProdComPrefix[];  // "type.googleprod.com/".
+
+std::string GetTypeUrl(StringPiece message_name,
+                       StringPiece type_url_prefix);
+
+// Helper class used to implement google::protobuf::Any.
+class PROTOBUF_EXPORT AnyMetadata {
+  typedef ArenaStringPtr UrlType;
+  typedef ArenaStringPtr ValueType;
+ public:
+  // AnyMetadata does not take ownership of "type_url" and "value".
+  AnyMetadata(UrlType* type_url, ValueType* value);
+
+  // Packs a message using the default type URL prefix: "type.googleapis.com".
+  // The resulted type URL will be "type.googleapis.com/<message_full_name>".
+  template <typename T>
+  void PackFrom(const T& message) {
+    InternalPackFrom(message, kTypeGoogleApisComPrefix, T::FullMessageName());
+  }
+
+  void PackFrom(const Message& message);
+
+  // Packs a message using the given type URL prefix. The type URL will be
+  // constructed by concatenating the message type's full name to the prefix
+  // with an optional "/" separator if the prefix doesn't already end with "/".
+  // For example, both PackFrom(message, "type.googleapis.com") and
+  // PackFrom(message, "type.googleapis.com/") yield the same result type
+  // URL: "type.googleapis.com/<message_full_name>".
+  template <typename T>
+  void PackFrom(const T& message, StringPiece type_url_prefix) {
+    InternalPackFrom(message, type_url_prefix, T::FullMessageName());
+  }
+
+  void PackFrom(const Message& message, const std::string& type_url_prefix);
+
+  // Unpacks the payload into the given message. Returns false if the message's
+  // type doesn't match the type specified in the type URL (i.e., the full
+  // name after the last "/" of the type URL doesn't match the message's actual
+  // full name) or parsing the payload has failed.
+  template <typename T>
+  bool UnpackTo(T* message) const {
+    return InternalUnpackTo(T::FullMessageName(), message);
+  }
+
+  bool UnpackTo(Message* message) const;
+
+  // Checks whether the type specified in the type URL matches the given type.
+  // A type is considered matching if its full name matches the full name after
+  // the last "/" in the type URL.
+  template <typename T>
+  bool Is() const {
+    return InternalIs(T::FullMessageName());
+  }
+
+ private:
+  void InternalPackFrom(const MessageLite& message,
+                        StringPiece type_url_prefix,
+                        StringPiece type_name);
+  bool InternalUnpackTo(StringPiece type_name,
+                        MessageLite* message) const;
+  bool InternalIs(StringPiece type_name) const;
+
+  UrlType* type_url_;
+  ValueType* value_;
+
+  GOOGLE_DISALLOW_EVIL_CONSTRUCTORS(AnyMetadata);
+};
+
+// Get the proto type name from Any::type_url value. For example, passing
+// "type.googleapis.com/rpc.QueryOrigin" will return "rpc.QueryOrigin" in
+// *full_type_name. Returns false if the type_url does not have a "/"
+// in the type url separating the full type name.
+//
+// NOTE: this function is available publicly as:
+//   google::protobuf::Any()  // static method on the generated message type.
+bool ParseAnyTypeUrl(const std::string& type_url, std::string* full_type_name);
+
+// Get the proto type name and prefix from Any::type_url value. For example,
+// passing "type.googleapis.com/rpc.QueryOrigin" will return
+// "type.googleapis.com/" in *url_prefix and "rpc.QueryOrigin" in
+// *full_type_name. Returns false if the type_url does not have a "/" in the
+// type url separating the full type name.
+bool ParseAnyTypeUrl(const std::string& type_url, std::string* url_prefix,
+                     std::string* full_type_name);
+
+// See if message is of type google.protobuf.Any, if so, return the descriptors
+// for "type_url" and "value" fields.
+bool GetAnyFieldDescriptors(const Message& message,
+                            const FieldDescriptor** type_url_field,
+                            const FieldDescriptor** value_field);
+
+}  // namespace internal
+}  // namespace protobuf
+}  // namespace google
+
+#include <google/protobuf/port_undef.inc>
+
+#endif  // GOOGLE_PROTOBUF_ANY_H__
+
+#else
+#error "This file should not be included when either TORCH_STABLE_ONLY or TORCH_TARGET_VERSION is defined."
+#endif  // !defined(TORCH_STABLE_ONLY) && !defined(TORCH_TARGET_VERSION)

Prism/LLaDA/LLaDA_Prism/.venv/lib/python3.12/site-packages/torch/include/google/protobuf/extension_set_inl.h

@@ -0,0 +1,281 @@
+#if !defined(TORCH_STABLE_ONLY) && !defined(TORCH_TARGET_VERSION)
+// Protocol Buffers - Google's data interchange format
+// Copyright 2008 Google Inc.  All rights reserved.
+// https://developers.google.com/protocol-buffers/
+//
+// 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 Google Inc. nor the names of its
+// contributors may be used to endorse or promote products derived from
+// this software without specific prior written permission.
+//
+// THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
+// "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
+// LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
+// A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT
+// OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
+// SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT
+// LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
+// DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
+// THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+// (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+// OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+
+#ifndef GOOGLE_PROTOBUF_EXTENSION_SET_INL_H__
+#define GOOGLE_PROTOBUF_EXTENSION_SET_INL_H__
+
+#include <google/protobuf/parse_context.h>
+#include <google/protobuf/extension_set.h>
+#include <google/protobuf/metadata_lite.h>
+
+namespace google {
+namespace protobuf {
+namespace internal {
+
+template <typename T>
+const char* ExtensionSet::ParseFieldWithExtensionInfo(
+    int number, bool was_packed_on_wire, const ExtensionInfo& extension,
+    InternalMetadata* metadata, const char* ptr, internal::ParseContext* ctx) {
+  if (was_packed_on_wire) {
+    switch (extension.type) {
+#define HANDLE_TYPE(UPPERCASE, CPP_CAMELCASE)                                \
+  case WireFormatLite::TYPE_##UPPERCASE:                                     \
+    return internal::Packed##CPP_CAMELCASE##Parser(                          \
+        MutableRawRepeatedField(number, extension.type, extension.is_packed, \
+                                extension.descriptor),                       \
+        ptr, ctx);
+      HANDLE_TYPE(INT32, Int32);
+      HANDLE_TYPE(INT64, Int64);
+      HANDLE_TYPE(UINT32, UInt32);
+      HANDLE_TYPE(UINT64, UInt64);
+      HANDLE_TYPE(SINT32, SInt32);
+      HANDLE_TYPE(SINT64, SInt64);
+      HANDLE_TYPE(FIXED32, Fixed32);
+      HANDLE_TYPE(FIXED64, Fixed64);
+      HANDLE_TYPE(SFIXED32, SFixed32);
+      HANDLE_TYPE(SFIXED64, SFixed64);
+      HANDLE_TYPE(FLOAT, Float);
+      HANDLE_TYPE(DOUBLE, Double);
+      HANDLE_TYPE(BOOL, Bool);
+#undef HANDLE_TYPE
+
+      case WireFormatLite::TYPE_ENUM:
+        return internal::PackedEnumParserArg<T>(
+            MutableRawRepeatedField(number, extension.type, extension.is_packed,
+                                    extension.descriptor),
+            ptr, ctx, extension.enum_validity_check.func,
+            extension.enum_validity_check.arg, metadata, number);
+      case WireFormatLite::TYPE_STRING:
+      case WireFormatLite::TYPE_BYTES:
+      case WireFormatLite::TYPE_GROUP:
+      case WireFormatLite::TYPE_MESSAGE:
+        GOOGLE_LOG(FATAL) << "Non-primitive types can't be packed.";
+        break;
+    }
+  } else {
+    switch (extension.type) {
+#define HANDLE_VARINT_TYPE(UPPERCASE, CPP_CAMELCASE)                        \
+  case WireFormatLite::TYPE_##UPPERCASE: {                                  \
+    uint64 value;                                                           \
+    ptr = VarintParse(ptr, &value);                                         \
+    GOOGLE_PROTOBUF_PARSER_ASSERT(ptr);                                    \
+    if (extension.is_repeated) {                                            \
+      Add##CPP_CAMELCASE(number, WireFormatLite::TYPE_##UPPERCASE,          \
+                         extension.is_packed, value, extension.descriptor); \
+    } else {                                                                \
+      Set##CPP_CAMELCASE(number, WireFormatLite::TYPE_##UPPERCASE, value,   \
+                         extension.descriptor);                             \
+    }                                                                       \
+  } break
+
+      HANDLE_VARINT_TYPE(INT32, Int32);
+      HANDLE_VARINT_TYPE(INT64, Int64);
+      HANDLE_VARINT_TYPE(UINT32, UInt32);
+      HANDLE_VARINT_TYPE(UINT64, UInt64);
+      HANDLE_VARINT_TYPE(BOOL, Bool);
+#undef HANDLE_VARINT_TYPE
+#define HANDLE_SVARINT_TYPE(UPPERCASE, CPP_CAMELCASE, SIZE)                 \
+  case WireFormatLite::TYPE_##UPPERCASE: {                                  \
+    uint64 val;                                                             \
+    ptr = VarintParse(ptr, &val);                                           \
+    GOOGLE_PROTOBUF_PARSER_ASSERT(ptr);                                    \
+    auto value = WireFormatLite::ZigZagDecode##SIZE(val);                   \
+    if (extension.is_repeated) {                                            \
+      Add##CPP_CAMELCASE(number, WireFormatLite::TYPE_##UPPERCASE,          \
+                         extension.is_packed, value, extension.descriptor); \
+    } else {                                                                \
+      Set##CPP_CAMELCASE(number, WireFormatLite::TYPE_##UPPERCASE, value,   \
+                         extension.descriptor);                             \
+    }                                                                       \
+  } break
+
+      HANDLE_SVARINT_TYPE(SINT32, Int32, 32);
+      HANDLE_SVARINT_TYPE(SINT64, Int64, 64);
+#undef HANDLE_SVARINT_TYPE
+#define HANDLE_FIXED_TYPE(UPPERCASE, CPP_CAMELCASE, CPPTYPE)                \
+  case WireFormatLite::TYPE_##UPPERCASE: {                                  \
+    auto value = UnalignedLoad<CPPTYPE>(ptr);                               \
+    ptr += sizeof(CPPTYPE);                                                 \
+    if (extension.is_repeated) {                                            \
+      Add##CPP_CAMELCASE(number, WireFormatLite::TYPE_##UPPERCASE,          \
+                         extension.is_packed, value, extension.descriptor); \
+    } else {                                                                \
+      Set##CPP_CAMELCASE(number, WireFormatLite::TYPE_##UPPERCASE, value,   \
+                         extension.descriptor);                             \
+    }                                                                       \
+  } break
+
+      HANDLE_FIXED_TYPE(FIXED32, UInt32, uint32);
+      HANDLE_FIXED_TYPE(FIXED64, UInt64, uint64);
+      HANDLE_FIXED_TYPE(SFIXED32, Int32, int32);
+      HANDLE_FIXED_TYPE(SFIXED64, Int64, int64);
+      HANDLE_FIXED_TYPE(FLOAT, Float, float);
+      HANDLE_FIXED_TYPE(DOUBLE, Double, double);
+#undef HANDLE_FIXED_TYPE
+
+      case WireFormatLite::TYPE_ENUM: {
+        uint64 val;
+        ptr = VarintParse(ptr, &val);
+        GOOGLE_PROTOBUF_PARSER_ASSERT(ptr);
+        int value = val;
+
+        if (!extension.enum_validity_check.func(
+                extension.enum_validity_check.arg, value)) {
+          WriteVarint(number, val, metadata->mutable_unknown_fields<T>());
+        } else if (extension.is_repeated) {
+          AddEnum(number, WireFormatLite::TYPE_ENUM, extension.is_packed, value,
+                  extension.descriptor);
+        } else {
+          SetEnum(number, WireFormatLite::TYPE_ENUM, value,
+                  extension.descriptor);
+        }
+        break;
+      }
+
+      case WireFormatLite::TYPE_BYTES:
+      case WireFormatLite::TYPE_STRING: {
+        std::string* value =
+            extension.is_repeated
+                ? AddString(number, WireFormatLite::TYPE_STRING,
+                            extension.descriptor)
+                : MutableString(number, WireFormatLite::TYPE_STRING,
+                                extension.descriptor);
+        int size = ReadSize(&ptr);
+        GOOGLE_PROTOBUF_PARSER_ASSERT(ptr);
+        return ctx->ReadString(ptr, size, value);
+      }
+
+      case WireFormatLite::TYPE_GROUP: {
+        MessageLite* value =
+            extension.is_repeated
+                ? AddMessage(number, WireFormatLite::TYPE_GROUP,
+                             *extension.message_info.prototype,
+                             extension.descriptor)
+                : MutableMessage(number, WireFormatLite::TYPE_GROUP,
+                                 *extension.message_info.prototype,
+                                 extension.descriptor);
+        uint32 tag = (number << 3) + WireFormatLite::WIRETYPE_START_GROUP;
+        return ctx->ParseGroup(value, ptr, tag);
+      }
+
+      case WireFormatLite::TYPE_MESSAGE: {
+        MessageLite* value =
+            extension.is_repeated
+                ? AddMessage(number, WireFormatLite::TYPE_MESSAGE,
+                             *extension.message_info.prototype,
+                             extension.descriptor)
+                : MutableMessage(number, WireFormatLite::TYPE_MESSAGE,
+                                 *extension.message_info.prototype,
+                                 extension.descriptor);
+        return ctx->ParseMessage(value, ptr);
+      }
+    }
+  }
+  return ptr;
+}
+
+template <typename Msg, typename T>
+const char* ExtensionSet::ParseMessageSetItemTmpl(
+    const char* ptr, const Msg* containing_type,
+    internal::InternalMetadata* metadata, internal::ParseContext* ctx) {
+  std::string payload;
+  uint32 type_id = 0;
+  bool payload_read = false;
+  while (!ctx->Done(&ptr)) {
+    uint32 tag = static_cast<uint8>(*ptr++);
+    if (tag == WireFormatLite::kMessageSetTypeIdTag) {
+      uint64 tmp;
+      ptr = ParseBigVarint(ptr, &tmp);
+      GOOGLE_PROTOBUF_PARSER_ASSERT(ptr);
+      type_id = tmp;
+      if (payload_read) {
+        ExtensionInfo extension;
+        bool was_packed_on_wire;
+        if (!FindExtension(2, type_id, containing_type, ctx, &extension,
+                           &was_packed_on_wire)) {
+          WriteLengthDelimited(type_id, payload,
+                               metadata->mutable_unknown_fields<T>());
+        } else {
+          MessageLite* value =
+              extension.is_repeated
+                  ? AddMessage(type_id, WireFormatLite::TYPE_MESSAGE,
+                               *extension.message_info.prototype,
+                               extension.descriptor)
+                  : MutableMessage(type_id, WireFormatLite::TYPE_MESSAGE,
+                                   *extension.message_info.prototype,
+                                   extension.descriptor);
+
+          const char* p;
+          // We can't use regular parse from string as we have to track
+          // proper recursion depth and descriptor pools.
+          ParseContext tmp_ctx(ctx->depth(), false, &p, payload);
+          tmp_ctx.data().pool = ctx->data().pool;
+          tmp_ctx.data().factory = ctx->data().factory;
+          GOOGLE_PROTOBUF_PARSER_ASSERT(value->_InternalParse(p, &tmp_ctx) &&
+                                         tmp_ctx.EndedAtLimit());
+        }
+        type_id = 0;
+      }
+    } else if (tag == WireFormatLite::kMessageSetMessageTag) {
+      if (type_id != 0) {
+        ptr = ParseFieldMaybeLazily(static_cast<uint64>(type_id) * 8 + 2, ptr,
+                                    containing_type, metadata, ctx);
+        GOOGLE_PROTOBUF_PARSER_ASSERT(ptr != nullptr);
+        type_id = 0;
+      } else {
+        int32 size = ReadSize(&ptr);
+        GOOGLE_PROTOBUF_PARSER_ASSERT(ptr);
+        ptr = ctx->ReadString(ptr, size, &payload);
+        GOOGLE_PROTOBUF_PARSER_ASSERT(ptr);
+        payload_read = true;
+      }
+    } else {
+      ptr = ReadTag(ptr - 1, &tag);
+      if (tag == 0 || (tag & 7) == 4) {
+        ctx->SetLastTag(tag);
+        return ptr;
+      }
+      ptr = ParseField(tag, ptr, containing_type, metadata, ctx);
+      GOOGLE_PROTOBUF_PARSER_ASSERT(ptr);
+    }
+  }
+  return ptr;
+}
+
+}  // namespace internal
+}  // namespace protobuf
+}  // namespace google
+
+#endif  // GOOGLE_PROTOBUF_EXTENSION_SET_INL_H__
+
+#else
+#error "This file should not be included when either TORCH_STABLE_ONLY or TORCH_TARGET_VERSION is defined."
+#endif  // !defined(TORCH_STABLE_ONLY) && !defined(TORCH_TARGET_VERSION)

Prism/LLaDA/LLaDA_Prism/.venv/lib/python3.12/site-packages/torch/include/google/protobuf/field_mask.pb.h

@@ -0,0 +1,320 @@
+#if !defined(TORCH_STABLE_ONLY) && !defined(TORCH_TARGET_VERSION)
+// Generated by the protocol buffer compiler.  DO NOT EDIT!
+// source: google/protobuf/field_mask.proto
+
+#ifndef GOOGLE_PROTOBUF_INCLUDED_google_2fprotobuf_2ffield_5fmask_2eproto
+#define GOOGLE_PROTOBUF_INCLUDED_google_2fprotobuf_2ffield_5fmask_2eproto
+
+#include <limits>
+#include <string>
+
+#include <google/protobuf/port_def.inc>
+#if PROTOBUF_VERSION < 3013000
+#error This file was generated by a newer version of protoc which is
+#error incompatible with your Protocol Buffer headers. Please update
+#error your headers.
+#endif
+#if 3013000 < PROTOBUF_MIN_PROTOC_VERSION
+#error This file was generated by an older version of protoc which is
+#error incompatible with your Protocol Buffer headers. Please
+#error regenerate this file with a newer version of protoc.
+#endif
+
+#include <google/protobuf/port_undef.inc>
+#include <google/protobuf/io/coded_stream.h>
+#include <google/protobuf/arena.h>
+#include <google/protobuf/arenastring.h>
+#include <google/protobuf/generated_message_table_driven.h>
+#include <google/protobuf/generated_message_util.h>
+#include <google/protobuf/inlined_string_field.h>
+#include <google/protobuf/metadata_lite.h>
+#include <google/protobuf/generated_message_reflection.h>
+#include <google/protobuf/message.h>
+#include <google/protobuf/repeated_field.h>  // IWYU pragma: export
+#include <google/protobuf/extension_set.h>  // IWYU pragma: export
+#include <google/protobuf/unknown_field_set.h>
+// @@protoc_insertion_point(includes)
+#include <google/protobuf/port_def.inc>
+#define PROTOBUF_INTERNAL_EXPORT_google_2fprotobuf_2ffield_5fmask_2eproto PROTOBUF_EXPORT
+PROTOBUF_NAMESPACE_OPEN
+namespace internal {
+class AnyMetadata;
+}  // namespace internal
+PROTOBUF_NAMESPACE_CLOSE
+
+// Internal implementation detail -- do not use these members.
+struct PROTOBUF_EXPORT TableStruct_google_2fprotobuf_2ffield_5fmask_2eproto {
+  static const ::PROTOBUF_NAMESPACE_ID::internal::ParseTableField entries[]
+    PROTOBUF_SECTION_VARIABLE(protodesc_cold);
+  static const ::PROTOBUF_NAMESPACE_ID::internal::AuxiliaryParseTableField aux[]
+    PROTOBUF_SECTION_VARIABLE(protodesc_cold);
+  static const ::PROTOBUF_NAMESPACE_ID::internal::ParseTable schema[1]
+    PROTOBUF_SECTION_VARIABLE(protodesc_cold);
+  static const ::PROTOBUF_NAMESPACE_ID::internal::FieldMetadata field_metadata[];
+  static const ::PROTOBUF_NAMESPACE_ID::internal::SerializationTable serialization_table[];
+  static const ::PROTOBUF_NAMESPACE_ID::uint32 offsets[];
+};
+extern PROTOBUF_EXPORT const ::PROTOBUF_NAMESPACE_ID::internal::DescriptorTable descriptor_table_google_2fprotobuf_2ffield_5fmask_2eproto;
+PROTOBUF_NAMESPACE_OPEN
+class FieldMask;
+class FieldMaskDefaultTypeInternal;
+PROTOBUF_EXPORT extern FieldMaskDefaultTypeInternal _FieldMask_default_instance_;
+PROTOBUF_NAMESPACE_CLOSE
+PROTOBUF_NAMESPACE_OPEN
+template<> PROTOBUF_EXPORT PROTOBUF_NAMESPACE_ID::FieldMask* Arena::CreateMaybeMessage<PROTOBUF_NAMESPACE_ID::FieldMask>(Arena*);
+PROTOBUF_NAMESPACE_CLOSE
+PROTOBUF_NAMESPACE_OPEN
+
+// ===================================================================
+
+class PROTOBUF_EXPORT FieldMask PROTOBUF_FINAL :
+    public ::PROTOBUF_NAMESPACE_ID::Message /* @@protoc_insertion_point(class_definition:google.protobuf.FieldMask) */ {
+ public:
+  inline FieldMask() : FieldMask(nullptr) {}
+  virtual ~FieldMask();
+
+  FieldMask(const FieldMask& from);
+  FieldMask(FieldMask&& from) noexcept
+    : FieldMask() {
+    *this = ::std::move(from);
+  }
+
+  inline FieldMask& operator=(const FieldMask& from) {
+    CopyFrom(from);
+    return *this;
+  }
+  inline FieldMask& operator=(FieldMask&& from) noexcept {
+    if (GetArena() == from.GetArena()) {
+      if (this != &from) InternalSwap(&from);
+    } else {
+      CopyFrom(from);
+    }
+    return *this;
+  }
+
+  static const ::PROTOBUF_NAMESPACE_ID::Descriptor* descriptor() {
+    return GetDescriptor();
+  }
+  static const ::PROTOBUF_NAMESPACE_ID::Descriptor* GetDescriptor() {
+    return GetMetadataStatic().descriptor;
+  }
+  static const ::PROTOBUF_NAMESPACE_ID::Reflection* GetReflection() {
+    return GetMetadataStatic().reflection;
+  }
+  static const FieldMask& default_instance();
+
+  static void InitAsDefaultInstance();  // FOR INTERNAL USE ONLY
+  static inline const FieldMask* internal_default_instance() {
+    return reinterpret_cast<const FieldMask*>(
+               &_FieldMask_default_instance_);
+  }
+  static constexpr int kIndexInFileMessages =
+    0;
+
+  friend void swap(FieldMask& a, FieldMask& b) {
+    a.Swap(&b);
+  }
+  inline void Swap(FieldMask* other) {
+    if (other == this) return;
+    if (GetArena() == other->GetArena()) {
+      InternalSwap(other);
+    } else {
+      ::PROTOBUF_NAMESPACE_ID::internal::GenericSwap(this, other);
+    }
+  }
+  void UnsafeArenaSwap(FieldMask* other) {
+    if (other == this) return;
+    GOOGLE_DCHECK(GetArena() == other->GetArena());
+    InternalSwap(other);
+  }
+
+  // implements Message ----------------------------------------------
+
+  inline FieldMask* New() const final {
+    return CreateMaybeMessage<FieldMask>(nullptr);
+  }
+
+  FieldMask* New(::PROTOBUF_NAMESPACE_ID::Arena* arena) const final {
+    return CreateMaybeMessage<FieldMask>(arena);
+  }
+  void CopyFrom(const ::PROTOBUF_NAMESPACE_ID::Message& from) final;
+  void MergeFrom(const ::PROTOBUF_NAMESPACE_ID::Message& from) final;
+  void CopyFrom(const FieldMask& from);
+  void MergeFrom(const FieldMask& from);
+  PROTOBUF_ATTRIBUTE_REINITIALIZES void Clear() final;
+  bool IsInitialized() const final;
+
+  size_t ByteSizeLong() const final;
+  const char* _InternalParse(const char* ptr, ::PROTOBUF_NAMESPACE_ID::internal::ParseContext* ctx) final;
+  ::PROTOBUF_NAMESPACE_ID::uint8* _InternalSerialize(
+      ::PROTOBUF_NAMESPACE_ID::uint8* target, ::PROTOBUF_NAMESPACE_ID::io::EpsCopyOutputStream* stream) const final;
+  int GetCachedSize() const final { return _cached_size_.Get(); }
+
+  private:
+  inline void SharedCtor();
+  inline void SharedDtor();
+  void SetCachedSize(int size) const final;
+  void InternalSwap(FieldMask* other);
+  friend class ::PROTOBUF_NAMESPACE_ID::internal::AnyMetadata;
+  static ::PROTOBUF_NAMESPACE_ID::StringPiece FullMessageName() {
+    return "google.protobuf.FieldMask";
+  }
+  protected:
+  explicit FieldMask(::PROTOBUF_NAMESPACE_ID::Arena* arena);
+  private:
+  static void ArenaDtor(void* object);
+  inline void RegisterArenaDtor(::PROTOBUF_NAMESPACE_ID::Arena* arena);
+  public:
+
+  ::PROTOBUF_NAMESPACE_ID::Metadata GetMetadata() const final;
+  private:
+  static ::PROTOBUF_NAMESPACE_ID::Metadata GetMetadataStatic() {
+    ::PROTOBUF_NAMESPACE_ID::internal::AssignDescriptors(&::descriptor_table_google_2fprotobuf_2ffield_5fmask_2eproto);
+    return ::descriptor_table_google_2fprotobuf_2ffield_5fmask_2eproto.file_level_metadata[kIndexInFileMessages];
+  }
+
+  public:
+
+  // nested types ----------------------------------------------------
+
+  // accessors -------------------------------------------------------
+
+  enum : int {
+    kPathsFieldNumber = 1,
+  };
+  // repeated string paths = 1;
+  int paths_size() const;
+  private:
+  int _internal_paths_size() const;
+  public:
+  void clear_paths();
+  const std::string& paths(int index) const;
+  std::string* mutable_paths(int index);
+  void set_paths(int index, const std::string& value);
+  void set_paths(int index, std::string&& value);
+  void set_paths(int index, const char* value);
+  void set_paths(int index, const char* value, size_t size);
+  std::string* add_paths();
+  void add_paths(const std::string& value);
+  void add_paths(std::string&& value);
+  void add_paths(const char* value);
+  void add_paths(const char* value, size_t size);
+  const ::PROTOBUF_NAMESPACE_ID::RepeatedPtrField<std::string>& paths() const;
+  ::PROTOBUF_NAMESPACE_ID::RepeatedPtrField<std::string>* mutable_paths();
+  private:
+  const std::string& _internal_paths(int index) const;
+  std::string* _internal_add_paths();
+  public:
+
+  // @@protoc_insertion_point(class_scope:google.protobuf.FieldMask)
+ private:
+  class _Internal;
+
+  template <typename T> friend class ::PROTOBUF_NAMESPACE_ID::Arena::InternalHelper;
+  typedef void InternalArenaConstructable_;
+  typedef void DestructorSkippable_;
+  ::PROTOBUF_NAMESPACE_ID::RepeatedPtrField<std::string> paths_;
+  mutable ::PROTOBUF_NAMESPACE_ID::internal::CachedSize _cached_size_;
+  friend struct ::TableStruct_google_2fprotobuf_2ffield_5fmask_2eproto;
+};
+// ===================================================================
+
+
+// ===================================================================
+
+#ifdef __GNUC__
+  #pragma GCC diagnostic push
+  #pragma GCC diagnostic ignored "-Wstrict-aliasing"
+#endif  // __GNUC__
+// FieldMask
+
+// repeated string paths = 1;
+inline int FieldMask::_internal_paths_size() const {
+  return paths_.size();
+}
+inline int FieldMask::paths_size() const {
+  return _internal_paths_size();
+}
+inline void FieldMask::clear_paths() {
+  paths_.Clear();
+}
+inline std::string* FieldMask::add_paths() {
+  // @@protoc_insertion_point(field_add_mutable:google.protobuf.FieldMask.paths)
+  return _internal_add_paths();
+}
+inline const std::string& FieldMask::_internal_paths(int index) const {
+  return paths_.Get(index);
+}
+inline const std::string& FieldMask::paths(int index) const {
+  // @@protoc_insertion_point(field_get:google.protobuf.FieldMask.paths)
+  return _internal_paths(index);
+}
+inline std::string* FieldMask::mutable_paths(int index) {
+  // @@protoc_insertion_point(field_mutable:google.protobuf.FieldMask.paths)
+  return paths_.Mutable(index);
+}
+inline void FieldMask::set_paths(int index, const std::string& value) {
+  // @@protoc_insertion_point(field_set:google.protobuf.FieldMask.paths)
+  paths_.Mutable(index)->assign(value);
+}
+inline void FieldMask::set_paths(int index, std::string&& value) {
+  // @@protoc_insertion_point(field_set:google.protobuf.FieldMask.paths)
+  paths_.Mutable(index)->assign(std::move(value));
+}
+inline void FieldMask::set_paths(int index, const char* value) {
+  GOOGLE_DCHECK(value != nullptr);
+  paths_.Mutable(index)->assign(value);
+  // @@protoc_insertion_point(field_set_char:google.protobuf.FieldMask.paths)
+}
+inline void FieldMask::set_paths(int index, const char* value, size_t size) {
+  paths_.Mutable(index)->assign(
+    reinterpret_cast<const char*>(value), size);
+  // @@protoc_insertion_point(field_set_pointer:google.protobuf.FieldMask.paths)
+}
+inline std::string* FieldMask::_internal_add_paths() {
+  return paths_.Add();
+}
+inline void FieldMask::add_paths(const std::string& value) {
+  paths_.Add()->assign(value);
+  // @@protoc_insertion_point(field_add:google.protobuf.FieldMask.paths)
+}
+inline void FieldMask::add_paths(std::string&& value) {
+  paths_.Add(std::move(value));
+  // @@protoc_insertion_point(field_add:google.protobuf.FieldMask.paths)
+}
+inline void FieldMask::add_paths(const char* value) {
+  GOOGLE_DCHECK(value != nullptr);
+  paths_.Add()->assign(value);
+  // @@protoc_insertion_point(field_add_char:google.protobuf.FieldMask.paths)
+}
+inline void FieldMask::add_paths(const char* value, size_t size) {
+  paths_.Add()->assign(reinterpret_cast<const char*>(value), size);
+  // @@protoc_insertion_point(field_add_pointer:google.protobuf.FieldMask.paths)
+}
+inline const ::PROTOBUF_NAMESPACE_ID::RepeatedPtrField<std::string>&
+FieldMask::paths() const {
+  // @@protoc_insertion_point(field_list:google.protobuf.FieldMask.paths)
+  return paths_;
+}
+inline ::PROTOBUF_NAMESPACE_ID::RepeatedPtrField<std::string>*
+FieldMask::mutable_paths() {
+  // @@protoc_insertion_point(field_mutable_list:google.protobuf.FieldMask.paths)
+  return &paths_;
+}
+
+#ifdef __GNUC__
+  #pragma GCC diagnostic pop
+#endif  // __GNUC__
+
+// @@protoc_insertion_point(namespace_scope)
+
+PROTOBUF_NAMESPACE_CLOSE
+
+// @@protoc_insertion_point(global_scope)
+
+#include <google/protobuf/port_undef.inc>
+#endif  // GOOGLE_PROTOBUF_INCLUDED_GOOGLE_PROTOBUF_INCLUDED_google_2fprotobuf_2ffield_5fmask_2eproto
+
+#else
+#error "This file should not be included when either TORCH_STABLE_ONLY or TORCH_TARGET_VERSION is defined."
+#endif  // !defined(TORCH_STABLE_ONLY) && !defined(TORCH_TARGET_VERSION)

Prism/LLaDA/LLaDA_Prism/.venv/lib/python3.12/site-packages/torch/include/google/protobuf/generated_message_table_driven.h

@@ -0,0 +1,344 @@
+#if !defined(TORCH_STABLE_ONLY) && !defined(TORCH_TARGET_VERSION)
+// Protocol Buffers - Google's data interchange format
+// Copyright 2008 Google Inc.  All rights reserved.
+// https://developers.google.com/protocol-buffers/
+//
+// 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 Google Inc. nor the names of its
+// contributors may be used to endorse or promote products derived from
+// this software without specific prior written permission.
+//
+// THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
+// "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
+// LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
+// A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT
+// OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
+// SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT
+// LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
+// DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
+// THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+// (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+// OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+
+#ifndef GOOGLE_PROTOBUF_GENERATED_MESSAGE_TABLE_DRIVEN_H__
+#define GOOGLE_PROTOBUF_GENERATED_MESSAGE_TABLE_DRIVEN_H__
+
+#include <google/protobuf/map.h>
+#include <google/protobuf/map_entry_lite.h>
+#include <google/protobuf/map_field_lite.h>
+#include <google/protobuf/message_lite.h>
+#include <google/protobuf/wire_format_lite.h>
+
+// We require C++11 and Clang to use constexpr for variables, as GCC 4.8
+// requires constexpr to be consistent between declarations of variables
+// unnecessarily (see https://gcc.gnu.org/bugzilla/show_bug.cgi?id=58541).
+// VS 2017 Update 3 also supports this usage of constexpr.
+#if defined(__clang__) || (defined(_MSC_VER) && _MSC_VER >= 1911)
+#define PROTOBUF_CONSTEXPR_VAR constexpr
+#else  // !__clang__
+#define PROTOBUF_CONSTEXPR_VAR
+#endif  // !_clang
+
+#ifdef SWIG
+#error "You cannot SWIG proto headers"
+#endif
+
+#include <google/protobuf/port_def.inc>
+
+namespace google {
+namespace protobuf {
+namespace internal {
+
+// Processing-type masks.
+static constexpr const unsigned char kOneofMask = 0x40;
+static constexpr const unsigned char kRepeatedMask = 0x20;
+// Mask for the raw type: either a WireFormatLite::FieldType or one of the
+// ProcessingTypes below, without the oneof or repeated flag.
+static constexpr const unsigned char kTypeMask = 0x1f;
+
+// Wire type masks.
+static constexpr const unsigned char kNotPackedMask = 0x10;
+static constexpr const unsigned char kInvalidMask = 0x20;
+
+enum ProcessingTypes {
+  TYPE_STRING_CORD = 19,
+  TYPE_STRING_STRING_PIECE = 20,
+  TYPE_BYTES_CORD = 21,
+  TYPE_BYTES_STRING_PIECE = 22,
+  TYPE_STRING_INLINED = 23,
+  TYPE_BYTES_INLINED = 24,
+  TYPE_MAP = 25,
+};
+
+static_assert(TYPE_MAP < kRepeatedMask, "Invalid enum");
+
+struct PROTOBUF_EXPORT FieldMetadata {
+  uint32 offset;  // offset of this field in the struct
+  uint32 tag;     // field * 8 + wire_type
+  // byte offset * 8 + bit_offset;
+  // if the high bit is set then this is the byte offset of the oneof_case
+  // for this field.
+  uint32 has_offset;
+  uint32 type;      // the type of this field.
+  const void* ptr;  // auxiliary data
+
+  // From the serializer point of view each fundamental type can occur in
+  // 4 different ways. For simplicity we treat all combinations as a cartesion
+  // product although not all combinations are allowed.
+  enum FieldTypeClass {
+    kPresence,
+    kNoPresence,
+    kRepeated,
+    kPacked,
+    kOneOf,
+    kNumTypeClasses  // must be last enum
+  };
+  // C++ protobuf has 20 fundamental types, were we added Cord and StringPiece
+  // and also distinquish the same types if they have different wire format.
+  enum {
+    kCordType = 19,
+    kStringPieceType = 20,
+    kInlinedType = 21,
+    kNumTypes = 21,
+    kSpecial = kNumTypes * kNumTypeClasses,
+  };
+
+  static int CalculateType(int fundamental_type, FieldTypeClass type_class);
+};
+
+// TODO(ckennelly):  Add a static assertion to ensure that these masks do not
+// conflict with wiretypes.
+
+// ParseTableField is kept small to help simplify instructions for computing
+// offsets, as we will always need this information to parse a field.
+// Additional data, needed for some types, is stored in
+// AuxiliaryParseTableField.
+struct ParseTableField {
+  uint32 offset;
+  // The presence_index ordinarily represents a has_bit index, but for fields
+  // inside a oneof it represents the index in _oneof_case_.
+  uint32 presence_index;
+  unsigned char normal_wiretype;
+  unsigned char packed_wiretype;
+
+  // processing_type is given by:
+  //   (FieldDescriptor->type() << 1) | FieldDescriptor->is_packed()
+  unsigned char processing_type;
+
+  unsigned char tag_size;
+};
+
+struct ParseTable;
+
+union AuxiliaryParseTableField {
+  typedef bool (*EnumValidator)(int);
+
+  // Enums
+  struct enum_aux {
+    EnumValidator validator;
+  };
+  enum_aux enums;
+  // Group, messages
+  struct message_aux {
+    // ExplicitlyInitialized<T> -> T requires a reinterpret_cast, which prevents
+    // the tables from being constructed as a constexpr.  We use void to avoid
+    // the cast.
+    const void* default_message_void;
+    const MessageLite* default_message() const {
+      return static_cast<const MessageLite*>(default_message_void);
+    }
+  };
+  message_aux messages;
+  // Strings
+  struct string_aux {
+    const void* default_ptr;
+    const char* field_name;
+  };
+  string_aux strings;
+
+  struct map_aux {
+    bool (*parse_map)(io::CodedInputStream*, void*);
+  };
+  map_aux maps;
+
+  AuxiliaryParseTableField() = default;
+  constexpr AuxiliaryParseTableField(AuxiliaryParseTableField::enum_aux e)
+      : enums(e) {}
+  constexpr AuxiliaryParseTableField(AuxiliaryParseTableField::message_aux m)
+      : messages(m) {}
+  constexpr AuxiliaryParseTableField(AuxiliaryParseTableField::string_aux s)
+      : strings(s) {}
+  constexpr AuxiliaryParseTableField(AuxiliaryParseTableField::map_aux m)
+      : maps(m) {}
+};
+
+struct ParseTable {
+  const ParseTableField* fields;
+  const AuxiliaryParseTableField* aux;
+  int max_field_number;
+  // TODO(ckennelly): Do something with this padding.
+
+  // TODO(ckennelly): Vet these for sign extension.
+  int64 has_bits_offset;
+  int64 oneof_case_offset;
+  int64 extension_offset;
+  int64 arena_offset;
+
+  // ExplicitlyInitialized<T> -> T requires a reinterpret_cast, which prevents
+  // the tables from being constructed as a constexpr.  We use void to avoid
+  // the cast.
+  const void* default_instance_void;
+  const MessageLite* default_instance() const {
+    return static_cast<const MessageLite*>(default_instance_void);
+  }
+
+  bool unknown_field_set;
+};
+
+static_assert(sizeof(ParseTableField) <= 16, "ParseTableField is too large");
+// The tables must be composed of POD components to ensure link-time
+// initialization.
+static_assert(std::is_pod<ParseTableField>::value, "");
+static_assert(std::is_pod<AuxiliaryParseTableField>::value, "");
+static_assert(std::is_pod<AuxiliaryParseTableField::enum_aux>::value, "");
+static_assert(std::is_pod<AuxiliaryParseTableField::message_aux>::value, "");
+static_assert(std::is_pod<AuxiliaryParseTableField::string_aux>::value, "");
+static_assert(std::is_pod<ParseTable>::value, "");
+
+// TODO(ckennelly): Consolidate these implementations into a single one, using
+// dynamic dispatch to the appropriate unknown field handler.
+bool MergePartialFromCodedStream(MessageLite* msg, const ParseTable& table,
+                                 io::CodedInputStream* input);
+bool MergePartialFromCodedStreamLite(MessageLite* msg, const ParseTable& table,
+                                     io::CodedInputStream* input);
+
+template <typename Entry>
+bool ParseMap(io::CodedInputStream* input, void* map_field) {
+  typedef typename MapEntryToMapField<Entry>::MapFieldType MapFieldType;
+  typedef Map<typename Entry::EntryKeyType, typename Entry::EntryValueType>
+      MapType;
+  typedef typename Entry::template Parser<MapFieldType, MapType> ParserType;
+
+  ParserType parser(static_cast<MapFieldType*>(map_field));
+  return WireFormatLite::ReadMessageNoVirtual(input, &parser);
+}
+
+struct SerializationTable {
+  int num_fields;
+  const FieldMetadata* field_table;
+};
+
+PROTOBUF_EXPORT void SerializeInternal(const uint8* base,
+                                       const FieldMetadata* table,
+                                       int32 num_fields,
+                                       io::CodedOutputStream* output);
+
+inline void TableSerialize(const MessageLite& msg,
+                           const SerializationTable* table,
+                           io::CodedOutputStream* output) {
+  const FieldMetadata* field_table = table->field_table;
+  int num_fields = table->num_fields - 1;
+  const uint8* base = reinterpret_cast<const uint8*>(&msg);
+  // TODO(gerbens) This skips the first test if we could use the fast
+  // array serialization path, we should make this
+  // int cached_size =
+  //    *reinterpret_cast<const int32*>(base + field_table->offset);
+  // SerializeWithCachedSize(msg, field_table + 1, num_fields, cached_size, ...)
+  // But we keep conformance with the old way for now.
+  SerializeInternal(base, field_table + 1, num_fields, output);
+}
+
+uint8* SerializeInternalToArray(const uint8* base, const FieldMetadata* table,
+                                int32 num_fields, bool is_deterministic,
+                                uint8* buffer);
+
+inline uint8* TableSerializeToArray(const MessageLite& msg,
+                                    const SerializationTable* table,
+                                    bool is_deterministic, uint8* buffer) {
+  const uint8* base = reinterpret_cast<const uint8*>(&msg);
+  const FieldMetadata* field_table = table->field_table + 1;
+  int num_fields = table->num_fields - 1;
+  return SerializeInternalToArray(base, field_table, num_fields,
+                                  is_deterministic, buffer);
+}
+
+template <typename T>
+struct CompareHelper {
+  bool operator()(const T& a, const T& b) const { return a < b; }
+};
+
+template <>
+struct CompareHelper<ArenaStringPtr> {
+  bool operator()(const ArenaStringPtr& a, const ArenaStringPtr& b) const {
+    return a.Get() < b.Get();
+  }
+};
+
+struct CompareMapKey {
+  template <typename T>
+  bool operator()(const MapEntryHelper<T>& a,
+                  const MapEntryHelper<T>& b) const {
+    return Compare(a.key_, b.key_);
+  }
+  template <typename T>
+  bool Compare(const T& a, const T& b) const {
+    return CompareHelper<T>()(a, b);
+  }
+};
+
+template <typename MapFieldType, const SerializationTable* table>
+void MapFieldSerializer(const uint8* base, uint32 offset, uint32 tag,
+                        uint32 has_offset, io::CodedOutputStream* output) {
+  typedef MapEntryHelper<typename MapFieldType::EntryTypeTrait> Entry;
+  typedef typename MapFieldType::MapType::const_iterator Iter;
+
+  const MapFieldType& map_field =
+      *reinterpret_cast<const MapFieldType*>(base + offset);
+  const SerializationTable* t =
+      table +
+      has_offset;  // has_offset is overloaded for maps to mean table offset
+  if (!output->IsSerializationDeterministic()) {
+    for (Iter it = map_field.GetMap().begin(); it != map_field.GetMap().end();
+         ++it) {
+      Entry map_entry(*it);
+      output->WriteVarint32(tag);
+      output->WriteVarint32(map_entry._cached_size_);
+      SerializeInternal(reinterpret_cast<const uint8*>(&map_entry),
+                        t->field_table, t->num_fields, output);
+    }
+  } else {
+    std::vector<Entry> v;
+    for (Iter it = map_field.GetMap().begin(); it != map_field.GetMap().end();
+         ++it) {
+      v.push_back(Entry(*it));
+    }
+    std::sort(v.begin(), v.end(), CompareMapKey());
+    for (int i = 0; i < v.size(); i++) {
+      output->WriteVarint32(tag);
+      output->WriteVarint32(v[i]._cached_size_);
+      SerializeInternal(reinterpret_cast<const uint8*>(&v[i]), t->field_table,
+                        t->num_fields, output);
+    }
+  }
+}
+
+}  // namespace internal
+}  // namespace protobuf
+}  // namespace google
+
+#include <google/protobuf/port_undef.inc>
+
+#endif  // GOOGLE_PROTOBUF_GENERATED_MESSAGE_TABLE_DRIVEN_H__
+
+#else
+#error "This file should not be included when either TORCH_STABLE_ONLY or TORCH_TARGET_VERSION is defined."
+#endif  // !defined(TORCH_STABLE_ONLY) && !defined(TORCH_TARGET_VERSION)