diff --git a/env-llmeval/lib/python3.10/site-packages/numpy/_utils/__init__.py b/env-llmeval/lib/python3.10/site-packages/numpy/_utils/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..388dd9174f356c74d6cdd6ad9a8b1ad603234420 --- /dev/null +++ b/env-llmeval/lib/python3.10/site-packages/numpy/_utils/__init__.py @@ -0,0 +1,29 @@ +""" +This is a module for defining private helpers which do not depend on the +rest of NumPy. + +Everything in here must be self-contained so that it can be +imported anywhere else without creating circular imports. +If a utility requires the import of NumPy, it probably belongs +in ``numpy.core``. +""" + +from ._convertions import asunicode, asbytes + + +def set_module(module): + """Private decorator for overriding __module__ on a function or class. + + Example usage:: + + @set_module('numpy') + def example(): + pass + + assert example.__module__ == 'numpy' + """ + def decorator(func): + if module is not None: + func.__module__ = module + return func + return decorator diff --git a/env-llmeval/lib/python3.10/site-packages/numpy/_utils/__pycache__/__init__.cpython-310.pyc b/env-llmeval/lib/python3.10/site-packages/numpy/_utils/__pycache__/__init__.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..3956d7387043d1386cbd7709a89ff1c87956c072 Binary files /dev/null and b/env-llmeval/lib/python3.10/site-packages/numpy/_utils/__pycache__/__init__.cpython-310.pyc differ diff --git a/env-llmeval/lib/python3.10/site-packages/numpy/_utils/__pycache__/_convertions.cpython-310.pyc b/env-llmeval/lib/python3.10/site-packages/numpy/_utils/__pycache__/_convertions.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..3fc454b7164fef7ce21bca338297065ac20e011a Binary files /dev/null and b/env-llmeval/lib/python3.10/site-packages/numpy/_utils/__pycache__/_convertions.cpython-310.pyc differ diff --git a/env-llmeval/lib/python3.10/site-packages/numpy/_utils/__pycache__/_inspect.cpython-310.pyc b/env-llmeval/lib/python3.10/site-packages/numpy/_utils/__pycache__/_inspect.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..1e25bd5db5f07af8d0a6158f20665c3939318345 Binary files /dev/null and b/env-llmeval/lib/python3.10/site-packages/numpy/_utils/__pycache__/_inspect.cpython-310.pyc differ diff --git a/env-llmeval/lib/python3.10/site-packages/numpy/_utils/__pycache__/_pep440.cpython-310.pyc b/env-llmeval/lib/python3.10/site-packages/numpy/_utils/__pycache__/_pep440.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..02bb52a7d348d634ec24dd146a5394e79ee257d8 Binary files /dev/null and b/env-llmeval/lib/python3.10/site-packages/numpy/_utils/__pycache__/_pep440.cpython-310.pyc differ diff --git a/env-llmeval/lib/python3.10/site-packages/numpy/_utils/_convertions.py b/env-llmeval/lib/python3.10/site-packages/numpy/_utils/_convertions.py new file mode 100644 index 0000000000000000000000000000000000000000..ab15a8ba019f1b6a40ac3f562897fa4581323efc --- /dev/null +++ b/env-llmeval/lib/python3.10/site-packages/numpy/_utils/_convertions.py @@ -0,0 +1,18 @@ +""" +A set of methods retained from np.compat module that +are still used across codebase. +""" + +__all__ = ["asunicode", "asbytes"] + + +def asunicode(s): + if isinstance(s, bytes): + return s.decode('latin1') + return str(s) + + +def asbytes(s): + if isinstance(s, bytes): + return s + return str(s).encode('latin1') diff --git a/env-llmeval/lib/python3.10/site-packages/numpy/_utils/_inspect.py b/env-llmeval/lib/python3.10/site-packages/numpy/_utils/_inspect.py new file mode 100644 index 0000000000000000000000000000000000000000..9a874a71dd0a53e25a671c51bfdceec850702bfe --- /dev/null +++ b/env-llmeval/lib/python3.10/site-packages/numpy/_utils/_inspect.py @@ -0,0 +1,191 @@ +"""Subset of inspect module from upstream python + +We use this instead of upstream because upstream inspect is slow to import, and +significantly contributes to numpy import times. Importing this copy has almost +no overhead. + +""" +import types + +__all__ = ['getargspec', 'formatargspec'] + +# ----------------------------------------------------------- type-checking +def ismethod(object): + """Return true if the object is an instance method. + + Instance method objects provide these attributes: + __doc__ documentation string + __name__ name with which this method was defined + im_class class object in which this method belongs + im_func function object containing implementation of method + im_self instance to which this method is bound, or None + + """ + return isinstance(object, types.MethodType) + +def isfunction(object): + """Return true if the object is a user-defined function. + + Function objects provide these attributes: + __doc__ documentation string + __name__ name with which this function was defined + func_code code object containing compiled function bytecode + func_defaults tuple of any default values for arguments + func_doc (same as __doc__) + func_globals global namespace in which this function was defined + func_name (same as __name__) + + """ + return isinstance(object, types.FunctionType) + +def iscode(object): + """Return true if the object is a code object. + + Code objects provide these attributes: + co_argcount number of arguments (not including * or ** args) + co_code string of raw compiled bytecode + co_consts tuple of constants used in the bytecode + co_filename name of file in which this code object was created + co_firstlineno number of first line in Python source code + co_flags bitmap: 1=optimized | 2=newlocals | 4=*arg | 8=**arg + co_lnotab encoded mapping of line numbers to bytecode indices + co_name name with which this code object was defined + co_names tuple of names of local variables + co_nlocals number of local variables + co_stacksize virtual machine stack space required + co_varnames tuple of names of arguments and local variables + + """ + return isinstance(object, types.CodeType) + +# ------------------------------------------------ argument list extraction +# These constants are from Python's compile.h. +CO_OPTIMIZED, CO_NEWLOCALS, CO_VARARGS, CO_VARKEYWORDS = 1, 2, 4, 8 + +def getargs(co): + """Get information about the arguments accepted by a code object. + + Three things are returned: (args, varargs, varkw), where 'args' is + a list of argument names (possibly containing nested lists), and + 'varargs' and 'varkw' are the names of the * and ** arguments or None. + + """ + + if not iscode(co): + raise TypeError('arg is not a code object') + + nargs = co.co_argcount + names = co.co_varnames + args = list(names[:nargs]) + + # The following acrobatics are for anonymous (tuple) arguments. + # Which we do not need to support, so remove to avoid importing + # the dis module. + for i in range(nargs): + if args[i][:1] in ['', '.']: + raise TypeError("tuple function arguments are not supported") + varargs = None + if co.co_flags & CO_VARARGS: + varargs = co.co_varnames[nargs] + nargs = nargs + 1 + varkw = None + if co.co_flags & CO_VARKEYWORDS: + varkw = co.co_varnames[nargs] + return args, varargs, varkw + +def getargspec(func): + """Get the names and default values of a function's arguments. + + A tuple of four things is returned: (args, varargs, varkw, defaults). + 'args' is a list of the argument names (it may contain nested lists). + 'varargs' and 'varkw' are the names of the * and ** arguments or None. + 'defaults' is an n-tuple of the default values of the last n arguments. + + """ + + if ismethod(func): + func = func.__func__ + if not isfunction(func): + raise TypeError('arg is not a Python function') + args, varargs, varkw = getargs(func.__code__) + return args, varargs, varkw, func.__defaults__ + +def getargvalues(frame): + """Get information about arguments passed into a particular frame. + + A tuple of four things is returned: (args, varargs, varkw, locals). + 'args' is a list of the argument names (it may contain nested lists). + 'varargs' and 'varkw' are the names of the * and ** arguments or None. + 'locals' is the locals dictionary of the given frame. + + """ + args, varargs, varkw = getargs(frame.f_code) + return args, varargs, varkw, frame.f_locals + +def joinseq(seq): + if len(seq) == 1: + return '(' + seq[0] + ',)' + else: + return '(' + ', '.join(seq) + ')' + +def strseq(object, convert, join=joinseq): + """Recursively walk a sequence, stringifying each element. + + """ + if type(object) in [list, tuple]: + return join([strseq(_o, convert, join) for _o in object]) + else: + return convert(object) + +def formatargspec(args, varargs=None, varkw=None, defaults=None, + formatarg=str, + formatvarargs=lambda name: '*' + name, + formatvarkw=lambda name: '**' + name, + formatvalue=lambda value: '=' + repr(value), + join=joinseq): + """Format an argument spec from the 4 values returned by getargspec. + + The first four arguments are (args, varargs, varkw, defaults). The + other four arguments are the corresponding optional formatting functions + that are called to turn names and values into strings. The ninth + argument is an optional function to format the sequence of arguments. + + """ + specs = [] + if defaults: + firstdefault = len(args) - len(defaults) + for i in range(len(args)): + spec = strseq(args[i], formatarg, join) + if defaults and i >= firstdefault: + spec = spec + formatvalue(defaults[i - firstdefault]) + specs.append(spec) + if varargs is not None: + specs.append(formatvarargs(varargs)) + if varkw is not None: + specs.append(formatvarkw(varkw)) + return '(' + ', '.join(specs) + ')' + +def formatargvalues(args, varargs, varkw, locals, + formatarg=str, + formatvarargs=lambda name: '*' + name, + formatvarkw=lambda name: '**' + name, + formatvalue=lambda value: '=' + repr(value), + join=joinseq): + """Format an argument spec from the 4 values returned by getargvalues. + + The first four arguments are (args, varargs, varkw, locals). The + next four arguments are the corresponding optional formatting functions + that are called to turn names and values into strings. The ninth + argument is an optional function to format the sequence of arguments. + + """ + def convert(name, locals=locals, + formatarg=formatarg, formatvalue=formatvalue): + return formatarg(name) + formatvalue(locals[name]) + specs = [strseq(arg, convert, join) for arg in args] + + if varargs: + specs.append(formatvarargs(varargs) + formatvalue(locals[varargs])) + if varkw: + specs.append(formatvarkw(varkw) + formatvalue(locals[varkw])) + return '(' + ', '.join(specs) + ')' diff --git a/env-llmeval/lib/python3.10/site-packages/numpy/_utils/_pep440.py b/env-llmeval/lib/python3.10/site-packages/numpy/_utils/_pep440.py new file mode 100644 index 0000000000000000000000000000000000000000..73d0afb5e95f099f8b04253177e8a3ab3d80d0c4 --- /dev/null +++ b/env-llmeval/lib/python3.10/site-packages/numpy/_utils/_pep440.py @@ -0,0 +1,487 @@ +"""Utility to compare pep440 compatible version strings. + +The LooseVersion and StrictVersion classes that distutils provides don't +work; they don't recognize anything like alpha/beta/rc/dev versions. +""" + +# Copyright (c) Donald Stufft and individual contributors. +# All rights reserved. + +# Redistribution and use in source and binary forms, with or without +# modification, are permitted provided that the following conditions are met: + +# 1. Redistributions of source code must retain the above copyright notice, +# this list of conditions and the following disclaimer. + +# 2. Redistributions in binary form must reproduce the above copyright +# notice, this list of conditions and the following disclaimer in the +# documentation and/or other materials provided with the distribution. + +# 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 HOLDER 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. + +import collections +import itertools +import re + + +__all__ = [ + "parse", "Version", "LegacyVersion", "InvalidVersion", "VERSION_PATTERN", +] + + +# BEGIN packaging/_structures.py + + +class Infinity: + def __repr__(self): + return "Infinity" + + def __hash__(self): + return hash(repr(self)) + + def __lt__(self, other): + return False + + def __le__(self, other): + return False + + def __eq__(self, other): + return isinstance(other, self.__class__) + + def __ne__(self, other): + return not isinstance(other, self.__class__) + + def __gt__(self, other): + return True + + def __ge__(self, other): + return True + + def __neg__(self): + return NegativeInfinity + + +Infinity = Infinity() + + +class NegativeInfinity: + def __repr__(self): + return "-Infinity" + + def __hash__(self): + return hash(repr(self)) + + def __lt__(self, other): + return True + + def __le__(self, other): + return True + + def __eq__(self, other): + return isinstance(other, self.__class__) + + def __ne__(self, other): + return not isinstance(other, self.__class__) + + def __gt__(self, other): + return False + + def __ge__(self, other): + return False + + def __neg__(self): + return Infinity + + +# BEGIN packaging/version.py + + +NegativeInfinity = NegativeInfinity() + +_Version = collections.namedtuple( + "_Version", + ["epoch", "release", "dev", "pre", "post", "local"], +) + + +def parse(version): + """ + Parse the given version string and return either a :class:`Version` object + or a :class:`LegacyVersion` object depending on if the given version is + a valid PEP 440 version or a legacy version. + """ + try: + return Version(version) + except InvalidVersion: + return LegacyVersion(version) + + +class InvalidVersion(ValueError): + """ + An invalid version was found, users should refer to PEP 440. + """ + + +class _BaseVersion: + + def __hash__(self): + return hash(self._key) + + def __lt__(self, other): + return self._compare(other, lambda s, o: s < o) + + def __le__(self, other): + return self._compare(other, lambda s, o: s <= o) + + def __eq__(self, other): + return self._compare(other, lambda s, o: s == o) + + def __ge__(self, other): + return self._compare(other, lambda s, o: s >= o) + + def __gt__(self, other): + return self._compare(other, lambda s, o: s > o) + + def __ne__(self, other): + return self._compare(other, lambda s, o: s != o) + + def _compare(self, other, method): + if not isinstance(other, _BaseVersion): + return NotImplemented + + return method(self._key, other._key) + + +class LegacyVersion(_BaseVersion): + + def __init__(self, version): + self._version = str(version) + self._key = _legacy_cmpkey(self._version) + + def __str__(self): + return self._version + + def __repr__(self): + return "".format(repr(str(self))) + + @property + def public(self): + return self._version + + @property + def base_version(self): + return self._version + + @property + def local(self): + return None + + @property + def is_prerelease(self): + return False + + @property + def is_postrelease(self): + return False + + +_legacy_version_component_re = re.compile( + r"(\d+ | [a-z]+ | \.| -)", re.VERBOSE, +) + +_legacy_version_replacement_map = { + "pre": "c", "preview": "c", "-": "final-", "rc": "c", "dev": "@", +} + + +def _parse_version_parts(s): + for part in _legacy_version_component_re.split(s): + part = _legacy_version_replacement_map.get(part, part) + + if not part or part == ".": + continue + + if part[:1] in "0123456789": + # pad for numeric comparison + yield part.zfill(8) + else: + yield "*" + part + + # ensure that alpha/beta/candidate are before final + yield "*final" + + +def _legacy_cmpkey(version): + # We hardcode an epoch of -1 here. A PEP 440 version can only have an epoch + # greater than or equal to 0. This will effectively put the LegacyVersion, + # which uses the defacto standard originally implemented by setuptools, + # as before all PEP 440 versions. + epoch = -1 + + # This scheme is taken from pkg_resources.parse_version setuptools prior to + # its adoption of the packaging library. + parts = [] + for part in _parse_version_parts(version.lower()): + if part.startswith("*"): + # remove "-" before a prerelease tag + if part < "*final": + while parts and parts[-1] == "*final-": + parts.pop() + + # remove trailing zeros from each series of numeric parts + while parts and parts[-1] == "00000000": + parts.pop() + + parts.append(part) + parts = tuple(parts) + + return epoch, parts + + +# Deliberately not anchored to the start and end of the string, to make it +# easier for 3rd party code to reuse +VERSION_PATTERN = r""" + v? + (?: + (?:(?P[0-9]+)!)? # epoch + (?P[0-9]+(?:\.[0-9]+)*) # release segment + (?P
                                          # pre-release
+            [-_\.]?
+            (?P(a|b|c|rc|alpha|beta|pre|preview))
+            [-_\.]?
+            (?P[0-9]+)?
+        )?
+        (?P                                         # post release
+            (?:-(?P[0-9]+))
+            |
+            (?:
+                [-_\.]?
+                (?Ppost|rev|r)
+                [-_\.]?
+                (?P[0-9]+)?
+            )
+        )?
+        (?P                                          # dev release
+            [-_\.]?
+            (?Pdev)
+            [-_\.]?
+            (?P[0-9]+)?
+        )?
+    )
+    (?:\+(?P[a-z0-9]+(?:[-_\.][a-z0-9]+)*))?       # local version
+"""
+
+
+class Version(_BaseVersion):
+
+    _regex = re.compile(
+        r"^\s*" + VERSION_PATTERN + r"\s*$",
+        re.VERBOSE | re.IGNORECASE,
+    )
+
+    def __init__(self, version):
+        # Validate the version and parse it into pieces
+        match = self._regex.search(version)
+        if not match:
+            raise InvalidVersion("Invalid version: '{0}'".format(version))
+
+        # Store the parsed out pieces of the version
+        self._version = _Version(
+            epoch=int(match.group("epoch")) if match.group("epoch") else 0,
+            release=tuple(int(i) for i in match.group("release").split(".")),
+            pre=_parse_letter_version(
+                match.group("pre_l"),
+                match.group("pre_n"),
+            ),
+            post=_parse_letter_version(
+                match.group("post_l"),
+                match.group("post_n1") or match.group("post_n2"),
+            ),
+            dev=_parse_letter_version(
+                match.group("dev_l"),
+                match.group("dev_n"),
+            ),
+            local=_parse_local_version(match.group("local")),
+        )
+
+        # Generate a key which will be used for sorting
+        self._key = _cmpkey(
+            self._version.epoch,
+            self._version.release,
+            self._version.pre,
+            self._version.post,
+            self._version.dev,
+            self._version.local,
+        )
+
+    def __repr__(self):
+        return "".format(repr(str(self)))
+
+    def __str__(self):
+        parts = []
+
+        # Epoch
+        if self._version.epoch != 0:
+            parts.append("{0}!".format(self._version.epoch))
+
+        # Release segment
+        parts.append(".".join(str(x) for x in self._version.release))
+
+        # Pre-release
+        if self._version.pre is not None:
+            parts.append("".join(str(x) for x in self._version.pre))
+
+        # Post-release
+        if self._version.post is not None:
+            parts.append(".post{0}".format(self._version.post[1]))
+
+        # Development release
+        if self._version.dev is not None:
+            parts.append(".dev{0}".format(self._version.dev[1]))
+
+        # Local version segment
+        if self._version.local is not None:
+            parts.append(
+                "+{0}".format(".".join(str(x) for x in self._version.local))
+            )
+
+        return "".join(parts)
+
+    @property
+    def public(self):
+        return str(self).split("+", 1)[0]
+
+    @property
+    def base_version(self):
+        parts = []
+
+        # Epoch
+        if self._version.epoch != 0:
+            parts.append("{0}!".format(self._version.epoch))
+
+        # Release segment
+        parts.append(".".join(str(x) for x in self._version.release))
+
+        return "".join(parts)
+
+    @property
+    def local(self):
+        version_string = str(self)
+        if "+" in version_string:
+            return version_string.split("+", 1)[1]
+
+    @property
+    def is_prerelease(self):
+        return bool(self._version.dev or self._version.pre)
+
+    @property
+    def is_postrelease(self):
+        return bool(self._version.post)
+
+
+def _parse_letter_version(letter, number):
+    if letter:
+        # We assume there is an implicit 0 in a pre-release if there is
+        # no numeral associated with it.
+        if number is None:
+            number = 0
+
+        # We normalize any letters to their lower-case form
+        letter = letter.lower()
+
+        # We consider some words to be alternate spellings of other words and
+        # in those cases we want to normalize the spellings to our preferred
+        # spelling.
+        if letter == "alpha":
+            letter = "a"
+        elif letter == "beta":
+            letter = "b"
+        elif letter in ["c", "pre", "preview"]:
+            letter = "rc"
+        elif letter in ["rev", "r"]:
+            letter = "post"
+
+        return letter, int(number)
+    if not letter and number:
+        # We assume that if we are given a number but not given a letter,
+        # then this is using the implicit post release syntax (e.g., 1.0-1)
+        letter = "post"
+
+        return letter, int(number)
+
+
+_local_version_seperators = re.compile(r"[\._-]")
+
+
+def _parse_local_version(local):
+    """
+    Takes a string like abc.1.twelve and turns it into ("abc", 1, "twelve").
+    """
+    if local is not None:
+        return tuple(
+            part.lower() if not part.isdigit() else int(part)
+            for part in _local_version_seperators.split(local)
+        )
+
+
+def _cmpkey(epoch, release, pre, post, dev, local):
+    # When we compare a release version, we want to compare it with all of the
+    # trailing zeros removed. So we'll use a reverse the list, drop all the now
+    # leading zeros until we come to something non-zero, then take the rest,
+    # re-reverse it back into the correct order, and make it a tuple and use
+    # that for our sorting key.
+    release = tuple(
+        reversed(list(
+            itertools.dropwhile(
+                lambda x: x == 0,
+                reversed(release),
+            )
+        ))
+    )
+
+    # We need to "trick" the sorting algorithm to put 1.0.dev0 before 1.0a0.
+    # We'll do this by abusing the pre-segment, but we _only_ want to do this
+    # if there is no pre- or a post-segment. If we have one of those, then
+    # the normal sorting rules will handle this case correctly.
+    if pre is None and post is None and dev is not None:
+        pre = -Infinity
+    # Versions without a pre-release (except as noted above) should sort after
+    # those with one.
+    elif pre is None:
+        pre = Infinity
+
+    # Versions without a post-segment should sort before those with one.
+    if post is None:
+        post = -Infinity
+
+    # Versions without a development segment should sort after those with one.
+    if dev is None:
+        dev = Infinity
+
+    if local is None:
+        # Versions without a local segment should sort before those with one.
+        local = -Infinity
+    else:
+        # Versions with a local segment need that segment parsed to implement
+        # the sorting rules in PEP440.
+        # - Alphanumeric segments sort before numeric segments
+        # - Alphanumeric segments sort lexicographically
+        # - Numeric segments sort numerically
+        # - Shorter versions sort before longer versions when the prefixes
+        #   match exactly
+        local = tuple(
+            (i, "") if isinstance(i, int) else (-Infinity, i)
+            for i in local
+        )
+
+    return epoch, release, pre, post, dev, local
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--- /dev/null
+++ b/env-llmeval/lib/python3.10/site-packages/numpy/array_api/_array_object.py
@@ -0,0 +1,1133 @@
+"""
+Wrapper class around the ndarray object for the array API standard.
+
+The array API standard defines some behaviors differently than ndarray, in
+particular, type promotion rules are different (the standard has no
+value-based casting). The standard also specifies a more limited subset of
+array methods and functionalities than are implemented on ndarray. Since the
+goal of the array_api namespace is to be a minimal implementation of the array
+API standard, we need to define a separate wrapper class for the array_api
+namespace.
+
+The standard compliant class is only a wrapper class. It is *not* a subclass
+of ndarray.
+"""
+
+from __future__ import annotations
+
+import operator
+from enum import IntEnum
+from ._creation_functions import asarray
+from ._dtypes import (
+    _all_dtypes,
+    _boolean_dtypes,
+    _integer_dtypes,
+    _integer_or_boolean_dtypes,
+    _floating_dtypes,
+    _complex_floating_dtypes,
+    _numeric_dtypes,
+    _result_type,
+    _dtype_categories,
+)
+
+from typing import TYPE_CHECKING, Optional, Tuple, Union, Any, SupportsIndex
+import types
+
+if TYPE_CHECKING:
+    from ._typing import Any, PyCapsule, Device, Dtype
+    import numpy.typing as npt
+
+import numpy as np
+
+from numpy import array_api
+
+
+class Array:
+    """
+    n-d array object for the array API namespace.
+
+    See the docstring of :py:obj:`np.ndarray ` for more
+    information.
+
+    This is a wrapper around numpy.ndarray that restricts the usage to only
+    those things that are required by the array API namespace. Note,
+    attributes on this object that start with a single underscore are not part
+    of the API specification and should only be used internally. This object
+    should not be constructed directly. Rather, use one of the creation
+    functions, such as asarray().
+
+    """
+    _array: np.ndarray[Any, Any]
+
+    # Use a custom constructor instead of __init__, as manually initializing
+    # this class is not supported API.
+    @classmethod
+    def _new(cls, x, /):
+        """
+        This is a private method for initializing the array API Array
+        object.
+
+        Functions outside of the array_api submodule should not use this
+        method. Use one of the creation functions instead, such as
+        ``asarray``.
+
+        """
+        obj = super().__new__(cls)
+        # Note: The spec does not have array scalars, only 0-D arrays.
+        if isinstance(x, np.generic):
+            # Convert the array scalar to a 0-D array
+            x = np.asarray(x)
+        if x.dtype not in _all_dtypes:
+            raise TypeError(
+                f"The array_api namespace does not support the dtype '{x.dtype}'"
+            )
+        obj._array = x
+        return obj
+
+    # Prevent Array() from working
+    def __new__(cls, *args, **kwargs):
+        raise TypeError(
+            "The array_api Array object should not be instantiated directly. Use an array creation function, such as asarray(), instead."
+        )
+
+    # These functions are not required by the spec, but are implemented for
+    # the sake of usability.
+
+    def __str__(self: Array, /) -> str:
+        """
+        Performs the operation __str__.
+        """
+        return self._array.__str__().replace("array", "Array")
+
+    def __repr__(self: Array, /) -> str:
+        """
+        Performs the operation __repr__.
+        """
+        suffix = f", dtype={self.dtype.name})"
+        if 0 in self.shape:
+            prefix = "empty("
+            mid = str(self.shape)
+        else:
+            prefix = "Array("
+            mid = np.array2string(self._array, separator=', ', prefix=prefix, suffix=suffix)
+        return prefix + mid + suffix
+
+    # This function is not required by the spec, but we implement it here for
+    # convenience so that np.asarray(np.array_api.Array) will work.
+    def __array__(self, dtype: None | np.dtype[Any] = None) -> npt.NDArray[Any]:
+        """
+        Warning: this method is NOT part of the array API spec. Implementers
+        of other libraries need not include it, and users should not assume it
+        will be present in other implementations.
+
+        """
+        return np.asarray(self._array, dtype=dtype)
+
+    # These are various helper functions to make the array behavior match the
+    # spec in places where it either deviates from or is more strict than
+    # NumPy behavior
+
+    def _check_allowed_dtypes(self, other: bool | int | float | Array, dtype_category: str, op: str) -> Array:
+        """
+        Helper function for operators to only allow specific input dtypes
+
+        Use like
+
+            other = self._check_allowed_dtypes(other, 'numeric', '__add__')
+            if other is NotImplemented:
+                return other
+        """
+
+        if self.dtype not in _dtype_categories[dtype_category]:
+            raise TypeError(f"Only {dtype_category} dtypes are allowed in {op}")
+        if isinstance(other, (int, complex, float, bool)):
+            other = self._promote_scalar(other)
+        elif isinstance(other, Array):
+            if other.dtype not in _dtype_categories[dtype_category]:
+                raise TypeError(f"Only {dtype_category} dtypes are allowed in {op}")
+        else:
+            return NotImplemented
+
+        # This will raise TypeError for type combinations that are not allowed
+        # to promote in the spec (even if the NumPy array operator would
+        # promote them).
+        res_dtype = _result_type(self.dtype, other.dtype)
+        if op.startswith("__i"):
+            # Note: NumPy will allow in-place operators in some cases where
+            # the type promoted operator does not match the left-hand side
+            # operand. For example,
+
+            # >>> a = np.array(1, dtype=np.int8)
+            # >>> a += np.array(1, dtype=np.int16)
+
+            # The spec explicitly disallows this.
+            if res_dtype != self.dtype:
+                raise TypeError(
+                    f"Cannot perform {op} with dtypes {self.dtype} and {other.dtype}"
+                )
+
+        return other
+
+    # Helper function to match the type promotion rules in the spec
+    def _promote_scalar(self, scalar):
+        """
+        Returns a promoted version of a Python scalar appropriate for use with
+        operations on self.
+
+        This may raise an OverflowError in cases where the scalar is an
+        integer that is too large to fit in a NumPy integer dtype, or
+        TypeError when the scalar type is incompatible with the dtype of self.
+        """
+        # Note: Only Python scalar types that match the array dtype are
+        # allowed.
+        if isinstance(scalar, bool):
+            if self.dtype not in _boolean_dtypes:
+                raise TypeError(
+                    "Python bool scalars can only be promoted with bool arrays"
+                )
+        elif isinstance(scalar, int):
+            if self.dtype in _boolean_dtypes:
+                raise TypeError(
+                    "Python int scalars cannot be promoted with bool arrays"
+                )
+            if self.dtype in _integer_dtypes:
+                info = np.iinfo(self.dtype)
+                if not (info.min <= scalar <= info.max):
+                    raise OverflowError(
+                        "Python int scalars must be within the bounds of the dtype for integer arrays"
+                    )
+            # int + array(floating) is allowed
+        elif isinstance(scalar, float):
+            if self.dtype not in _floating_dtypes:
+                raise TypeError(
+                    "Python float scalars can only be promoted with floating-point arrays."
+                )
+        elif isinstance(scalar, complex):
+            if self.dtype not in _complex_floating_dtypes:
+                raise TypeError(
+                    "Python complex scalars can only be promoted with complex floating-point arrays."
+                )
+        else:
+            raise TypeError("'scalar' must be a Python scalar")
+
+        # Note: scalars are unconditionally cast to the same dtype as the
+        # array.
+
+        # Note: the spec only specifies integer-dtype/int promotion
+        # behavior for integers within the bounds of the integer dtype.
+        # Outside of those bounds we use the default NumPy behavior (either
+        # cast or raise OverflowError).
+        return Array._new(np.array(scalar, self.dtype))
+
+    @staticmethod
+    def _normalize_two_args(x1, x2) -> Tuple[Array, Array]:
+        """
+        Normalize inputs to two arg functions to fix type promotion rules
+
+        NumPy deviates from the spec type promotion rules in cases where one
+        argument is 0-dimensional and the other is not. For example:
+
+        >>> import numpy as np
+        >>> a = np.array([1.0], dtype=np.float32)
+        >>> b = np.array(1.0, dtype=np.float64)
+        >>> np.add(a, b) # The spec says this should be float64
+        array([2.], dtype=float32)
+
+        To fix this, we add a dimension to the 0-dimension array before passing it
+        through. This works because a dimension would be added anyway from
+        broadcasting, so the resulting shape is the same, but this prevents NumPy
+        from not promoting the dtype.
+        """
+        # Another option would be to use signature=(x1.dtype, x2.dtype, None),
+        # but that only works for ufuncs, so we would have to call the ufuncs
+        # directly in the operator methods. One should also note that this
+        # sort of trick wouldn't work for functions like searchsorted, which
+        # don't do normal broadcasting, but there aren't any functions like
+        # that in the array API namespace.
+        if x1.ndim == 0 and x2.ndim != 0:
+            # The _array[None] workaround was chosen because it is relatively
+            # performant. broadcast_to(x1._array, x2.shape) is much slower. We
+            # could also manually type promote x2, but that is more complicated
+            # and about the same performance as this.
+            x1 = Array._new(x1._array[None])
+        elif x2.ndim == 0 and x1.ndim != 0:
+            x2 = Array._new(x2._array[None])
+        return (x1, x2)
+
+    # Note: A large fraction of allowed indices are disallowed here (see the
+    # docstring below)
+    def _validate_index(self, key):
+        """
+        Validate an index according to the array API.
+
+        The array API specification only requires a subset of indices that are
+        supported by NumPy. This function will reject any index that is
+        allowed by NumPy but not required by the array API specification. We
+        always raise ``IndexError`` on such indices (the spec does not require
+        any specific behavior on them, but this makes the NumPy array API
+        namespace a minimal implementation of the spec). See
+        https://data-apis.org/array-api/latest/API_specification/indexing.html
+        for the full list of required indexing behavior
+
+        This function raises IndexError if the index ``key`` is invalid. It
+        only raises ``IndexError`` on indices that are not already rejected by
+        NumPy, as NumPy will already raise the appropriate error on such
+        indices. ``shape`` may be None, in which case, only cases that are
+        independent of the array shape are checked.
+
+        The following cases are allowed by NumPy, but not specified by the array
+        API specification:
+
+        - Indices to not include an implicit ellipsis at the end. That is,
+          every axis of an array must be explicitly indexed or an ellipsis
+          included. This behaviour is sometimes referred to as flat indexing.
+
+        - The start and stop of a slice may not be out of bounds. In
+          particular, for a slice ``i:j:k`` on an axis of size ``n``, only the
+          following are allowed:
+
+          - ``i`` or ``j`` omitted (``None``).
+          - ``-n <= i <= max(0, n - 1)``.
+          - For ``k > 0`` or ``k`` omitted (``None``), ``-n <= j <= n``.
+          - For ``k < 0``, ``-n - 1 <= j <= max(0, n - 1)``.
+
+        - Boolean array indices are not allowed as part of a larger tuple
+          index.
+
+        - Integer array indices are not allowed (with the exception of 0-D
+          arrays, which are treated the same as scalars).
+
+        Additionally, it should be noted that indices that would return a
+        scalar in NumPy will return a 0-D array. Array scalars are not allowed
+        in the specification, only 0-D arrays. This is done in the
+        ``Array._new`` constructor, not this function.
+
+        """
+        _key = key if isinstance(key, tuple) else (key,)
+        for i in _key:
+            if isinstance(i, bool) or not (
+                isinstance(i, SupportsIndex)  # i.e. ints
+                or isinstance(i, slice)
+                or i == Ellipsis
+                or i is None
+                or isinstance(i, Array)
+                or isinstance(i, np.ndarray)
+            ):
+                raise IndexError(
+                    f"Single-axes index {i} has {type(i)=}, but only "
+                    "integers, slices (:), ellipsis (...), newaxis (None), "
+                    "zero-dimensional integer arrays and boolean arrays "
+                    "are specified in the Array API."
+                )
+
+        nonexpanding_key = []
+        single_axes = []
+        n_ellipsis = 0
+        key_has_mask = False
+        for i in _key:
+            if i is not None:
+                nonexpanding_key.append(i)
+                if isinstance(i, Array) or isinstance(i, np.ndarray):
+                    if i.dtype in _boolean_dtypes:
+                        key_has_mask = True
+                    single_axes.append(i)
+                else:
+                    # i must not be an array here, to avoid elementwise equals
+                    if i == Ellipsis:
+                        n_ellipsis += 1
+                    else:
+                        single_axes.append(i)
+
+        n_single_axes = len(single_axes)
+        if n_ellipsis > 1:
+            return  # handled by ndarray
+        elif n_ellipsis == 0:
+            # Note boolean masks must be the sole index, which we check for
+            # later on.
+            if not key_has_mask and n_single_axes < self.ndim:
+                raise IndexError(
+                    f"{self.ndim=}, but the multi-axes index only specifies "
+                    f"{n_single_axes} dimensions. If this was intentional, "
+                    "add a trailing ellipsis (...) which expands into as many "
+                    "slices (:) as necessary - this is what np.ndarray arrays "
+                    "implicitly do, but such flat indexing behaviour is not "
+                    "specified in the Array API."
+                )
+
+        if n_ellipsis == 0:
+            indexed_shape = self.shape
+        else:
+            ellipsis_start = None
+            for pos, i in enumerate(nonexpanding_key):
+                if not (isinstance(i, Array) or isinstance(i, np.ndarray)):
+                    if i == Ellipsis:
+                        ellipsis_start = pos
+                        break
+            assert ellipsis_start is not None  # sanity check
+            ellipsis_end = self.ndim - (n_single_axes - ellipsis_start)
+            indexed_shape = (
+                self.shape[:ellipsis_start] + self.shape[ellipsis_end:]
+            )
+        for i, side in zip(single_axes, indexed_shape):
+            if isinstance(i, slice):
+                if side == 0:
+                    f_range = "0 (or None)"
+                else:
+                    f_range = f"between -{side} and {side - 1} (or None)"
+                if i.start is not None:
+                    try:
+                        start = operator.index(i.start)
+                    except TypeError:
+                        pass  # handled by ndarray
+                    else:
+                        if not (-side <= start <= side):
+                            raise IndexError(
+                                f"Slice {i} contains {start=}, but should be "
+                                f"{f_range} for an axis of size {side} "
+                                "(out-of-bounds starts are not specified in "
+                                "the Array API)"
+                            )
+                if i.stop is not None:
+                    try:
+                        stop = operator.index(i.stop)
+                    except TypeError:
+                        pass  # handled by ndarray
+                    else:
+                        if not (-side <= stop <= side):
+                            raise IndexError(
+                                f"Slice {i} contains {stop=}, but should be "
+                                f"{f_range} for an axis of size {side} "
+                                "(out-of-bounds stops are not specified in "
+                                "the Array API)"
+                            )
+            elif isinstance(i, Array):
+                if i.dtype in _boolean_dtypes and len(_key) != 1:
+                    assert isinstance(key, tuple)  # sanity check
+                    raise IndexError(
+                        f"Single-axes index {i} is a boolean array and "
+                        f"{len(key)=}, but masking is only specified in the "
+                        "Array API when the array is the sole index."
+                    )
+                elif i.dtype in _integer_dtypes and i.ndim != 0:
+                    raise IndexError(
+                        f"Single-axes index {i} is a non-zero-dimensional "
+                        "integer array, but advanced integer indexing is not "
+                        "specified in the Array API."
+                    )
+            elif isinstance(i, tuple):
+                raise IndexError(
+                    f"Single-axes index {i} is a tuple, but nested tuple "
+                    "indices are not specified in the Array API."
+                )
+
+    # Everything below this line is required by the spec.
+
+    def __abs__(self: Array, /) -> Array:
+        """
+        Performs the operation __abs__.
+        """
+        if self.dtype not in _numeric_dtypes:
+            raise TypeError("Only numeric dtypes are allowed in __abs__")
+        res = self._array.__abs__()
+        return self.__class__._new(res)
+
+    def __add__(self: Array, other: Union[int, float, Array], /) -> Array:
+        """
+        Performs the operation __add__.
+        """
+        other = self._check_allowed_dtypes(other, "numeric", "__add__")
+        if other is NotImplemented:
+            return other
+        self, other = self._normalize_two_args(self, other)
+        res = self._array.__add__(other._array)
+        return self.__class__._new(res)
+
+    def __and__(self: Array, other: Union[int, bool, Array], /) -> Array:
+        """
+        Performs the operation __and__.
+        """
+        other = self._check_allowed_dtypes(other, "integer or boolean", "__and__")
+        if other is NotImplemented:
+            return other
+        self, other = self._normalize_two_args(self, other)
+        res = self._array.__and__(other._array)
+        return self.__class__._new(res)
+
+    def __array_namespace__(
+        self: Array, /, *, api_version: Optional[str] = None
+    ) -> types.ModuleType:
+        if api_version is not None and not api_version.startswith("2021."):
+            raise ValueError(f"Unrecognized array API version: {api_version!r}")
+        return array_api
+
+    def __bool__(self: Array, /) -> bool:
+        """
+        Performs the operation __bool__.
+        """
+        # Note: This is an error here.
+        if self._array.ndim != 0:
+            raise TypeError("bool is only allowed on arrays with 0 dimensions")
+        res = self._array.__bool__()
+        return res
+
+    def __complex__(self: Array, /) -> complex:
+        """
+        Performs the operation __complex__.
+        """
+        # Note: This is an error here.
+        if self._array.ndim != 0:
+            raise TypeError("complex is only allowed on arrays with 0 dimensions")
+        res = self._array.__complex__()
+        return res
+
+    def __dlpack__(self: Array, /, *, stream: None = None) -> PyCapsule:
+        """
+        Performs the operation __dlpack__.
+        """
+        return self._array.__dlpack__(stream=stream)
+
+    def __dlpack_device__(self: Array, /) -> Tuple[IntEnum, int]:
+        """
+        Performs the operation __dlpack_device__.
+        """
+        # Note: device support is required for this
+        return self._array.__dlpack_device__()
+
+    def __eq__(self: Array, other: Union[int, float, bool, Array], /) -> Array:
+        """
+        Performs the operation __eq__.
+        """
+        # Even though "all" dtypes are allowed, we still require them to be
+        # promotable with each other.
+        other = self._check_allowed_dtypes(other, "all", "__eq__")
+        if other is NotImplemented:
+            return other
+        self, other = self._normalize_two_args(self, other)
+        res = self._array.__eq__(other._array)
+        return self.__class__._new(res)
+
+    def __float__(self: Array, /) -> float:
+        """
+        Performs the operation __float__.
+        """
+        # Note: This is an error here.
+        if self._array.ndim != 0:
+            raise TypeError("float is only allowed on arrays with 0 dimensions")
+        if self.dtype in _complex_floating_dtypes:
+            raise TypeError("float is not allowed on complex floating-point arrays")
+        res = self._array.__float__()
+        return res
+
+    def __floordiv__(self: Array, other: Union[int, float, Array], /) -> Array:
+        """
+        Performs the operation __floordiv__.
+        """
+        other = self._check_allowed_dtypes(other, "real numeric", "__floordiv__")
+        if other is NotImplemented:
+            return other
+        self, other = self._normalize_two_args(self, other)
+        res = self._array.__floordiv__(other._array)
+        return self.__class__._new(res)
+
+    def __ge__(self: Array, other: Union[int, float, Array], /) -> Array:
+        """
+        Performs the operation __ge__.
+        """
+        other = self._check_allowed_dtypes(other, "real numeric", "__ge__")
+        if other is NotImplemented:
+            return other
+        self, other = self._normalize_two_args(self, other)
+        res = self._array.__ge__(other._array)
+        return self.__class__._new(res)
+
+    def __getitem__(
+        self: Array,
+        key: Union[
+            int,
+            slice,
+            ellipsis,
+            Tuple[Union[int, slice, ellipsis, None], ...],
+            Array,
+        ],
+        /,
+    ) -> Array:
+        """
+        Performs the operation __getitem__.
+        """
+        # Note: Only indices required by the spec are allowed. See the
+        # docstring of _validate_index
+        self._validate_index(key)
+        if isinstance(key, Array):
+            # Indexing self._array with array_api arrays can be erroneous
+            key = key._array
+        res = self._array.__getitem__(key)
+        return self._new(res)
+
+    def __gt__(self: Array, other: Union[int, float, Array], /) -> Array:
+        """
+        Performs the operation __gt__.
+        """
+        other = self._check_allowed_dtypes(other, "real numeric", "__gt__")
+        if other is NotImplemented:
+            return other
+        self, other = self._normalize_two_args(self, other)
+        res = self._array.__gt__(other._array)
+        return self.__class__._new(res)
+
+    def __int__(self: Array, /) -> int:
+        """
+        Performs the operation __int__.
+        """
+        # Note: This is an error here.
+        if self._array.ndim != 0:
+            raise TypeError("int is only allowed on arrays with 0 dimensions")
+        if self.dtype in _complex_floating_dtypes:
+            raise TypeError("int is not allowed on complex floating-point arrays")
+        res = self._array.__int__()
+        return res
+
+    def __index__(self: Array, /) -> int:
+        """
+        Performs the operation __index__.
+        """
+        res = self._array.__index__()
+        return res
+
+    def __invert__(self: Array, /) -> Array:
+        """
+        Performs the operation __invert__.
+        """
+        if self.dtype not in _integer_or_boolean_dtypes:
+            raise TypeError("Only integer or boolean dtypes are allowed in __invert__")
+        res = self._array.__invert__()
+        return self.__class__._new(res)
+
+    def __le__(self: Array, other: Union[int, float, Array], /) -> Array:
+        """
+        Performs the operation __le__.
+        """
+        other = self._check_allowed_dtypes(other, "real numeric", "__le__")
+        if other is NotImplemented:
+            return other
+        self, other = self._normalize_two_args(self, other)
+        res = self._array.__le__(other._array)
+        return self.__class__._new(res)
+
+    def __lshift__(self: Array, other: Union[int, Array], /) -> Array:
+        """
+        Performs the operation __lshift__.
+        """
+        other = self._check_allowed_dtypes(other, "integer", "__lshift__")
+        if other is NotImplemented:
+            return other
+        self, other = self._normalize_two_args(self, other)
+        res = self._array.__lshift__(other._array)
+        return self.__class__._new(res)
+
+    def __lt__(self: Array, other: Union[int, float, Array], /) -> Array:
+        """
+        Performs the operation __lt__.
+        """
+        other = self._check_allowed_dtypes(other, "real numeric", "__lt__")
+        if other is NotImplemented:
+            return other
+        self, other = self._normalize_two_args(self, other)
+        res = self._array.__lt__(other._array)
+        return self.__class__._new(res)
+
+    def __matmul__(self: Array, other: Array, /) -> Array:
+        """
+        Performs the operation __matmul__.
+        """
+        # matmul is not defined for scalars, but without this, we may get
+        # the wrong error message from asarray.
+        other = self._check_allowed_dtypes(other, "numeric", "__matmul__")
+        if other is NotImplemented:
+            return other
+        res = self._array.__matmul__(other._array)
+        return self.__class__._new(res)
+
+    def __mod__(self: Array, other: Union[int, float, Array], /) -> Array:
+        """
+        Performs the operation __mod__.
+        """
+        other = self._check_allowed_dtypes(other, "real numeric", "__mod__")
+        if other is NotImplemented:
+            return other
+        self, other = self._normalize_two_args(self, other)
+        res = self._array.__mod__(other._array)
+        return self.__class__._new(res)
+
+    def __mul__(self: Array, other: Union[int, float, Array], /) -> Array:
+        """
+        Performs the operation __mul__.
+        """
+        other = self._check_allowed_dtypes(other, "numeric", "__mul__")
+        if other is NotImplemented:
+            return other
+        self, other = self._normalize_two_args(self, other)
+        res = self._array.__mul__(other._array)
+        return self.__class__._new(res)
+
+    def __ne__(self: Array, other: Union[int, float, bool, Array], /) -> Array:
+        """
+        Performs the operation __ne__.
+        """
+        other = self._check_allowed_dtypes(other, "all", "__ne__")
+        if other is NotImplemented:
+            return other
+        self, other = self._normalize_two_args(self, other)
+        res = self._array.__ne__(other._array)
+        return self.__class__._new(res)
+
+    def __neg__(self: Array, /) -> Array:
+        """
+        Performs the operation __neg__.
+        """
+        if self.dtype not in _numeric_dtypes:
+            raise TypeError("Only numeric dtypes are allowed in __neg__")
+        res = self._array.__neg__()
+        return self.__class__._new(res)
+
+    def __or__(self: Array, other: Union[int, bool, Array], /) -> Array:
+        """
+        Performs the operation __or__.
+        """
+        other = self._check_allowed_dtypes(other, "integer or boolean", "__or__")
+        if other is NotImplemented:
+            return other
+        self, other = self._normalize_two_args(self, other)
+        res = self._array.__or__(other._array)
+        return self.__class__._new(res)
+
+    def __pos__(self: Array, /) -> Array:
+        """
+        Performs the operation __pos__.
+        """
+        if self.dtype not in _numeric_dtypes:
+            raise TypeError("Only numeric dtypes are allowed in __pos__")
+        res = self._array.__pos__()
+        return self.__class__._new(res)
+
+    def __pow__(self: Array, other: Union[int, float, Array], /) -> Array:
+        """
+        Performs the operation __pow__.
+        """
+        from ._elementwise_functions import pow
+
+        other = self._check_allowed_dtypes(other, "numeric", "__pow__")
+        if other is NotImplemented:
+            return other
+        # Note: NumPy's __pow__ does not follow type promotion rules for 0-d
+        # arrays, so we use pow() here instead.
+        return pow(self, other)
+
+    def __rshift__(self: Array, other: Union[int, Array], /) -> Array:
+        """
+        Performs the operation __rshift__.
+        """
+        other = self._check_allowed_dtypes(other, "integer", "__rshift__")
+        if other is NotImplemented:
+            return other
+        self, other = self._normalize_two_args(self, other)
+        res = self._array.__rshift__(other._array)
+        return self.__class__._new(res)
+
+    def __setitem__(
+        self,
+        key: Union[
+            int, slice, ellipsis, Tuple[Union[int, slice, ellipsis], ...], Array
+        ],
+        value: Union[int, float, bool, Array],
+        /,
+    ) -> None:
+        """
+        Performs the operation __setitem__.
+        """
+        # Note: Only indices required by the spec are allowed. See the
+        # docstring of _validate_index
+        self._validate_index(key)
+        if isinstance(key, Array):
+            # Indexing self._array with array_api arrays can be erroneous
+            key = key._array
+        self._array.__setitem__(key, asarray(value)._array)
+
+    def __sub__(self: Array, other: Union[int, float, Array], /) -> Array:
+        """
+        Performs the operation __sub__.
+        """
+        other = self._check_allowed_dtypes(other, "numeric", "__sub__")
+        if other is NotImplemented:
+            return other
+        self, other = self._normalize_two_args(self, other)
+        res = self._array.__sub__(other._array)
+        return self.__class__._new(res)
+
+    # PEP 484 requires int to be a subtype of float, but __truediv__ should
+    # not accept int.
+    def __truediv__(self: Array, other: Union[float, Array], /) -> Array:
+        """
+        Performs the operation __truediv__.
+        """
+        other = self._check_allowed_dtypes(other, "floating-point", "__truediv__")
+        if other is NotImplemented:
+            return other
+        self, other = self._normalize_two_args(self, other)
+        res = self._array.__truediv__(other._array)
+        return self.__class__._new(res)
+
+    def __xor__(self: Array, other: Union[int, bool, Array], /) -> Array:
+        """
+        Performs the operation __xor__.
+        """
+        other = self._check_allowed_dtypes(other, "integer or boolean", "__xor__")
+        if other is NotImplemented:
+            return other
+        self, other = self._normalize_two_args(self, other)
+        res = self._array.__xor__(other._array)
+        return self.__class__._new(res)
+
+    def __iadd__(self: Array, other: Union[int, float, Array], /) -> Array:
+        """
+        Performs the operation __iadd__.
+        """
+        other = self._check_allowed_dtypes(other, "numeric", "__iadd__")
+        if other is NotImplemented:
+            return other
+        self._array.__iadd__(other._array)
+        return self
+
+    def __radd__(self: Array, other: Union[int, float, Array], /) -> Array:
+        """
+        Performs the operation __radd__.
+        """
+        other = self._check_allowed_dtypes(other, "numeric", "__radd__")
+        if other is NotImplemented:
+            return other
+        self, other = self._normalize_two_args(self, other)
+        res = self._array.__radd__(other._array)
+        return self.__class__._new(res)
+
+    def __iand__(self: Array, other: Union[int, bool, Array], /) -> Array:
+        """
+        Performs the operation __iand__.
+        """
+        other = self._check_allowed_dtypes(other, "integer or boolean", "__iand__")
+        if other is NotImplemented:
+            return other
+        self._array.__iand__(other._array)
+        return self
+
+    def __rand__(self: Array, other: Union[int, bool, Array], /) -> Array:
+        """
+        Performs the operation __rand__.
+        """
+        other = self._check_allowed_dtypes(other, "integer or boolean", "__rand__")
+        if other is NotImplemented:
+            return other
+        self, other = self._normalize_two_args(self, other)
+        res = self._array.__rand__(other._array)
+        return self.__class__._new(res)
+
+    def __ifloordiv__(self: Array, other: Union[int, float, Array], /) -> Array:
+        """
+        Performs the operation __ifloordiv__.
+        """
+        other = self._check_allowed_dtypes(other, "real numeric", "__ifloordiv__")
+        if other is NotImplemented:
+            return other
+        self._array.__ifloordiv__(other._array)
+        return self
+
+    def __rfloordiv__(self: Array, other: Union[int, float, Array], /) -> Array:
+        """
+        Performs the operation __rfloordiv__.
+        """
+        other = self._check_allowed_dtypes(other, "real numeric", "__rfloordiv__")
+        if other is NotImplemented:
+            return other
+        self, other = self._normalize_two_args(self, other)
+        res = self._array.__rfloordiv__(other._array)
+        return self.__class__._new(res)
+
+    def __ilshift__(self: Array, other: Union[int, Array], /) -> Array:
+        """
+        Performs the operation __ilshift__.
+        """
+        other = self._check_allowed_dtypes(other, "integer", "__ilshift__")
+        if other is NotImplemented:
+            return other
+        self._array.__ilshift__(other._array)
+        return self
+
+    def __rlshift__(self: Array, other: Union[int, Array], /) -> Array:
+        """
+        Performs the operation __rlshift__.
+        """
+        other = self._check_allowed_dtypes(other, "integer", "__rlshift__")
+        if other is NotImplemented:
+            return other
+        self, other = self._normalize_two_args(self, other)
+        res = self._array.__rlshift__(other._array)
+        return self.__class__._new(res)
+
+    def __imatmul__(self: Array, other: Array, /) -> Array:
+        """
+        Performs the operation __imatmul__.
+        """
+        # matmul is not defined for scalars, but without this, we may get
+        # the wrong error message from asarray.
+        other = self._check_allowed_dtypes(other, "numeric", "__imatmul__")
+        if other is NotImplemented:
+            return other
+        res = self._array.__imatmul__(other._array)
+        return self.__class__._new(res)
+
+    def __rmatmul__(self: Array, other: Array, /) -> Array:
+        """
+        Performs the operation __rmatmul__.
+        """
+        # matmul is not defined for scalars, but without this, we may get
+        # the wrong error message from asarray.
+        other = self._check_allowed_dtypes(other, "numeric", "__rmatmul__")
+        if other is NotImplemented:
+            return other
+        res = self._array.__rmatmul__(other._array)
+        return self.__class__._new(res)
+
+    def __imod__(self: Array, other: Union[int, float, Array], /) -> Array:
+        """
+        Performs the operation __imod__.
+        """
+        other = self._check_allowed_dtypes(other, "real numeric", "__imod__")
+        if other is NotImplemented:
+            return other
+        self._array.__imod__(other._array)
+        return self
+
+    def __rmod__(self: Array, other: Union[int, float, Array], /) -> Array:
+        """
+        Performs the operation __rmod__.
+        """
+        other = self._check_allowed_dtypes(other, "real numeric", "__rmod__")
+        if other is NotImplemented:
+            return other
+        self, other = self._normalize_two_args(self, other)
+        res = self._array.__rmod__(other._array)
+        return self.__class__._new(res)
+
+    def __imul__(self: Array, other: Union[int, float, Array], /) -> Array:
+        """
+        Performs the operation __imul__.
+        """
+        other = self._check_allowed_dtypes(other, "numeric", "__imul__")
+        if other is NotImplemented:
+            return other
+        self._array.__imul__(other._array)
+        return self
+
+    def __rmul__(self: Array, other: Union[int, float, Array], /) -> Array:
+        """
+        Performs the operation __rmul__.
+        """
+        other = self._check_allowed_dtypes(other, "numeric", "__rmul__")
+        if other is NotImplemented:
+            return other
+        self, other = self._normalize_two_args(self, other)
+        res = self._array.__rmul__(other._array)
+        return self.__class__._new(res)
+
+    def __ior__(self: Array, other: Union[int, bool, Array], /) -> Array:
+        """
+        Performs the operation __ior__.
+        """
+        other = self._check_allowed_dtypes(other, "integer or boolean", "__ior__")
+        if other is NotImplemented:
+            return other
+        self._array.__ior__(other._array)
+        return self
+
+    def __ror__(self: Array, other: Union[int, bool, Array], /) -> Array:
+        """
+        Performs the operation __ror__.
+        """
+        other = self._check_allowed_dtypes(other, "integer or boolean", "__ror__")
+        if other is NotImplemented:
+            return other
+        self, other = self._normalize_two_args(self, other)
+        res = self._array.__ror__(other._array)
+        return self.__class__._new(res)
+
+    def __ipow__(self: Array, other: Union[int, float, Array], /) -> Array:
+        """
+        Performs the operation __ipow__.
+        """
+        other = self._check_allowed_dtypes(other, "numeric", "__ipow__")
+        if other is NotImplemented:
+            return other
+        self._array.__ipow__(other._array)
+        return self
+
+    def __rpow__(self: Array, other: Union[int, float, Array], /) -> Array:
+        """
+        Performs the operation __rpow__.
+        """
+        from ._elementwise_functions import pow
+
+        other = self._check_allowed_dtypes(other, "numeric", "__rpow__")
+        if other is NotImplemented:
+            return other
+        # Note: NumPy's __pow__ does not follow the spec type promotion rules
+        # for 0-d arrays, so we use pow() here instead.
+        return pow(other, self)
+
+    def __irshift__(self: Array, other: Union[int, Array], /) -> Array:
+        """
+        Performs the operation __irshift__.
+        """
+        other = self._check_allowed_dtypes(other, "integer", "__irshift__")
+        if other is NotImplemented:
+            return other
+        self._array.__irshift__(other._array)
+        return self
+
+    def __rrshift__(self: Array, other: Union[int, Array], /) -> Array:
+        """
+        Performs the operation __rrshift__.
+        """
+        other = self._check_allowed_dtypes(other, "integer", "__rrshift__")
+        if other is NotImplemented:
+            return other
+        self, other = self._normalize_two_args(self, other)
+        res = self._array.__rrshift__(other._array)
+        return self.__class__._new(res)
+
+    def __isub__(self: Array, other: Union[int, float, Array], /) -> Array:
+        """
+        Performs the operation __isub__.
+        """
+        other = self._check_allowed_dtypes(other, "numeric", "__isub__")
+        if other is NotImplemented:
+            return other
+        self._array.__isub__(other._array)
+        return self
+
+    def __rsub__(self: Array, other: Union[int, float, Array], /) -> Array:
+        """
+        Performs the operation __rsub__.
+        """
+        other = self._check_allowed_dtypes(other, "numeric", "__rsub__")
+        if other is NotImplemented:
+            return other
+        self, other = self._normalize_two_args(self, other)
+        res = self._array.__rsub__(other._array)
+        return self.__class__._new(res)
+
+    def __itruediv__(self: Array, other: Union[float, Array], /) -> Array:
+        """
+        Performs the operation __itruediv__.
+        """
+        other = self._check_allowed_dtypes(other, "floating-point", "__itruediv__")
+        if other is NotImplemented:
+            return other
+        self._array.__itruediv__(other._array)
+        return self
+
+    def __rtruediv__(self: Array, other: Union[float, Array], /) -> Array:
+        """
+        Performs the operation __rtruediv__.
+        """
+        other = self._check_allowed_dtypes(other, "floating-point", "__rtruediv__")
+        if other is NotImplemented:
+            return other
+        self, other = self._normalize_two_args(self, other)
+        res = self._array.__rtruediv__(other._array)
+        return self.__class__._new(res)
+
+    def __ixor__(self: Array, other: Union[int, bool, Array], /) -> Array:
+        """
+        Performs the operation __ixor__.
+        """
+        other = self._check_allowed_dtypes(other, "integer or boolean", "__ixor__")
+        if other is NotImplemented:
+            return other
+        self._array.__ixor__(other._array)
+        return self
+
+    def __rxor__(self: Array, other: Union[int, bool, Array], /) -> Array:
+        """
+        Performs the operation __rxor__.
+        """
+        other = self._check_allowed_dtypes(other, "integer or boolean", "__rxor__")
+        if other is NotImplemented:
+            return other
+        self, other = self._normalize_two_args(self, other)
+        res = self._array.__rxor__(other._array)
+        return self.__class__._new(res)
+
+    def to_device(self: Array, device: Device, /, stream: None = None) -> Array:
+        if stream is not None:
+            raise ValueError("The stream argument to to_device() is not supported")
+        if device == 'cpu':
+            return self
+        raise ValueError(f"Unsupported device {device!r}")
+
+    @property
+    def dtype(self) -> Dtype:
+        """
+        Array API compatible wrapper for :py:meth:`np.ndarray.dtype `.
+
+        See its docstring for more information.
+        """
+        return self._array.dtype
+
+    @property
+    def device(self) -> Device:
+        return "cpu"
+
+    # Note: mT is new in array API spec (see matrix_transpose)
+    @property
+    def mT(self) -> Array:
+        from .linalg import matrix_transpose
+        return matrix_transpose(self)
+
+    @property
+    def ndim(self) -> int:
+        """
+        Array API compatible wrapper for :py:meth:`np.ndarray.ndim `.
+
+        See its docstring for more information.
+        """
+        return self._array.ndim
+
+    @property
+    def shape(self) -> Tuple[int, ...]:
+        """
+        Array API compatible wrapper for :py:meth:`np.ndarray.shape `.
+
+        See its docstring for more information.
+        """
+        return self._array.shape
+
+    @property
+    def size(self) -> int:
+        """
+        Array API compatible wrapper for :py:meth:`np.ndarray.size `.
+
+        See its docstring for more information.
+        """
+        return self._array.size
+
+    @property
+    def T(self) -> Array:
+        """
+        Array API compatible wrapper for :py:meth:`np.ndarray.T `.
+
+        See its docstring for more information.
+        """
+        # Note: T only works on 2-dimensional arrays. See the corresponding
+        # note in the specification:
+        # https://data-apis.org/array-api/latest/API_specification/array_object.html#t
+        if self.ndim != 2:
+            raise ValueError("x.T requires x to have 2 dimensions. Use x.mT to transpose stacks of matrices and permute_dims() to permute dimensions.")
+        return self.__class__._new(self._array.T)
diff --git a/env-llmeval/lib/python3.10/site-packages/numpy/array_api/_data_type_functions.py b/env-llmeval/lib/python3.10/site-packages/numpy/array_api/_data_type_functions.py
new file mode 100644
index 0000000000000000000000000000000000000000..6f972c3b54244fdedf2bf2ce77f91c173c09b540
--- /dev/null
+++ b/env-llmeval/lib/python3.10/site-packages/numpy/array_api/_data_type_functions.py
@@ -0,0 +1,197 @@
+from __future__ import annotations
+
+from ._array_object import Array
+from ._dtypes import (
+    _all_dtypes,
+    _boolean_dtypes,
+    _signed_integer_dtypes,
+    _unsigned_integer_dtypes,
+    _integer_dtypes,
+    _real_floating_dtypes,
+    _complex_floating_dtypes,
+    _numeric_dtypes,
+    _result_type,
+)
+
+from dataclasses import dataclass
+from typing import TYPE_CHECKING, List, Tuple, Union
+
+if TYPE_CHECKING:
+    from ._typing import Dtype
+    from collections.abc import Sequence
+
+import numpy as np
+
+
+# Note: astype is a function, not an array method as in NumPy.
+def astype(x: Array, dtype: Dtype, /, *, copy: bool = True) -> Array:
+    if not copy and dtype == x.dtype:
+        return x
+    return Array._new(x._array.astype(dtype=dtype, copy=copy))
+
+
+def broadcast_arrays(*arrays: Array) -> List[Array]:
+    """
+    Array API compatible wrapper for :py:func:`np.broadcast_arrays `.
+
+    See its docstring for more information.
+    """
+    from ._array_object import Array
+
+    return [
+        Array._new(array) for array in np.broadcast_arrays(*[a._array for a in arrays])
+    ]
+
+
+def broadcast_to(x: Array, /, shape: Tuple[int, ...]) -> Array:
+    """
+    Array API compatible wrapper for :py:func:`np.broadcast_to `.
+
+    See its docstring for more information.
+    """
+    from ._array_object import Array
+
+    return Array._new(np.broadcast_to(x._array, shape))
+
+
+def can_cast(from_: Union[Dtype, Array], to: Dtype, /) -> bool:
+    """
+    Array API compatible wrapper for :py:func:`np.can_cast `.
+
+    See its docstring for more information.
+    """
+    if isinstance(from_, Array):
+        from_ = from_.dtype
+    elif from_ not in _all_dtypes:
+        raise TypeError(f"{from_=}, but should be an array_api array or dtype")
+    if to not in _all_dtypes:
+        raise TypeError(f"{to=}, but should be a dtype")
+    # Note: We avoid np.can_cast() as it has discrepancies with the array API,
+    # since NumPy allows cross-kind casting (e.g., NumPy allows bool -> int8).
+    # See https://github.com/numpy/numpy/issues/20870
+    try:
+        # We promote `from_` and `to` together. We then check if the promoted
+        # dtype is `to`, which indicates if `from_` can (up)cast to `to`.
+        dtype = _result_type(from_, to)
+        return to == dtype
+    except TypeError:
+        # _result_type() raises if the dtypes don't promote together
+        return False
+
+
+# These are internal objects for the return types of finfo and iinfo, since
+# the NumPy versions contain extra data that isn't part of the spec.
+@dataclass
+class finfo_object:
+    bits: int
+    # Note: The types of the float data here are float, whereas in NumPy they
+    # are scalars of the corresponding float dtype.
+    eps: float
+    max: float
+    min: float
+    smallest_normal: float
+    dtype: Dtype
+
+
+@dataclass
+class iinfo_object:
+    bits: int
+    max: int
+    min: int
+    dtype: Dtype
+
+
+def finfo(type: Union[Dtype, Array], /) -> finfo_object:
+    """
+    Array API compatible wrapper for :py:func:`np.finfo `.
+
+    See its docstring for more information.
+    """
+    fi = np.finfo(type)
+    # Note: The types of the float data here are float, whereas in NumPy they
+    # are scalars of the corresponding float dtype.
+    return finfo_object(
+        fi.bits,
+        float(fi.eps),
+        float(fi.max),
+        float(fi.min),
+        float(fi.smallest_normal),
+        fi.dtype,
+    )
+
+
+def iinfo(type: Union[Dtype, Array], /) -> iinfo_object:
+    """
+    Array API compatible wrapper for :py:func:`np.iinfo `.
+
+    See its docstring for more information.
+    """
+    ii = np.iinfo(type)
+    return iinfo_object(ii.bits, ii.max, ii.min, ii.dtype)
+
+
+# Note: isdtype is a new function from the 2022.12 array API specification.
+def isdtype(
+    dtype: Dtype, kind: Union[Dtype, str, Tuple[Union[Dtype, str], ...]]
+) -> bool:
+    """
+    Returns a boolean indicating whether a provided dtype is of a specified data type ``kind``.
+
+    See
+    https://data-apis.org/array-api/latest/API_specification/generated/array_api.isdtype.html
+    for more details
+    """
+    if isinstance(kind, tuple):
+        # Disallow nested tuples
+        if any(isinstance(k, tuple) for k in kind):
+            raise TypeError("'kind' must be a dtype, str, or tuple of dtypes and strs")
+        return any(isdtype(dtype, k) for k in kind)
+    elif isinstance(kind, str):
+        if kind == 'bool':
+            return dtype in _boolean_dtypes
+        elif kind == 'signed integer':
+            return dtype in _signed_integer_dtypes
+        elif kind == 'unsigned integer':
+            return dtype in _unsigned_integer_dtypes
+        elif kind == 'integral':
+            return dtype in _integer_dtypes
+        elif kind == 'real floating':
+            return dtype in _real_floating_dtypes
+        elif kind == 'complex floating':
+            return dtype in _complex_floating_dtypes
+        elif kind == 'numeric':
+            return dtype in _numeric_dtypes
+        else:
+            raise ValueError(f"Unrecognized data type kind: {kind!r}")
+    elif kind in _all_dtypes:
+        return dtype == kind
+    else:
+        raise TypeError(f"'kind' must be a dtype, str, or tuple of dtypes and strs, not {type(kind).__name__}")
+
+def result_type(*arrays_and_dtypes: Union[Array, Dtype]) -> Dtype:
+    """
+    Array API compatible wrapper for :py:func:`np.result_type `.
+
+    See its docstring for more information.
+    """
+    # Note: we use a custom implementation that gives only the type promotions
+    # required by the spec rather than using np.result_type. NumPy implements
+    # too many extra type promotions like int64 + uint64 -> float64, and does
+    # value-based casting on scalar arrays.
+    A = []
+    for a in arrays_and_dtypes:
+        if isinstance(a, Array):
+            a = a.dtype
+        elif isinstance(a, np.ndarray) or a not in _all_dtypes:
+            raise TypeError("result_type() inputs must be array_api arrays or dtypes")
+        A.append(a)
+
+    if len(A) == 0:
+        raise ValueError("at least one array or dtype is required")
+    elif len(A) == 1:
+        return A[0]
+    else:
+        t = A[0]
+        for t2 in A[1:]:
+            t = _result_type(t, t2)
+        return t
diff --git a/env-llmeval/lib/python3.10/site-packages/numpy/array_api/_dtypes.py b/env-llmeval/lib/python3.10/site-packages/numpy/array_api/_dtypes.py
new file mode 100644
index 0000000000000000000000000000000000000000..0e8f666eeeddb1e78f4c0d0437971bfa66fea887
--- /dev/null
+++ b/env-llmeval/lib/python3.10/site-packages/numpy/array_api/_dtypes.py
@@ -0,0 +1,180 @@
+import numpy as np
+
+# Note: we use dtype objects instead of dtype classes. The spec does not
+# require any behavior on dtypes other than equality.
+int8 = np.dtype("int8")
+int16 = np.dtype("int16")
+int32 = np.dtype("int32")
+int64 = np.dtype("int64")
+uint8 = np.dtype("uint8")
+uint16 = np.dtype("uint16")
+uint32 = np.dtype("uint32")
+uint64 = np.dtype("uint64")
+float32 = np.dtype("float32")
+float64 = np.dtype("float64")
+complex64 = np.dtype("complex64")
+complex128 = np.dtype("complex128")
+# Note: This name is changed
+bool = np.dtype("bool")
+
+_all_dtypes = (
+    int8,
+    int16,
+    int32,
+    int64,
+    uint8,
+    uint16,
+    uint32,
+    uint64,
+    float32,
+    float64,
+    complex64,
+    complex128,
+    bool,
+)
+_boolean_dtypes = (bool,)
+_real_floating_dtypes = (float32, float64)
+_floating_dtypes = (float32, float64, complex64, complex128)
+_complex_floating_dtypes = (complex64, complex128)
+_integer_dtypes = (int8, int16, int32, int64, uint8, uint16, uint32, uint64)
+_signed_integer_dtypes = (int8, int16, int32, int64)
+_unsigned_integer_dtypes = (uint8, uint16, uint32, uint64)
+_integer_or_boolean_dtypes = (
+    bool,
+    int8,
+    int16,
+    int32,
+    int64,
+    uint8,
+    uint16,
+    uint32,
+    uint64,
+)
+_real_numeric_dtypes = (
+    float32,
+    float64,
+    int8,
+    int16,
+    int32,
+    int64,
+    uint8,
+    uint16,
+    uint32,
+    uint64,
+)
+_numeric_dtypes = (
+    float32,
+    float64,
+    complex64,
+    complex128,
+    int8,
+    int16,
+    int32,
+    int64,
+    uint8,
+    uint16,
+    uint32,
+    uint64,
+)
+
+_dtype_categories = {
+    "all": _all_dtypes,
+    "real numeric": _real_numeric_dtypes,
+    "numeric": _numeric_dtypes,
+    "integer": _integer_dtypes,
+    "integer or boolean": _integer_or_boolean_dtypes,
+    "boolean": _boolean_dtypes,
+    "real floating-point": _floating_dtypes,
+    "complex floating-point": _complex_floating_dtypes,
+    "floating-point": _floating_dtypes,
+}
+
+
+# Note: the spec defines a restricted type promotion table compared to NumPy.
+# In particular, cross-kind promotions like integer + float or boolean +
+# integer are not allowed, even for functions that accept both kinds.
+# Additionally, NumPy promotes signed integer + uint64 to float64, but this
+# promotion is not allowed here. To be clear, Python scalar int objects are
+# allowed to promote to floating-point dtypes, but only in array operators
+# (see Array._promote_scalar) method in _array_object.py.
+_promotion_table = {
+    (int8, int8): int8,
+    (int8, int16): int16,
+    (int8, int32): int32,
+    (int8, int64): int64,
+    (int16, int8): int16,
+    (int16, int16): int16,
+    (int16, int32): int32,
+    (int16, int64): int64,
+    (int32, int8): int32,
+    (int32, int16): int32,
+    (int32, int32): int32,
+    (int32, int64): int64,
+    (int64, int8): int64,
+    (int64, int16): int64,
+    (int64, int32): int64,
+    (int64, int64): int64,
+    (uint8, uint8): uint8,
+    (uint8, uint16): uint16,
+    (uint8, uint32): uint32,
+    (uint8, uint64): uint64,
+    (uint16, uint8): uint16,
+    (uint16, uint16): uint16,
+    (uint16, uint32): uint32,
+    (uint16, uint64): uint64,
+    (uint32, uint8): uint32,
+    (uint32, uint16): uint32,
+    (uint32, uint32): uint32,
+    (uint32, uint64): uint64,
+    (uint64, uint8): uint64,
+    (uint64, uint16): uint64,
+    (uint64, uint32): uint64,
+    (uint64, uint64): uint64,
+    (int8, uint8): int16,
+    (int8, uint16): int32,
+    (int8, uint32): int64,
+    (int16, uint8): int16,
+    (int16, uint16): int32,
+    (int16, uint32): int64,
+    (int32, uint8): int32,
+    (int32, uint16): int32,
+    (int32, uint32): int64,
+    (int64, uint8): int64,
+    (int64, uint16): int64,
+    (int64, uint32): int64,
+    (uint8, int8): int16,
+    (uint16, int8): int32,
+    (uint32, int8): int64,
+    (uint8, int16): int16,
+    (uint16, int16): int32,
+    (uint32, int16): int64,
+    (uint8, int32): int32,
+    (uint16, int32): int32,
+    (uint32, int32): int64,
+    (uint8, int64): int64,
+    (uint16, int64): int64,
+    (uint32, int64): int64,
+    (float32, float32): float32,
+    (float32, float64): float64,
+    (float64, float32): float64,
+    (float64, float64): float64,
+    (complex64, complex64): complex64,
+    (complex64, complex128): complex128,
+    (complex128, complex64): complex128,
+    (complex128, complex128): complex128,
+    (float32, complex64): complex64,
+    (float32, complex128): complex128,
+    (float64, complex64): complex128,
+    (float64, complex128): complex128,
+    (complex64, float32): complex64,
+    (complex64, float64): complex128,
+    (complex128, float32): complex128,
+    (complex128, float64): complex128,
+    (bool, bool): bool,
+}
+
+
+def _result_type(type1, type2):
+    if (type1, type2) in _promotion_table:
+        return _promotion_table[type1, type2]
+    raise TypeError(f"{type1} and {type2} cannot be type promoted together")
diff --git a/env-llmeval/lib/python3.10/site-packages/numpy/array_api/_manipulation_functions.py b/env-llmeval/lib/python3.10/site-packages/numpy/array_api/_manipulation_functions.py
new file mode 100644
index 0000000000000000000000000000000000000000..556bde7d0b07c14a3f7c35c57859b6fe253f8c18
--- /dev/null
+++ b/env-llmeval/lib/python3.10/site-packages/numpy/array_api/_manipulation_functions.py
@@ -0,0 +1,112 @@
+from __future__ import annotations
+
+from ._array_object import Array
+from ._data_type_functions import result_type
+
+from typing import List, Optional, Tuple, Union
+
+import numpy as np
+
+# Note: the function name is different here
+def concat(
+    arrays: Union[Tuple[Array, ...], List[Array]], /, *, axis: Optional[int] = 0
+) -> Array:
+    """
+    Array API compatible wrapper for :py:func:`np.concatenate `.
+
+    See its docstring for more information.
+    """
+    # Note: Casting rules here are different from the np.concatenate default
+    # (no for scalars with axis=None, no cross-kind casting)
+    dtype = result_type(*arrays)
+    arrays = tuple(a._array for a in arrays)
+    return Array._new(np.concatenate(arrays, axis=axis, dtype=dtype))
+
+
+def expand_dims(x: Array, /, *, axis: int) -> Array:
+    """
+    Array API compatible wrapper for :py:func:`np.expand_dims `.
+
+    See its docstring for more information.
+    """
+    return Array._new(np.expand_dims(x._array, axis))
+
+
+def flip(x: Array, /, *, axis: Optional[Union[int, Tuple[int, ...]]] = None) -> Array:
+    """
+    Array API compatible wrapper for :py:func:`np.flip `.
+
+    See its docstring for more information.
+    """
+    return Array._new(np.flip(x._array, axis=axis))
+
+
+# Note: The function name is different here (see also matrix_transpose).
+# Unlike transpose(), the axes argument is required.
+def permute_dims(x: Array, /, axes: Tuple[int, ...]) -> Array:
+    """
+    Array API compatible wrapper for :py:func:`np.transpose `.
+
+    See its docstring for more information.
+    """
+    return Array._new(np.transpose(x._array, axes))
+
+
+# Note: the optional argument is called 'shape', not 'newshape'
+def reshape(x: Array, 
+            /, 
+            shape: Tuple[int, ...],
+            *,
+            copy: Optional[Bool] = None) -> Array:
+    """
+    Array API compatible wrapper for :py:func:`np.reshape `.
+
+    See its docstring for more information.
+    """
+
+    data = x._array
+    if copy:
+        data = np.copy(data)
+
+    reshaped = np.reshape(data, shape)
+
+    if copy is False and not np.shares_memory(data, reshaped):
+        raise AttributeError("Incompatible shape for in-place modification.")
+
+    return Array._new(reshaped)
+
+
+def roll(
+    x: Array,
+    /,
+    shift: Union[int, Tuple[int, ...]],
+    *,
+    axis: Optional[Union[int, Tuple[int, ...]]] = None,
+) -> Array:
+    """
+    Array API compatible wrapper for :py:func:`np.roll `.
+
+    See its docstring for more information.
+    """
+    return Array._new(np.roll(x._array, shift, axis=axis))
+
+
+def squeeze(x: Array, /, axis: Union[int, Tuple[int, ...]]) -> Array:
+    """
+    Array API compatible wrapper for :py:func:`np.squeeze `.
+
+    See its docstring for more information.
+    """
+    return Array._new(np.squeeze(x._array, axis=axis))
+
+
+def stack(arrays: Union[Tuple[Array, ...], List[Array]], /, *, axis: int = 0) -> Array:
+    """
+    Array API compatible wrapper for :py:func:`np.stack `.
+
+    See its docstring for more information.
+    """
+    # Call result type here just to raise on disallowed type combinations
+    result_type(*arrays)
+    arrays = tuple(a._array for a in arrays)
+    return Array._new(np.stack(arrays, axis=axis))
diff --git a/env-llmeval/lib/python3.10/site-packages/numpy/array_api/_searching_functions.py b/env-llmeval/lib/python3.10/site-packages/numpy/array_api/_searching_functions.py
new file mode 100644
index 0000000000000000000000000000000000000000..a1f4b0c904c11834b4ccbcf202b639628c8aaf5c
--- /dev/null
+++ b/env-llmeval/lib/python3.10/site-packages/numpy/array_api/_searching_functions.py
@@ -0,0 +1,51 @@
+from __future__ import annotations
+
+from ._array_object import Array
+from ._dtypes import _result_type, _real_numeric_dtypes
+
+from typing import Optional, Tuple
+
+import numpy as np
+
+
+def argmax(x: Array, /, *, axis: Optional[int] = None, keepdims: bool = False) -> Array:
+    """
+    Array API compatible wrapper for :py:func:`np.argmax `.
+
+    See its docstring for more information.
+    """
+    if x.dtype not in _real_numeric_dtypes:
+        raise TypeError("Only real numeric dtypes are allowed in argmax")
+    return Array._new(np.asarray(np.argmax(x._array, axis=axis, keepdims=keepdims)))
+
+
+def argmin(x: Array, /, *, axis: Optional[int] = None, keepdims: bool = False) -> Array:
+    """
+    Array API compatible wrapper for :py:func:`np.argmin `.
+
+    See its docstring for more information.
+    """
+    if x.dtype not in _real_numeric_dtypes:
+        raise TypeError("Only real numeric dtypes are allowed in argmin")
+    return Array._new(np.asarray(np.argmin(x._array, axis=axis, keepdims=keepdims)))
+
+
+def nonzero(x: Array, /) -> Tuple[Array, ...]:
+    """
+    Array API compatible wrapper for :py:func:`np.nonzero `.
+
+    See its docstring for more information.
+    """
+    return tuple(Array._new(i) for i in np.nonzero(x._array))
+
+
+def where(condition: Array, x1: Array, x2: Array, /) -> Array:
+    """
+    Array API compatible wrapper for :py:func:`np.where `.
+
+    See its docstring for more information.
+    """
+    # Call result type here just to raise on disallowed type combinations
+    _result_type(x1.dtype, x2.dtype)
+    x1, x2 = Array._normalize_two_args(x1, x2)
+    return Array._new(np.where(condition._array, x1._array, x2._array))
diff --git a/env-llmeval/lib/python3.10/site-packages/numpy/array_api/_sorting_functions.py b/env-llmeval/lib/python3.10/site-packages/numpy/array_api/_sorting_functions.py
new file mode 100644
index 0000000000000000000000000000000000000000..9b8cb044d88a992da63d6e1a68c5b4c998a49680
--- /dev/null
+++ b/env-llmeval/lib/python3.10/site-packages/numpy/array_api/_sorting_functions.py
@@ -0,0 +1,54 @@
+from __future__ import annotations
+
+from ._array_object import Array
+from ._dtypes import _real_numeric_dtypes
+
+import numpy as np
+
+
+# Note: the descending keyword argument is new in this function
+def argsort(
+    x: Array, /, *, axis: int = -1, descending: bool = False, stable: bool = True
+) -> Array:
+    """
+    Array API compatible wrapper for :py:func:`np.argsort `.
+
+    See its docstring for more information.
+    """
+    if x.dtype not in _real_numeric_dtypes:
+        raise TypeError("Only real numeric dtypes are allowed in argsort")
+    # Note: this keyword argument is different, and the default is different.
+    kind = "stable" if stable else "quicksort"
+    if not descending:
+        res = np.argsort(x._array, axis=axis, kind=kind)
+    else:
+        # As NumPy has no native descending sort, we imitate it here. Note that
+        # simply flipping the results of np.argsort(x._array, ...) would not
+        # respect the relative order like it would in native descending sorts.
+        res = np.flip(
+            np.argsort(np.flip(x._array, axis=axis), axis=axis, kind=kind),
+            axis=axis,
+        )
+        # Rely on flip()/argsort() to validate axis
+        normalised_axis = axis if axis >= 0 else x.ndim + axis
+        max_i = x.shape[normalised_axis] - 1
+        res = max_i - res
+    return Array._new(res)
+
+# Note: the descending keyword argument is new in this function
+def sort(
+    x: Array, /, *, axis: int = -1, descending: bool = False, stable: bool = True
+) -> Array:
+    """
+    Array API compatible wrapper for :py:func:`np.sort `.
+
+    See its docstring for more information.
+    """
+    if x.dtype not in _real_numeric_dtypes:
+        raise TypeError("Only real numeric dtypes are allowed in sort")
+    # Note: this keyword argument is different, and the default is different.
+    kind = "stable" if stable else "quicksort"
+    res = np.sort(x._array, axis=axis, kind=kind)
+    if descending:
+        res = np.flip(res, axis=axis)
+    return Array._new(res)
diff --git a/env-llmeval/lib/python3.10/site-packages/numpy/array_api/_typing.py b/env-llmeval/lib/python3.10/site-packages/numpy/array_api/_typing.py
new file mode 100644
index 0000000000000000000000000000000000000000..e63a375b5f6696fc9ba639c8d355d1b1840273bb
--- /dev/null
+++ b/env-llmeval/lib/python3.10/site-packages/numpy/array_api/_typing.py
@@ -0,0 +1,76 @@
+"""
+This file defines the types for type annotations.
+
+These names aren't part of the module namespace, but they are used in the
+annotations in the function signatures. The functions in the module are only
+valid for inputs that match the given type annotations.
+"""
+
+from __future__ import annotations
+
+__all__ = [
+    "Array",
+    "Device",
+    "Dtype",
+    "SupportsDLPack",
+    "SupportsBufferProtocol",
+    "PyCapsule",
+]
+
+import sys
+
+from typing import (
+    Any,
+    Literal,
+    Sequence,
+    Type,
+    Union,
+    TypeVar,
+    Protocol,
+)
+
+from ._array_object import Array
+from numpy import (
+    dtype,
+    int8,
+    int16,
+    int32,
+    int64,
+    uint8,
+    uint16,
+    uint32,
+    uint64,
+    float32,
+    float64,
+)
+
+_T_co = TypeVar("_T_co", covariant=True)
+
+class NestedSequence(Protocol[_T_co]):
+    def __getitem__(self, key: int, /) -> _T_co | NestedSequence[_T_co]: ...
+    def __len__(self, /) -> int: ...
+
+Device = Literal["cpu"]
+
+Dtype = dtype[Union[
+    int8,
+    int16,
+    int32,
+    int64,
+    uint8,
+    uint16,
+    uint32,
+    uint64,
+    float32,
+    float64,
+]]
+
+if sys.version_info >= (3, 12):
+    from collections.abc import Buffer as SupportsBufferProtocol
+else:
+    SupportsBufferProtocol = Any
+
+PyCapsule = Any
+
+class SupportsDLPack(Protocol):
+    def __dlpack__(self, /, *, stream: None = ...) -> PyCapsule: ...
diff --git a/env-llmeval/lib/python3.10/site-packages/numpy/array_api/_utility_functions.py b/env-llmeval/lib/python3.10/site-packages/numpy/array_api/_utility_functions.py
new file mode 100644
index 0000000000000000000000000000000000000000..5ecb4bd9fef732bf5ca6aeec38ce2e7557ed19c2
--- /dev/null
+++ b/env-llmeval/lib/python3.10/site-packages/numpy/array_api/_utility_functions.py
@@ -0,0 +1,37 @@
+from __future__ import annotations
+
+from ._array_object import Array
+
+from typing import Optional, Tuple, Union
+
+import numpy as np
+
+
+def all(
+    x: Array,
+    /,
+    *,
+    axis: Optional[Union[int, Tuple[int, ...]]] = None,
+    keepdims: bool = False,
+) -> Array:
+    """
+    Array API compatible wrapper for :py:func:`np.all `.
+
+    See its docstring for more information.
+    """
+    return Array._new(np.asarray(np.all(x._array, axis=axis, keepdims=keepdims)))
+
+
+def any(
+    x: Array,
+    /,
+    *,
+    axis: Optional[Union[int, Tuple[int, ...]]] = None,
+    keepdims: bool = False,
+) -> Array:
+    """
+    Array API compatible wrapper for :py:func:`np.any `.
+
+    See its docstring for more information.
+    """
+    return Array._new(np.asarray(np.any(x._array, axis=axis, keepdims=keepdims)))
diff --git a/env-llmeval/lib/python3.10/site-packages/numpy/array_api/tests/__init__.py b/env-llmeval/lib/python3.10/site-packages/numpy/array_api/tests/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..536062e3827921df637105680ea9e8ea879b8f3e
--- /dev/null
+++ b/env-llmeval/lib/python3.10/site-packages/numpy/array_api/tests/__init__.py
@@ -0,0 +1,7 @@
+"""
+Tests for the array API namespace.
+
+Note, full compliance with the array API can be tested with the official array API test
+suite https://github.com/data-apis/array-api-tests. This test suite primarily
+focuses on those things that are not tested by the official test suite.
+"""
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diff --git a/env-llmeval/lib/python3.10/site-packages/numpy/array_api/tests/test_array_object.py b/env-llmeval/lib/python3.10/site-packages/numpy/array_api/tests/test_array_object.py
new file mode 100644
index 0000000000000000000000000000000000000000..0feb72c4ea33a632545e6329854644ac9b817a4a
--- /dev/null
+++ b/env-llmeval/lib/python3.10/site-packages/numpy/array_api/tests/test_array_object.py
@@ -0,0 +1,395 @@
+import operator
+
+from numpy.testing import assert_raises, suppress_warnings
+import numpy as np
+import pytest
+
+from .. import ones, asarray, reshape, result_type, all, equal
+from .._array_object import Array
+from .._dtypes import (
+    _all_dtypes,
+    _boolean_dtypes,
+    _real_floating_dtypes,
+    _floating_dtypes,
+    _complex_floating_dtypes,
+    _integer_dtypes,
+    _integer_or_boolean_dtypes,
+    _real_numeric_dtypes,
+    _numeric_dtypes,
+    int8,
+    int16,
+    int32,
+    int64,
+    uint64,
+    bool as bool_,
+)
+
+
+def test_validate_index():
+    # The indexing tests in the official array API test suite test that the
+    # array object correctly handles the subset of indices that are required
+    # by the spec. But the NumPy array API implementation specifically
+    # disallows any index not required by the spec, via Array._validate_index.
+    # This test focuses on testing that non-valid indices are correctly
+    # rejected. See
+    # https://data-apis.org/array-api/latest/API_specification/indexing.html
+    # and the docstring of Array._validate_index for the exact indexing
+    # behavior that should be allowed. This does not test indices that are
+    # already invalid in NumPy itself because Array will generally just pass
+    # such indices directly to the underlying np.ndarray.
+
+    a = ones((3, 4))
+
+    # Out of bounds slices are not allowed
+    assert_raises(IndexError, lambda: a[:4])
+    assert_raises(IndexError, lambda: a[:-4])
+    assert_raises(IndexError, lambda: a[:3:-1])
+    assert_raises(IndexError, lambda: a[:-5:-1])
+    assert_raises(IndexError, lambda: a[4:])
+    assert_raises(IndexError, lambda: a[-4:])
+    assert_raises(IndexError, lambda: a[4::-1])
+    assert_raises(IndexError, lambda: a[-4::-1])
+
+    assert_raises(IndexError, lambda: a[...,:5])
+    assert_raises(IndexError, lambda: a[...,:-5])
+    assert_raises(IndexError, lambda: a[...,:5:-1])
+    assert_raises(IndexError, lambda: a[...,:-6:-1])
+    assert_raises(IndexError, lambda: a[...,5:])
+    assert_raises(IndexError, lambda: a[...,-5:])
+    assert_raises(IndexError, lambda: a[...,5::-1])
+    assert_raises(IndexError, lambda: a[...,-5::-1])
+
+    # Boolean indices cannot be part of a larger tuple index
+    assert_raises(IndexError, lambda: a[a[:,0]==1,0])
+    assert_raises(IndexError, lambda: a[a[:,0]==1,...])
+    assert_raises(IndexError, lambda: a[..., a[0]==1])
+    assert_raises(IndexError, lambda: a[[True, True, True]])
+    assert_raises(IndexError, lambda: a[(True, True, True),])
+
+    # Integer array indices are not allowed (except for 0-D)
+    idx = asarray([[0, 1]])
+    assert_raises(IndexError, lambda: a[idx])
+    assert_raises(IndexError, lambda: a[idx,])
+    assert_raises(IndexError, lambda: a[[0, 1]])
+    assert_raises(IndexError, lambda: a[(0, 1), (0, 1)])
+    assert_raises(IndexError, lambda: a[[0, 1]])
+    assert_raises(IndexError, lambda: a[np.array([[0, 1]])])
+
+    # Multiaxis indices must contain exactly as many indices as dimensions
+    assert_raises(IndexError, lambda: a[()])
+    assert_raises(IndexError, lambda: a[0,])
+    assert_raises(IndexError, lambda: a[0])
+    assert_raises(IndexError, lambda: a[:])
+
+def test_operators():
+    # For every operator, we test that it works for the required type
+    # combinations and raises TypeError otherwise
+    binary_op_dtypes = {
+        "__add__": "numeric",
+        "__and__": "integer_or_boolean",
+        "__eq__": "all",
+        "__floordiv__": "real numeric",
+        "__ge__": "real numeric",
+        "__gt__": "real numeric",
+        "__le__": "real numeric",
+        "__lshift__": "integer",
+        "__lt__": "real numeric",
+        "__mod__": "real numeric",
+        "__mul__": "numeric",
+        "__ne__": "all",
+        "__or__": "integer_or_boolean",
+        "__pow__": "numeric",
+        "__rshift__": "integer",
+        "__sub__": "numeric",
+        "__truediv__": "floating",
+        "__xor__": "integer_or_boolean",
+    }
+    # Recompute each time because of in-place ops
+    def _array_vals():
+        for d in _integer_dtypes:
+            yield asarray(1, dtype=d)
+        for d in _boolean_dtypes:
+            yield asarray(False, dtype=d)
+        for d in _floating_dtypes:
+            yield asarray(1.0, dtype=d)
+
+
+    BIG_INT = int(1e30)
+    for op, dtypes in binary_op_dtypes.items():
+        ops = [op]
+        if op not in ["__eq__", "__ne__", "__le__", "__ge__", "__lt__", "__gt__"]:
+            rop = "__r" + op[2:]
+            iop = "__i" + op[2:]
+            ops += [rop, iop]
+        for s in [1, 1.0, 1j, BIG_INT, False]:
+            for _op in ops:
+                for a in _array_vals():
+                    # Test array op scalar. From the spec, the following combinations
+                    # are supported:
+
+                    # - Python bool for a bool array dtype,
+                    # - a Python int within the bounds of the given dtype for integer array dtypes,
+                    # - a Python int or float for real floating-point array dtypes
+                    # - a Python int, float, or complex for complex floating-point array dtypes
+
+                    if ((dtypes == "all"
+                         or dtypes == "numeric" and a.dtype in _numeric_dtypes
+                         or dtypes == "real numeric" and a.dtype in _real_numeric_dtypes
+                         or dtypes == "integer" and a.dtype in _integer_dtypes
+                         or dtypes == "integer_or_boolean" and a.dtype in _integer_or_boolean_dtypes
+                         or dtypes == "boolean" and a.dtype in _boolean_dtypes
+                         or dtypes == "floating" and a.dtype in _floating_dtypes
+                        )
+                        # bool is a subtype of int, which is why we avoid
+                        # isinstance here.
+                        and (a.dtype in _boolean_dtypes and type(s) == bool
+                             or a.dtype in _integer_dtypes and type(s) == int
+                             or a.dtype in _real_floating_dtypes and type(s) in [float, int]
+                             or a.dtype in _complex_floating_dtypes and type(s) in [complex, float, int]
+                        )):
+                        if a.dtype in _integer_dtypes and s == BIG_INT:
+                            assert_raises(OverflowError, lambda: getattr(a, _op)(s))
+                        else:
+                            # Only test for no error
+                            with suppress_warnings() as sup:
+                                # ignore warnings from pow(BIG_INT)
+                                sup.filter(RuntimeWarning,
+                                           "invalid value encountered in power")
+                                getattr(a, _op)(s)
+                    else:
+                        assert_raises(TypeError, lambda: getattr(a, _op)(s))
+
+                # Test array op array.
+                for _op in ops:
+                    for x in _array_vals():
+                        for y in _array_vals():
+                            # See the promotion table in NEP 47 or the array
+                            # API spec page on type promotion. Mixed kind
+                            # promotion is not defined.
+                            if (x.dtype == uint64 and y.dtype in [int8, int16, int32, int64]
+                                or y.dtype == uint64 and x.dtype in [int8, int16, int32, int64]
+                                or x.dtype in _integer_dtypes and y.dtype not in _integer_dtypes
+                                or y.dtype in _integer_dtypes and x.dtype not in _integer_dtypes
+                                or x.dtype in _boolean_dtypes and y.dtype not in _boolean_dtypes
+                                or y.dtype in _boolean_dtypes and x.dtype not in _boolean_dtypes
+                                or x.dtype in _floating_dtypes and y.dtype not in _floating_dtypes
+                                or y.dtype in _floating_dtypes and x.dtype not in _floating_dtypes
+                                ):
+                                assert_raises(TypeError, lambda: getattr(x, _op)(y))
+                            # Ensure in-place operators only promote to the same dtype as the left operand.
+                            elif (
+                                _op.startswith("__i")
+                                and result_type(x.dtype, y.dtype) != x.dtype
+                            ):
+                                assert_raises(TypeError, lambda: getattr(x, _op)(y))
+                            # Ensure only those dtypes that are required for every operator are allowed.
+                            elif (dtypes == "all" and (x.dtype in _boolean_dtypes and y.dtype in _boolean_dtypes
+                                                      or x.dtype in _numeric_dtypes and y.dtype in _numeric_dtypes)
+                                or (dtypes == "real numeric" and x.dtype in _real_numeric_dtypes and y.dtype in _real_numeric_dtypes)
+                                or (dtypes == "numeric" and x.dtype in _numeric_dtypes and y.dtype in _numeric_dtypes)
+                                or dtypes == "integer" and x.dtype in _integer_dtypes and y.dtype in _integer_dtypes
+                                or dtypes == "integer_or_boolean" and (x.dtype in _integer_dtypes and y.dtype in _integer_dtypes
+                                                                       or x.dtype in _boolean_dtypes and y.dtype in _boolean_dtypes)
+                                or dtypes == "boolean" and x.dtype in _boolean_dtypes and y.dtype in _boolean_dtypes
+                                or dtypes == "floating" and x.dtype in _floating_dtypes and y.dtype in _floating_dtypes
+                            ):
+                                getattr(x, _op)(y)
+                            else:
+                                assert_raises(TypeError, lambda: getattr(x, _op)(y))
+
+    unary_op_dtypes = {
+        "__abs__": "numeric",
+        "__invert__": "integer_or_boolean",
+        "__neg__": "numeric",
+        "__pos__": "numeric",
+    }
+    for op, dtypes in unary_op_dtypes.items():
+        for a in _array_vals():
+            if (
+                dtypes == "numeric"
+                and a.dtype in _numeric_dtypes
+                or dtypes == "integer_or_boolean"
+                and a.dtype in _integer_or_boolean_dtypes
+            ):
+                # Only test for no error
+                getattr(a, op)()
+            else:
+                assert_raises(TypeError, lambda: getattr(a, op)())
+
+    # Finally, matmul() must be tested separately, because it works a bit
+    # different from the other operations.
+    def _matmul_array_vals():
+        for a in _array_vals():
+            yield a
+        for d in _all_dtypes:
+            yield ones((3, 4), dtype=d)
+            yield ones((4, 2), dtype=d)
+            yield ones((4, 4), dtype=d)
+
+    # Scalars always error
+    for _op in ["__matmul__", "__rmatmul__", "__imatmul__"]:
+        for s in [1, 1.0, False]:
+            for a in _matmul_array_vals():
+                if (type(s) in [float, int] and a.dtype in _floating_dtypes
+                    or type(s) == int and a.dtype in _integer_dtypes):
+                    # Type promotion is valid, but @ is not allowed on 0-D
+                    # inputs, so the error is a ValueError
+                    assert_raises(ValueError, lambda: getattr(a, _op)(s))
+                else:
+                    assert_raises(TypeError, lambda: getattr(a, _op)(s))
+
+    for x in _matmul_array_vals():
+        for y in _matmul_array_vals():
+            if (x.dtype == uint64 and y.dtype in [int8, int16, int32, int64]
+                or y.dtype == uint64 and x.dtype in [int8, int16, int32, int64]
+                or x.dtype in _integer_dtypes and y.dtype not in _integer_dtypes
+                or y.dtype in _integer_dtypes and x.dtype not in _integer_dtypes
+                or x.dtype in _floating_dtypes and y.dtype not in _floating_dtypes
+                or y.dtype in _floating_dtypes and x.dtype not in _floating_dtypes
+                or x.dtype in _boolean_dtypes
+                or y.dtype in _boolean_dtypes
+                ):
+                assert_raises(TypeError, lambda: x.__matmul__(y))
+                assert_raises(TypeError, lambda: y.__rmatmul__(x))
+                assert_raises(TypeError, lambda: x.__imatmul__(y))
+            elif x.shape == () or y.shape == () or x.shape[1] != y.shape[0]:
+                assert_raises(ValueError, lambda: x.__matmul__(y))
+                assert_raises(ValueError, lambda: y.__rmatmul__(x))
+                if result_type(x.dtype, y.dtype) != x.dtype:
+                    assert_raises(TypeError, lambda: x.__imatmul__(y))
+                else:
+                    assert_raises(ValueError, lambda: x.__imatmul__(y))
+            else:
+                x.__matmul__(y)
+                y.__rmatmul__(x)
+                if result_type(x.dtype, y.dtype) != x.dtype:
+                    assert_raises(TypeError, lambda: x.__imatmul__(y))
+                elif y.shape[0] != y.shape[1]:
+                    # This one fails because x @ y has a different shape from x
+                    assert_raises(ValueError, lambda: x.__imatmul__(y))
+                else:
+                    x.__imatmul__(y)
+
+
+def test_python_scalar_construtors():
+    b = asarray(False)
+    i = asarray(0)
+    f = asarray(0.0)
+    c = asarray(0j)
+
+    assert bool(b) == False
+    assert int(i) == 0
+    assert float(f) == 0.0
+    assert operator.index(i) == 0
+
+    # bool/int/float/complex should only be allowed on 0-D arrays.
+    assert_raises(TypeError, lambda: bool(asarray([False])))
+    assert_raises(TypeError, lambda: int(asarray([0])))
+    assert_raises(TypeError, lambda: float(asarray([0.0])))
+    assert_raises(TypeError, lambda: complex(asarray([0j])))
+    assert_raises(TypeError, lambda: operator.index(asarray([0])))
+
+    # bool should work on all types of arrays
+    assert bool(b) is bool(i) is bool(f) is bool(c) is False
+
+    # int should fail on complex arrays
+    assert int(b) == int(i) == int(f) == 0
+    assert_raises(TypeError, lambda: int(c))
+
+    # float should fail on complex arrays
+    assert float(b) == float(i) == float(f) == 0.0
+    assert_raises(TypeError, lambda: float(c))
+
+    # complex should work on all types of arrays
+    assert complex(b) == complex(i) == complex(f) == complex(c) == 0j
+
+    # index should only work on integer arrays
+    assert operator.index(i) == 0
+    assert_raises(TypeError, lambda: operator.index(b))
+    assert_raises(TypeError, lambda: operator.index(f))
+    assert_raises(TypeError, lambda: operator.index(c))
+
+
+def test_device_property():
+    a = ones((3, 4))
+    assert a.device == 'cpu'
+
+    assert all(equal(a.to_device('cpu'), a))
+    assert_raises(ValueError, lambda: a.to_device('gpu'))
+
+    assert all(equal(asarray(a, device='cpu'), a))
+    assert_raises(ValueError, lambda: asarray(a, device='gpu'))
+
+def test_array_properties():
+    a = ones((1, 2, 3))
+    b = ones((2, 3))
+    assert_raises(ValueError, lambda: a.T)
+
+    assert isinstance(b.T, Array)
+    assert b.T.shape == (3, 2)
+
+    assert isinstance(a.mT, Array)
+    assert a.mT.shape == (1, 3, 2)
+    assert isinstance(b.mT, Array)
+    assert b.mT.shape == (3, 2)
+
+def test___array__():
+    a = ones((2, 3), dtype=int16)
+    assert np.asarray(a) is a._array
+    b = np.asarray(a, dtype=np.float64)
+    assert np.all(np.equal(b, np.ones((2, 3), dtype=np.float64)))
+    assert b.dtype == np.float64
+
+def test_allow_newaxis():
+    a = ones(5)
+    indexed_a = a[None, :]
+    assert indexed_a.shape == (1, 5)
+
+def test_disallow_flat_indexing_with_newaxis():
+    a = ones((3, 3, 3))
+    with pytest.raises(IndexError):
+        a[None, 0, 0]
+
+def test_disallow_mask_with_newaxis():
+    a = ones((3, 3, 3))
+    with pytest.raises(IndexError):
+        a[None, asarray(True)]
+
+@pytest.mark.parametrize("shape", [(), (5,), (3, 3, 3)])
+@pytest.mark.parametrize("index", ["string", False, True])
+def test_error_on_invalid_index(shape, index):
+    a = ones(shape)
+    with pytest.raises(IndexError):
+        a[index]
+
+def test_mask_0d_array_without_errors():
+    a = ones(())
+    a[asarray(True)]
+
+@pytest.mark.parametrize(
+    "i", [slice(5), slice(5, 0), asarray(True), asarray([0, 1])]
+)
+def test_error_on_invalid_index_with_ellipsis(i):
+    a = ones((3, 3, 3))
+    with pytest.raises(IndexError):
+        a[..., i]
+    with pytest.raises(IndexError):
+        a[i, ...]
+
+def test_array_keys_use_private_array():
+    """
+    Indexing operations convert array keys before indexing the internal array
+
+    Fails when array_api array keys are not converted into NumPy-proper arrays
+    in __getitem__(). This is achieved by passing array_api arrays with 0-sized
+    dimensions, which NumPy-proper treats erroneously - not sure why!
+
+    TODO: Find and use appropriate __setitem__() case.
+    """
+    a = ones((0, 0), dtype=bool_)
+    assert a[a].shape == (0,)
+
+    a = ones((0,), dtype=bool_)
+    key = ones((0, 0), dtype=bool_)
+    with pytest.raises(IndexError):
+        a[key]
diff --git a/env-llmeval/lib/python3.10/site-packages/numpy/array_api/tests/test_creation_functions.py b/env-llmeval/lib/python3.10/site-packages/numpy/array_api/tests/test_creation_functions.py
new file mode 100644
index 0000000000000000000000000000000000000000..be9eaa38378cb19f4c445ec4138245564c13b28a
--- /dev/null
+++ b/env-llmeval/lib/python3.10/site-packages/numpy/array_api/tests/test_creation_functions.py
@@ -0,0 +1,142 @@
+from numpy.testing import assert_raises
+import numpy as np
+
+from .. import all
+from .._creation_functions import (
+    asarray,
+    arange,
+    empty,
+    empty_like,
+    eye,
+    full,
+    full_like,
+    linspace,
+    meshgrid,
+    ones,
+    ones_like,
+    zeros,
+    zeros_like,
+)
+from .._dtypes import float32, float64
+from .._array_object import Array
+
+
+def test_asarray_errors():
+    # Test various protections against incorrect usage
+    assert_raises(TypeError, lambda: Array([1]))
+    assert_raises(TypeError, lambda: asarray(["a"]))
+    assert_raises(ValueError, lambda: asarray([1.0], dtype=np.float16))
+    assert_raises(OverflowError, lambda: asarray(2**100))
+    # Preferably this would be OverflowError
+    # assert_raises(OverflowError, lambda: asarray([2**100]))
+    assert_raises(TypeError, lambda: asarray([2**100]))
+    asarray([1], device="cpu")  # Doesn't error
+    assert_raises(ValueError, lambda: asarray([1], device="gpu"))
+
+    assert_raises(ValueError, lambda: asarray([1], dtype=int))
+    assert_raises(ValueError, lambda: asarray([1], dtype="i"))
+
+
+def test_asarray_copy():
+    a = asarray([1])
+    b = asarray(a, copy=True)
+    a[0] = 0
+    assert all(b[0] == 1)
+    assert all(a[0] == 0)
+    a = asarray([1])
+    b = asarray(a, copy=np._CopyMode.ALWAYS)
+    a[0] = 0
+    assert all(b[0] == 1)
+    assert all(a[0] == 0)
+    a = asarray([1])
+    b = asarray(a, copy=np._CopyMode.NEVER)
+    a[0] = 0
+    assert all(b[0] == 0)
+    assert_raises(NotImplementedError, lambda: asarray(a, copy=False))
+    assert_raises(NotImplementedError,
+                  lambda: asarray(a, copy=np._CopyMode.IF_NEEDED))
+
+
+def test_arange_errors():
+    arange(1, device="cpu")  # Doesn't error
+    assert_raises(ValueError, lambda: arange(1, device="gpu"))
+    assert_raises(ValueError, lambda: arange(1, dtype=int))
+    assert_raises(ValueError, lambda: arange(1, dtype="i"))
+
+
+def test_empty_errors():
+    empty((1,), device="cpu")  # Doesn't error
+    assert_raises(ValueError, lambda: empty((1,), device="gpu"))
+    assert_raises(ValueError, lambda: empty((1,), dtype=int))
+    assert_raises(ValueError, lambda: empty((1,), dtype="i"))
+
+
+def test_empty_like_errors():
+    empty_like(asarray(1), device="cpu")  # Doesn't error
+    assert_raises(ValueError, lambda: empty_like(asarray(1), device="gpu"))
+    assert_raises(ValueError, lambda: empty_like(asarray(1), dtype=int))
+    assert_raises(ValueError, lambda: empty_like(asarray(1), dtype="i"))
+
+
+def test_eye_errors():
+    eye(1, device="cpu")  # Doesn't error
+    assert_raises(ValueError, lambda: eye(1, device="gpu"))
+    assert_raises(ValueError, lambda: eye(1, dtype=int))
+    assert_raises(ValueError, lambda: eye(1, dtype="i"))
+
+
+def test_full_errors():
+    full((1,), 0, device="cpu")  # Doesn't error
+    assert_raises(ValueError, lambda: full((1,), 0, device="gpu"))
+    assert_raises(ValueError, lambda: full((1,), 0, dtype=int))
+    assert_raises(ValueError, lambda: full((1,), 0, dtype="i"))
+
+
+def test_full_like_errors():
+    full_like(asarray(1), 0, device="cpu")  # Doesn't error
+    assert_raises(ValueError, lambda: full_like(asarray(1), 0, device="gpu"))
+    assert_raises(ValueError, lambda: full_like(asarray(1), 0, dtype=int))
+    assert_raises(ValueError, lambda: full_like(asarray(1), 0, dtype="i"))
+
+
+def test_linspace_errors():
+    linspace(0, 1, 10, device="cpu")  # Doesn't error
+    assert_raises(ValueError, lambda: linspace(0, 1, 10, device="gpu"))
+    assert_raises(ValueError, lambda: linspace(0, 1, 10, dtype=float))
+    assert_raises(ValueError, lambda: linspace(0, 1, 10, dtype="f"))
+
+
+def test_ones_errors():
+    ones((1,), device="cpu")  # Doesn't error
+    assert_raises(ValueError, lambda: ones((1,), device="gpu"))
+    assert_raises(ValueError, lambda: ones((1,), dtype=int))
+    assert_raises(ValueError, lambda: ones((1,), dtype="i"))
+
+
+def test_ones_like_errors():
+    ones_like(asarray(1), device="cpu")  # Doesn't error
+    assert_raises(ValueError, lambda: ones_like(asarray(1), device="gpu"))
+    assert_raises(ValueError, lambda: ones_like(asarray(1), dtype=int))
+    assert_raises(ValueError, lambda: ones_like(asarray(1), dtype="i"))
+
+
+def test_zeros_errors():
+    zeros((1,), device="cpu")  # Doesn't error
+    assert_raises(ValueError, lambda: zeros((1,), device="gpu"))
+    assert_raises(ValueError, lambda: zeros((1,), dtype=int))
+    assert_raises(ValueError, lambda: zeros((1,), dtype="i"))
+
+
+def test_zeros_like_errors():
+    zeros_like(asarray(1), device="cpu")  # Doesn't error
+    assert_raises(ValueError, lambda: zeros_like(asarray(1), device="gpu"))
+    assert_raises(ValueError, lambda: zeros_like(asarray(1), dtype=int))
+    assert_raises(ValueError, lambda: zeros_like(asarray(1), dtype="i"))
+
+def test_meshgrid_dtype_errors():
+    # Doesn't raise
+    meshgrid()
+    meshgrid(asarray([1.], dtype=float32))
+    meshgrid(asarray([1.], dtype=float32), asarray([1.], dtype=float32))
+
+    assert_raises(ValueError, lambda: meshgrid(asarray([1.], dtype=float32), asarray([1.], dtype=float64)))
diff --git a/env-llmeval/lib/python3.10/site-packages/numpy/array_api/tests/test_data_type_functions.py b/env-llmeval/lib/python3.10/site-packages/numpy/array_api/tests/test_data_type_functions.py
new file mode 100644
index 0000000000000000000000000000000000000000..61d56ca45b1edf9a6dbc4681d1f5ac52448f96c4
--- /dev/null
+++ b/env-llmeval/lib/python3.10/site-packages/numpy/array_api/tests/test_data_type_functions.py
@@ -0,0 +1,31 @@
+import pytest
+
+from numpy.testing import assert_raises
+from numpy import array_api as xp
+import numpy as np
+
+@pytest.mark.parametrize(
+    "from_, to, expected",
+    [
+        (xp.int8, xp.int16, True),
+        (xp.int16, xp.int8, False),
+        (xp.bool, xp.int8, False),
+        (xp.asarray(0, dtype=xp.uint8), xp.int8, False),
+    ],
+)
+def test_can_cast(from_, to, expected):
+    """
+    can_cast() returns correct result
+    """
+    assert xp.can_cast(from_, to) == expected
+
+def test_isdtype_strictness():
+    assert_raises(TypeError, lambda: xp.isdtype(xp.float64, 64))
+    assert_raises(ValueError, lambda: xp.isdtype(xp.float64, 'f8'))
+
+    assert_raises(TypeError, lambda: xp.isdtype(xp.float64, (('integral',),)))
+    assert_raises(TypeError, lambda: xp.isdtype(xp.float64, np.object_))
+
+    # TODO: These will require https://github.com/numpy/numpy/issues/23883
+    # assert_raises(TypeError, lambda: xp.isdtype(xp.float64, None))
+    # assert_raises(TypeError, lambda: xp.isdtype(xp.float64, np.float64))
diff --git a/env-llmeval/lib/python3.10/site-packages/numpy/array_api/tests/test_elementwise_functions.py b/env-llmeval/lib/python3.10/site-packages/numpy/array_api/tests/test_elementwise_functions.py
new file mode 100644
index 0000000000000000000000000000000000000000..1228d0af2e6a6a8ae504297c7562d3beb5ba9516
--- /dev/null
+++ b/env-llmeval/lib/python3.10/site-packages/numpy/array_api/tests/test_elementwise_functions.py
@@ -0,0 +1,114 @@
+from inspect import getfullargspec
+
+from numpy.testing import assert_raises
+
+from .. import asarray, _elementwise_functions
+from .._elementwise_functions import bitwise_left_shift, bitwise_right_shift
+from .._dtypes import (
+    _dtype_categories,
+    _boolean_dtypes,
+    _floating_dtypes,
+    _integer_dtypes,
+)
+
+
+def nargs(func):
+    return len(getfullargspec(func).args)
+
+
+def test_function_types():
+    # Test that every function accepts only the required input types. We only
+    # test the negative cases here (error). The positive cases are tested in
+    # the array API test suite.
+
+    elementwise_function_input_types = {
+        "abs": "numeric",
+        "acos": "floating-point",
+        "acosh": "floating-point",
+        "add": "numeric",
+        "asin": "floating-point",
+        "asinh": "floating-point",
+        "atan": "floating-point",
+        "atan2": "real floating-point",
+        "atanh": "floating-point",
+        "bitwise_and": "integer or boolean",
+        "bitwise_invert": "integer or boolean",
+        "bitwise_left_shift": "integer",
+        "bitwise_or": "integer or boolean",
+        "bitwise_right_shift": "integer",
+        "bitwise_xor": "integer or boolean",
+        "ceil": "real numeric",
+        "conj": "complex floating-point",
+        "cos": "floating-point",
+        "cosh": "floating-point",
+        "divide": "floating-point",
+        "equal": "all",
+        "exp": "floating-point",
+        "expm1": "floating-point",
+        "floor": "real numeric",
+        "floor_divide": "real numeric",
+        "greater": "real numeric",
+        "greater_equal": "real numeric",
+        "imag": "complex floating-point",
+        "isfinite": "numeric",
+        "isinf": "numeric",
+        "isnan": "numeric",
+        "less": "real numeric",
+        "less_equal": "real numeric",
+        "log": "floating-point",
+        "logaddexp": "real floating-point",
+        "log10": "floating-point",
+        "log1p": "floating-point",
+        "log2": "floating-point",
+        "logical_and": "boolean",
+        "logical_not": "boolean",
+        "logical_or": "boolean",
+        "logical_xor": "boolean",
+        "multiply": "numeric",
+        "negative": "numeric",
+        "not_equal": "all",
+        "positive": "numeric",
+        "pow": "numeric",
+        "real": "complex floating-point",
+        "remainder": "real numeric",
+        "round": "numeric",
+        "sign": "numeric",
+        "sin": "floating-point",
+        "sinh": "floating-point",
+        "sqrt": "floating-point",
+        "square": "numeric",
+        "subtract": "numeric",
+        "tan": "floating-point",
+        "tanh": "floating-point",
+        "trunc": "real numeric",
+    }
+
+    def _array_vals():
+        for d in _integer_dtypes:
+            yield asarray(1, dtype=d)
+        for d in _boolean_dtypes:
+            yield asarray(False, dtype=d)
+        for d in _floating_dtypes:
+            yield asarray(1.0, dtype=d)
+
+    for x in _array_vals():
+        for func_name, types in elementwise_function_input_types.items():
+            dtypes = _dtype_categories[types]
+            func = getattr(_elementwise_functions, func_name)
+            if nargs(func) == 2:
+                for y in _array_vals():
+                    if x.dtype not in dtypes or y.dtype not in dtypes:
+                        assert_raises(TypeError, lambda: func(x, y))
+            else:
+                if x.dtype not in dtypes:
+                    assert_raises(TypeError, lambda: func(x))
+
+
+def test_bitwise_shift_error():
+    # bitwise shift functions should raise when the second argument is negative
+    assert_raises(
+        ValueError, lambda: bitwise_left_shift(asarray([1, 1]), asarray([1, -1]))
+    )
+    assert_raises(
+        ValueError, lambda: bitwise_right_shift(asarray([1, 1]), asarray([1, -1]))
+    )
diff --git a/env-llmeval/lib/python3.10/site-packages/numpy/array_api/tests/test_indexing_functions.py b/env-llmeval/lib/python3.10/site-packages/numpy/array_api/tests/test_indexing_functions.py
new file mode 100644
index 0000000000000000000000000000000000000000..9e05c63863a6fca5a24dfaa26e1fd9569dea9580
--- /dev/null
+++ b/env-llmeval/lib/python3.10/site-packages/numpy/array_api/tests/test_indexing_functions.py
@@ -0,0 +1,24 @@
+import pytest
+
+from numpy import array_api as xp
+
+
+@pytest.mark.parametrize(
+    "x, indices, axis, expected",
+    [
+        ([2, 3], [1, 1, 0], 0,  [3, 3, 2]),
+        ([2, 3], [1, 1, 0], -1, [3, 3, 2]),
+        ([[2, 3]], [1], -1, [[3]]),
+        ([[2, 3]], [0, 0], 0, [[2, 3], [2, 3]]),
+    ],
+)
+def test_take_function(x, indices, axis, expected):
+    """
+    Indices respect relative order of a descending stable-sort
+
+    See https://github.com/numpy/numpy/issues/20778
+    """
+    x = xp.asarray(x)
+    indices = xp.asarray(indices)
+    out = xp.take(x, indices, axis=axis)
+    assert xp.all(out == xp.asarray(expected))
diff --git a/env-llmeval/lib/python3.10/site-packages/numpy/array_api/tests/test_manipulation_functions.py b/env-llmeval/lib/python3.10/site-packages/numpy/array_api/tests/test_manipulation_functions.py
new file mode 100644
index 0000000000000000000000000000000000000000..aec57c38ba6a60f3e8dbf3e9b8e8241182f788de
--- /dev/null
+++ b/env-llmeval/lib/python3.10/site-packages/numpy/array_api/tests/test_manipulation_functions.py
@@ -0,0 +1,37 @@
+from numpy.testing import assert_raises
+import numpy as np
+
+from .. import all
+from .._creation_functions import asarray
+from .._dtypes import float64, int8
+from .._manipulation_functions import (
+        concat,
+        reshape,
+        stack
+)
+
+
+def test_concat_errors():
+    assert_raises(TypeError, lambda: concat((1, 1), axis=None))
+    assert_raises(TypeError, lambda: concat([asarray([1], dtype=int8),
+                                             asarray([1], dtype=float64)]))
+
+
+def test_stack_errors():
+    assert_raises(TypeError, lambda: stack([asarray([1, 1], dtype=int8),
+                                            asarray([2, 2], dtype=float64)]))
+
+
+def test_reshape_copy():
+    a = asarray(np.ones((2, 3)))
+    b = reshape(a, (3, 2), copy=True)
+    assert not np.shares_memory(a._array, b._array)
+    
+    a = asarray(np.ones((2, 3)))
+    b = reshape(a, (3, 2), copy=False)
+    assert np.shares_memory(a._array, b._array)
+
+    a = asarray(np.ones((2, 3)).T)
+    b = reshape(a, (3, 2), copy=True)
+    assert_raises(AttributeError, lambda: reshape(a, (2, 3), copy=False))
+
diff --git a/env-llmeval/lib/python3.10/site-packages/numpy/array_api/tests/test_set_functions.py b/env-llmeval/lib/python3.10/site-packages/numpy/array_api/tests/test_set_functions.py
new file mode 100644
index 0000000000000000000000000000000000000000..b8eb65d430dc4f03e2ff662c61823adf17f2d502
--- /dev/null
+++ b/env-llmeval/lib/python3.10/site-packages/numpy/array_api/tests/test_set_functions.py
@@ -0,0 +1,19 @@
+import pytest
+from hypothesis import given
+from hypothesis.extra.array_api import make_strategies_namespace
+
+from numpy import array_api as xp
+
+xps = make_strategies_namespace(xp)
+
+
+@pytest.mark.parametrize("func", [xp.unique_all, xp.unique_inverse])
+@given(xps.arrays(dtype=xps.scalar_dtypes(), shape=xps.array_shapes()))
+def test_inverse_indices_shape(func, x):
+    """
+    Inverse indices share shape of input array
+
+    See https://github.com/numpy/numpy/issues/20638
+    """
+    out = func(x)
+    assert out.inverse_indices.shape == x.shape
diff --git a/env-llmeval/lib/python3.10/site-packages/numpy/array_api/tests/test_sorting_functions.py b/env-llmeval/lib/python3.10/site-packages/numpy/array_api/tests/test_sorting_functions.py
new file mode 100644
index 0000000000000000000000000000000000000000..9848bbfeb7bdee1a7183a6bda641e0611c756ee6
--- /dev/null
+++ b/env-llmeval/lib/python3.10/site-packages/numpy/array_api/tests/test_sorting_functions.py
@@ -0,0 +1,23 @@
+import pytest
+
+from numpy import array_api as xp
+
+
+@pytest.mark.parametrize(
+    "obj, axis, expected",
+    [
+        ([0, 0], -1, [0, 1]),
+        ([0, 1, 0], -1, [1, 0, 2]),
+        ([[0, 1], [1, 1]], 0, [[1, 0], [0, 1]]),
+        ([[0, 1], [1, 1]], 1, [[1, 0], [0, 1]]),
+    ],
+)
+def test_stable_desc_argsort(obj, axis, expected):
+    """
+    Indices respect relative order of a descending stable-sort
+
+    See https://github.com/numpy/numpy/issues/20778
+    """
+    x = xp.asarray(obj)
+    out = xp.argsort(x, axis=axis, stable=True, descending=True)
+    assert xp.all(out == xp.asarray(expected))
diff --git a/env-llmeval/lib/python3.10/site-packages/numpy/array_api/tests/test_validation.py b/env-llmeval/lib/python3.10/site-packages/numpy/array_api/tests/test_validation.py
new file mode 100644
index 0000000000000000000000000000000000000000..0dd100d159242a1dcd0da5277dd655625c602fad
--- /dev/null
+++ b/env-llmeval/lib/python3.10/site-packages/numpy/array_api/tests/test_validation.py
@@ -0,0 +1,27 @@
+from typing import Callable
+
+import pytest
+
+from numpy import array_api as xp
+
+
+def p(func: Callable, *args, **kwargs):
+    f_sig = ", ".join(
+        [str(a) for a in args] + [f"{k}={v}" for k, v in kwargs.items()]
+    )
+    id_ = f"{func.__name__}({f_sig})"
+    return pytest.param(func, args, kwargs, id=id_)
+
+
+@pytest.mark.parametrize(
+    "func, args, kwargs",
+    [
+        p(xp.can_cast, 42, xp.int8),
+        p(xp.can_cast, xp.int8, 42),
+        p(xp.result_type, 42),
+    ],
+)
+def test_raises_on_invalid_types(func, args, kwargs):
+    """Function raises TypeError when passed invalidly-typed inputs"""
+    with pytest.raises(TypeError):
+        func(*args, **kwargs)
diff --git a/env-llmeval/lib/python3.10/site-packages/numpy/f2py/_backends/__pycache__/_backend.cpython-310.pyc b/env-llmeval/lib/python3.10/site-packages/numpy/f2py/_backends/__pycache__/_backend.cpython-310.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..88f9789e0003e03529e1994566d4b803c438a760
Binary files /dev/null and b/env-llmeval/lib/python3.10/site-packages/numpy/f2py/_backends/__pycache__/_backend.cpython-310.pyc differ
diff --git a/env-llmeval/lib/python3.10/site-packages/numpy/f2py/tests/src/callback/foo.f b/env-llmeval/lib/python3.10/site-packages/numpy/f2py/tests/src/callback/foo.f
new file mode 100644
index 0000000000000000000000000000000000000000..ba397bb38133faa8d502807368074e6b296749b9
--- /dev/null
+++ b/env-llmeval/lib/python3.10/site-packages/numpy/f2py/tests/src/callback/foo.f
@@ -0,0 +1,62 @@
+       subroutine t(fun,a)
+       integer a
+cf2py  intent(out) a
+       external fun
+       call fun(a)
+       end
+
+       subroutine func(a)
+cf2py  intent(in,out) a
+       integer a
+       a = a + 11
+       end
+
+       subroutine func0(a)
+cf2py  intent(out) a
+       integer a
+       a = 11
+       end
+
+       subroutine t2(a)
+cf2py  intent(callback) fun
+       integer a
+cf2py  intent(out) a
+       external fun
+       call fun(a)
+       end
+
+       subroutine string_callback(callback, a)
+       external callback
+       double precision callback
+       double precision a
+       character*1 r
+cf2py  intent(out) a
+       r = 'r'
+       a = callback(r)
+       end
+
+       subroutine string_callback_array(callback, cu, lencu, a)
+       external callback
+       integer callback
+       integer lencu
+       character*8 cu(lencu)
+       integer a
+cf2py  intent(out) a
+
+       a = callback(cu, lencu)
+       end
+
+       subroutine hidden_callback(a, r)
+       external global_f
+cf2py  intent(callback, hide) global_f
+       integer a, r, global_f
+cf2py  intent(out) r
+       r = global_f(a)
+       end
+
+       subroutine hidden_callback2(a, r)
+       external global_f
+       integer a, r, global_f
+cf2py  intent(out) r
+       r = global_f(a)
+       end
diff --git a/env-llmeval/lib/python3.10/site-packages/numpy/f2py/tests/src/callback/gh17797.f90 b/env-llmeval/lib/python3.10/site-packages/numpy/f2py/tests/src/callback/gh17797.f90
new file mode 100644
index 0000000000000000000000000000000000000000..49853afd766a90e521104081bf77236a252d3c70
--- /dev/null
+++ b/env-llmeval/lib/python3.10/site-packages/numpy/f2py/tests/src/callback/gh17797.f90
@@ -0,0 +1,7 @@
+function gh17797(f, y) result(r)
+  external f
+  integer(8) :: r, f
+  integer(8), dimension(:) :: y
+  r = f(0)
+  r = r + sum(y)
+end function gh17797
diff --git a/env-llmeval/lib/python3.10/site-packages/numpy/f2py/tests/src/callback/gh18335.f90 b/env-llmeval/lib/python3.10/site-packages/numpy/f2py/tests/src/callback/gh18335.f90
new file mode 100644
index 0000000000000000000000000000000000000000..92b6d7540c827d20c7d2169c56f14653954d7ff9
--- /dev/null
+++ b/env-llmeval/lib/python3.10/site-packages/numpy/f2py/tests/src/callback/gh18335.f90
@@ -0,0 +1,17 @@
+        ! When gh18335_workaround is defined as an extension,
+        ! the issue cannot be reproduced.
+        !subroutine gh18335_workaround(f, y)
+        !  implicit none
+        !  external f
+        !  integer(kind=1) :: y(1)
+        !  call f(y)
+        !end subroutine gh18335_workaround
+
+        function gh18335(f) result (r)
+          implicit none
+          external f
+          integer(kind=1) :: y(1), r
+          y(1) = 123
+          call f(y)
+          r = y(1)
+        end function gh18335
diff --git a/env-llmeval/lib/python3.10/site-packages/numpy/f2py/tests/src/quoted_character/foo.f b/env-llmeval/lib/python3.10/site-packages/numpy/f2py/tests/src/quoted_character/foo.f
new file mode 100644
index 0000000000000000000000000000000000000000..9dc1cfa4446d8c05c0fc0bb1c69360a687d003c3
--- /dev/null
+++ b/env-llmeval/lib/python3.10/site-packages/numpy/f2py/tests/src/quoted_character/foo.f
@@ -0,0 +1,14 @@
+      SUBROUTINE FOO(OUT1, OUT2, OUT3, OUT4, OUT5, OUT6)
+      CHARACTER SINGLE, DOUBLE, SEMICOL, EXCLA, OPENPAR, CLOSEPAR
+      PARAMETER (SINGLE="'", DOUBLE='"', SEMICOL=';', EXCLA="!",
+     1           OPENPAR="(", CLOSEPAR=")")
+      CHARACTER OUT1, OUT2, OUT3, OUT4, OUT5, OUT6
+Cf2py intent(out) OUT1, OUT2, OUT3, OUT4, OUT5, OUT6
+      OUT1 = SINGLE
+      OUT2 = DOUBLE
+      OUT3 = SEMICOL
+      OUT4 = EXCLA
+      OUT5 = OPENPAR
+      OUT6 = CLOSEPAR
+      RETURN
+      END
diff --git a/env-llmeval/lib/python3.10/site-packages/numpy/f2py/tests/src/return_complex/foo77.f b/env-llmeval/lib/python3.10/site-packages/numpy/f2py/tests/src/return_complex/foo77.f
new file mode 100644
index 0000000000000000000000000000000000000000..37a1ec845ecacc7fbc228f1ee5f628ec73075c12
--- /dev/null
+++ b/env-llmeval/lib/python3.10/site-packages/numpy/f2py/tests/src/return_complex/foo77.f
@@ -0,0 +1,45 @@
+       function t0(value)
+         complex value
+         complex t0
+         t0 = value
+       end
+       function t8(value)
+         complex*8 value
+         complex*8 t8
+         t8 = value
+       end
+       function t16(value)
+         complex*16 value
+         complex*16 t16
+         t16 = value
+       end
+       function td(value)
+         double complex value
+         double complex td
+         td = value
+       end
+
+       subroutine s0(t0,value)
+         complex value
+         complex t0
+cf2py    intent(out) t0
+         t0 = value
+       end
+       subroutine s8(t8,value)
+         complex*8 value
+         complex*8 t8
+cf2py    intent(out) t8
+         t8 = value
+       end
+       subroutine s16(t16,value)
+         complex*16 value
+         complex*16 t16
+cf2py    intent(out) t16
+         t16 = value
+       end
+       subroutine sd(td,value)
+         double complex value
+         double complex td
+cf2py    intent(out) td
+         td = value
+       end
diff --git a/env-llmeval/lib/python3.10/site-packages/numpy/f2py/tests/src/return_complex/foo90.f90 b/env-llmeval/lib/python3.10/site-packages/numpy/f2py/tests/src/return_complex/foo90.f90
new file mode 100644
index 0000000000000000000000000000000000000000..adc27b470538bc663416fb512a29c4b2bbe8d3dd
--- /dev/null
+++ b/env-llmeval/lib/python3.10/site-packages/numpy/f2py/tests/src/return_complex/foo90.f90
@@ -0,0 +1,48 @@
+module f90_return_complex
+  contains
+       function t0(value)
+         complex :: value
+         complex :: t0
+         t0 = value
+       end function t0
+       function t8(value)
+         complex(kind=4) :: value
+         complex(kind=4) :: t8
+         t8 = value
+       end function t8
+       function t16(value)
+         complex(kind=8) :: value
+         complex(kind=8) :: t16
+         t16 = value
+       end function t16
+       function td(value)
+         double complex :: value
+         double complex :: td
+         td = value
+       end function td
+
+       subroutine s0(t0,value)
+         complex :: value
+         complex :: t0
+!f2py    intent(out) t0
+         t0 = value
+       end subroutine s0
+       subroutine s8(t8,value)
+         complex(kind=4) :: value
+         complex(kind=4) :: t8
+!f2py    intent(out) t8
+         t8 = value
+       end subroutine s8
+       subroutine s16(t16,value)
+         complex(kind=8) :: value
+         complex(kind=8) :: t16
+!f2py    intent(out) t16
+         t16 = value
+       end subroutine s16
+       subroutine sd(td,value)
+         double complex :: value
+         double complex :: td
+!f2py    intent(out) td
+         td = value
+       end subroutine sd
+end module f90_return_complex
diff --git a/env-llmeval/lib/python3.10/site-packages/numpy/f2py/tests/src/return_integer/foo77.f b/env-llmeval/lib/python3.10/site-packages/numpy/f2py/tests/src/return_integer/foo77.f
new file mode 100644
index 0000000000000000000000000000000000000000..1ab895b9ac340ca91c5d3a4080334bab9f033a55
--- /dev/null
+++ b/env-llmeval/lib/python3.10/site-packages/numpy/f2py/tests/src/return_integer/foo77.f
@@ -0,0 +1,56 @@
+       function t0(value)
+         integer value
+         integer t0
+         t0 = value
+       end
+       function t1(value)
+         integer*1 value
+         integer*1 t1
+         t1 = value
+       end
+       function t2(value)
+         integer*2 value
+         integer*2 t2
+         t2 = value
+       end
+       function t4(value)
+         integer*4 value
+         integer*4 t4
+         t4 = value
+       end
+       function t8(value)
+         integer*8 value
+         integer*8 t8
+         t8 = value
+       end
+
+       subroutine s0(t0,value)
+         integer value
+         integer t0
+cf2py    intent(out) t0
+         t0 = value
+       end
+       subroutine s1(t1,value)
+         integer*1 value
+         integer*1 t1
+cf2py    intent(out) t1
+         t1 = value
+       end
+       subroutine s2(t2,value)
+         integer*2 value
+         integer*2 t2
+cf2py    intent(out) t2
+         t2 = value
+       end
+       subroutine s4(t4,value)
+         integer*4 value
+         integer*4 t4
+cf2py    intent(out) t4
+         t4 = value
+       end
+       subroutine s8(t8,value)
+         integer*8 value
+         integer*8 t8
+cf2py    intent(out) t8
+         t8 = value
+       end
diff --git a/env-llmeval/lib/python3.10/site-packages/numpy/f2py/tests/src/return_integer/foo90.f90 b/env-llmeval/lib/python3.10/site-packages/numpy/f2py/tests/src/return_integer/foo90.f90
new file mode 100644
index 0000000000000000000000000000000000000000..ba9249aa20f941dbf00f060ad5d7e8820745b0f4
--- /dev/null
+++ b/env-llmeval/lib/python3.10/site-packages/numpy/f2py/tests/src/return_integer/foo90.f90
@@ -0,0 +1,59 @@
+module f90_return_integer
+  contains
+       function t0(value)
+         integer :: value
+         integer :: t0
+         t0 = value
+       end function t0
+       function t1(value)
+         integer(kind=1) :: value
+         integer(kind=1) :: t1
+         t1 = value
+       end function t1
+       function t2(value)
+         integer(kind=2) :: value
+         integer(kind=2) :: t2
+         t2 = value
+       end function t2
+       function t4(value)
+         integer(kind=4) :: value
+         integer(kind=4) :: t4
+         t4 = value
+       end function t4
+       function t8(value)
+         integer(kind=8) :: value
+         integer(kind=8) :: t8
+         t8 = value
+       end function t8
+
+       subroutine s0(t0,value)
+         integer :: value
+         integer :: t0
+!f2py    intent(out) t0
+         t0 = value
+       end subroutine s0
+       subroutine s1(t1,value)
+         integer(kind=1) :: value
+         integer(kind=1) :: t1
+!f2py    intent(out) t1
+         t1 = value
+       end subroutine s1
+       subroutine s2(t2,value)
+         integer(kind=2) :: value
+         integer(kind=2) :: t2
+!f2py    intent(out) t2
+         t2 = value
+       end subroutine s2
+       subroutine s4(t4,value)
+         integer(kind=4) :: value
+         integer(kind=4) :: t4
+!f2py    intent(out) t4
+         t4 = value
+       end subroutine s4
+       subroutine s8(t8,value)
+         integer(kind=8) :: value
+         integer(kind=8) :: t8
+!f2py    intent(out) t8
+         t8 = value
+       end subroutine s8
+end module f90_return_integer
diff --git a/env-llmeval/lib/python3.10/site-packages/numpy/f2py/tests/src/return_logical/foo77.f b/env-llmeval/lib/python3.10/site-packages/numpy/f2py/tests/src/return_logical/foo77.f
new file mode 100644
index 0000000000000000000000000000000000000000..ef530145fedf8b5cf3a05bdf0a46a4e22150007b
--- /dev/null
+++ b/env-llmeval/lib/python3.10/site-packages/numpy/f2py/tests/src/return_logical/foo77.f
@@ -0,0 +1,56 @@
+       function t0(value)
+         logical value
+         logical t0
+         t0 = value
+       end
+       function t1(value)
+         logical*1 value
+         logical*1 t1
+         t1 = value
+       end
+       function t2(value)
+         logical*2 value
+         logical*2 t2
+         t2 = value
+       end
+       function t4(value)
+         logical*4 value
+         logical*4 t4
+         t4 = value
+       end
+c       function t8(value)
+c         logical*8 value
+c         logical*8 t8
+c         t8 = value
+c       end
+
+       subroutine s0(t0,value)
+         logical value
+         logical t0
+cf2py    intent(out) t0
+         t0 = value
+       end
+       subroutine s1(t1,value)
+         logical*1 value
+         logical*1 t1
+cf2py    intent(out) t1
+         t1 = value
+       end
+       subroutine s2(t2,value)
+         logical*2 value
+         logical*2 t2
+cf2py    intent(out) t2
+         t2 = value
+       end
+       subroutine s4(t4,value)
+         logical*4 value
+         logical*4 t4
+cf2py    intent(out) t4
+         t4 = value
+       end
+c       subroutine s8(t8,value)
+c         logical*8 value
+c         logical*8 t8
+cf2py    intent(out) t8
+c         t8 = value
+c       end
diff --git a/env-llmeval/lib/python3.10/site-packages/numpy/f2py/tests/src/return_logical/foo90.f90 b/env-llmeval/lib/python3.10/site-packages/numpy/f2py/tests/src/return_logical/foo90.f90
new file mode 100644
index 0000000000000000000000000000000000000000..a4526468e3719140f0ed7d50a5f3a31d78d1d2de
--- /dev/null
+++ b/env-llmeval/lib/python3.10/site-packages/numpy/f2py/tests/src/return_logical/foo90.f90
@@ -0,0 +1,59 @@
+module f90_return_logical
+  contains
+       function t0(value)
+         logical :: value
+         logical :: t0
+         t0 = value
+       end function t0
+       function t1(value)
+         logical(kind=1) :: value
+         logical(kind=1) :: t1
+         t1 = value
+       end function t1
+       function t2(value)
+         logical(kind=2) :: value
+         logical(kind=2) :: t2
+         t2 = value
+       end function t2
+       function t4(value)
+         logical(kind=4) :: value
+         logical(kind=4) :: t4
+         t4 = value
+       end function t4
+       function t8(value)
+         logical(kind=8) :: value
+         logical(kind=8) :: t8
+         t8 = value
+       end function t8
+
+       subroutine s0(t0,value)
+         logical :: value
+         logical :: t0
+!f2py    intent(out) t0
+         t0 = value
+       end subroutine s0
+       subroutine s1(t1,value)
+         logical(kind=1) :: value
+         logical(kind=1) :: t1
+!f2py    intent(out) t1
+         t1 = value
+       end subroutine s1
+       subroutine s2(t2,value)
+         logical(kind=2) :: value
+         logical(kind=2) :: t2
+!f2py    intent(out) t2
+         t2 = value
+       end subroutine s2
+       subroutine s4(t4,value)
+         logical(kind=4) :: value
+         logical(kind=4) :: t4
+!f2py    intent(out) t4
+         t4 = value
+       end subroutine s4
+       subroutine s8(t8,value)
+         logical(kind=8) :: value
+         logical(kind=8) :: t8
+!f2py    intent(out) t8
+         t8 = value
+       end subroutine s8
+end module f90_return_logical
diff --git a/env-llmeval/lib/python3.10/site-packages/numpy/tests/__init__.py b/env-llmeval/lib/python3.10/site-packages/numpy/tests/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/env-llmeval/lib/python3.10/site-packages/numpy/tests/test__all__.py b/env-llmeval/lib/python3.10/site-packages/numpy/tests/test__all__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e44bda3d58ab92e614905f6f20f102242d6d6b0c
--- /dev/null
+++ b/env-llmeval/lib/python3.10/site-packages/numpy/tests/test__all__.py
@@ -0,0 +1,9 @@
+
+import collections
+import numpy as np
+
+
+def test_no_duplicates_in_np__all__():
+    # Regression test for gh-10198.
+    dups = {k: v for k, v in collections.Counter(np.__all__).items() if v > 1}
+    assert len(dups) == 0
diff --git a/env-llmeval/lib/python3.10/site-packages/numpy/tests/test_ctypeslib.py b/env-llmeval/lib/python3.10/site-packages/numpy/tests/test_ctypeslib.py
new file mode 100644
index 0000000000000000000000000000000000000000..965e547e7c977a755885b5410d198dc912968eef
--- /dev/null
+++ b/env-llmeval/lib/python3.10/site-packages/numpy/tests/test_ctypeslib.py
@@ -0,0 +1,370 @@
+import sys
+import sysconfig
+import weakref
+from pathlib import Path
+
+import pytest
+
+import numpy as np
+from numpy.ctypeslib import ndpointer, load_library, as_array
+from numpy.testing import assert_, assert_array_equal, assert_raises, assert_equal
+
+try:
+    import ctypes
+except ImportError:
+    ctypes = None
+else:
+    cdll = None
+    test_cdll = None
+    if hasattr(sys, 'gettotalrefcount'):
+        try:
+            cdll = load_library('_multiarray_umath_d', np.core._multiarray_umath.__file__)
+        except OSError:
+            pass
+        try:
+            test_cdll = load_library('_multiarray_tests', np.core._multiarray_tests.__file__)
+        except OSError:
+            pass
+    if cdll is None:
+        cdll = load_library('_multiarray_umath', np.core._multiarray_umath.__file__)
+    if test_cdll is None:
+        test_cdll = load_library('_multiarray_tests', np.core._multiarray_tests.__file__)
+
+    c_forward_pointer = test_cdll.forward_pointer
+
+
+@pytest.mark.skipif(ctypes is None,
+                    reason="ctypes not available in this python")
+@pytest.mark.skipif(sys.platform == 'cygwin',
+                    reason="Known to fail on cygwin")
+class TestLoadLibrary:
+    def test_basic(self):
+        loader_path = np.core._multiarray_umath.__file__
+
+        out1 = load_library('_multiarray_umath', loader_path)
+        out2 = load_library(Path('_multiarray_umath'), loader_path)
+        out3 = load_library('_multiarray_umath', Path(loader_path))
+        out4 = load_library(b'_multiarray_umath', loader_path)
+
+        assert isinstance(out1, ctypes.CDLL)
+        assert out1 is out2 is out3 is out4
+
+    def test_basic2(self):
+        # Regression for #801: load_library with a full library name
+        # (including extension) does not work.
+        try:
+            so_ext = sysconfig.get_config_var('EXT_SUFFIX')
+            load_library('_multiarray_umath%s' % so_ext,
+                         np.core._multiarray_umath.__file__)
+        except ImportError as e:
+            msg = ("ctypes is not available on this python: skipping the test"
+                   " (import error was: %s)" % str(e))
+            print(msg)
+
+
+class TestNdpointer:
+    def test_dtype(self):
+        dt = np.intc
+        p = ndpointer(dtype=dt)
+        assert_(p.from_param(np.array([1], dt)))
+        dt = 'i4')
+        p = ndpointer(dtype=dt)
+        p.from_param(np.array([1], dt))
+        assert_raises(TypeError, p.from_param,
+                          np.array([1], dt.newbyteorder('swap')))
+        dtnames = ['x', 'y']
+        dtformats = [np.intc, np.float64]
+        dtdescr = {'names': dtnames, 'formats': dtformats}
+        dt = np.dtype(dtdescr)
+        p = ndpointer(dtype=dt)
+        assert_(p.from_param(np.zeros((10,), dt)))
+        samedt = np.dtype(dtdescr)
+        p = ndpointer(dtype=samedt)
+        assert_(p.from_param(np.zeros((10,), dt)))
+        dt2 = np.dtype(dtdescr, align=True)
+        if dt.itemsize != dt2.itemsize:
+            assert_raises(TypeError, p.from_param, np.zeros((10,), dt2))
+        else:
+            assert_(p.from_param(np.zeros((10,), dt2)))
+
+    def test_ndim(self):
+        p = ndpointer(ndim=0)
+        assert_(p.from_param(np.array(1)))
+        assert_raises(TypeError, p.from_param, np.array([1]))
+        p = ndpointer(ndim=1)
+        assert_raises(TypeError, p.from_param, np.array(1))
+        assert_(p.from_param(np.array([1])))
+        p = ndpointer(ndim=2)
+        assert_(p.from_param(np.array([[1]])))
+
+    def test_shape(self):
+        p = ndpointer(shape=(1, 2))
+        assert_(p.from_param(np.array([[1, 2]])))
+        assert_raises(TypeError, p.from_param, np.array([[1], [2]]))
+        p = ndpointer(shape=())
+        assert_(p.from_param(np.array(1)))
+
+    def test_flags(self):
+        x = np.array([[1, 2], [3, 4]], order='F')
+        p = ndpointer(flags='FORTRAN')
+        assert_(p.from_param(x))
+        p = ndpointer(flags='CONTIGUOUS')
+        assert_raises(TypeError, p.from_param, x)
+        p = ndpointer(flags=x.flags.num)
+        assert_(p.from_param(x))
+        assert_raises(TypeError, p.from_param, np.array([[1, 2], [3, 4]]))
+
+    def test_cache(self):
+        assert_(ndpointer(dtype=np.float64) is ndpointer(dtype=np.float64))
+
+        # shapes are normalized
+        assert_(ndpointer(shape=2) is ndpointer(shape=(2,)))
+
+        # 1.12 <= v < 1.16 had a bug that made these fail
+        assert_(ndpointer(shape=2) is not ndpointer(ndim=2))
+        assert_(ndpointer(ndim=2) is not ndpointer(shape=2))
+
+@pytest.mark.skipif(ctypes is None,
+                    reason="ctypes not available on this python installation")
+class TestNdpointerCFunc:
+    def test_arguments(self):
+        """ Test that arguments are coerced from arrays """
+        c_forward_pointer.restype = ctypes.c_void_p
+        c_forward_pointer.argtypes = (ndpointer(ndim=2),)
+
+        c_forward_pointer(np.zeros((2, 3)))
+        # too many dimensions
+        assert_raises(
+            ctypes.ArgumentError, c_forward_pointer, np.zeros((2, 3, 4)))
+
+    @pytest.mark.parametrize(
+        'dt', [
+            float,
+            np.dtype(dict(
+                formats=['u2')
+        ct = np.ctypeslib.as_ctypes_type(dt)
+        assert_equal(ct, ctypes.c_uint16.__ctype_be__)
+
+        dt = np.dtype('u2')
+        ct = np.ctypeslib.as_ctypes_type(dt)
+        assert_equal(ct, ctypes.c_uint16)
+
+    def test_subarray(self):
+        dt = np.dtype((np.int32, (2, 3)))
+        ct = np.ctypeslib.as_ctypes_type(dt)
+        assert_equal(ct, 2 * (3 * ctypes.c_int32))
+
+    def test_structure(self):
+        dt = np.dtype([
+            ('a', np.uint16),
+            ('b', np.uint32),
+        ])
+
+        ct = np.ctypeslib.as_ctypes_type(dt)
+        assert_(issubclass(ct, ctypes.Structure))
+        assert_equal(ctypes.sizeof(ct), dt.itemsize)
+        assert_equal(ct._fields_, [
+            ('a', ctypes.c_uint16),
+            ('b', ctypes.c_uint32),
+        ])
+
+    def test_structure_aligned(self):
+        dt = np.dtype([
+            ('a', np.uint16),
+            ('b', np.uint32),
+        ], align=True)
+
+        ct = np.ctypeslib.as_ctypes_type(dt)
+        assert_(issubclass(ct, ctypes.Structure))
+        assert_equal(ctypes.sizeof(ct), dt.itemsize)
+        assert_equal(ct._fields_, [
+            ('a', ctypes.c_uint16),
+            ('', ctypes.c_char * 2),  # padding
+            ('b', ctypes.c_uint32),
+        ])
+
+    def test_union(self):
+        dt = np.dtype(dict(
+            names=['a', 'b'],
+            offsets=[0, 0],
+            formats=[np.uint16, np.uint32]
+        ))
+
+        ct = np.ctypeslib.as_ctypes_type(dt)
+        assert_(issubclass(ct, ctypes.Union))
+        assert_equal(ctypes.sizeof(ct), dt.itemsize)
+        assert_equal(ct._fields_, [
+            ('a', ctypes.c_uint16),
+            ('b', ctypes.c_uint32),
+        ])
+
+    def test_padded_union(self):
+        dt = np.dtype(dict(
+            names=['a', 'b'],
+            offsets=[0, 0],
+            formats=[np.uint16, np.uint32],
+            itemsize=5,
+        ))
+
+        ct = np.ctypeslib.as_ctypes_type(dt)
+        assert_(issubclass(ct, ctypes.Union))
+        assert_equal(ctypes.sizeof(ct), dt.itemsize)
+        assert_equal(ct._fields_, [
+            ('a', ctypes.c_uint16),
+            ('b', ctypes.c_uint32),
+            ('', ctypes.c_char * 5),  # padding
+        ])
+
+    def test_overlapping(self):
+        dt = np.dtype(dict(
+            names=['a', 'b'],
+            offsets=[0, 2],
+            formats=[np.uint32, np.uint32]
+        ))
+        assert_raises(NotImplementedError, np.ctypeslib.as_ctypes_type, dt)
diff --git a/env-llmeval/lib/python3.10/site-packages/numpy/tests/test_lazyloading.py b/env-llmeval/lib/python3.10/site-packages/numpy/tests/test_lazyloading.py
new file mode 100644
index 0000000000000000000000000000000000000000..f31a4eab79d04d95f07a365f9ceafe5b168194fb
--- /dev/null
+++ b/env-llmeval/lib/python3.10/site-packages/numpy/tests/test_lazyloading.py
@@ -0,0 +1,38 @@
+import sys
+import importlib
+from importlib.util import LazyLoader, find_spec, module_from_spec
+import pytest
+
+
+# Warning raised by _reload_guard() in numpy/__init__.py
+@pytest.mark.filterwarnings("ignore:The NumPy module was reloaded")
+def test_lazy_load():
+    # gh-22045. lazyload doesn't import submodule names into the namespace
+    # muck with sys.modules to test the importing system
+    old_numpy = sys.modules.pop("numpy")
+
+    numpy_modules = {}
+    for mod_name, mod in list(sys.modules.items()):
+        if mod_name[:6] == "numpy.":
+            numpy_modules[mod_name] = mod
+            sys.modules.pop(mod_name)
+
+    try:
+        # create lazy load of numpy as np
+        spec = find_spec("numpy")
+        module = module_from_spec(spec)
+        sys.modules["numpy"] = module
+        loader = LazyLoader(spec.loader)
+        loader.exec_module(module)
+        np = module
+
+        # test a subpackage import
+        from numpy.lib import recfunctions
+
+        # test triggering the import of the package
+        np.ndarray
+
+    finally:
+        if old_numpy:
+            sys.modules["numpy"] = old_numpy
+            sys.modules.update(numpy_modules)
diff --git a/env-llmeval/lib/python3.10/site-packages/numpy/tests/test_matlib.py b/env-llmeval/lib/python3.10/site-packages/numpy/tests/test_matlib.py
new file mode 100644
index 0000000000000000000000000000000000000000..0e93c4848d75432c97189273f4f2e0cbc6c04e20
--- /dev/null
+++ b/env-llmeval/lib/python3.10/site-packages/numpy/tests/test_matlib.py
@@ -0,0 +1,58 @@
+import numpy as np
+import numpy.matlib
+from numpy.testing import assert_array_equal, assert_
+
+def test_empty():
+    x = numpy.matlib.empty((2,))
+    assert_(isinstance(x, np.matrix))
+    assert_(x.shape, (1, 2))
+
+def test_ones():
+    assert_array_equal(numpy.matlib.ones((2, 3)),
+                       np.matrix([[ 1.,  1.,  1.],
+                                 [ 1.,  1.,  1.]]))
+
+    assert_array_equal(numpy.matlib.ones(2), np.matrix([[ 1.,  1.]]))
+
+def test_zeros():
+    assert_array_equal(numpy.matlib.zeros((2, 3)),
+                       np.matrix([[ 0.,  0.,  0.],
+                                 [ 0.,  0.,  0.]]))
+
+    assert_array_equal(numpy.matlib.zeros(2), np.matrix([[ 0.,  0.]]))
+
+def test_identity():
+    x = numpy.matlib.identity(2, dtype=int)
+    assert_array_equal(x, np.matrix([[1, 0], [0, 1]]))
+
+def test_eye():
+    xc = numpy.matlib.eye(3, k=1, dtype=int)
+    assert_array_equal(xc, np.matrix([[ 0,  1,  0],
+                                      [ 0,  0,  1],
+                                      [ 0,  0,  0]]))
+    assert xc.flags.c_contiguous
+    assert not xc.flags.f_contiguous
+
+    xf = numpy.matlib.eye(3, 4, dtype=int, order='F')
+    assert_array_equal(xf, np.matrix([[ 1,  0,  0,  0],
+                                      [ 0,  1,  0,  0],
+                                      [ 0,  0,  1,  0]]))
+    assert not xf.flags.c_contiguous
+    assert xf.flags.f_contiguous
+
+def test_rand():
+    x = numpy.matlib.rand(3)
+    # check matrix type, array would have shape (3,)
+    assert_(x.ndim == 2)
+
+def test_randn():
+    x = np.matlib.randn(3)
+    # check matrix type, array would have shape (3,)
+    assert_(x.ndim == 2)
+
+def test_repmat():
+    a1 = np.arange(4)
+    x = numpy.matlib.repmat(a1, 2, 2)
+    y = np.array([[0, 1, 2, 3, 0, 1, 2, 3],
+                  [0, 1, 2, 3, 0, 1, 2, 3]])
+    assert_array_equal(x, y)
diff --git a/env-llmeval/lib/python3.10/site-packages/numpy/tests/test_numpy_config.py b/env-llmeval/lib/python3.10/site-packages/numpy/tests/test_numpy_config.py
new file mode 100644
index 0000000000000000000000000000000000000000..82c1ad70b93015f71ce386a9388ccad0eff19047
--- /dev/null
+++ b/env-llmeval/lib/python3.10/site-packages/numpy/tests/test_numpy_config.py
@@ -0,0 +1,44 @@
+"""
+Check the numpy config is valid.
+"""
+import numpy as np
+import pytest
+from unittest.mock import Mock, patch
+
+pytestmark = pytest.mark.skipif(
+    not hasattr(np.__config__, "_built_with_meson"),
+    reason="Requires Meson builds",
+)
+
+
+class TestNumPyConfigs:
+    REQUIRED_CONFIG_KEYS = [
+        "Compilers",
+        "Machine Information",
+        "Python Information",
+    ]
+
+    @patch("numpy.__config__._check_pyyaml")
+    def test_pyyaml_not_found(self, mock_yaml_importer):
+        mock_yaml_importer.side_effect = ModuleNotFoundError()
+        with pytest.warns(UserWarning):
+            np.show_config()
+
+    def test_dict_mode(self):
+        config = np.show_config(mode="dicts")
+
+        assert isinstance(config, dict)
+        assert all([key in config for key in self.REQUIRED_CONFIG_KEYS]), (
+            "Required key missing,"
+            " see index of `False` with `REQUIRED_CONFIG_KEYS`"
+        )
+
+    def test_invalid_mode(self):
+        with pytest.raises(AttributeError):
+            np.show_config(mode="foo")
+
+    def test_warn_to_add_tests(self):
+        assert len(np.__config__.DisplayModes) == 2, (
+            "New mode detected,"
+            " please add UT if applicable and increment this count"
+        )
diff --git a/env-llmeval/lib/python3.10/site-packages/numpy/tests/test_numpy_version.py b/env-llmeval/lib/python3.10/site-packages/numpy/tests/test_numpy_version.py
new file mode 100644
index 0000000000000000000000000000000000000000..61643426c8d757c8367dc7e8d19f6d4c106314a3
--- /dev/null
+++ b/env-llmeval/lib/python3.10/site-packages/numpy/tests/test_numpy_version.py
@@ -0,0 +1,41 @@
+"""
+Check the numpy version is valid.
+
+Note that a development version is marked by the presence of 'dev0' or '+'
+in the version string, all else is treated as a release. The version string
+itself is set from the output of ``git describe`` which relies on tags.
+
+Examples
+--------
+
+Valid Development: 1.22.0.dev0 1.22.0.dev0+5-g7999db4df2 1.22.0+5-g7999db4df2
+Valid Release: 1.21.0.rc1, 1.21.0.b1, 1.21.0
+Invalid: 1.22.0.dev, 1.22.0.dev0-5-g7999db4dfB, 1.21.0.d1, 1.21.a
+
+Note that a release is determined by the version string, which in turn
+is controlled by the result of the ``git describe`` command.
+"""
+import re
+
+import numpy as np
+from numpy.testing import assert_
+
+
+def test_valid_numpy_version():
+    # Verify that the numpy version is a valid one (no .post suffix or other
+    # nonsense).  See gh-6431 for an issue caused by an invalid version.
+    version_pattern = r"^[0-9]+\.[0-9]+\.[0-9]+(a[0-9]|b[0-9]|rc[0-9])?"
+    dev_suffix = r"(\.dev[0-9]+(\+git[0-9]+\.[0-9a-f]+)?)?"
+    res = re.match(version_pattern + dev_suffix + '$', np.__version__)
+
+    assert_(res is not None, np.__version__)
+
+
+def test_short_version():
+    # Check numpy.short_version actually exists
+    if np.version.release:
+        assert_(np.__version__ == np.version.short_version,
+                "short_version mismatch in release version")
+    else:
+        assert_(np.__version__.split("+")[0] == np.version.short_version,
+                "short_version mismatch in development version")
diff --git a/env-llmeval/lib/python3.10/site-packages/numpy/tests/test_public_api.py b/env-llmeval/lib/python3.10/site-packages/numpy/tests/test_public_api.py
new file mode 100644
index 0000000000000000000000000000000000000000..54bf3dacf9722004d51cb13d8b5dd7c1105a655a
--- /dev/null
+++ b/env-llmeval/lib/python3.10/site-packages/numpy/tests/test_public_api.py
@@ -0,0 +1,551 @@
+import sys
+import sysconfig
+import subprocess
+import pkgutil
+import types
+import importlib
+import warnings
+
+import numpy as np
+import numpy
+import pytest
+from numpy.testing import IS_WASM
+
+try:
+    import ctypes
+except ImportError:
+    ctypes = None
+
+
+def check_dir(module, module_name=None):
+    """Returns a mapping of all objects with the wrong __module__ attribute."""
+    if module_name is None:
+        module_name = module.__name__
+    results = {}
+    for name in dir(module):
+        item = getattr(module, name)
+        if (hasattr(item, '__module__') and hasattr(item, '__name__')
+                and item.__module__ != module_name):
+            results[name] = item.__module__ + '.' + item.__name__
+    return results
+
+
+def test_numpy_namespace():
+    # None of these objects are publicly documented to be part of the main
+    # NumPy namespace (some are useful though, others need to be cleaned up)
+    undocumented = {
+        '_add_newdoc_ufunc': 'numpy.core._multiarray_umath._add_newdoc_ufunc',
+        'add_docstring': 'numpy.core._multiarray_umath.add_docstring',
+        'add_newdoc': 'numpy.core.function_base.add_newdoc',
+        'add_newdoc_ufunc': 'numpy.core._multiarray_umath._add_newdoc_ufunc',
+        'byte_bounds': 'numpy.lib.utils.byte_bounds',
+        'compare_chararrays': 'numpy.core._multiarray_umath.compare_chararrays',
+        'deprecate': 'numpy.lib.utils.deprecate',
+        'deprecate_with_doc': 'numpy.lib.utils.deprecate_with_doc',
+        'disp': 'numpy.lib.function_base.disp',
+        'fastCopyAndTranspose': 'numpy.core._multiarray_umath.fastCopyAndTranspose',
+        'get_array_wrap': 'numpy.lib.shape_base.get_array_wrap',
+        'get_include': 'numpy.lib.utils.get_include',
+        'recfromcsv': 'numpy.lib.npyio.recfromcsv',
+        'recfromtxt': 'numpy.lib.npyio.recfromtxt',
+        'safe_eval': 'numpy.lib.utils.safe_eval',
+        'set_string_function': 'numpy.core.arrayprint.set_string_function',
+        'show_config': 'numpy.__config__.show',
+        'show_runtime': 'numpy.lib.utils.show_runtime',
+        'who': 'numpy.lib.utils.who',
+    }
+    # We override dir to not show these members
+    allowlist = undocumented
+    bad_results = check_dir(np)
+    # pytest gives better error messages with the builtin assert than with
+    # assert_equal
+    assert bad_results == allowlist
+
+
+@pytest.mark.skipif(IS_WASM, reason="can't start subprocess")
+@pytest.mark.parametrize('name', ['testing'])
+def test_import_lazy_import(name):
+    """Make sure we can actually use the modules we lazy load.
+
+    While not exported as part of the public API, it was accessible.  With the
+    use of __getattr__ and __dir__, this isn't always true It can happen that
+    an infinite recursion may happen.
+
+    This is the only way I found that would force the failure to appear on the
+    badly implemented code.
+
+    We also test for the presence of the lazily imported modules in dir
+
+    """
+    exe = (sys.executable, '-c', "import numpy; numpy." + name)
+    result = subprocess.check_output(exe)
+    assert not result
+
+    # Make sure they are still in the __dir__
+    assert name in dir(np)
+
+
+def test_dir_testing():
+    """Assert that output of dir has only one "testing/tester"
+    attribute without duplicate"""
+    assert len(dir(np)) == len(set(dir(np)))
+
+
+def test_numpy_linalg():
+    bad_results = check_dir(np.linalg)
+    assert bad_results == {}
+
+
+def test_numpy_fft():
+    bad_results = check_dir(np.fft)
+    assert bad_results == {}
+
+
+@pytest.mark.skipif(ctypes is None,
+                    reason="ctypes not available in this python")
+def test_NPY_NO_EXPORT():
+    cdll = ctypes.CDLL(np.core._multiarray_tests.__file__)
+    # Make sure an arbitrary NPY_NO_EXPORT function is actually hidden
+    f = getattr(cdll, 'test_not_exported', None)
+    assert f is None, ("'test_not_exported' is mistakenly exported, "
+                      "NPY_NO_EXPORT does not work")
+
+
+# Historically NumPy has not used leading underscores for private submodules
+# much.  This has resulted in lots of things that look like public modules
+# (i.e. things that can be imported as `import numpy.somesubmodule.somefile`),
+# but were never intended to be public.  The PUBLIC_MODULES list contains
+# modules that are either public because they were meant to be, or because they
+# contain public functions/objects that aren't present in any other namespace
+# for whatever reason and therefore should be treated as public.
+#
+# The PRIVATE_BUT_PRESENT_MODULES list contains modules that look public (lack
+# of underscores) but should not be used.  For many of those modules the
+# current status is fine.  For others it may make sense to work on making them
+# private, to clean up our public API and avoid confusion.
+PUBLIC_MODULES = ['numpy.' + s for s in [
+    "array_api",
+    "array_api.linalg",
+    "ctypeslib",
+    "doc",
+    "doc.constants",
+    "doc.ufuncs",
+    "dtypes",
+    "exceptions",
+    "f2py",
+    "fft",
+    "lib",
+    "lib.format",  # was this meant to be public?
+    "lib.mixins",
+    "lib.recfunctions",
+    "lib.scimath",
+    "lib.stride_tricks",
+    "linalg",
+    "ma",
+    "ma.extras",
+    "ma.mrecords",
+    "matlib",
+    "polynomial",
+    "polynomial.chebyshev",
+    "polynomial.hermite",
+    "polynomial.hermite_e",
+    "polynomial.laguerre",
+    "polynomial.legendre",
+    "polynomial.polynomial",
+    "random",
+    "testing",
+    "testing.overrides",
+    "typing",
+    "typing.mypy_plugin",
+    "version"  # Should be removed for NumPy 2.0
+]]
+if sys.version_info < (3, 12):
+    PUBLIC_MODULES += [
+        'numpy.' + s for s in [
+            "distutils",
+            "distutils.cpuinfo",
+            "distutils.exec_command",
+            "distutils.misc_util",
+            "distutils.log",
+            "distutils.system_info",
+        ]
+    ]
+
+
+
+PUBLIC_ALIASED_MODULES = [
+    "numpy.char",
+    "numpy.emath",
+    "numpy.rec",
+]
+
+
+PRIVATE_BUT_PRESENT_MODULES = ['numpy.' + s for s in [
+    "compat",
+    "compat.py3k",
+    "conftest",
+    "core",
+    "core.arrayprint",
+    "core.defchararray",
+    "core.einsumfunc",
+    "core.fromnumeric",
+    "core.function_base",
+    "core.getlimits",
+    "core.memmap",
+    "core.multiarray",
+    "core.numeric",
+    "core.numerictypes",
+    "core.overrides",
+    "core.records",
+    "core.shape_base",
+    "core.umath",
+    "f2py.auxfuncs",
+    "f2py.capi_maps",
+    "f2py.cb_rules",
+    "f2py.cfuncs",
+    "f2py.common_rules",
+    "f2py.crackfortran",
+    "f2py.diagnose",
+    "f2py.f2py2e",
+    "f2py.f90mod_rules",
+    "f2py.func2subr",
+    "f2py.rules",
+    "f2py.symbolic",
+    "f2py.use_rules",
+    "fft.helper",
+    "lib.arraypad",
+    "lib.arraysetops",
+    "lib.arrayterator",
+    "lib.function_base",
+    "lib.histograms",
+    "lib.index_tricks",
+    "lib.nanfunctions",
+    "lib.npyio",
+    "lib.polynomial",
+    "lib.shape_base",
+    "lib.twodim_base",
+    "lib.type_check",
+    "lib.ufunclike",
+    "lib.user_array",  # note: not in np.lib, but probably should just be deleted
+    "lib.utils",
+    "linalg.lapack_lite",
+    "linalg.linalg",
+    "ma.core",
+    "ma.testutils",
+    "ma.timer_comparison",
+    "matrixlib",
+    "matrixlib.defmatrix",
+    "polynomial.polyutils",
+    "random.mtrand",
+    "random.bit_generator",
+    "testing.print_coercion_tables",
+]]
+if sys.version_info < (3, 12):
+    PRIVATE_BUT_PRESENT_MODULES += [
+        'numpy.' + s for s in [
+            "distutils.armccompiler",
+            "distutils.fujitsuccompiler",
+            "distutils.ccompiler",
+            'distutils.ccompiler_opt',
+            "distutils.command",
+            "distutils.command.autodist",
+            "distutils.command.bdist_rpm",
+            "distutils.command.build",
+            "distutils.command.build_clib",
+            "distutils.command.build_ext",
+            "distutils.command.build_py",
+            "distutils.command.build_scripts",
+            "distutils.command.build_src",
+            "distutils.command.config",
+            "distutils.command.config_compiler",
+            "distutils.command.develop",
+            "distutils.command.egg_info",
+            "distutils.command.install",
+            "distutils.command.install_clib",
+            "distutils.command.install_data",
+            "distutils.command.install_headers",
+            "distutils.command.sdist",
+            "distutils.conv_template",
+            "distutils.core",
+            "distutils.extension",
+            "distutils.fcompiler",
+            "distutils.fcompiler.absoft",
+            "distutils.fcompiler.arm",
+            "distutils.fcompiler.compaq",
+            "distutils.fcompiler.environment",
+            "distutils.fcompiler.g95",
+            "distutils.fcompiler.gnu",
+            "distutils.fcompiler.hpux",
+            "distutils.fcompiler.ibm",
+            "distutils.fcompiler.intel",
+            "distutils.fcompiler.lahey",
+            "distutils.fcompiler.mips",
+            "distutils.fcompiler.nag",
+            "distutils.fcompiler.none",
+            "distutils.fcompiler.pathf95",
+            "distutils.fcompiler.pg",
+            "distutils.fcompiler.nv",
+            "distutils.fcompiler.sun",
+            "distutils.fcompiler.vast",
+            "distutils.fcompiler.fujitsu",
+            "distutils.from_template",
+            "distutils.intelccompiler",
+            "distutils.lib2def",
+            "distutils.line_endings",
+            "distutils.mingw32ccompiler",
+            "distutils.msvccompiler",
+            "distutils.npy_pkg_config",
+            "distutils.numpy_distribution",
+            "distutils.pathccompiler",
+            "distutils.unixccompiler",
+        ]
+    ]
+
+
+def is_unexpected(name):
+    """Check if this needs to be considered."""
+    if '._' in name or '.tests' in name or '.setup' in name:
+        return False
+
+    if name in PUBLIC_MODULES:
+        return False
+
+    if name in PUBLIC_ALIASED_MODULES:
+        return False
+
+    if name in PRIVATE_BUT_PRESENT_MODULES:
+        return False
+
+    return True
+
+
+# These are present in a directory with an __init__.py but cannot be imported
+# code_generators/ isn't installed, but present for an inplace build
+SKIP_LIST = [
+    "numpy.core.code_generators",
+    "numpy.core.code_generators.genapi",
+    "numpy.core.code_generators.generate_umath",
+    "numpy.core.code_generators.ufunc_docstrings",
+    "numpy.core.code_generators.generate_numpy_api",
+    "numpy.core.code_generators.generate_ufunc_api",
+    "numpy.core.code_generators.numpy_api",
+    "numpy.core.code_generators.generate_umath_doc",
+    "numpy.core.code_generators.verify_c_api_version",
+    "numpy.core.cversions",
+    "numpy.core.generate_numpy_api",
+    "numpy.core.umath_tests",
+]
+if sys.version_info < (3, 12):
+    SKIP_LIST += ["numpy.distutils.msvc9compiler"]
+
+
+# suppressing warnings from deprecated modules
+@pytest.mark.filterwarnings("ignore:.*np.compat.*:DeprecationWarning")
+def test_all_modules_are_expected():
+    """
+    Test that we don't add anything that looks like a new public module by
+    accident.  Check is based on filenames.
+    """
+
+    modnames = []
+    for _, modname, ispkg in pkgutil.walk_packages(path=np.__path__,
+                                                   prefix=np.__name__ + '.',
+                                                   onerror=None):
+        if is_unexpected(modname) and modname not in SKIP_LIST:
+            # We have a name that is new.  If that's on purpose, add it to
+            # PUBLIC_MODULES.  We don't expect to have to add anything to
+            # PRIVATE_BUT_PRESENT_MODULES.  Use an underscore in the name!
+            modnames.append(modname)
+
+    if modnames:
+        raise AssertionError(f'Found unexpected modules: {modnames}')
+
+
+# Stuff that clearly shouldn't be in the API and is detected by the next test
+# below
+SKIP_LIST_2 = [
+    'numpy.math',
+    'numpy.doc.constants.re',
+    'numpy.doc.constants.textwrap',
+    'numpy.lib.emath',
+    'numpy.lib.math',
+    'numpy.matlib.char',
+    'numpy.matlib.rec',
+    'numpy.matlib.emath',
+    'numpy.matlib.exceptions',
+    'numpy.matlib.math',
+    'numpy.matlib.linalg',
+    'numpy.matlib.fft',
+    'numpy.matlib.random',
+    'numpy.matlib.ctypeslib',
+    'numpy.matlib.ma',
+]
+if sys.version_info < (3, 12):
+    SKIP_LIST_2 += [
+        'numpy.distutils.log.sys',
+        'numpy.distutils.log.logging',
+        'numpy.distutils.log.warnings',
+    ]
+
+
+def test_all_modules_are_expected_2():
+    """
+    Method checking all objects. The pkgutil-based method in
+    `test_all_modules_are_expected` does not catch imports into a namespace,
+    only filenames.  So this test is more thorough, and checks this like:
+
+        import .lib.scimath as emath
+
+    To check if something in a module is (effectively) public, one can check if
+    there's anything in that namespace that's a public function/object but is
+    not exposed in a higher-level namespace.  For example for a `numpy.lib`
+    submodule::
+
+        mod = np.lib.mixins
+        for obj in mod.__all__:
+            if obj in np.__all__:
+                continue
+            elif obj in np.lib.__all__:
+                continue
+
+            else:
+                print(obj)
+
+    """
+
+    def find_unexpected_members(mod_name):
+        members = []
+        module = importlib.import_module(mod_name)
+        if hasattr(module, '__all__'):
+            objnames = module.__all__
+        else:
+            objnames = dir(module)
+
+        for objname in objnames:
+            if not objname.startswith('_'):
+                fullobjname = mod_name + '.' + objname
+                if isinstance(getattr(module, objname), types.ModuleType):
+                    if is_unexpected(fullobjname):
+                        if fullobjname not in SKIP_LIST_2:
+                            members.append(fullobjname)
+
+        return members
+
+    unexpected_members = find_unexpected_members("numpy")
+    for modname in PUBLIC_MODULES:
+        unexpected_members.extend(find_unexpected_members(modname))
+
+    if unexpected_members:
+        raise AssertionError("Found unexpected object(s) that look like "
+                             "modules: {}".format(unexpected_members))
+
+
+def test_api_importable():
+    """
+    Check that all submodules listed higher up in this file can be imported
+
+    Note that if a PRIVATE_BUT_PRESENT_MODULES entry goes missing, it may
+    simply need to be removed from the list (deprecation may or may not be
+    needed - apply common sense).
+    """
+    def check_importable(module_name):
+        try:
+            importlib.import_module(module_name)
+        except (ImportError, AttributeError):
+            return False
+
+        return True
+
+    module_names = []
+    for module_name in PUBLIC_MODULES:
+        if not check_importable(module_name):
+            module_names.append(module_name)
+
+    if module_names:
+        raise AssertionError("Modules in the public API that cannot be "
+                             "imported: {}".format(module_names))
+
+    for module_name in PUBLIC_ALIASED_MODULES:
+        try:
+            eval(module_name)
+        except AttributeError:
+            module_names.append(module_name)
+
+    if module_names:
+        raise AssertionError("Modules in the public API that were not "
+                             "found: {}".format(module_names))
+
+    with warnings.catch_warnings(record=True) as w:
+        warnings.filterwarnings('always', category=DeprecationWarning)
+        warnings.filterwarnings('always', category=ImportWarning)
+        for module_name in PRIVATE_BUT_PRESENT_MODULES:
+            if not check_importable(module_name):
+                module_names.append(module_name)
+
+    if module_names:
+        raise AssertionError("Modules that are not really public but looked "
+                             "public and can not be imported: "
+                             "{}".format(module_names))
+
+
+@pytest.mark.xfail(
+    sysconfig.get_config_var("Py_DEBUG") not in (None, 0, "0"),
+    reason=(
+        "NumPy possibly built with `USE_DEBUG=True ./tools/travis-test.sh`, "
+        "which does not expose the `array_api` entry point. "
+        "See https://github.com/numpy/numpy/pull/19800"
+    ),
+)
+def test_array_api_entry_point():
+    """
+    Entry point for Array API implementation can be found with importlib and
+    returns the numpy.array_api namespace.
+    """
+    # For a development install that did not go through meson-python,
+    # the entrypoint will not have been installed. So ensure this test fails
+    # only if numpy is inside site-packages.
+    numpy_in_sitepackages = sysconfig.get_path('platlib') in np.__file__
+
+    eps = importlib.metadata.entry_points()
+    try:
+        xp_eps = eps.select(group="array_api")
+    except AttributeError:
+        # The select interface for entry_points was introduced in py3.10,
+        # deprecating its dict interface. We fallback to dict keys for finding
+        # Array API entry points so that running this test in <=3.9 will
+        # still work - see https://github.com/numpy/numpy/pull/19800.
+        xp_eps = eps.get("array_api", [])
+    if len(xp_eps) == 0:
+        if numpy_in_sitepackages:
+            msg = "No entry points for 'array_api' found"
+            raise AssertionError(msg) from None
+        return
+
+    try:
+        ep = next(ep for ep in xp_eps if ep.name == "numpy")
+    except StopIteration:
+        if numpy_in_sitepackages:
+            msg = "'numpy' not in array_api entry points"
+            raise AssertionError(msg) from None
+        return
+
+    xp = ep.load()
+    msg = (
+        f"numpy entry point value '{ep.value}' "
+        "does not point to our Array API implementation"
+    )
+    assert xp is numpy.array_api, msg
+
+
+@pytest.mark.parametrize("name", [
+        'ModuleDeprecationWarning', 'VisibleDeprecationWarning',
+        'ComplexWarning', 'TooHardError', 'AxisError'])
+def test_moved_exceptions(name):
+    # These were moved to the exceptions namespace, but currently still
+    # available
+    assert name in np.__all__
+    assert name not in np.__dir__()
+    # Fetching works, but __module__ is set correctly:
+    assert getattr(np, name).__module__ == "numpy.exceptions"
+    assert name in np.exceptions.__all__
+    getattr(np.exceptions, name)
diff --git a/env-llmeval/lib/python3.10/site-packages/numpy/tests/test_reloading.py b/env-llmeval/lib/python3.10/site-packages/numpy/tests/test_reloading.py
new file mode 100644
index 0000000000000000000000000000000000000000..a1f360089a547bb4c81ef7a43884823ba4734227
--- /dev/null
+++ b/env-llmeval/lib/python3.10/site-packages/numpy/tests/test_reloading.py
@@ -0,0 +1,72 @@
+from numpy.testing import (
+    assert_raises,
+    assert_warns,
+    assert_,
+    assert_equal,
+    IS_WASM,
+)
+from numpy.compat import pickle
+
+import pytest
+import sys
+import subprocess
+import textwrap
+from importlib import reload
+
+
+def test_numpy_reloading():
+    # gh-7844. Also check that relevant globals retain their identity.
+    import numpy as np
+    import numpy._globals
+
+    _NoValue = np._NoValue
+    VisibleDeprecationWarning = np.VisibleDeprecationWarning
+    ModuleDeprecationWarning = np.ModuleDeprecationWarning
+
+    with assert_warns(UserWarning):
+        reload(np)
+    assert_(_NoValue is np._NoValue)
+    assert_(ModuleDeprecationWarning is np.ModuleDeprecationWarning)
+    assert_(VisibleDeprecationWarning is np.VisibleDeprecationWarning)
+
+    assert_raises(RuntimeError, reload, numpy._globals)
+    with assert_warns(UserWarning):
+        reload(np)
+    assert_(_NoValue is np._NoValue)
+    assert_(ModuleDeprecationWarning is np.ModuleDeprecationWarning)
+    assert_(VisibleDeprecationWarning is np.VisibleDeprecationWarning)
+
+def test_novalue():
+    import numpy as np
+    for proto in range(2, pickle.HIGHEST_PROTOCOL + 1):
+        assert_equal(repr(np._NoValue), '')
+        assert_(pickle.loads(pickle.dumps(np._NoValue,
+                                          protocol=proto)) is np._NoValue)
+
+
+@pytest.mark.skipif(IS_WASM, reason="can't start subprocess")
+def test_full_reimport():
+    """At the time of writing this, it is *not* truly supported, but
+    apparently enough users rely on it, for it to be an annoying change
+    when it started failing previously.
+    """
+    # Test within a new process, to ensure that we do not mess with the
+    # global state during the test run (could lead to cryptic test failures).
+    # This is generally unsafe, especially, since we also reload the C-modules.
+    code = textwrap.dedent(r"""
+        import sys
+        from pytest import warns
+        import numpy as np
+
+        for k in list(sys.modules.keys()):
+            if "numpy" in k:
+                del sys.modules[k]
+
+        with warns(UserWarning):
+            import numpy as np
+        """)
+    p = subprocess.run([sys.executable, '-c', code], capture_output=True)
+    if p.returncode:
+        raise AssertionError(
+            f"Non-zero return code: {p.returncode!r}\n\n{p.stderr.decode()}"
+        )
diff --git a/env-llmeval/lib/python3.10/site-packages/numpy/tests/test_scripts.py b/env-llmeval/lib/python3.10/site-packages/numpy/tests/test_scripts.py
new file mode 100644
index 0000000000000000000000000000000000000000..892c04eef0bed4b9d92408419c547f8258a005e3
--- /dev/null
+++ b/env-llmeval/lib/python3.10/site-packages/numpy/tests/test_scripts.py
@@ -0,0 +1,47 @@
+""" Test scripts
+
+Test that we can run executable scripts that have been installed with numpy.
+"""
+import sys
+import os
+import pytest
+from os.path import join as pathjoin, isfile, dirname
+import subprocess
+
+import numpy as np
+from numpy.testing import assert_equal, IS_WASM
+
+is_inplace = isfile(pathjoin(dirname(np.__file__),  '..', 'setup.py'))
+
+
+def find_f2py_commands():
+    if sys.platform == 'win32':
+        exe_dir = dirname(sys.executable)
+        if exe_dir.endswith('Scripts'): # virtualenv
+            return [os.path.join(exe_dir, 'f2py')]
+        else:
+            return [os.path.join(exe_dir, "Scripts", 'f2py')]
+    else:
+        # Three scripts are installed in Unix-like systems:
+        # 'f2py', 'f2py{major}', and 'f2py{major.minor}'. For example,
+        # if installed with python3.9 the scripts would be named
+        # 'f2py', 'f2py3', and 'f2py3.9'.
+        version = sys.version_info
+        major = str(version.major)
+        minor = str(version.minor)
+        return ['f2py', 'f2py' + major, 'f2py' + major + '.' + minor]
+
+
+@pytest.mark.skipif(is_inplace, reason="Cannot test f2py command inplace")
+@pytest.mark.xfail(reason="Test is unreliable")
+@pytest.mark.parametrize('f2py_cmd', find_f2py_commands())
+def test_f2py(f2py_cmd):
+    # test that we can run f2py script
+    stdout = subprocess.check_output([f2py_cmd, '-v'])
+    assert_equal(stdout.strip(), np.__version__.encode('ascii'))
+
+
+@pytest.mark.skipif(IS_WASM, reason="Cannot start subprocess")
+def test_pep338():
+    stdout = subprocess.check_output([sys.executable, '-mnumpy.f2py', '-v'])
+    assert_equal(stdout.strip(), np.__version__.encode('ascii'))
diff --git a/env-llmeval/lib/python3.10/site-packages/numpy/tests/test_warnings.py b/env-llmeval/lib/python3.10/site-packages/numpy/tests/test_warnings.py
new file mode 100644
index 0000000000000000000000000000000000000000..df90fcef8c599ec1808bfb5d21f553d5f466e42d
--- /dev/null
+++ b/env-llmeval/lib/python3.10/site-packages/numpy/tests/test_warnings.py
@@ -0,0 +1,74 @@
+"""
+Tests which scan for certain occurrences in the code, they may not find
+all of these occurrences but should catch almost all.
+"""
+import pytest
+
+from pathlib import Path
+import ast
+import tokenize
+import numpy
+
+class ParseCall(ast.NodeVisitor):
+    def __init__(self):
+        self.ls = []
+
+    def visit_Attribute(self, node):
+        ast.NodeVisitor.generic_visit(self, node)
+        self.ls.append(node.attr)
+
+    def visit_Name(self, node):
+        self.ls.append(node.id)
+
+
+class FindFuncs(ast.NodeVisitor):
+    def __init__(self, filename):
+        super().__init__()
+        self.__filename = filename
+
+    def visit_Call(self, node):
+        p = ParseCall()
+        p.visit(node.func)
+        ast.NodeVisitor.generic_visit(self, node)
+
+        if p.ls[-1] == 'simplefilter' or p.ls[-1] == 'filterwarnings':
+            if node.args[0].value == "ignore":
+                raise AssertionError(
+                    "warnings should have an appropriate stacklevel; found in "
+                    "{} on line {}".format(self.__filename, node.lineno))
+
+        if p.ls[-1] == 'warn' and (
+                len(p.ls) == 1 or p.ls[-2] == 'warnings'):
+
+            if "testing/tests/test_warnings.py" == self.__filename:
+                # This file
+                return
+
+            # See if stacklevel exists:
+            if len(node.args) == 3:
+                return
+            args = {kw.arg for kw in node.keywords}
+            if "stacklevel" in args:
+                return
+            raise AssertionError(
+                "warnings should have an appropriate stacklevel; found in "
+                "{} on line {}".format(self.__filename, node.lineno))
+
+
+@pytest.mark.slow
+def test_warning_calls():
+    # combined "ignore" and stacklevel error
+    base = Path(numpy.__file__).parent
+
+    for path in base.rglob("*.py"):
+        if base / "testing" in path.parents:
+            continue
+        if path == base / "__init__.py":
+            continue
+        if path == base / "random" / "__init__.py":
+            continue
+        # use tokenize to auto-detect encoding on systems where no
+        # default encoding is defined (e.g. LANG='C')
+        with tokenize.open(str(path)) as file:
+            tree = ast.parse(file.read())
+            FindFuncs(path).visit(tree)