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diff --git a/numpy/array_api/__init__.py b/numpy/array_api/__init__.py
new file mode 100644
index 000000000..4650e3db8
--- /dev/null
+++ b/numpy/array_api/__init__.py
@@ -0,0 +1,171 @@
+"""
+A NumPy sub-namespace that conforms to the Python array API standard.
+
+This submodule accompanies NEP 47, which proposes its inclusion in NumPy.
+
+This is a proof-of-concept namespace that wraps the corresponding NumPy
+functions to give a conforming implementation of the Python array API standard
+(https://data-apis.github.io/array-api/latest/). The standard is currently in
+an RFC phase and comments on it are both welcome and encouraged. Comments
+should be made either at https://github.com/data-apis/array-api or at
+https://github.com/data-apis/consortium-feedback/discussions.
+
+NumPy already follows the proposed spec for the most part, so this module
+serves mostly as a thin wrapper around it. However, NumPy also implements a
+lot of behavior that is not included in the spec, so this serves as a
+restricted subset of the API. Only those functions that are part of the spec
+are included in this namespace, and all functions are given with the exact
+signature given in the spec, including the use of position-only arguments, and
+omitting any extra keyword arguments implemented by NumPy but not part of the
+spec. The behavior of some functions is also modified from the NumPy behavior
+to conform to the standard. Note that the underlying array object itself is
+wrapped in a wrapper Array() class, but is otherwise unchanged. This submodule
+is implemented in pure Python with no C extensions.
+
+The array API spec is designed as a "minimal API subset" and explicitly allows
+libraries to include behaviors not specified by it. But users of this module
+that intend to write portable code should be aware that only those behaviors
+that are listed in the spec are guaranteed to be implemented across libraries.
+Consequently, the NumPy implementation was chosen to be both conforming and
+minimal, so that users can use this implementation of the array API namespace
+and be sure that behaviors that it defines will be available in conforming
+namespaces from other libraries.
+
+A few notes about the current state of this submodule:
+
+- There is a test suite that tests modules against the array API standard at
+  https://github.com/data-apis/array-api-tests. The test suite is still a work
+  in progress, but the existing tests pass on this module, with a few
+  exceptions:
+
+  - Device support is not yet implemented in NumPy
+    (https://data-apis.github.io/array-api/latest/design_topics/device_support.html).
+    As a result, the `device` attribute of the array object is missing, and
+    array creation functions that take the `device` keyword argument will fail
+    with NotImplementedError.
+
+  - DLPack support (see https://github.com/data-apis/array-api/pull/106) is
+    not included here, as it requires a full implementation in NumPy proper
+    first.
+
+  - The linear algebra extension in the spec will be added in a future pull
+request.
+
+  The test suite is not yet complete, and even the tests that exist are not
+  guaranteed to give a comprehensive coverage of the spec. Therefore, those
+  reviewing this submodule should refer to the standard documents themselves.
+
+- There is a custom array object, numpy.array_api.Array, which is returned
+  by all functions in this module. All functions in the array API namespace
+  implicitly assume that they will only receive this object as input. The only
+  way to create instances of this object is to use one of the array creation
+  functions. It does not have a public constructor on the object itself. The
+  object is a small wrapper Python class around numpy.ndarray. The main
+  purpose of it is to restrict the namespace of the array object to only those
+  dtypes and only those methods that are required by the spec, as well as to
+  limit/change certain behavior that differs in the spec. In particular:
+
+  - The array API namespace does not have scalar objects, only 0-d arrays.
+    Operations in on Array that would create a scalar in NumPy create a 0-d
+    array.
+
+  - Indexing: Only a subset of indices supported by NumPy are required by the
+    spec. The Array object restricts indexing to only allow those types of
+    indices that are required by the spec. See the docstring of the
+    numpy.array_api.Array._validate_indices helper function for more
+    information.
+
+  - Type promotion: Some type promotion rules are different in the spec. In
+    particular, the spec does not have any value-based casting. The
+    Array._promote_scalar method promotes Python scalars to arrays,
+    disallowing cross-type promotions like int -> float64 that are not allowed
+    in the spec. Array._normalize_two_args works around some type promotion
+    quirks in NumPy, particularly, value-based casting that occurs when one
+    argument of an operation is a 0-d array.
+
+- All functions include type annotations, corresponding to those given in the
+  spec (see _typing.py for definitions of some custom types). These do not
+  currently fully pass mypy due to some limitations in mypy.
+
+- Dtype objects are just the NumPy dtype objects, e.g., float64 =
+  np.dtype('float64'). The spec does not require any behavior on these dtype
+  objects other than that they be accessible by name and be comparable by
+  equality, but it was considered too much extra complexity to create custom
+  objects to represent dtypes.
+
+- The wrapper functions in this module do not do any type checking for things
+  that would be impossible without leaving the array_api namespace. For
+  example, since the array API dtype objects are just the NumPy dtype objects,
+  one could pass in a non-spec NumPy dtype into a function.
+
+- All places where the implementations in this submodule are known to deviate
+  from their corresponding functions in NumPy are marked with "# Note"
+  comments. Reviewers should make note of these comments.
+
+Still TODO in this module are:
+
+- Device support and DLPack support are not yet implemented. These require
+  support in NumPy itself first.
+
+- The a non-default value for the `copy` keyword argument is not yet
+  implemented on asarray. This requires support in numpy.asarray() first.
+
+- Some functions are not yet fully tested in the array API test suite, and may
+  require updates that are not yet known until the tests are written.
+
+"""
+
+__all__ = []
+
+from ._constants import e, inf, nan, pi
+
+__all__ += ['e', 'inf', 'nan', 'pi']
+
+from ._creation_functions import asarray, arange, empty, empty_like, eye, from_dlpack, full, full_like, linspace, meshgrid, ones, ones_like, zeros, zeros_like
+
+__all__ += ['asarray', 'arange', 'empty', 'empty_like', 'eye', 'from_dlpack', 'full', 'full_like', 'linspace', 'meshgrid', 'ones', 'ones_like', 'zeros', 'zeros_like']
+
+from ._data_type_functions import broadcast_arrays, broadcast_to, can_cast, finfo, iinfo, result_type
+
+__all__ += ['broadcast_arrays', 'broadcast_to', 'can_cast', 'finfo', 'iinfo', 'result_type']
+
+from ._dtypes import int8, int16, int32, int64, uint8, uint16, uint32, uint64, float32, float64, bool
+
+__all__ += ['int8', 'int16', 'int32', 'int64', 'uint8', 'uint16', 'uint32', 'uint64', 'float32', 'float64', 'bool']
+
+from ._elementwise_functions import abs, acos, acosh, add, asin, asinh, atan, atan2, atanh, bitwise_and, bitwise_left_shift, bitwise_invert, bitwise_or, bitwise_right_shift, bitwise_xor, ceil, cos, cosh, divide, equal, exp, expm1, floor, floor_divide, greater, greater_equal, isfinite, isinf, isnan, less, less_equal, log, log1p, log2, log10, logaddexp, logical_and, logical_not, logical_or, logical_xor, multiply, negative, not_equal, positive, pow, remainder, round, sign, sin, sinh, square, sqrt, subtract, tan, tanh, trunc
+
+__all__ += ['abs', 'acos', 'acosh', 'add', 'asin', 'asinh', 'atan', 'atan2', 'atanh', 'bitwise_and', 'bitwise_left_shift', 'bitwise_invert', 'bitwise_or', 'bitwise_right_shift', 'bitwise_xor', 'ceil', 'cos', 'cosh', 'divide', 'equal', 'exp', 'expm1', 'floor', 'floor_divide', 'greater', 'greater_equal', 'isfinite', 'isinf', 'isnan', 'less', 'less_equal', 'log', 'log1p', 'log2', 'log10', 'logaddexp', 'logical_and', 'logical_not', 'logical_or', 'logical_xor', 'multiply', 'negative', 'not_equal', 'positive', 'pow', 'remainder', 'round', 'sign', 'sin', 'sinh', 'square', 'sqrt', 'subtract', 'tan', 'tanh', 'trunc']
+
+# einsum is not yet implemented in the array API spec.
+
+# from ._linear_algebra_functions import einsum
+# __all__ += ['einsum']
+
+from ._linear_algebra_functions import matmul, tensordot, transpose, vecdot
+
+__all__ += ['matmul', 'tensordot', 'transpose', 'vecdot']
+
+from ._manipulation_functions import concat, expand_dims, flip, reshape, roll, squeeze, stack
+
+__all__ += ['concat', 'expand_dims', 'flip', 'reshape', 'roll', 'squeeze', 'stack']
+
+from ._searching_functions import argmax, argmin, nonzero, where
+
+__all__ += ['argmax', 'argmin', 'nonzero', 'where']
+
+from ._set_functions import unique
+
+__all__ += ['unique']
+
+from ._sorting_functions import argsort, sort
+
+__all__ += ['argsort', 'sort']
+
+from ._statistical_functions import max, mean, min, prod, std, sum, var
+
+__all__ += ['max', 'mean', 'min', 'prod', 'std', 'sum', 'var']
+
+from ._utility_functions import all, any
+
+__all__ += ['all', 'any']
diff --git a/numpy/array_api/_array_object.py b/numpy/array_api/_array_object.py
new file mode 100644
index 000000000..24957fde6
--- /dev/null
+++ b/numpy/array_api/_array_object.py
@@ -0,0 +1,983 @@
+"""
+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, _numeric_dtypes)
+
+from typing import TYPE_CHECKING, Any, Optional, Tuple, Union
+if TYPE_CHECKING:
+    from ._typing import PyCapsule, Device, Dtype
+
+import numpy as np
+
+class Array:
+    """
+    n-d array object for the array API namespace.
+
+    See the docstring of :py:obj:`np.ndarray <numpy.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().
+
+    """
+    # 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__.
+        """
+        return f"Array({np.array2string(self._array, separator=', ')}, dtype={self.dtype.name})"
+
+    # 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, dtype_category, op):
+        """
+        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
+        """
+        from ._dtypes import _result_type
+
+        _dtypes = {
+            'all': _all_dtypes,
+            'numeric': _numeric_dtypes,
+            'integer': _integer_dtypes,
+            'integer or boolean': _integer_or_boolean_dtypes,
+            'boolean': _boolean_dtypes,
+            'floating-point': _floating_dtypes,
+        }
+
+        if self.dtype not in _dtypes[dtype_category]:
+            raise TypeError(f'Only {dtype_category} dtypes are allowed in {op}')
+        if isinstance(other, (int, float, bool)):
+            other = self._promote_scalar(other)
+        elif isinstance(other, Array):
+            if other.dtype not in _dtypes[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.
+        """
+        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")
+        elif isinstance(scalar, float):
+            if self.dtype not in _floating_dtypes:
+                raise TypeError("Python float scalars can only be promoted with floating-point arrays.")
+        else:
+            raise TypeError("'scalar' must be a Python scalar")
+
+        # 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):
+        """
+        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)
+    @staticmethod
+    def _validate_index(key, shape):
+        """
+        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 either raises IndexError if the index ``key`` is
+        invalid, or a new key to be used in place of ``key`` in indexing. 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:
+
+        - 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.
+
+        """
+        if isinstance(key, slice):
+            if shape is None:
+                return key
+            if shape == ():
+                return key
+            size = shape[0]
+            # Ensure invalid slice entries are passed through.
+            if key.start is not None:
+                try:
+                    operator.index(key.start)
+                except TypeError:
+                    return key
+                if not (-size <= key.start <= max(0, size - 1)):
+                    raise IndexError("Slices with out-of-bounds start are not allowed in the array API namespace")
+            if key.stop is not None:
+                try:
+                    operator.index(key.stop)
+                except TypeError:
+                    return key
+                step = 1 if key.step is None else key.step
+                if (step > 0 and not (-size <= key.stop <= size)
+                    or step < 0 and not (-size - 1 <= key.stop <= max(0, size - 1))):
+                    raise IndexError("Slices with out-of-bounds stop are not allowed in the array API namespace")
+            return key
+
+        elif isinstance(key, tuple):
+            key = tuple(Array._validate_index(idx, None) for idx in key)
+
+            for idx in key:
+                if isinstance(idx, np.ndarray) and idx.dtype in _boolean_dtypes or isinstance(idx, (bool, np.bool_)):
+                    if len(key) == 1:
+                        return key
+                    raise IndexError("Boolean array indices combined with other indices are not allowed in the array API namespace")
+                if isinstance(idx, tuple):
+                    raise IndexError("Nested tuple indices are not allowed in the array API namespace")
+
+            if shape is None:
+                return key
+            n_ellipsis = key.count(...)
+            if n_ellipsis > 1:
+                return key
+            ellipsis_i = key.index(...) if n_ellipsis else len(key)
+
+            for idx, size in list(zip(key[:ellipsis_i], shape)) + list(zip(key[:ellipsis_i:-1], shape[:ellipsis_i:-1])):
+                Array._validate_index(idx, (size,))
+            return key
+        elif isinstance(key, bool):
+            return key
+        elif isinstance(key, Array):
+            if key.dtype in _integer_dtypes:
+                if key.ndim != 0:
+                    raise IndexError("Non-zero dimensional integer array indices are not allowed in the array API namespace")
+            return key._array
+        elif key is Ellipsis:
+            return key
+        elif key is None:
+            raise IndexError("newaxis indices are not allowed in the array API namespace")
+        try:
+            return operator.index(key)
+        except TypeError:
+            # Note: This also omits boolean arrays that are not already in
+            # Array() form, like a list of booleans.
+            raise IndexError("Only integers, slices (`:`), ellipsis (`...`), and boolean arrays are valid indices in the array API namespace")
+
+    # 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) -> object:
+        if api_version is not None and not api_version.startswith('2021.'):
+            raise ValueError(f"Unrecognized array API version: {api_version!r}")
+        from numpy import array_api
+        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 __dlpack__(self: Array, /, *, stream: None = None) -> PyCapsule:
+        """
+        Performs the operation __dlpack__.
+        """
+        res = self._array.__dlpack__(stream=stream)
+        return self.__class__._new(res)
+
+    def __dlpack_device__(self: Array, /) -> Tuple[IntEnum, int]:
+        """
+        Performs the operation __dlpack_device__.
+        """
+        # Note: device support is required for this
+        res = self._array.__dlpack_device__()
+        return self.__class__._new(res)
+
+    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")
+        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, '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, '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], ...], Array], /) -> Array:
+        """
+        Performs the operation __getitem__.
+        """
+        # Note: Only indices required by the spec are allowed. See the
+        # docstring of _validate_index
+        key = self._validate_index(key, self.shape)
+        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, '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")
+        res = self._array.__int__()
+        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, '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)
+
+    # Note: __len__ may end up being removed from the array API spec.
+    def __len__(self, /) -> int:
+        """
+        Performs the operation __len__.
+        """
+        res = self._array.__len__()
+        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, '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, '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)
+
+    # PEP 484 requires int to be a subtype of float, but __pow__ should not
+    # accept int.
+    def __pow__(self: Array, other: Union[float, Array], /) -> Array:
+        """
+        Performs the operation __pow__.
+        """
+        from ._elementwise_functions import pow
+
+        other = self._check_allowed_dtypes(other, 'floating-point', '__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], /) -> Array:
+        """
+        Performs the operation __setitem__.
+        """
+        # Note: Only indices required by the spec are allowed. See the
+        # docstring of _validate_index
+        key = self._validate_index(key, self.shape)
+        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, '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, '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__.
+        """
+        # Note: NumPy does not implement __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
+
+        # __imatmul__ can only be allowed when it would not change the shape
+        # of self.
+        other_shape = other.shape
+        if self.shape == () or other_shape == ():
+            raise ValueError("@= requires at least one dimension")
+        if len(other_shape) == 1 or other_shape[-1] != other_shape[-2]:
+            raise ValueError("@= cannot change the shape of the input array")
+        self._array[:] = self._array.__matmul__(other._array)
+        return self
+
+    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, '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, '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[float, Array], /) -> Array:
+        """
+        Performs the operation __ipow__.
+        """
+        other = self._check_allowed_dtypes(other, 'floating-point', '__ipow__')
+        if other is NotImplemented:
+            return other
+        self._array.__ipow__(other._array)
+        return self
+
+    def __rpow__(self: Array, other: Union[float, Array], /) -> Array:
+        """
+        Performs the operation __rpow__.
+        """
+        from ._elementwise_functions import pow
+
+        other = self._check_allowed_dtypes(other, 'floating-point', '__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)
+
+    @property
+    def dtype(self) -> Dtype:
+        """
+        Array API compatible wrapper for :py:meth:`np.ndarray.dtype <numpy.ndarray.dtype>`.
+
+        See its docstring for more information.
+        """
+        return self._array.dtype
+
+    @property
+    def device(self) -> Device:
+        return 'cpu'
+
+    @property
+    def ndim(self) -> int:
+        """
+        Array API compatible wrapper for :py:meth:`np.ndarray.ndim <numpy.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 <numpy.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 <numpy.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 <numpy.ndarray.T>`.
+
+        See its docstring for more information.
+        """
+        return self._array.T
diff --git a/numpy/array_api/_constants.py b/numpy/array_api/_constants.py
new file mode 100644
index 000000000..9541941e7
--- /dev/null
+++ b/numpy/array_api/_constants.py
@@ -0,0 +1,6 @@
+import numpy as np
+
+e = np.e
+inf = np.inf
+nan = np.nan
+pi = np.pi
diff --git a/numpy/array_api/_creation_functions.py b/numpy/array_api/_creation_functions.py
new file mode 100644
index 000000000..acf78056a
--- /dev/null
+++ b/numpy/array_api/_creation_functions.py
@@ -0,0 +1,216 @@
+from __future__ import annotations
+
+
+from typing import TYPE_CHECKING, List, Optional, Tuple, Union
+if TYPE_CHECKING:
+    from ._typing import (Array, Device, Dtype, NestedSequence,
+                          SupportsDLPack, SupportsBufferProtocol)
+    from collections.abc import Sequence
+from ._dtypes import _all_dtypes
+
+import numpy as np
+
+def _check_valid_dtype(dtype):
+    # Note: Only spelling dtypes as the dtype objects is supported.
+
+    # We use this instead of "dtype in _all_dtypes" because the dtype objects
+    # define equality with the sorts of things we want to disallw.
+    for d in (None,) + _all_dtypes:
+        if dtype is d:
+            return
+    raise ValueError("dtype must be one of the supported dtypes")
+
+def asarray(obj: Union[Array, bool, int, float, NestedSequence[bool|int|float], SupportsDLPack, SupportsBufferProtocol], /, *, dtype: Optional[Dtype] = None, device: Optional[Device] = None, copy: Optional[bool] = None) -> Array:
+    """
+    Array API compatible wrapper for :py:func:`np.asarray <numpy.asarray>`.
+
+    See its docstring for more information.
+    """
+    # _array_object imports in this file are inside the functions to avoid
+    # circular imports
+    from ._array_object import Array
+
+    _check_valid_dtype(dtype)
+    if device not in ['cpu', None]:
+        raise ValueError(f"Unsupported device {device!r}")
+    if copy is False:
+        # Note: copy=False is not yet implemented in np.asarray
+        raise NotImplementedError("copy=False is not yet implemented")
+    if isinstance(obj, Array) and (dtype is None or obj.dtype == dtype):
+        if copy is True:
+            return Array._new(np.array(obj._array, copy=True, dtype=dtype))
+        return obj
+    if dtype is None and isinstance(obj, int) and (obj > 2**64 or obj < -2**63):
+        # Give a better error message in this case. NumPy would convert this
+        # to an object array. TODO: This won't handle large integers in lists.
+        raise OverflowError("Integer out of bounds for array dtypes")
+    res = np.asarray(obj, dtype=dtype)
+    return Array._new(res)
+
+def arange(start: Union[int, float], /, stop: Optional[Union[int, float]] = None, step: Union[int, float] = 1, *, dtype: Optional[Dtype] = None, device: Optional[Device] = None) -> Array:
+    """
+    Array API compatible wrapper for :py:func:`np.arange <numpy.arange>`.
+
+    See its docstring for more information.
+    """
+    from ._array_object import Array
+
+    _check_valid_dtype(dtype)
+    if device not in ['cpu', None]:
+        raise ValueError(f"Unsupported device {device!r}")
+    return Array._new(np.arange(start, stop=stop, step=step, dtype=dtype))
+
+def empty(shape: Union[int, Tuple[int, ...]], *, dtype: Optional[Dtype] = None, device: Optional[Device] = None) -> Array:
+    """
+    Array API compatible wrapper for :py:func:`np.empty <numpy.empty>`.
+
+    See its docstring for more information.
+    """
+    from ._array_object import Array
+
+    _check_valid_dtype(dtype)
+    if device not in ['cpu', None]:
+        raise ValueError(f"Unsupported device {device!r}")
+    return Array._new(np.empty(shape, dtype=dtype))
+
+def empty_like(x: Array, /, *, dtype: Optional[Dtype] = None, device: Optional[Device] = None) -> Array:
+    """
+    Array API compatible wrapper for :py:func:`np.empty_like <numpy.empty_like>`.
+
+    See its docstring for more information.
+    """
+    from ._array_object import Array
+
+    _check_valid_dtype(dtype)
+    if device not in ['cpu', None]:
+        raise ValueError(f"Unsupported device {device!r}")
+    return Array._new(np.empty_like(x._array, dtype=dtype))
+
+def eye(n_rows: int, n_cols: Optional[int] = None, /, *, k: Optional[int] = 0, dtype: Optional[Dtype] = None, device: Optional[Device] = None) -> Array:
+    """
+    Array API compatible wrapper for :py:func:`np.eye <numpy.eye>`.
+
+    See its docstring for more information.
+    """
+    from ._array_object import Array
+
+    _check_valid_dtype(dtype)
+    if device not in ['cpu', None]:
+        raise ValueError(f"Unsupported device {device!r}")
+    return Array._new(np.eye(n_rows, M=n_cols, k=k, dtype=dtype))
+
+def from_dlpack(x: object, /) -> Array:
+    # Note: dlpack support is not yet implemented on Array
+    raise NotImplementedError("DLPack support is not yet implemented")
+
+def full(shape: Union[int, Tuple[int, ...]], fill_value: Union[int, float], *, dtype: Optional[Dtype] = None, device: Optional[Device] = None) -> Array:
+    """
+    Array API compatible wrapper for :py:func:`np.full <numpy.full>`.
+
+    See its docstring for more information.
+    """
+    from ._array_object import Array
+
+    _check_valid_dtype(dtype)
+    if device not in ['cpu', None]:
+        raise ValueError(f"Unsupported device {device!r}")
+    if isinstance(fill_value, Array) and fill_value.ndim == 0:
+        fill_value = fill_value._array
+    res = np.full(shape, fill_value, dtype=dtype)
+    if res.dtype not in _all_dtypes:
+        # This will happen if the fill value is not something that NumPy
+        # coerces to one of the acceptable dtypes.
+        raise TypeError("Invalid input to full")
+    return Array._new(res)
+
+def full_like(x: Array, /, fill_value: Union[int, float], *, dtype: Optional[Dtype] = None, device: Optional[Device] = None) -> Array:
+    """
+    Array API compatible wrapper for :py:func:`np.full_like <numpy.full_like>`.
+
+    See its docstring for more information.
+    """
+    from ._array_object import Array
+
+    _check_valid_dtype(dtype)
+    if device not in ['cpu', None]:
+        raise ValueError(f"Unsupported device {device!r}")
+    res = np.full_like(x._array, fill_value, dtype=dtype)
+    if res.dtype not in _all_dtypes:
+        # This will happen if the fill value is not something that NumPy
+        # coerces to one of the acceptable dtypes.
+        raise TypeError("Invalid input to full_like")
+    return Array._new(res)
+
+def linspace(start: Union[int, float], stop: Union[int, float], /, num: int, *, dtype: Optional[Dtype] = None, device: Optional[Device] = None, endpoint: bool = True) -> Array:
+    """
+    Array API compatible wrapper for :py:func:`np.linspace <numpy.linspace>`.
+
+    See its docstring for more information.
+    """
+    from ._array_object import Array
+
+    _check_valid_dtype(dtype)
+    if device not in ['cpu', None]:
+        raise ValueError(f"Unsupported device {device!r}")
+    return Array._new(np.linspace(start, stop, num, dtype=dtype, endpoint=endpoint))
+
+def meshgrid(*arrays: Sequence[Array], indexing: str = 'xy') -> List[Array, ...]:
+    """
+    Array API compatible wrapper for :py:func:`np.meshgrid <numpy.meshgrid>`.
+
+    See its docstring for more information.
+    """
+    from ._array_object import Array
+    return [Array._new(array) for array in np.meshgrid(*[a._array for a in arrays], indexing=indexing)]
+
+def ones(shape: Union[int, Tuple[int, ...]], *, dtype: Optional[Dtype] = None, device: Optional[Device] = None) -> Array:
+    """
+    Array API compatible wrapper for :py:func:`np.ones <numpy.ones>`.
+
+    See its docstring for more information.
+    """
+    from ._array_object import Array
+
+    _check_valid_dtype(dtype)
+    if device not in ['cpu', None]:
+        raise ValueError(f"Unsupported device {device!r}")
+    return Array._new(np.ones(shape, dtype=dtype))
+
+def ones_like(x: Array, /, *, dtype: Optional[Dtype] = None, device: Optional[Device] = None) -> Array:
+    """
+    Array API compatible wrapper for :py:func:`np.ones_like <numpy.ones_like>`.
+
+    See its docstring for more information.
+    """
+    from ._array_object import Array
+
+    _check_valid_dtype(dtype)
+    if device not in ['cpu', None]:
+        raise ValueError(f"Unsupported device {device!r}")
+    return Array._new(np.ones_like(x._array, dtype=dtype))
+
+def zeros(shape: Union[int, Tuple[int, ...]], *, dtype: Optional[Dtype] = None, device: Optional[Device] = None) -> Array:
+    """
+    Array API compatible wrapper for :py:func:`np.zeros <numpy.zeros>`.
+
+    See its docstring for more information.
+    """
+    from ._array_object import Array
+
+    _check_valid_dtype(dtype)
+    if device not in ['cpu', None]:
+        raise ValueError(f"Unsupported device {device!r}")
+    return Array._new(np.zeros(shape, dtype=dtype))
+
+def zeros_like(x: Array, /, *, dtype: Optional[Dtype] = None, device: Optional[Device] = None) -> Array:
+    """
+    Array API compatible wrapper for :py:func:`np.zeros_like <numpy.zeros_like>`.
+
+    See its docstring for more information.
+    """
+    from ._array_object import Array
+
+    _check_valid_dtype(dtype)
+    if device not in ['cpu', None]:
+        raise ValueError(f"Unsupported device {device!r}")
+    return Array._new(np.zeros_like(x._array, dtype=dtype))
diff --git a/numpy/array_api/_data_type_functions.py b/numpy/array_api/_data_type_functions.py
new file mode 100644
index 000000000..17a00cc6d
--- /dev/null
+++ b/numpy/array_api/_data_type_functions.py
@@ -0,0 +1,117 @@
+from __future__ import annotations
+
+from ._array_object import Array
+from ._dtypes import _all_dtypes, _result_type
+
+from dataclasses import dataclass
+from typing import TYPE_CHECKING, List, Tuple, Union
+if TYPE_CHECKING:
+    from ._typing import Dtype
+    from collections.abc import Sequence
+
+import numpy as np
+
+def broadcast_arrays(*arrays: Sequence[Array]) -> List[Array]:
+    """
+    Array API compatible wrapper for :py:func:`np.broadcast_arrays <numpy.broadcast_arrays>`.
+
+    See its docstring for more information.
+    """
+    from ._array_object import Array
+    return [Array._new(array) for array in np.broadcast_arrays(*[a._array for a in arrays])]
+
+def broadcast_to(x: Array, /, shape: Tuple[int, ...]) -> Array:
+    """
+    Array API compatible wrapper for :py:func:`np.broadcast_to <numpy.broadcast_to>`.
+
+    See its docstring for more information.
+    """
+    from ._array_object import Array
+    return Array._new(np.broadcast_to(x._array, shape))
+
+def can_cast(from_: Union[Dtype, Array], to: Dtype, /) -> bool:
+    """
+    Array API compatible wrapper for :py:func:`np.can_cast <numpy.can_cast>`.
+
+    See its docstring for more information.
+    """
+    from ._array_object import Array
+    if isinstance(from_, Array):
+        from_ = from_._array
+    return np.can_cast(from_, to)
+
+# 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
+    # Note: smallest_normal is part of the array API spec, but cannot be used
+    # until https://github.com/numpy/numpy/pull/18536 is merged.
+
+    # smallest_normal: float
+
+@dataclass
+class iinfo_object:
+    bits: int
+    max: int
+    min: int
+
+def finfo(type: Union[Dtype, Array], /) -> finfo_object:
+    """
+    Array API compatible wrapper for :py:func:`np.finfo <numpy.finfo>`.
+
+    See its docstring for more information.
+    """
+    fi = np.finfo(type)
+    # Note: The types of the float data here are float, whereas in NumPy they
+    # are scalars of the corresponding float dtype.
+    return finfo_object(
+        fi.bits,
+        float(fi.eps),
+        float(fi.max),
+        float(fi.min),
+        # TODO: Uncomment this when #18536 is merged.
+        # float(fi.smallest_normal),
+    )
+
+def iinfo(type: Union[Dtype, Array], /) -> iinfo_object:
+    """
+    Array API compatible wrapper for :py:func:`np.iinfo <numpy.iinfo>`.
+
+    See its docstring for more information.
+    """
+    ii = np.iinfo(type)
+    return iinfo_object(ii.bits, ii.max, ii.min)
+
+def result_type(*arrays_and_dtypes: Sequence[Union[Array, Dtype]]) -> Dtype:
+    """
+    Array API compatible wrapper for :py:func:`np.result_type <numpy.result_type>`.
+
+    See its docstring for more information.
+    """
+    # Note: we use a custom implementation that gives only the type promotions
+    # required by the spec rather than using np.result_type. NumPy implements
+    # too many extra type promotions like int64 + uint64 -> float64, and does
+    # value-based casting on scalar arrays.
+    A = []
+    for a in arrays_and_dtypes:
+        if isinstance(a, Array):
+            a = a.dtype
+        elif isinstance(a, np.ndarray) or a not in _all_dtypes:
+            raise TypeError("result_type() inputs must be array_api arrays or dtypes")
+        A.append(a)
+
+    if len(A) == 0:
+        raise ValueError("at least one array or dtype is required")
+    elif len(A) == 1:
+        return A[0]
+    else:
+        t = A[0]
+        for t2 in A[1:]:
+            t = _result_type(t, t2)
+        return t
diff --git a/numpy/array_api/_dtypes.py b/numpy/array_api/_dtypes.py
new file mode 100644
index 000000000..fcdb562da
--- /dev/null
+++ b/numpy/array_api/_dtypes.py
@@ -0,0 +1,100 @@
+import numpy as np
+
+# Note: we use dtype objects instead of dtype classes. The spec does not
+# require any behavior on dtypes other than equality.
+int8 = np.dtype('int8')
+int16 = np.dtype('int16')
+int32 = np.dtype('int32')
+int64 = np.dtype('int64')
+uint8 = np.dtype('uint8')
+uint16 = np.dtype('uint16')
+uint32 = np.dtype('uint32')
+uint64 = np.dtype('uint64')
+float32 = np.dtype('float32')
+float64 = np.dtype('float64')
+# Note: This name is changed
+bool = np.dtype('bool')
+
+_all_dtypes = (int8, int16, int32, int64, uint8, uint16, uint32, uint64,
+               float32, float64, bool)
+_boolean_dtypes = (bool,)
+_floating_dtypes = (float32, float64)
+_integer_dtypes = (int8, int16, int32, int64, uint8, uint16, uint32, uint64)
+_integer_or_boolean_dtypes = (bool, int8, int16, int32, int64, uint8, uint16, uint32, uint64)
+_numeric_dtypes = (float32, float64, int8, int16, int32, int64, uint8, uint16, uint32, uint64)
+
+# Note: the spec defines a restricted type promotion table compared to NumPy.
+# In particular, cross-kind promotions like integer + float or boolean +
+# integer are not allowed, even for functions that accept both kinds.
+# Additionally, NumPy promotes signed integer + uint64 to float64, but this
+# promotion is not allowed here. To be clear, Python scalar int objects are
+# allowed to promote to floating-point dtypes, but only in array operators
+# (see Array._promote_scalar) method in _array_object.py.
+_promotion_table = {
+    (int8, int8): int8,
+    (int8, int16): int16,
+    (int8, int32): int32,
+    (int8, int64): int64,
+    (int16, int8): int16,
+    (int16, int16): int16,
+    (int16, int32): int32,
+    (int16, int64): int64,
+    (int32, int8): int32,
+    (int32, int16): int32,
+    (int32, int32): int32,
+    (int32, int64): int64,
+    (int64, int8): int64,
+    (int64, int16): int64,
+    (int64, int32): int64,
+    (int64, int64): int64,
+    (uint8, uint8): uint8,
+    (uint8, uint16): uint16,
+    (uint8, uint32): uint32,
+    (uint8, uint64): uint64,
+    (uint16, uint8): uint16,
+    (uint16, uint16): uint16,
+    (uint16, uint32): uint32,
+    (uint16, uint64): uint64,
+    (uint32, uint8): uint32,
+    (uint32, uint16): uint32,
+    (uint32, uint32): uint32,
+    (uint32, uint64): uint64,
+    (uint64, uint8): uint64,
+    (uint64, uint16): uint64,
+    (uint64, uint32): uint64,
+    (uint64, uint64): uint64,
+    (int8, uint8): int16,
+    (int8, uint16): int32,
+    (int8, uint32): int64,
+    (int16, uint8): int16,
+    (int16, uint16): int32,
+    (int16, uint32): int64,
+    (int32, uint8): int32,
+    (int32, uint16): int32,
+    (int32, uint32): int64,
+    (int64, uint8): int64,
+    (int64, uint16): int64,
+    (int64, uint32): int64,
+    (uint8, int8): int16,
+    (uint16, int8): int32,
+    (uint32, int8): int64,
+    (uint8, int16): int16,
+    (uint16, int16): int32,
+    (uint32, int16): int64,
+    (uint8, int32): int32,
+    (uint16, int32): int32,
+    (uint32, int32): int64,
+    (uint8, int64): int64,
+    (uint16, int64): int64,
+    (uint32, int64): int64,
+    (float32, float32): float32,
+    (float32, float64): float64,
+    (float64, float32): float64,
+    (float64, float64): float64,
+    (bool, bool): bool,
+}
+
+def _result_type(type1, type2):
+    if (type1, type2) in _promotion_table:
+        return _promotion_table[type1, type2]
+    raise TypeError(f"{type1} and {type2} cannot be type promoted together")
diff --git a/numpy/array_api/_elementwise_functions.py b/numpy/array_api/_elementwise_functions.py
new file mode 100644
index 000000000..7833ebe54
--- /dev/null
+++ b/numpy/array_api/_elementwise_functions.py
@@ -0,0 +1,659 @@
+from __future__ import annotations
+
+from ._dtypes import (_boolean_dtypes, _floating_dtypes,
+                      _integer_dtypes, _integer_or_boolean_dtypes,
+                      _numeric_dtypes, _result_type)
+from ._array_object import Array
+
+import numpy as np
+
+def abs(x: Array, /) -> Array:
+    """
+    Array API compatible wrapper for :py:func:`np.abs <numpy.abs>`.
+
+    See its docstring for more information.
+    """
+    if x.dtype not in _numeric_dtypes:
+        raise TypeError('Only numeric dtypes are allowed in abs')
+    return Array._new(np.abs(x._array))
+
+# Note: the function name is different here
+def acos(x: Array, /) -> Array:
+    """
+    Array API compatible wrapper for :py:func:`np.arccos <numpy.arccos>`.
+
+    See its docstring for more information.
+    """
+    if x.dtype not in _floating_dtypes:
+        raise TypeError('Only floating-point dtypes are allowed in acos')
+    return Array._new(np.arccos(x._array))
+
+# Note: the function name is different here
+def acosh(x: Array, /) -> Array:
+    """
+    Array API compatible wrapper for :py:func:`np.arccosh <numpy.arccosh>`.
+
+    See its docstring for more information.
+    """
+    if x.dtype not in _floating_dtypes:
+        raise TypeError('Only floating-point dtypes are allowed in acosh')
+    return Array._new(np.arccosh(x._array))
+
+def add(x1: Array, x2: Array, /) -> Array:
+    """
+    Array API compatible wrapper for :py:func:`np.add <numpy.add>`.
+
+    See its docstring for more information.
+    """
+    if x1.dtype not in _numeric_dtypes or x2.dtype not in _numeric_dtypes:
+        raise TypeError('Only numeric dtypes are allowed in add')
+    # Call result type here just to raise on disallowed type combinations
+    _result_type(x1.dtype, x2.dtype)
+    x1, x2 = Array._normalize_two_args(x1, x2)
+    return Array._new(np.add(x1._array, x2._array))
+
+# Note: the function name is different here
+def asin(x: Array, /) -> Array:
+    """
+    Array API compatible wrapper for :py:func:`np.arcsin <numpy.arcsin>`.
+
+    See its docstring for more information.
+    """
+    if x.dtype not in _floating_dtypes:
+        raise TypeError('Only floating-point dtypes are allowed in asin')
+    return Array._new(np.arcsin(x._array))
+
+# Note: the function name is different here
+def asinh(x: Array, /) -> Array:
+    """
+    Array API compatible wrapper for :py:func:`np.arcsinh <numpy.arcsinh>`.
+
+    See its docstring for more information.
+    """
+    if x.dtype not in _floating_dtypes:
+        raise TypeError('Only floating-point dtypes are allowed in asinh')
+    return Array._new(np.arcsinh(x._array))
+
+# Note: the function name is different here
+def atan(x: Array, /) -> Array:
+    """
+    Array API compatible wrapper for :py:func:`np.arctan <numpy.arctan>`.
+
+    See its docstring for more information.
+    """
+    if x.dtype not in _floating_dtypes:
+        raise TypeError('Only floating-point dtypes are allowed in atan')
+    return Array._new(np.arctan(x._array))
+
+# Note: the function name is different here
+def atan2(x1: Array, x2: Array, /) -> Array:
+    """
+    Array API compatible wrapper for :py:func:`np.arctan2 <numpy.arctan2>`.
+
+    See its docstring for more information.
+    """
+    if x1.dtype not in _floating_dtypes or x2.dtype not in _floating_dtypes:
+        raise TypeError('Only floating-point dtypes are allowed in atan2')
+    # Call result type here just to raise on disallowed type combinations
+    _result_type(x1.dtype, x2.dtype)
+    x1, x2 = Array._normalize_two_args(x1, x2)
+    return Array._new(np.arctan2(x1._array, x2._array))
+
+# Note: the function name is different here
+def atanh(x: Array, /) -> Array:
+    """
+    Array API compatible wrapper for :py:func:`np.arctanh <numpy.arctanh>`.
+
+    See its docstring for more information.
+    """
+    if x.dtype not in _floating_dtypes:
+        raise TypeError('Only floating-point dtypes are allowed in atanh')
+    return Array._new(np.arctanh(x._array))
+
+def bitwise_and(x1: Array, x2: Array, /) -> Array:
+    """
+    Array API compatible wrapper for :py:func:`np.bitwise_and <numpy.bitwise_and>`.
+
+    See its docstring for more information.
+    """
+    if x1.dtype not in _integer_or_boolean_dtypes or x2.dtype not in _integer_or_boolean_dtypes:
+        raise TypeError('Only integer or boolean dtypes are allowed in bitwise_and')
+    # Call result type here just to raise on disallowed type combinations
+    _result_type(x1.dtype, x2.dtype)
+    x1, x2 = Array._normalize_two_args(x1, x2)
+    return Array._new(np.bitwise_and(x1._array, x2._array))
+
+# Note: the function name is different here
+def bitwise_left_shift(x1: Array, x2: Array, /) -> Array:
+    """
+    Array API compatible wrapper for :py:func:`np.left_shift <numpy.left_shift>`.
+
+    See its docstring for more information.
+    """
+    if x1.dtype not in _integer_dtypes or x2.dtype not in _integer_dtypes:
+        raise TypeError('Only integer dtypes are allowed in bitwise_left_shift')
+    # Call result type here just to raise on disallowed type combinations
+    _result_type(x1.dtype, x2.dtype)
+    x1, x2 = Array._normalize_two_args(x1, x2)
+    # Note: bitwise_left_shift is only defined for x2 nonnegative.
+    if np.any(x2._array < 0):
+        raise ValueError('bitwise_left_shift(x1, x2) is only defined for x2 >= 0')
+    return Array._new(np.left_shift(x1._array, x2._array))
+
+# Note: the function name is different here
+def bitwise_invert(x: Array, /) -> Array:
+    """
+    Array API compatible wrapper for :py:func:`np.invert <numpy.invert>`.
+
+    See its docstring for more information.
+    """
+    if x.dtype not in _integer_or_boolean_dtypes:
+        raise TypeError('Only integer or boolean dtypes are allowed in bitwise_invert')
+    return Array._new(np.invert(x._array))
+
+def bitwise_or(x1: Array, x2: Array, /) -> Array:
+    """
+    Array API compatible wrapper for :py:func:`np.bitwise_or <numpy.bitwise_or>`.
+
+    See its docstring for more information.
+    """
+    if x1.dtype not in _integer_or_boolean_dtypes or x2.dtype not in _integer_or_boolean_dtypes:
+        raise TypeError('Only integer or boolean dtypes are allowed in bitwise_or')
+    # Call result type here just to raise on disallowed type combinations
+    _result_type(x1.dtype, x2.dtype)
+    x1, x2 = Array._normalize_two_args(x1, x2)
+    return Array._new(np.bitwise_or(x1._array, x2._array))
+
+# Note: the function name is different here
+def bitwise_right_shift(x1: Array, x2: Array, /) -> Array:
+    """
+    Array API compatible wrapper for :py:func:`np.right_shift <numpy.right_shift>`.
+
+    See its docstring for more information.
+    """
+    if x1.dtype not in _integer_dtypes or x2.dtype not in _integer_dtypes:
+        raise TypeError('Only integer dtypes are allowed in bitwise_right_shift')
+    # Call result type here just to raise on disallowed type combinations
+    _result_type(x1.dtype, x2.dtype)
+    x1, x2 = Array._normalize_two_args(x1, x2)
+    # Note: bitwise_right_shift is only defined for x2 nonnegative.
+    if np.any(x2._array < 0):
+        raise ValueError('bitwise_right_shift(x1, x2) is only defined for x2 >= 0')
+    return Array._new(np.right_shift(x1._array, x2._array))
+
+def bitwise_xor(x1: Array, x2: Array, /) -> Array:
+    """
+    Array API compatible wrapper for :py:func:`np.bitwise_xor <numpy.bitwise_xor>`.
+
+    See its docstring for more information.
+    """
+    if x1.dtype not in _integer_or_boolean_dtypes or x2.dtype not in _integer_or_boolean_dtypes:
+        raise TypeError('Only integer or boolean dtypes are allowed in bitwise_xor')
+    # Call result type here just to raise on disallowed type combinations
+    _result_type(x1.dtype, x2.dtype)
+    x1, x2 = Array._normalize_two_args(x1, x2)
+    return Array._new(np.bitwise_xor(x1._array, x2._array))
+
+def ceil(x: Array, /) -> Array:
+    """
+    Array API compatible wrapper for :py:func:`np.ceil <numpy.ceil>`.
+
+    See its docstring for more information.
+    """
+    if x.dtype not in _numeric_dtypes:
+        raise TypeError('Only numeric dtypes are allowed in ceil')
+    if x.dtype in _integer_dtypes:
+        # Note: The return dtype of ceil is the same as the input
+        return x
+    return Array._new(np.ceil(x._array))
+
+def cos(x: Array, /) -> Array:
+    """
+    Array API compatible wrapper for :py:func:`np.cos <numpy.cos>`.
+
+    See its docstring for more information.
+    """
+    if x.dtype not in _floating_dtypes:
+        raise TypeError('Only floating-point dtypes are allowed in cos')
+    return Array._new(np.cos(x._array))
+
+def cosh(x: Array, /) -> Array:
+    """
+    Array API compatible wrapper for :py:func:`np.cosh <numpy.cosh>`.
+
+    See its docstring for more information.
+    """
+    if x.dtype not in _floating_dtypes:
+        raise TypeError('Only floating-point dtypes are allowed in cosh')
+    return Array._new(np.cosh(x._array))
+
+def divide(x1: Array, x2: Array, /) -> Array:
+    """
+    Array API compatible wrapper for :py:func:`np.divide <numpy.divide>`.
+
+    See its docstring for more information.
+    """
+    if x1.dtype not in _floating_dtypes or x2.dtype not in _floating_dtypes:
+        raise TypeError('Only floating-point dtypes are allowed in divide')
+    # Call result type here just to raise on disallowed type combinations
+    _result_type(x1.dtype, x2.dtype)
+    x1, x2 = Array._normalize_two_args(x1, x2)
+    return Array._new(np.divide(x1._array, x2._array))
+
+def equal(x1: Array, x2: Array, /) -> Array:
+    """
+    Array API compatible wrapper for :py:func:`np.equal <numpy.equal>`.
+
+    See its docstring for more information.
+    """
+    # Call result type here just to raise on disallowed type combinations
+    _result_type(x1.dtype, x2.dtype)
+    x1, x2 = Array._normalize_two_args(x1, x2)
+    return Array._new(np.equal(x1._array, x2._array))
+
+def exp(x: Array, /) -> Array:
+    """
+    Array API compatible wrapper for :py:func:`np.exp <numpy.exp>`.
+
+    See its docstring for more information.
+    """
+    if x.dtype not in _floating_dtypes:
+        raise TypeError('Only floating-point dtypes are allowed in exp')
+    return Array._new(np.exp(x._array))
+
+def expm1(x: Array, /) -> Array:
+    """
+    Array API compatible wrapper for :py:func:`np.expm1 <numpy.expm1>`.
+
+    See its docstring for more information.
+    """
+    if x.dtype not in _floating_dtypes:
+        raise TypeError('Only floating-point dtypes are allowed in expm1')
+    return Array._new(np.expm1(x._array))
+
+def floor(x: Array, /) -> Array:
+    """
+    Array API compatible wrapper for :py:func:`np.floor <numpy.floor>`.
+
+    See its docstring for more information.
+    """
+    if x.dtype not in _numeric_dtypes:
+        raise TypeError('Only numeric dtypes are allowed in floor')
+    if x.dtype in _integer_dtypes:
+        # Note: The return dtype of floor is the same as the input
+        return x
+    return Array._new(np.floor(x._array))
+
+def floor_divide(x1: Array, x2: Array, /) -> Array:
+    """
+    Array API compatible wrapper for :py:func:`np.floor_divide <numpy.floor_divide>`.
+
+    See its docstring for more information.
+    """
+    if x1.dtype not in _numeric_dtypes or x2.dtype not in _numeric_dtypes:
+        raise TypeError('Only numeric dtypes are allowed in floor_divide')
+    # Call result type here just to raise on disallowed type combinations
+    _result_type(x1.dtype, x2.dtype)
+    x1, x2 = Array._normalize_two_args(x1, x2)
+    return Array._new(np.floor_divide(x1._array, x2._array))
+
+def greater(x1: Array, x2: Array, /) -> Array:
+    """
+    Array API compatible wrapper for :py:func:`np.greater <numpy.greater>`.
+
+    See its docstring for more information.
+    """
+    if x1.dtype not in _numeric_dtypes or x2.dtype not in _numeric_dtypes:
+        raise TypeError('Only numeric dtypes are allowed in greater')
+    # Call result type here just to raise on disallowed type combinations
+    _result_type(x1.dtype, x2.dtype)
+    x1, x2 = Array._normalize_two_args(x1, x2)
+    return Array._new(np.greater(x1._array, x2._array))
+
+def greater_equal(x1: Array, x2: Array, /) -> Array:
+    """
+    Array API compatible wrapper for :py:func:`np.greater_equal <numpy.greater_equal>`.
+
+    See its docstring for more information.
+    """
+    if x1.dtype not in _numeric_dtypes or x2.dtype not in _numeric_dtypes:
+        raise TypeError('Only numeric dtypes are allowed in greater_equal')
+    # Call result type here just to raise on disallowed type combinations
+    _result_type(x1.dtype, x2.dtype)
+    x1, x2 = Array._normalize_two_args(x1, x2)
+    return Array._new(np.greater_equal(x1._array, x2._array))
+
+def isfinite(x: Array, /) -> Array:
+    """
+    Array API compatible wrapper for :py:func:`np.isfinite <numpy.isfinite>`.
+
+    See its docstring for more information.
+    """
+    if x.dtype not in _numeric_dtypes:
+        raise TypeError('Only numeric dtypes are allowed in isfinite')
+    return Array._new(np.isfinite(x._array))
+
+def isinf(x: Array, /) -> Array:
+    """
+    Array API compatible wrapper for :py:func:`np.isinf <numpy.isinf>`.
+
+    See its docstring for more information.
+    """
+    if x.dtype not in _numeric_dtypes:
+        raise TypeError('Only numeric dtypes are allowed in isinf')
+    return Array._new(np.isinf(x._array))
+
+def isnan(x: Array, /) -> Array:
+    """
+    Array API compatible wrapper for :py:func:`np.isnan <numpy.isnan>`.
+
+    See its docstring for more information.
+    """
+    if x.dtype not in _numeric_dtypes:
+        raise TypeError('Only numeric dtypes are allowed in isnan')
+    return Array._new(np.isnan(x._array))
+
+def less(x1: Array, x2: Array, /) -> Array:
+    """
+    Array API compatible wrapper for :py:func:`np.less <numpy.less>`.
+
+    See its docstring for more information.
+    """
+    if x1.dtype not in _numeric_dtypes or x2.dtype not in _numeric_dtypes:
+        raise TypeError('Only numeric dtypes are allowed in less')
+    # Call result type here just to raise on disallowed type combinations
+    _result_type(x1.dtype, x2.dtype)
+    x1, x2 = Array._normalize_two_args(x1, x2)
+    return Array._new(np.less(x1._array, x2._array))
+
+def less_equal(x1: Array, x2: Array, /) -> Array:
+    """
+    Array API compatible wrapper for :py:func:`np.less_equal <numpy.less_equal>`.
+
+    See its docstring for more information.
+    """
+    if x1.dtype not in _numeric_dtypes or x2.dtype not in _numeric_dtypes:
+        raise TypeError('Only numeric dtypes are allowed in less_equal')
+    # Call result type here just to raise on disallowed type combinations
+    _result_type(x1.dtype, x2.dtype)
+    x1, x2 = Array._normalize_two_args(x1, x2)
+    return Array._new(np.less_equal(x1._array, x2._array))
+
+def log(x: Array, /) -> Array:
+    """
+    Array API compatible wrapper for :py:func:`np.log <numpy.log>`.
+
+    See its docstring for more information.
+    """
+    if x.dtype not in _floating_dtypes:
+        raise TypeError('Only floating-point dtypes are allowed in log')
+    return Array._new(np.log(x._array))
+
+def log1p(x: Array, /) -> Array:
+    """
+    Array API compatible wrapper for :py:func:`np.log1p <numpy.log1p>`.
+
+    See its docstring for more information.
+    """
+    if x.dtype not in _floating_dtypes:
+        raise TypeError('Only floating-point dtypes are allowed in log1p')
+    return Array._new(np.log1p(x._array))
+
+def log2(x: Array, /) -> Array:
+    """
+    Array API compatible wrapper for :py:func:`np.log2 <numpy.log2>`.
+
+    See its docstring for more information.
+    """
+    if x.dtype not in _floating_dtypes:
+        raise TypeError('Only floating-point dtypes are allowed in log2')
+    return Array._new(np.log2(x._array))
+
+def log10(x: Array, /) -> Array:
+    """
+    Array API compatible wrapper for :py:func:`np.log10 <numpy.log10>`.
+
+    See its docstring for more information.
+    """
+    if x.dtype not in _floating_dtypes:
+        raise TypeError('Only floating-point dtypes are allowed in log10')
+    return Array._new(np.log10(x._array))
+
+def logaddexp(x1: Array, x2: Array) -> Array:
+    """
+    Array API compatible wrapper for :py:func:`np.logaddexp <numpy.logaddexp>`.
+
+    See its docstring for more information.
+    """
+    if x1.dtype not in _floating_dtypes or x2.dtype not in _floating_dtypes:
+        raise TypeError('Only floating-point dtypes are allowed in logaddexp')
+    # Call result type here just to raise on disallowed type combinations
+    _result_type(x1.dtype, x2.dtype)
+    x1, x2 = Array._normalize_two_args(x1, x2)
+    return Array._new(np.logaddexp(x1._array, x2._array))
+
+def logical_and(x1: Array, x2: Array, /) -> Array:
+    """
+    Array API compatible wrapper for :py:func:`np.logical_and <numpy.logical_and>`.
+
+    See its docstring for more information.
+    """
+    if x1.dtype not in _boolean_dtypes or x2.dtype not in _boolean_dtypes:
+        raise TypeError('Only boolean dtypes are allowed in logical_and')
+    # Call result type here just to raise on disallowed type combinations
+    _result_type(x1.dtype, x2.dtype)
+    x1, x2 = Array._normalize_two_args(x1, x2)
+    return Array._new(np.logical_and(x1._array, x2._array))
+
+def logical_not(x: Array, /) -> Array:
+    """
+    Array API compatible wrapper for :py:func:`np.logical_not <numpy.logical_not>`.
+
+    See its docstring for more information.
+    """
+    if x.dtype not in _boolean_dtypes:
+        raise TypeError('Only boolean dtypes are allowed in logical_not')
+    return Array._new(np.logical_not(x._array))
+
+def logical_or(x1: Array, x2: Array, /) -> Array:
+    """
+    Array API compatible wrapper for :py:func:`np.logical_or <numpy.logical_or>`.
+
+    See its docstring for more information.
+    """
+    if x1.dtype not in _boolean_dtypes or x2.dtype not in _boolean_dtypes:
+        raise TypeError('Only boolean dtypes are allowed in logical_or')
+    # Call result type here just to raise on disallowed type combinations
+    _result_type(x1.dtype, x2.dtype)
+    x1, x2 = Array._normalize_two_args(x1, x2)
+    return Array._new(np.logical_or(x1._array, x2._array))
+
+def logical_xor(x1: Array, x2: Array, /) -> Array:
+    """
+    Array API compatible wrapper for :py:func:`np.logical_xor <numpy.logical_xor>`.
+
+    See its docstring for more information.
+    """
+    if x1.dtype not in _boolean_dtypes or x2.dtype not in _boolean_dtypes:
+        raise TypeError('Only boolean dtypes are allowed in logical_xor')
+    # Call result type here just to raise on disallowed type combinations
+    _result_type(x1.dtype, x2.dtype)
+    x1, x2 = Array._normalize_two_args(x1, x2)
+    return Array._new(np.logical_xor(x1._array, x2._array))
+
+def multiply(x1: Array, x2: Array, /) -> Array:
+    """
+    Array API compatible wrapper for :py:func:`np.multiply <numpy.multiply>`.
+
+    See its docstring for more information.
+    """
+    if x1.dtype not in _numeric_dtypes or x2.dtype not in _numeric_dtypes:
+        raise TypeError('Only numeric dtypes are allowed in multiply')
+    # Call result type here just to raise on disallowed type combinations
+    _result_type(x1.dtype, x2.dtype)
+    x1, x2 = Array._normalize_two_args(x1, x2)
+    return Array._new(np.multiply(x1._array, x2._array))
+
+def negative(x: Array, /) -> Array:
+    """
+    Array API compatible wrapper for :py:func:`np.negative <numpy.negative>`.
+
+    See its docstring for more information.
+    """
+    if x.dtype not in _numeric_dtypes:
+        raise TypeError('Only numeric dtypes are allowed in negative')
+    return Array._new(np.negative(x._array))
+
+def not_equal(x1: Array, x2: Array, /) -> Array:
+    """
+    Array API compatible wrapper for :py:func:`np.not_equal <numpy.not_equal>`.
+
+    See its docstring for more information.
+    """
+    # Call result type here just to raise on disallowed type combinations
+    _result_type(x1.dtype, x2.dtype)
+    x1, x2 = Array._normalize_two_args(x1, x2)
+    return Array._new(np.not_equal(x1._array, x2._array))
+
+def positive(x: Array, /) -> Array:
+    """
+    Array API compatible wrapper for :py:func:`np.positive <numpy.positive>`.
+
+    See its docstring for more information.
+    """
+    if x.dtype not in _numeric_dtypes:
+        raise TypeError('Only numeric dtypes are allowed in positive')
+    return Array._new(np.positive(x._array))
+
+# Note: the function name is different here
+def pow(x1: Array, x2: Array, /) -> Array:
+    """
+    Array API compatible wrapper for :py:func:`np.power <numpy.power>`.
+
+    See its docstring for more information.
+    """
+    if x1.dtype not in _floating_dtypes or x2.dtype not in _floating_dtypes:
+        raise TypeError('Only floating-point dtypes are allowed in pow')
+    # Call result type here just to raise on disallowed type combinations
+    _result_type(x1.dtype, x2.dtype)
+    x1, x2 = Array._normalize_two_args(x1, x2)
+    return Array._new(np.power(x1._array, x2._array))
+
+def remainder(x1: Array, x2: Array, /) -> Array:
+    """
+    Array API compatible wrapper for :py:func:`np.remainder <numpy.remainder>`.
+
+    See its docstring for more information.
+    """
+    if x1.dtype not in _numeric_dtypes or x2.dtype not in _numeric_dtypes:
+        raise TypeError('Only numeric dtypes are allowed in remainder')
+    # Call result type here just to raise on disallowed type combinations
+    _result_type(x1.dtype, x2.dtype)
+    x1, x2 = Array._normalize_two_args(x1, x2)
+    return Array._new(np.remainder(x1._array, x2._array))
+
+def round(x: Array, /) -> Array:
+    """
+    Array API compatible wrapper for :py:func:`np.round <numpy.round>`.
+
+    See its docstring for more information.
+    """
+    if x.dtype not in _numeric_dtypes:
+        raise TypeError('Only numeric dtypes are allowed in round')
+    return Array._new(np.round(x._array))
+
+def sign(x: Array, /) -> Array:
+    """
+    Array API compatible wrapper for :py:func:`np.sign <numpy.sign>`.
+
+    See its docstring for more information.
+    """
+    if x.dtype not in _numeric_dtypes:
+        raise TypeError('Only numeric dtypes are allowed in sign')
+    return Array._new(np.sign(x._array))
+
+def sin(x: Array, /) -> Array:
+    """
+    Array API compatible wrapper for :py:func:`np.sin <numpy.sin>`.
+
+    See its docstring for more information.
+    """
+    if x.dtype not in _floating_dtypes:
+        raise TypeError('Only floating-point dtypes are allowed in sin')
+    return Array._new(np.sin(x._array))
+
+def sinh(x: Array, /) -> Array:
+    """
+    Array API compatible wrapper for :py:func:`np.sinh <numpy.sinh>`.
+
+    See its docstring for more information.
+    """
+    if x.dtype not in _floating_dtypes:
+        raise TypeError('Only floating-point dtypes are allowed in sinh')
+    return Array._new(np.sinh(x._array))
+
+def square(x: Array, /) -> Array:
+    """
+    Array API compatible wrapper for :py:func:`np.square <numpy.square>`.
+
+    See its docstring for more information.
+    """
+    if x.dtype not in _numeric_dtypes:
+        raise TypeError('Only numeric dtypes are allowed in square')
+    return Array._new(np.square(x._array))
+
+def sqrt(x: Array, /) -> Array:
+    """
+    Array API compatible wrapper for :py:func:`np.sqrt <numpy.sqrt>`.
+
+    See its docstring for more information.
+    """
+    if x.dtype not in _floating_dtypes:
+        raise TypeError('Only floating-point dtypes are allowed in sqrt')
+    return Array._new(np.sqrt(x._array))
+
+def subtract(x1: Array, x2: Array, /) -> Array:
+    """
+    Array API compatible wrapper for :py:func:`np.subtract <numpy.subtract>`.
+
+    See its docstring for more information.
+    """
+    if x1.dtype not in _numeric_dtypes or x2.dtype not in _numeric_dtypes:
+        raise TypeError('Only numeric dtypes are allowed in subtract')
+    # Call result type here just to raise on disallowed type combinations
+    _result_type(x1.dtype, x2.dtype)
+    x1, x2 = Array._normalize_two_args(x1, x2)
+    return Array._new(np.subtract(x1._array, x2._array))
+
+def tan(x: Array, /) -> Array:
+    """
+    Array API compatible wrapper for :py:func:`np.tan <numpy.tan>`.
+
+    See its docstring for more information.
+    """
+    if x.dtype not in _floating_dtypes:
+        raise TypeError('Only floating-point dtypes are allowed in tan')
+    return Array._new(np.tan(x._array))
+
+def tanh(x: Array, /) -> Array:
+    """
+    Array API compatible wrapper for :py:func:`np.tanh <numpy.tanh>`.
+
+    See its docstring for more information.
+    """
+    if x.dtype not in _floating_dtypes:
+        raise TypeError('Only floating-point dtypes are allowed in tanh')
+    return Array._new(np.tanh(x._array))
+
+def trunc(x: Array, /) -> Array:
+    """
+    Array API compatible wrapper for :py:func:`np.trunc <numpy.trunc>`.
+
+    See its docstring for more information.
+    """
+    if x.dtype not in _numeric_dtypes:
+        raise TypeError('Only numeric dtypes are allowed in trunc')
+    if x.dtype in _integer_dtypes:
+        # Note: The return dtype of trunc is the same as the input
+        return x
+    return Array._new(np.trunc(x._array))
diff --git a/numpy/array_api/_linear_algebra_functions.py b/numpy/array_api/_linear_algebra_functions.py
new file mode 100644
index 000000000..f13f9c541
--- /dev/null
+++ b/numpy/array_api/_linear_algebra_functions.py
@@ -0,0 +1,58 @@
+from __future__ import annotations
+
+from ._array_object import Array
+from ._dtypes import _numeric_dtypes, _result_type
+
+from typing import Optional, Sequence, Tuple, Union
+
+import numpy as np
+
+# einsum is not yet implemented in the array API spec.
+
+# def einsum():
+#     """
+#     Array API compatible wrapper for :py:func:`np.einsum <numpy.einsum>`.
+#
+#     See its docstring for more information.
+#     """
+#     return np.einsum()
+
+def matmul(x1: Array, x2: Array, /) -> Array:
+    """
+    Array API compatible wrapper for :py:func:`np.matmul <numpy.matmul>`.
+
+    See its docstring for more information.
+    """
+    # Note: the restriction to numeric dtypes only is different from
+    # np.matmul.
+    if x1.dtype not in _numeric_dtypes or x2.dtype not in _numeric_dtypes:
+        raise TypeError('Only numeric dtypes are allowed in matmul')
+    # Call result type here just to raise on disallowed type combinations
+    _result_type(x1.dtype, x2.dtype)
+
+    return Array._new(np.matmul(x1._array, x2._array))
+
+# Note: axes must be a tuple, unlike np.tensordot where it can be an array or array-like.
+def tensordot(x1: Array, x2: Array, /, *, axes: Union[int, Tuple[Sequence[int], Sequence[int]]] = 2) -> Array:
+    # Note: the restriction to numeric dtypes only is different from
+    # np.tensordot.
+    if x1.dtype not in _numeric_dtypes or x2.dtype not in _numeric_dtypes:
+        raise TypeError('Only numeric dtypes are allowed in tensordot')
+    # Call result type here just to raise on disallowed type combinations
+    _result_type(x1.dtype, x2.dtype)
+
+    return Array._new(np.tensordot(x1._array, x2._array, axes=axes))
+
+def transpose(x: Array, /, *, axes: Optional[Tuple[int, ...]] = None) -> Array:
+    """
+    Array API compatible wrapper for :py:func:`np.transpose <numpy.transpose>`.
+
+    See its docstring for more information.
+    """
+    return Array._new(np.transpose(x._array, axes=axes))
+
+# Note: vecdot is not in NumPy
+def vecdot(x1: Array, x2: Array, /, *, axis: Optional[int] = None) -> Array:
+    if axis is None:
+        axis = -1
+    return tensordot(x1, x2, axes=((axis,), (axis,)))
diff --git a/numpy/array_api/_manipulation_functions.py b/numpy/array_api/_manipulation_functions.py
new file mode 100644
index 000000000..fa6344beb
--- /dev/null
+++ b/numpy/array_api/_manipulation_functions.py
@@ -0,0 +1,71 @@
+from __future__ import annotations
+
+from ._array_object import Array
+from ._data_type_functions import result_type
+
+from typing import List, Optional, Tuple, Union
+
+import numpy as np
+
+# Note: the function name is different here
+def concat(arrays: Union[Tuple[Array, ...], List[Array]], /, *, axis: Optional[int] = 0) -> Array:
+    """
+    Array API compatible wrapper for :py:func:`np.concatenate <numpy.concatenate>`.
+
+    See its docstring for more information.
+    """
+    arrays = tuple(a._array for a in arrays)
+    # Call result type here just to raise on disallowed type combinations
+    result_type(*arrays)
+    return Array._new(np.concatenate(arrays, axis=axis))
+
+def expand_dims(x: Array, /, *, axis: int) -> Array:
+    """
+    Array API compatible wrapper for :py:func:`np.expand_dims <numpy.expand_dims>`.
+
+    See its docstring for more information.
+    """
+    return Array._new(np.expand_dims(x._array, axis))
+
+def flip(x: Array, /, *, axis: Optional[Union[int, Tuple[int, ...]]] = None) -> Array:
+    """
+    Array API compatible wrapper for :py:func:`np.flip <numpy.flip>`.
+
+    See its docstring for more information.
+    """
+    return Array._new(np.flip(x._array, axis=axis))
+
+def reshape(x: Array, /, shape: Tuple[int, ...]) -> Array:
+    """
+    Array API compatible wrapper for :py:func:`np.reshape <numpy.reshape>`.
+
+    See its docstring for more information.
+    """
+    return Array._new(np.reshape(x._array, shape))
+
+def roll(x: Array, /, shift: Union[int, Tuple[int, ...]], *, axis: Optional[Union[int, Tuple[int, ...]]] = None) -> Array:
+    """
+    Array API compatible wrapper for :py:func:`np.roll <numpy.roll>`.
+
+    See its docstring for more information.
+    """
+    return Array._new(np.roll(x._array, shift, axis=axis))
+
+def squeeze(x: Array, /, axis: Optional[Union[int, Tuple[int, ...]]] = None) -> Array:
+    """
+    Array API compatible wrapper for :py:func:`np.squeeze <numpy.squeeze>`.
+
+    See its docstring for more information.
+    """
+    return Array._new(np.squeeze(x._array, axis=axis))
+
+def stack(arrays: Union[Tuple[Array, ...], List[Array]], /, *, axis: int = 0) -> Array:
+    """
+    Array API compatible wrapper for :py:func:`np.stack <numpy.stack>`.
+
+    See its docstring for more information.
+    """
+    arrays = tuple(a._array for a in arrays)
+    # Call result type here just to raise on disallowed type combinations
+    result_type(*arrays)
+    return Array._new(np.stack(arrays, axis=axis))
diff --git a/numpy/array_api/_searching_functions.py b/numpy/array_api/_searching_functions.py
new file mode 100644
index 000000000..d80720850
--- /dev/null
+++ b/numpy/array_api/_searching_functions.py
@@ -0,0 +1,44 @@
+from __future__ import annotations
+
+from ._array_object import Array
+from ._dtypes import _result_type
+
+from typing import Optional, Tuple
+
+import numpy as np
+
+def argmax(x: Array, /, *, axis: Optional[int] = None, keepdims: bool = False) -> Array:
+    """
+    Array API compatible wrapper for :py:func:`np.argmax <numpy.argmax>`.
+
+    See its docstring for more information.
+    """
+    # Note: this currently fails as np.argmax does not implement keepdims
+    return Array._new(np.asarray(np.argmax(x._array, axis=axis, keepdims=keepdims)))
+
+def argmin(x: Array, /, *, axis: Optional[int] = None, keepdims: bool = False) -> Array:
+    """
+    Array API compatible wrapper for :py:func:`np.argmin <numpy.argmin>`.
+
+    See its docstring for more information.
+    """
+    # Note: this currently fails as np.argmin does not implement keepdims
+    return Array._new(np.asarray(np.argmin(x._array, axis=axis, keepdims=keepdims)))
+
+def nonzero(x: Array, /) -> Tuple[Array, ...]:
+    """
+    Array API compatible wrapper for :py:func:`np.nonzero <numpy.nonzero>`.
+
+    See its docstring for more information.
+    """
+    return Array._new(np.nonzero(x._array))
+
+def where(condition: Array, x1: Array, x2: Array, /) -> Array:
+    """
+    Array API compatible wrapper for :py:func:`np.where <numpy.where>`.
+
+    See its docstring for more information.
+    """
+    # Call result type here just to raise on disallowed type combinations
+    _result_type(x1.dtype, x2.dtype)
+    return Array._new(np.where(condition._array, x1._array, x2._array))
diff --git a/numpy/array_api/_set_functions.py b/numpy/array_api/_set_functions.py
new file mode 100644
index 000000000..f28c2ee72
--- /dev/null
+++ b/numpy/array_api/_set_functions.py
@@ -0,0 +1,15 @@
+from __future__ import annotations
+
+from ._array_object import Array
+
+from typing import Tuple, Union
+
+import numpy as np
+
+def unique(x: Array, /, *, return_counts: bool = False, return_index: bool = False, return_inverse: bool = False) -> Union[Array, Tuple[Array, ...]]:
+    """
+    Array API compatible wrapper for :py:func:`np.unique <numpy.unique>`.
+
+    See its docstring for more information.
+    """
+    return Array._new(np.unique(x._array, return_counts=return_counts, return_index=return_index, return_inverse=return_inverse))
diff --git a/numpy/array_api/_sorting_functions.py b/numpy/array_api/_sorting_functions.py
new file mode 100644
index 000000000..a125e0718
--- /dev/null
+++ b/numpy/array_api/_sorting_functions.py
@@ -0,0 +1,31 @@
+from __future__ import annotations
+
+from ._array_object import Array
+
+import numpy as np
+
+def argsort(x: Array, /, *, axis: int = -1, descending: bool = False, stable: bool = True) -> Array:
+    """
+    Array API compatible wrapper for :py:func:`np.argsort <numpy.argsort>`.
+
+    See its docstring for more information.
+    """
+    # Note: this keyword argument is different, and the default is different.
+    kind = 'stable' if stable else 'quicksort'
+    res = np.argsort(x._array, axis=axis, kind=kind)
+    if descending:
+        res = np.flip(res, axis=axis)
+    return Array._new(res)
+
+def sort(x: Array, /, *, axis: int = -1, descending: bool = False, stable: bool = True) -> Array:
+    """
+    Array API compatible wrapper for :py:func:`np.sort <numpy.sort>`.
+
+    See its docstring for more information.
+    """
+    # Note: this keyword argument is different, and the default is different.
+    kind = 'stable' if stable else 'quicksort'
+    res = np.sort(x._array, axis=axis, kind=kind)
+    if descending:
+        res = np.flip(res, axis=axis)
+    return Array._new(res)
diff --git a/numpy/array_api/_statistical_functions.py b/numpy/array_api/_statistical_functions.py
new file mode 100644
index 000000000..61fc60c46
--- /dev/null
+++ b/numpy/array_api/_statistical_functions.py
@@ -0,0 +1,30 @@
+from __future__ import annotations
+
+from ._array_object import Array
+
+from typing import Optional, Tuple, Union
+
+import numpy as np
+
+def max(x: Array, /, *, axis: Optional[Union[int, Tuple[int, ...]]] = None, keepdims: bool = False) -> Array:
+    return Array._new(np.max(x._array, axis=axis, keepdims=keepdims))
+
+def mean(x: Array, /, *, axis: Optional[Union[int, Tuple[int, ...]]] = None, keepdims: bool = False) -> Array:
+    return Array._new(np.asarray(np.mean(x._array, axis=axis, keepdims=keepdims)))
+
+def min(x: Array, /, *, axis: Optional[Union[int, Tuple[int, ...]]] = None, keepdims: bool = False) -> Array:
+    return Array._new(np.min(x._array, axis=axis, keepdims=keepdims))
+
+def prod(x: Array, /, *, axis: Optional[Union[int, Tuple[int, ...]]] = None, keepdims: bool = False) -> Array:
+    return Array._new(np.asarray(np.prod(x._array, axis=axis, keepdims=keepdims)))
+
+def std(x: Array, /, *, axis: Optional[Union[int, Tuple[int, ...]]] = None, correction: Union[int, float] = 0.0, keepdims: bool = False) -> Array:
+    # Note: the keyword argument correction is different here
+    return Array._new(np.asarray(np.std(x._array, axis=axis, ddof=correction, keepdims=keepdims)))
+
+def sum(x: Array, /, *, axis: Optional[Union[int, Tuple[int, ...]]] = None, keepdims: bool = False) -> Array:
+    return Array._new(np.asarray(np.sum(x._array, axis=axis, keepdims=keepdims)))
+
+def var(x: Array, /, *, axis: Optional[Union[int, Tuple[int, ...]]] = None, correction: Union[int, float] = 0.0, keepdims: bool = False) -> Array:
+    # Note: the keyword argument correction is different here
+    return Array._new(np.asarray(np.var(x._array, axis=axis, ddof=correction, keepdims=keepdims)))
diff --git a/numpy/array_api/_typing.py b/numpy/array_api/_typing.py
new file mode 100644
index 000000000..4ff718205
--- /dev/null
+++ b/numpy/array_api/_typing.py
@@ -0,0 +1,26 @@
+"""
+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.
+"""
+
+__all__ = ['Array', 'Device', 'Dtype', 'SupportsDLPack',
+           'SupportsBufferProtocol', 'PyCapsule']
+
+from typing import Any, Sequence, Type, Union
+
+from . import (Array, int8, int16, int32, int64, uint8, uint16, uint32,
+               uint64, float32, float64)
+
+# This should really be recursive, but that isn't supported yet. See the
+# similar comment in numpy/typing/_array_like.py
+NestedSequence = Sequence[Sequence[Any]]
+
+Device = Any
+Dtype = Type[Union[[int8, int16, int32, int64, uint8, uint16,
+                   uint32, uint64, float32, float64]]]
+SupportsDLPack = Any
+SupportsBufferProtocol = Any
+PyCapsule = Any
diff --git a/numpy/array_api/_utility_functions.py b/numpy/array_api/_utility_functions.py
new file mode 100644
index 000000000..f243bfe68
--- /dev/null
+++ b/numpy/array_api/_utility_functions.py
@@ -0,0 +1,23 @@
+from __future__ import annotations
+
+from ._array_object import Array
+
+from typing import Optional, Tuple, Union
+
+import numpy as np
+
+def all(x: Array, /, *, axis: Optional[Union[int, Tuple[int, ...]]] = None, keepdims: bool = False) -> Array:
+    """
+    Array API compatible wrapper for :py:func:`np.all <numpy.all>`.
+
+    See its docstring for more information.
+    """
+    return Array._new(np.asarray(np.all(x._array, axis=axis, keepdims=keepdims)))
+
+def any(x: Array, /, *, axis: Optional[Union[int, Tuple[int, ...]]] = None, keepdims: bool = False) -> Array:
+    """
+    Array API compatible wrapper for :py:func:`np.any <numpy.any>`.
+
+    See its docstring for more information.
+    """
+    return Array._new(np.asarray(np.any(x._array, axis=axis, keepdims=keepdims)))
diff --git a/numpy/array_api/tests/__init__.py b/numpy/array_api/tests/__init__.py
new file mode 100644
index 000000000..536062e38
--- /dev/null
+++ b/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.
+"""
diff --git a/numpy/array_api/tests/test_array_object.py b/numpy/array_api/tests/test_array_object.py
new file mode 100644
index 000000000..22078bbee
--- /dev/null
+++ b/numpy/array_api/tests/test_array_object.py
@@ -0,0 +1,250 @@
+from numpy.testing import assert_raises
+import numpy as np
+
+from .. import ones, asarray, result_type
+from .._dtypes import (_all_dtypes, _boolean_dtypes, _floating_dtypes,
+                       _integer_dtypes, _integer_or_boolean_dtypes,
+                       _numeric_dtypes, int8, int16, int32, int64, uint64)
+
+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[3:])
+    assert_raises(IndexError, lambda: a[-4:])
+    assert_raises(IndexError, lambda: a[3::-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[...,:4:-1])
+    assert_raises(IndexError, lambda: a[...,:-6:-1])
+    assert_raises(IndexError, lambda: a[...,4:])
+    assert_raises(IndexError, lambda: a[...,-5:])
+    assert_raises(IndexError, lambda: a[...,4::-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]])])
+
+    # np.newaxis is not allowed
+    assert_raises(IndexError, lambda: a[None])
+    assert_raises(IndexError, lambda: a[None, ...])
+    assert_raises(IndexError, lambda: a[..., None])
+
+def test_operators():
+    # For every operator, we test that it works for the required type
+    # combinations and raises TypeError otherwise
+    binary_op_dtypes ={
+        '__add__': 'numeric',
+        '__and__': 'integer_or_boolean',
+        '__eq__': 'all',
+        '__floordiv__': 'numeric',
+        '__ge__': 'numeric',
+        '__gt__': 'numeric',
+        '__le__': 'numeric',
+        '__lshift__': 'integer',
+        '__lt__': 'numeric',
+        '__mod__': 'numeric',
+        '__mul__': 'numeric',
+        '__ne__': 'all',
+        '__or__': 'integer_or_boolean',
+        '__pow__': 'floating',
+        '__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., dtype=d)
+
+    for op, dtypes in binary_op_dtypes.items():
+        ops = [op]
+        if op not in ['__eq__', '__ne__', '__le__', '__ge__', '__lt__', '__gt__']:
+            rop = '__r' + op[2:]
+            iop = '__i' + op[2:]
+            ops += [rop, iop]
+        for s in [1, 1., False]:
+            for _op in ops:
+                for a in _array_vals():
+                    # Test array op scalar. From the spec, the following combinations
+                    # are supported:
+
+                    # - Python bool for a bool array dtype,
+                    # - a Python int within the bounds of the given dtype for integer array dtypes,
+                    # - a Python int or float for floating-point array dtypes
+
+                    # We do not do bounds checking for int scalars, but rather use the default
+                    # NumPy behavior for casting in that case.
+
+                    if ((dtypes == "all"
+                         or dtypes == "numeric" and a.dtype in _numeric_dtypes
+                         or dtypes == "integer" and a.dtype in _integer_dtypes
+                         or dtypes == "integer_or_boolean" and a.dtype in _integer_or_boolean_dtypes
+                         or dtypes == "boolean" and a.dtype in _boolean_dtypes
+                         or dtypes == "floating" and a.dtype in _floating_dtypes
+                        )
+                        # bool is a subtype of int, which is why we avoid
+                        # isinstance here.
+                        and (a.dtype in _boolean_dtypes and type(s) == bool
+                             or a.dtype in _integer_dtypes and type(s) == int
+                             or a.dtype in _floating_dtypes and type(s) in [float, int]
+                        )):
+                        # Only test for no error
+                        getattr(a, _op)(s)
+                    else:
+                        assert_raises(TypeError, lambda: getattr(a, _op)(s))
+
+                # Test array op array.
+                for _op in ops:
+                    for x in _array_vals():
+                        for y in _array_vals():
+                            # See the promotion table in NEP 47 or the array
+                            # API spec page on type promotion. Mixed kind
+                            # promotion is not defined.
+                            if (x.dtype == uint64 and y.dtype in [int8, int16, int32, int64]
+                                or y.dtype == uint64 and x.dtype in [int8, int16, int32, int64]
+                                or x.dtype in _integer_dtypes and y.dtype not in _integer_dtypes
+                                or y.dtype in _integer_dtypes and x.dtype not in _integer_dtypes
+                                or x.dtype in _boolean_dtypes and y.dtype not in _boolean_dtypes
+                                or y.dtype in _boolean_dtypes and x.dtype not in _boolean_dtypes
+                                or x.dtype in _floating_dtypes and y.dtype not in _floating_dtypes
+                                or y.dtype in _floating_dtypes and x.dtype not in _floating_dtypes
+                                ):
+                                assert_raises(TypeError, lambda: getattr(x, _op)(y))
+                            # Ensure in-place operators only promote to the same dtype as the left operand.
+                            elif _op.startswith('__i') and result_type(x.dtype, y.dtype) != x.dtype:
+                                assert_raises(TypeError, lambda: getattr(x, _op)(y))
+                            # Ensure only those dtypes that are required for every operator are allowed.
+                            elif (dtypes == "all" and (x.dtype in _boolean_dtypes and y.dtype in _boolean_dtypes
+                                                      or x.dtype in _numeric_dtypes and y.dtype in _numeric_dtypes)
+                                or (dtypes == "numeric" and x.dtype in _numeric_dtypes and y.dtype in _numeric_dtypes)
+                                or dtypes == "integer" and x.dtype in _integer_dtypes and y.dtype in _numeric_dtypes
+                                or dtypes == "integer_or_boolean" and (x.dtype in _integer_dtypes and y.dtype in _integer_dtypes
+                                                                       or x.dtype in _boolean_dtypes and y.dtype in _boolean_dtypes)
+                                or dtypes == "boolean" and x.dtype in _boolean_dtypes and y.dtype in _boolean_dtypes
+                                or dtypes == "floating" and x.dtype in _floating_dtypes and y.dtype in _floating_dtypes
+                            ):
+                                getattr(x, _op)(y)
+                            else:
+                                assert_raises(TypeError, lambda: getattr(x, _op)(y))
+
+    unary_op_dtypes ={
+        '__abs__': 'numeric',
+        '__invert__': 'integer_or_boolean',
+        '__neg__': 'numeric',
+        '__pos__': 'numeric',
+    }
+    for op, dtypes in unary_op_dtypes.items():
+        for a in _array_vals():
+            if (dtypes == "numeric" and a.dtype in _numeric_dtypes
+                or dtypes == "integer_or_boolean" and a.dtype in _integer_or_boolean_dtypes
+                ):
+                # Only test for no error
+                getattr(a, op)()
+            else:
+                assert_raises(TypeError, lambda: getattr(a, op)())
+
+    # Finally, matmul() must be tested separately, because it works a bit
+    # different from the other operations.
+    def _matmul_array_vals():
+        for a in _array_vals():
+            yield a
+        for d in _all_dtypes:
+            yield ones((3, 4), dtype=d)
+            yield ones((4, 2), dtype=d)
+            yield ones((4, 4), dtype=d)
+
+    # Scalars always error
+    for _op in ['__matmul__', '__rmatmul__', '__imatmul__']:
+        for s in [1, 1., 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():
+    a = asarray(False)
+    b = asarray(0)
+    c = asarray(0.)
+
+    assert bool(a) == bool(b) == bool(c) == False
+    assert int(a) == int(b) == int(c) == 0
+    assert float(a) == float(b) == float(c) == 0.
+
+    # bool/int/float should only be allowed on 0-D arrays.
+    assert_raises(TypeError, lambda: bool(asarray([False])))
+    assert_raises(TypeError, lambda: int(asarray([0])))
+    assert_raises(TypeError, lambda: float(asarray([0.])))
diff --git a/numpy/array_api/tests/test_creation_functions.py b/numpy/array_api/tests/test_creation_functions.py
new file mode 100644
index 000000000..654f1d9b3
--- /dev/null
+++ b/numpy/array_api/tests/test_creation_functions.py
@@ -0,0 +1,103 @@
+from numpy.testing import assert_raises
+import numpy as np
+
+from .. import all
+from .._creation_functions import (asarray, arange, empty, empty_like, eye, from_dlpack, full, full_like, linspace, meshgrid, ones, ones_like, zeros, zeros_like)
+from .._array_object import Array
+from .._dtypes import (_all_dtypes, _boolean_dtypes, _floating_dtypes,
+                       _integer_dtypes, _integer_or_boolean_dtypes,
+                       _numeric_dtypes, int8, int16, int32, int64, uint64)
+
+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.], 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)
+    # Once copy=False is implemented, replace this with
+    # a = asarray([1])
+    # b = asarray(a, copy=False)
+    # a[0] = 0
+    # assert all(b[0] == 0)
+    assert_raises(NotImplementedError, lambda: asarray(a, copy=False))
+
+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'))
diff --git a/numpy/array_api/tests/test_elementwise_functions.py b/numpy/array_api/tests/test_elementwise_functions.py
new file mode 100644
index 000000000..994cb0bf0
--- /dev/null
+++ b/numpy/array_api/tests/test_elementwise_functions.py
@@ -0,0 +1,110 @@
+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 (_all_dtypes, _boolean_dtypes, _floating_dtypes,
+                       _integer_dtypes, _integer_or_boolean_dtypes,
+                       _numeric_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',
+        'acosh': 'floating',
+        'add': 'numeric',
+        'asin': 'floating',
+        'asinh': 'floating',
+        'atan': 'floating',
+        'atan2': 'floating',
+        'atanh': 'floating',
+        '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': 'numeric',
+        'cos': 'floating',
+        'cosh': 'floating',
+        'divide': 'floating',
+        'equal': 'all',
+        'exp': 'floating',
+        'expm1': 'floating',
+        'floor': 'numeric',
+        'floor_divide': 'numeric',
+        'greater': 'numeric',
+        'greater_equal': 'numeric',
+        'isfinite': 'numeric',
+        'isinf': 'numeric',
+        'isnan': 'numeric',
+        'less': 'numeric',
+        'less_equal': 'numeric',
+        'log': 'floating',
+        'logaddexp': 'floating',
+        'log10': 'floating',
+        'log1p': 'floating',
+        'log2': 'floating',
+        'logical_and': 'boolean',
+        'logical_not': 'boolean',
+        'logical_or': 'boolean',
+        'logical_xor': 'boolean',
+        'multiply': 'numeric',
+        'negative': 'numeric',
+        'not_equal': 'all',
+        'positive': 'numeric',
+        'pow': 'floating',
+        'remainder': 'numeric',
+        'round': 'numeric',
+        'sign': 'numeric',
+        'sin': 'floating',
+        'sinh': 'floating',
+        'sqrt': 'floating',
+        'square': 'numeric',
+        'subtract': 'numeric',
+        'tan': 'floating',
+        'tanh': 'floating',
+        'trunc': 'numeric',
+    }
+
+    _dtypes = {
+        'all': _all_dtypes,
+        'numeric': _numeric_dtypes,
+        'integer': _integer_dtypes,
+        'integer_or_boolean': _integer_or_boolean_dtypes,
+        'boolean': _boolean_dtypes,
+        'floating': _floating_dtypes,
+    }
+
+    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., dtype=d)
+
+    for x in _array_vals():
+        for func_name, types in elementwise_function_input_types.items():
+            dtypes = _dtypes[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])))