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wemm/lib/python3.10/site-packages/numpy/array_api/_array_object.py

@@ -0,0 +1,1118 @@
+"""
+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,
+    _result_type,
+    _dtype_categories,
+)
+
+from typing import TYPE_CHECKING, Optional, Tuple, Union, Any, SupportsIndex
+import types
+
+if TYPE_CHECKING:
+    from ._typing import Any, PyCapsule, Device, Dtype
+    import numpy.typing as npt
+
+import numpy as np
+
+from numpy import array_api
+
+
+class Array:
+    """
+    n-d array object for the array API namespace.
+
+    See the docstring of :py:obj:`np.ndarray <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().
+
+    """
+    _array: np.ndarray
+
+    # Use a custom constructor instead of __init__, as manually initializing
+    # this class is not supported API.
+    @classmethod
+    def _new(cls, x, /):
+        """
+        This is a private method for initializing the array API Array
+        object.
+
+        Functions outside of the array_api submodule should not use this
+        method. Use one of the creation functions instead, such as
+        ``asarray``.
+
+        """
+        obj = super().__new__(cls)
+        # Note: The spec does not have array scalars, only 0-D arrays.
+        if isinstance(x, np.generic):
+            # Convert the array scalar to a 0-D array
+            x = np.asarray(x)
+        if x.dtype not in _all_dtypes:
+            raise TypeError(
+                f"The array_api namespace does not support the dtype '{x.dtype}'"
+            )
+        obj._array = x
+        return obj
+
+    # Prevent Array() from working
+    def __new__(cls, *args, **kwargs):
+        raise TypeError(
+            "The array_api Array object should not be instantiated directly. Use an array creation function, such as asarray(), instead."
+        )
+
+    # These functions are not required by the spec, but are implemented for
+    # the sake of usability.
+
+    def __str__(self: Array, /) -> str:
+        """
+        Performs the operation __str__.
+        """
+        return self._array.__str__().replace("array", "Array")
+
+    def __repr__(self: Array, /) -> str:
+        """
+        Performs the operation __repr__.
+        """
+        suffix = f", dtype={self.dtype.name})"
+        if 0 in self.shape:
+            prefix = "empty("
+            mid = str(self.shape)
+        else:
+            prefix = "Array("
+            mid = np.array2string(self._array, separator=', ', prefix=prefix, suffix=suffix)
+        return prefix + mid + suffix
+
+    # This function is not required by the spec, but we implement it here for
+    # convenience so that np.asarray(np.array_api.Array) will work.
+    def __array__(self, dtype: None | np.dtype[Any] = None) -> npt.NDArray[Any]:
+        """
+        Warning: this method is NOT part of the array API spec. Implementers
+        of other libraries need not include it, and users should not assume it
+        will be present in other implementations.
+
+        """
+        return np.asarray(self._array, dtype=dtype)
+
+    # These are various helper functions to make the array behavior match the
+    # spec in places where it either deviates from or is more strict than
+    # NumPy behavior
+
+    def _check_allowed_dtypes(self, other: bool | int | float | Array, dtype_category: str, op: str) -> Array:
+        """
+        Helper function for operators to only allow specific input dtypes
+
+        Use like
+
+            other = self._check_allowed_dtypes(other, 'numeric', '__add__')
+            if other is NotImplemented:
+                return other
+        """
+
+        if self.dtype not in _dtype_categories[dtype_category]:
+            raise TypeError(f"Only {dtype_category} dtypes are allowed in {op}")
+        if isinstance(other, (int, float, bool)):
+            other = self._promote_scalar(other)
+        elif isinstance(other, Array):
+            if other.dtype not in _dtype_categories[dtype_category]:
+                raise TypeError(f"Only {dtype_category} dtypes are allowed in {op}")
+        else:
+            return NotImplemented
+
+        # This will raise TypeError for type combinations that are not allowed
+        # to promote in the spec (even if the NumPy array operator would
+        # promote them).
+        res_dtype = _result_type(self.dtype, other.dtype)
+        if op.startswith("__i"):
+            # Note: NumPy will allow in-place operators in some cases where
+            # the type promoted operator does not match the left-hand side
+            # operand. For example,
+
+            # >>> a = np.array(1, dtype=np.int8)
+            # >>> a += np.array(1, dtype=np.int16)
+
+            # The spec explicitly disallows this.
+            if res_dtype != self.dtype:
+                raise TypeError(
+                    f"Cannot perform {op} with dtypes {self.dtype} and {other.dtype}"
+                )
+
+        return other
+
+    # Helper function to match the type promotion rules in the spec
+    def _promote_scalar(self, scalar):
+        """
+        Returns a promoted version of a Python scalar appropriate for use with
+        operations on self.
+
+        This may raise an OverflowError in cases where the scalar is an
+        integer that is too large to fit in a NumPy integer dtype, or
+        TypeError when the scalar type is incompatible with the dtype of self.
+        """
+        # Note: Only Python scalar types that match the array dtype are
+        # allowed.
+        if isinstance(scalar, bool):
+            if self.dtype not in _boolean_dtypes:
+                raise TypeError(
+                    "Python bool scalars can only be promoted with bool arrays"
+                )
+        elif isinstance(scalar, int):
+            if self.dtype in _boolean_dtypes:
+                raise TypeError(
+                    "Python int scalars cannot be promoted with bool arrays"
+                )
+        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: scalars are unconditionally cast to the same dtype as the
+        # array.
+
+        # Note: the spec only specifies integer-dtype/int promotion
+        # behavior for integers within the bounds of the integer dtype.
+        # Outside of those bounds we use the default NumPy behavior (either
+        # cast or raise OverflowError).
+        return Array._new(np.array(scalar, self.dtype))
+
+    @staticmethod
+    def _normalize_two_args(x1, x2) -> Tuple[Array, Array]:
+        """
+        Normalize inputs to two arg functions to fix type promotion rules
+
+        NumPy deviates from the spec type promotion rules in cases where one
+        argument is 0-dimensional and the other is not. For example:
+
+        >>> import numpy as np
+        >>> a = np.array([1.0], dtype=np.float32)
+        >>> b = np.array(1.0, dtype=np.float64)
+        >>> np.add(a, b) # The spec says this should be float64
+        array([2.], dtype=float32)
+
+        To fix this, we add a dimension to the 0-dimension array before passing it
+        through. This works because a dimension would be added anyway from
+        broadcasting, so the resulting shape is the same, but this prevents NumPy
+        from not promoting the dtype.
+        """
+        # Another option would be to use signature=(x1.dtype, x2.dtype, None),
+        # but that only works for ufuncs, so we would have to call the ufuncs
+        # directly in the operator methods. One should also note that this
+        # sort of trick wouldn't work for functions like searchsorted, which
+        # don't do normal broadcasting, but there aren't any functions like
+        # that in the array API namespace.
+        if x1.ndim == 0 and x2.ndim != 0:
+            # The _array[None] workaround was chosen because it is relatively
+            # performant. broadcast_to(x1._array, x2.shape) is much slower. We
+            # could also manually type promote x2, but that is more complicated
+            # and about the same performance as this.
+            x1 = Array._new(x1._array[None])
+        elif x2.ndim == 0 and x1.ndim != 0:
+            x2 = Array._new(x2._array[None])
+        return (x1, x2)
+
+    # Note: A large fraction of allowed indices are disallowed here (see the
+    # docstring below)
+    def _validate_index(self, key):
+        """
+        Validate an index according to the array API.
+
+        The array API specification only requires a subset of indices that are
+        supported by NumPy. This function will reject any index that is
+        allowed by NumPy but not required by the array API specification. We
+        always raise ``IndexError`` on such indices (the spec does not require
+        any specific behavior on them, but this makes the NumPy array API
+        namespace a minimal implementation of the spec). See
+        https://data-apis.org/array-api/latest/API_specification/indexing.html
+        for the full list of required indexing behavior
+
+        This function raises IndexError if the index ``key`` is invalid. It
+        only raises ``IndexError`` on indices that are not already rejected by
+        NumPy, as NumPy will already raise the appropriate error on such
+        indices. ``shape`` may be None, in which case, only cases that are
+        independent of the array shape are checked.
+
+        The following cases are allowed by NumPy, but not specified by the array
+        API specification:
+
+        - Indices to not include an implicit ellipsis at the end. That is,
+          every axis of an array must be explicitly indexed or an ellipsis
+          included. This behaviour is sometimes referred to as flat indexing.
+
+        - The start and stop of a slice may not be out of bounds. In
+          particular, for a slice ``i:j:k`` on an axis of size ``n``, only the
+          following are allowed:
+
+          - ``i`` or ``j`` omitted (``None``).
+          - ``-n <= i <= max(0, n - 1)``.
+          - For ``k > 0`` or ``k`` omitted (``None``), ``-n <= j <= n``.
+          - For ``k < 0``, ``-n - 1 <= j <= max(0, n - 1)``.
+
+        - Boolean array indices are not allowed as part of a larger tuple
+          index.
+
+        - Integer array indices are not allowed (with the exception of 0-D
+          arrays, which are treated the same as scalars).
+
+        Additionally, it should be noted that indices that would return a
+        scalar in NumPy will return a 0-D array. Array scalars are not allowed
+        in the specification, only 0-D arrays. This is done in the
+        ``Array._new`` constructor, not this function.
+
+        """
+        _key = key if isinstance(key, tuple) else (key,)
+        for i in _key:
+            if isinstance(i, bool) or not (
+                isinstance(i, SupportsIndex)  # i.e. ints
+                or isinstance(i, slice)
+                or i == Ellipsis
+                or i is None
+                or isinstance(i, Array)
+                or isinstance(i, np.ndarray)
+            ):
+                raise IndexError(
+                    f"Single-axes index {i} has {type(i)=}, but only "
+                    "integers, slices (:), ellipsis (...), newaxis (None), "
+                    "zero-dimensional integer arrays and boolean arrays "
+                    "are specified in the Array API."
+                )
+
+        nonexpanding_key = []
+        single_axes = []
+        n_ellipsis = 0
+        key_has_mask = False
+        for i in _key:
+            if i is not None:
+                nonexpanding_key.append(i)
+                if isinstance(i, Array) or isinstance(i, np.ndarray):
+                    if i.dtype in _boolean_dtypes:
+                        key_has_mask = True
+                    single_axes.append(i)
+                else:
+                    # i must not be an array here, to avoid elementwise equals
+                    if i == Ellipsis:
+                        n_ellipsis += 1
+                    else:
+                        single_axes.append(i)
+
+        n_single_axes = len(single_axes)
+        if n_ellipsis > 1:
+            return  # handled by ndarray
+        elif n_ellipsis == 0:
+            # Note boolean masks must be the sole index, which we check for
+            # later on.
+            if not key_has_mask and n_single_axes < self.ndim:
+                raise IndexError(
+                    f"{self.ndim=}, but the multi-axes index only specifies "
+                    f"{n_single_axes} dimensions. If this was intentional, "
+                    "add a trailing ellipsis (...) which expands into as many "
+                    "slices (:) as necessary - this is what np.ndarray arrays "
+                    "implicitly do, but such flat indexing behaviour is not "
+                    "specified in the Array API."
+                )
+
+        if n_ellipsis == 0:
+            indexed_shape = self.shape
+        else:
+            ellipsis_start = None
+            for pos, i in enumerate(nonexpanding_key):
+                if not (isinstance(i, Array) or isinstance(i, np.ndarray)):
+                    if i == Ellipsis:
+                        ellipsis_start = pos
+                        break
+            assert ellipsis_start is not None  # sanity check
+            ellipsis_end = self.ndim - (n_single_axes - ellipsis_start)
+            indexed_shape = (
+                self.shape[:ellipsis_start] + self.shape[ellipsis_end:]
+            )
+        for i, side in zip(single_axes, indexed_shape):
+            if isinstance(i, slice):
+                if side == 0:
+                    f_range = "0 (or None)"
+                else:
+                    f_range = f"between -{side} and {side - 1} (or None)"
+                if i.start is not None:
+                    try:
+                        start = operator.index(i.start)
+                    except TypeError:
+                        pass  # handled by ndarray
+                    else:
+                        if not (-side <= start <= side):
+                            raise IndexError(
+                                f"Slice {i} contains {start=}, but should be "
+                                f"{f_range} for an axis of size {side} "
+                                "(out-of-bounds starts are not specified in "
+                                "the Array API)"
+                            )
+                if i.stop is not None:
+                    try:
+                        stop = operator.index(i.stop)
+                    except TypeError:
+                        pass  # handled by ndarray
+                    else:
+                        if not (-side <= stop <= side):
+                            raise IndexError(
+                                f"Slice {i} contains {stop=}, but should be "
+                                f"{f_range} for an axis of size {side} "
+                                "(out-of-bounds stops are not specified in "
+                                "the Array API)"
+                            )
+            elif isinstance(i, Array):
+                if i.dtype in _boolean_dtypes and len(_key) != 1:
+                    assert isinstance(key, tuple)  # sanity check
+                    raise IndexError(
+                        f"Single-axes index {i} is a boolean array and "
+                        f"{len(key)=}, but masking is only specified in the "
+                        "Array API when the array is the sole index."
+                    )
+                elif i.dtype in _integer_dtypes and i.ndim != 0:
+                    raise IndexError(
+                        f"Single-axes index {i} is a non-zero-dimensional "
+                        "integer array, but advanced integer indexing is not "
+                        "specified in the Array API."
+                    )
+            elif isinstance(i, tuple):
+                raise IndexError(
+                    f"Single-axes index {i} is a tuple, but nested tuple "
+                    "indices are not specified in the Array API."
+                )
+
+    # Everything below this line is required by the spec.
+
+    def __abs__(self: Array, /) -> Array:
+        """
+        Performs the operation __abs__.
+        """
+        if self.dtype not in _numeric_dtypes:
+            raise TypeError("Only numeric dtypes are allowed in __abs__")
+        res = self._array.__abs__()
+        return self.__class__._new(res)
+
+    def __add__(self: Array, other: Union[int, float, Array], /) -> Array:
+        """
+        Performs the operation __add__.
+        """
+        other = self._check_allowed_dtypes(other, "numeric", "__add__")
+        if other is NotImplemented:
+            return other
+        self, other = self._normalize_two_args(self, other)
+        res = self._array.__add__(other._array)
+        return self.__class__._new(res)
+
+    def __and__(self: Array, other: Union[int, bool, Array], /) -> Array:
+        """
+        Performs the operation __and__.
+        """
+        other = self._check_allowed_dtypes(other, "integer or boolean", "__and__")
+        if other is NotImplemented:
+            return other
+        self, other = self._normalize_two_args(self, other)
+        res = self._array.__and__(other._array)
+        return self.__class__._new(res)
+
+    def __array_namespace__(
+        self: Array, /, *, api_version: Optional[str] = None
+    ) -> types.ModuleType:
+        if api_version is not None and not api_version.startswith("2021."):
+            raise ValueError(f"Unrecognized array API version: {api_version!r}")
+        return array_api
+
+    def __bool__(self: Array, /) -> bool:
+        """
+        Performs the operation __bool__.
+        """
+        # Note: This is an error here.
+        if self._array.ndim != 0:
+            raise TypeError("bool is only allowed on arrays with 0 dimensions")
+        if self.dtype not in _boolean_dtypes:
+            raise ValueError("bool is only allowed on boolean arrays")
+        res = self._array.__bool__()
+        return res
+
+    def __dlpack__(self: Array, /, *, stream: None = None) -> PyCapsule:
+        """
+        Performs the operation __dlpack__.
+        """
+        return self._array.__dlpack__(stream=stream)
+
+    def __dlpack_device__(self: Array, /) -> Tuple[IntEnum, int]:
+        """
+        Performs the operation __dlpack_device__.
+        """
+        # Note: device support is required for this
+        return self._array.__dlpack_device__()
+
+    def __eq__(self: Array, other: Union[int, float, bool, Array], /) -> Array:
+        """
+        Performs the operation __eq__.
+        """
+        # Even though "all" dtypes are allowed, we still require them to be
+        # promotable with each other.
+        other = self._check_allowed_dtypes(other, "all", "__eq__")
+        if other is NotImplemented:
+            return other
+        self, other = self._normalize_two_args(self, other)
+        res = self._array.__eq__(other._array)
+        return self.__class__._new(res)
+
+    def __float__(self: Array, /) -> float:
+        """
+        Performs the operation __float__.
+        """
+        # Note: This is an error here.
+        if self._array.ndim != 0:
+            raise TypeError("float is only allowed on arrays with 0 dimensions")
+        if self.dtype not in _floating_dtypes:
+            raise ValueError("float is only allowed on floating-point arrays")
+        res = self._array.__float__()
+        return res
+
+    def __floordiv__(self: Array, other: Union[int, float, Array], /) -> Array:
+        """
+        Performs the operation __floordiv__.
+        """
+        other = self._check_allowed_dtypes(other, "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
+        self._validate_index(key)
+        if isinstance(key, Array):
+            # Indexing self._array with array_api arrays can be erroneous
+            key = key._array
+        res = self._array.__getitem__(key)
+        return self._new(res)
+
+    def __gt__(self: Array, other: Union[int, float, Array], /) -> Array:
+        """
+        Performs the operation __gt__.
+        """
+        other = self._check_allowed_dtypes(other, "numeric", "__gt__")
+        if other is NotImplemented:
+            return other
+        self, other = self._normalize_two_args(self, other)
+        res = self._array.__gt__(other._array)
+        return self.__class__._new(res)
+
+    def __int__(self: Array, /) -> int:
+        """
+        Performs the operation __int__.
+        """
+        # Note: This is an error here.
+        if self._array.ndim != 0:
+            raise TypeError("int is only allowed on arrays with 0 dimensions")
+        if self.dtype not in _integer_dtypes:
+            raise ValueError("int is only allowed on integer arrays")
+        res = self._array.__int__()
+        return res
+
+    def __index__(self: Array, /) -> int:
+        """
+        Performs the operation __index__.
+        """
+        res = self._array.__index__()
+        return res
+
+    def __invert__(self: Array, /) -> Array:
+        """
+        Performs the operation __invert__.
+        """
+        if self.dtype not in _integer_or_boolean_dtypes:
+            raise TypeError("Only integer or boolean dtypes are allowed in __invert__")
+        res = self._array.__invert__()
+        return self.__class__._new(res)
+
+    def __le__(self: Array, other: Union[int, float, Array], /) -> Array:
+        """
+        Performs the operation __le__.
+        """
+        other = self._check_allowed_dtypes(other, "numeric", "__le__")
+        if other is NotImplemented:
+            return other
+        self, other = self._normalize_two_args(self, other)
+        res = self._array.__le__(other._array)
+        return self.__class__._new(res)
+
+    def __lshift__(self: Array, other: Union[int, Array], /) -> Array:
+        """
+        Performs the operation __lshift__.
+        """
+        other = self._check_allowed_dtypes(other, "integer", "__lshift__")
+        if other is NotImplemented:
+            return other
+        self, other = self._normalize_two_args(self, other)
+        res = self._array.__lshift__(other._array)
+        return self.__class__._new(res)
+
+    def __lt__(self: Array, other: Union[int, float, Array], /) -> Array:
+        """
+        Performs the operation __lt__.
+        """
+        other = self._check_allowed_dtypes(other, "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)
+
+    def __pow__(self: Array, other: Union[int, float, Array], /) -> Array:
+        """
+        Performs the operation __pow__.
+        """
+        from ._elementwise_functions import pow
+
+        other = self._check_allowed_dtypes(other, "numeric", "__pow__")
+        if other is NotImplemented:
+            return other
+        # Note: NumPy's __pow__ does not follow type promotion rules for 0-d
+        # arrays, so we use pow() here instead.
+        return pow(self, other)
+
+    def __rshift__(self: Array, other: Union[int, Array], /) -> Array:
+        """
+        Performs the operation __rshift__.
+        """
+        other = self._check_allowed_dtypes(other, "integer", "__rshift__")
+        if other is NotImplemented:
+            return other
+        self, other = self._normalize_two_args(self, other)
+        res = self._array.__rshift__(other._array)
+        return self.__class__._new(res)
+
+    def __setitem__(
+        self,
+        key: Union[
+            int, slice, ellipsis, Tuple[Union[int, slice, ellipsis], ...], Array
+        ],
+        value: Union[int, float, bool, Array],
+        /,
+    ) -> None:
+        """
+        Performs the operation __setitem__.
+        """
+        # Note: Only indices required by the spec are allowed. See the
+        # docstring of _validate_index
+        self._validate_index(key)
+        if isinstance(key, Array):
+            # Indexing self._array with array_api arrays can be erroneous
+            key = key._array
+        self._array.__setitem__(key, asarray(value)._array)
+
+    def __sub__(self: Array, other: Union[int, float, Array], /) -> Array:
+        """
+        Performs the operation __sub__.
+        """
+        other = self._check_allowed_dtypes(other, "numeric", "__sub__")
+        if other is NotImplemented:
+            return other
+        self, other = self._normalize_two_args(self, other)
+        res = self._array.__sub__(other._array)
+        return self.__class__._new(res)
+
+    # PEP 484 requires int to be a subtype of float, but __truediv__ should
+    # not accept int.
+    def __truediv__(self: Array, other: Union[float, Array], /) -> Array:
+        """
+        Performs the operation __truediv__.
+        """
+        other = self._check_allowed_dtypes(other, "floating-point", "__truediv__")
+        if other is NotImplemented:
+            return other
+        self, other = self._normalize_two_args(self, other)
+        res = self._array.__truediv__(other._array)
+        return self.__class__._new(res)
+
+    def __xor__(self: Array, other: Union[int, bool, Array], /) -> Array:
+        """
+        Performs the operation __xor__.
+        """
+        other = self._check_allowed_dtypes(other, "integer or boolean", "__xor__")
+        if other is NotImplemented:
+            return other
+        self, other = self._normalize_two_args(self, other)
+        res = self._array.__xor__(other._array)
+        return self.__class__._new(res)
+
+    def __iadd__(self: Array, other: Union[int, float, Array], /) -> Array:
+        """
+        Performs the operation __iadd__.
+        """
+        other = self._check_allowed_dtypes(other, "numeric", "__iadd__")
+        if other is NotImplemented:
+            return other
+        self._array.__iadd__(other._array)
+        return self
+
+    def __radd__(self: Array, other: Union[int, float, Array], /) -> Array:
+        """
+        Performs the operation __radd__.
+        """
+        other = self._check_allowed_dtypes(other, "numeric", "__radd__")
+        if other is NotImplemented:
+            return other
+        self, other = self._normalize_two_args(self, other)
+        res = self._array.__radd__(other._array)
+        return self.__class__._new(res)
+
+    def __iand__(self: Array, other: Union[int, bool, Array], /) -> Array:
+        """
+        Performs the operation __iand__.
+        """
+        other = self._check_allowed_dtypes(other, "integer or boolean", "__iand__")
+        if other is NotImplemented:
+            return other
+        self._array.__iand__(other._array)
+        return self
+
+    def __rand__(self: Array, other: Union[int, bool, Array], /) -> Array:
+        """
+        Performs the operation __rand__.
+        """
+        other = self._check_allowed_dtypes(other, "integer or boolean", "__rand__")
+        if other is NotImplemented:
+            return other
+        self, other = self._normalize_two_args(self, other)
+        res = self._array.__rand__(other._array)
+        return self.__class__._new(res)
+
+    def __ifloordiv__(self: Array, other: Union[int, float, Array], /) -> Array:
+        """
+        Performs the operation __ifloordiv__.
+        """
+        other = self._check_allowed_dtypes(other, "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[int, float, Array], /) -> Array:
+        """
+        Performs the operation __ipow__.
+        """
+        other = self._check_allowed_dtypes(other, "numeric", "__ipow__")
+        if other is NotImplemented:
+            return other
+        self._array.__ipow__(other._array)
+        return self
+
+    def __rpow__(self: Array, other: Union[int, float, Array], /) -> Array:
+        """
+        Performs the operation __rpow__.
+        """
+        from ._elementwise_functions import pow
+
+        other = self._check_allowed_dtypes(other, "numeric", "__rpow__")
+        if other is NotImplemented:
+            return other
+        # Note: NumPy's __pow__ does not follow the spec type promotion rules
+        # for 0-d arrays, so we use pow() here instead.
+        return pow(other, self)
+
+    def __irshift__(self: Array, other: Union[int, Array], /) -> Array:
+        """
+        Performs the operation __irshift__.
+        """
+        other = self._check_allowed_dtypes(other, "integer", "__irshift__")
+        if other is NotImplemented:
+            return other
+        self._array.__irshift__(other._array)
+        return self
+
+    def __rrshift__(self: Array, other: Union[int, Array], /) -> Array:
+        """
+        Performs the operation __rrshift__.
+        """
+        other = self._check_allowed_dtypes(other, "integer", "__rrshift__")
+        if other is NotImplemented:
+            return other
+        self, other = self._normalize_two_args(self, other)
+        res = self._array.__rrshift__(other._array)
+        return self.__class__._new(res)
+
+    def __isub__(self: Array, other: Union[int, float, Array], /) -> Array:
+        """
+        Performs the operation __isub__.
+        """
+        other = self._check_allowed_dtypes(other, "numeric", "__isub__")
+        if other is NotImplemented:
+            return other
+        self._array.__isub__(other._array)
+        return self
+
+    def __rsub__(self: Array, other: Union[int, float, Array], /) -> Array:
+        """
+        Performs the operation __rsub__.
+        """
+        other = self._check_allowed_dtypes(other, "numeric", "__rsub__")
+        if other is NotImplemented:
+            return other
+        self, other = self._normalize_two_args(self, other)
+        res = self._array.__rsub__(other._array)
+        return self.__class__._new(res)
+
+    def __itruediv__(self: Array, other: Union[float, Array], /) -> Array:
+        """
+        Performs the operation __itruediv__.
+        """
+        other = self._check_allowed_dtypes(other, "floating-point", "__itruediv__")
+        if other is NotImplemented:
+            return other
+        self._array.__itruediv__(other._array)
+        return self
+
+    def __rtruediv__(self: Array, other: Union[float, Array], /) -> Array:
+        """
+        Performs the operation __rtruediv__.
+        """
+        other = self._check_allowed_dtypes(other, "floating-point", "__rtruediv__")
+        if other is NotImplemented:
+            return other
+        self, other = self._normalize_two_args(self, other)
+        res = self._array.__rtruediv__(other._array)
+        return self.__class__._new(res)
+
+    def __ixor__(self: Array, other: Union[int, bool, Array], /) -> Array:
+        """
+        Performs the operation __ixor__.
+        """
+        other = self._check_allowed_dtypes(other, "integer or boolean", "__ixor__")
+        if other is NotImplemented:
+            return other
+        self._array.__ixor__(other._array)
+        return self
+
+    def __rxor__(self: Array, other: Union[int, bool, Array], /) -> Array:
+        """
+        Performs the operation __rxor__.
+        """
+        other = self._check_allowed_dtypes(other, "integer or boolean", "__rxor__")
+        if other is NotImplemented:
+            return other
+        self, other = self._normalize_two_args(self, other)
+        res = self._array.__rxor__(other._array)
+        return self.__class__._new(res)
+
+    def to_device(self: Array, device: Device, /, stream: None = None) -> Array:
+        if stream is not None:
+            raise ValueError("The stream argument to to_device() is not supported")
+        if device == 'cpu':
+            return self
+        raise ValueError(f"Unsupported device {device!r}")
+
+    @property
+    def dtype(self) -> Dtype:
+        """
+        Array API compatible wrapper for :py:meth:`np.ndarray.dtype <numpy.ndarray.dtype>`.
+
+        See its docstring for more information.
+        """
+        return self._array.dtype
+
+    @property
+    def device(self) -> Device:
+        return "cpu"
+
+    # Note: mT is new in array API spec (see matrix_transpose)
+    @property
+    def mT(self) -> Array:
+        from .linalg import matrix_transpose
+        return matrix_transpose(self)
+
+    @property
+    def ndim(self) -> int:
+        """
+        Array API compatible wrapper for :py:meth:`np.ndarray.ndim <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.
+        """
+        # Note: T only works on 2-dimensional arrays. See the corresponding
+        # note in the specification:
+        # https://data-apis.org/array-api/latest/API_specification/array_object.html#t
+        if self.ndim != 2:
+            raise ValueError("x.T requires x to have 2 dimensions. Use x.mT to transpose stacks of matrices and permute_dims() to permute dimensions.")
+        return self.__class__._new(self._array.T)

wemm/lib/python3.10/site-packages/numpy/array_api/_constants.py

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

wemm/lib/python3.10/site-packages/numpy/array_api/_manipulation_functions.py

@@ -0,0 +1,97 @@
+from __future__ import annotations
+
+from ._array_object import Array
+from ._data_type_functions import result_type
+
+from typing import List, Optional, Tuple, Union
+
+import numpy as np
+
+# Note: the function name is different here
+def concat(
+    arrays: Union[Tuple[Array, ...], List[Array]], /, *, axis: Optional[int] = 0
+) -> Array:
+    """
+    Array API compatible wrapper for :py:func:`np.concatenate <numpy.concatenate>`.
+
+    See its docstring for more information.
+    """
+    # Note: Casting rules here are different from the np.concatenate default
+    # (no for scalars with axis=None, no cross-kind casting)
+    dtype = result_type(*arrays)
+    arrays = tuple(a._array for a in arrays)
+    return Array._new(np.concatenate(arrays, axis=axis, dtype=dtype))
+
+
+def expand_dims(x: Array, /, *, axis: int) -> Array:
+    """
+    Array API compatible wrapper for :py:func:`np.expand_dims <numpy.expand_dims>`.
+
+    See its docstring for more information.
+    """
+    return Array._new(np.expand_dims(x._array, axis))
+
+
+def flip(x: Array, /, *, axis: Optional[Union[int, Tuple[int, ...]]] = None) -> Array:
+    """
+    Array API compatible wrapper for :py:func:`np.flip <numpy.flip>`.
+
+    See its docstring for more information.
+    """
+    return Array._new(np.flip(x._array, axis=axis))
+
+
+# Note: The function name is different here (see also matrix_transpose).
+# Unlike transpose(), the axes argument is required.
+def permute_dims(x: Array, /, axes: Tuple[int, ...]) -> Array:
+    """
+    Array API compatible wrapper for :py:func:`np.transpose <numpy.transpose>`.
+
+    See its docstring for more information.
+    """
+    return Array._new(np.transpose(x._array, axes))
+
+
+def reshape(x: Array, /, shape: Tuple[int, ...]) -> Array:
+    """
+    Array API compatible wrapper for :py:func:`np.reshape <numpy.reshape>`.
+
+    See its docstring for more information.
+    """
+    return Array._new(np.reshape(x._array, shape))
+
+
+def roll(
+    x: Array,
+    /,
+    shift: Union[int, Tuple[int, ...]],
+    *,
+    axis: Optional[Union[int, Tuple[int, ...]]] = None,
+) -> Array:
+    """
+    Array API compatible wrapper for :py:func:`np.roll <numpy.roll>`.
+
+    See its docstring for more information.
+    """
+    return Array._new(np.roll(x._array, shift, axis=axis))
+
+
+def squeeze(x: Array, /, axis: Union[int, Tuple[int, ...]]) -> Array:
+    """
+    Array API compatible wrapper for :py:func:`np.squeeze <numpy.squeeze>`.
+
+    See its docstring for more information.
+    """
+    return Array._new(np.squeeze(x._array, axis=axis))
+
+
+def stack(arrays: Union[Tuple[Array, ...], List[Array]], /, *, axis: int = 0) -> Array:
+    """
+    Array API compatible wrapper for :py:func:`np.stack <numpy.stack>`.
+
+    See its docstring for more information.
+    """
+    # Call result type here just to raise on disallowed type combinations
+    result_type(*arrays)
+    arrays = tuple(a._array for a in arrays)
+    return Array._new(np.stack(arrays, axis=axis))

wemm/lib/python3.10/site-packages/numpy/array_api/_set_functions.py

@@ -0,0 +1,102 @@
+from __future__ import annotations
+
+from ._array_object import Array
+
+from typing import NamedTuple
+
+import numpy as np
+
+# Note: np.unique() is split into four functions in the array API:
+# unique_all, unique_counts, unique_inverse, and unique_values (this is done
+# to remove polymorphic return types).
+
+# Note: The various unique() functions are supposed to return multiple NaNs.
+# This does not match the NumPy behavior, however, this is currently left as a
+# TODO in this implementation as this behavior may be reverted in np.unique().
+# See https://github.com/numpy/numpy/issues/20326.
+
+# Note: The functions here return a namedtuple (np.unique() returns a normal
+# tuple).
+
+class UniqueAllResult(NamedTuple):
+    values: Array
+    indices: Array
+    inverse_indices: Array
+    counts: Array
+
+
+class UniqueCountsResult(NamedTuple):
+    values: Array
+    counts: Array
+
+
+class UniqueInverseResult(NamedTuple):
+    values: Array
+    inverse_indices: Array
+
+
+def unique_all(x: Array, /) -> UniqueAllResult:
+    """
+    Array API compatible wrapper for :py:func:`np.unique <numpy.unique>`.
+
+    See its docstring for more information.
+    """
+    values, indices, inverse_indices, counts = np.unique(
+        x._array,
+        return_counts=True,
+        return_index=True,
+        return_inverse=True,
+    )
+    # np.unique() flattens inverse indices, but they need to share x's shape
+    # See https://github.com/numpy/numpy/issues/20638
+    inverse_indices = inverse_indices.reshape(x.shape)
+    return UniqueAllResult(
+        Array._new(values),
+        Array._new(indices),
+        Array._new(inverse_indices),
+        Array._new(counts),
+    )
+
+
+def unique_counts(x: Array, /) -> UniqueCountsResult:
+    res = np.unique(
+        x._array,
+        return_counts=True,
+        return_index=False,
+        return_inverse=False,
+    )
+
+    return UniqueCountsResult(*[Array._new(i) for i in res])
+
+
+def unique_inverse(x: Array, /) -> UniqueInverseResult:
+    """
+    Array API compatible wrapper for :py:func:`np.unique <numpy.unique>`.
+
+    See its docstring for more information.
+    """
+    values, inverse_indices = np.unique(
+        x._array,
+        return_counts=False,
+        return_index=False,
+        return_inverse=True,
+    )
+    # np.unique() flattens inverse indices, but they need to share x's shape
+    # See https://github.com/numpy/numpy/issues/20638
+    inverse_indices = inverse_indices.reshape(x.shape)
+    return UniqueInverseResult(Array._new(values), Array._new(inverse_indices))
+
+
+def unique_values(x: Array, /) -> Array:
+    """
+    Array API compatible wrapper for :py:func:`np.unique <numpy.unique>`.
+
+    See its docstring for more information.
+    """
+    res = np.unique(
+        x._array,
+        return_counts=False,
+        return_index=False,
+        return_inverse=False,
+    )
+    return Array._new(res)

wemm/lib/python3.10/site-packages/numpy/array_api/_sorting_functions.py

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

wemm/lib/python3.10/site-packages/numpy/array_api/_statistical_functions.py

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

wemm/lib/python3.10/site-packages/numpy/array_api/_utility_functions.py

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

wemm/lib/python3.10/site-packages/numpy/array_api/tests/__init__.py

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

wemm/lib/python3.10/site-packages/numpy/array_api/tests/test_creation_functions.py

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

wemm/lib/python3.10/site-packages/numpy/array_api/tests/test_data_type_functions.py

@@ -0,0 +1,19 @@
+import pytest
+
+from numpy import array_api as xp
+
+
+@pytest.mark.parametrize(
+    "from_, to, expected",
+    [
+        (xp.int8, xp.int16, True),
+        (xp.int16, xp.int8, False),
+        (xp.bool, xp.int8, False),
+        (xp.asarray(0, dtype=xp.uint8), xp.int8, False),
+    ],
+)
+def test_can_cast(from_, to, expected):
+    """
+    can_cast() returns correct result
+    """
+    assert xp.can_cast(from_, to) == expected

wemm/lib/python3.10/site-packages/numpy/array_api/tests/test_set_functions.py

@@ -0,0 +1,19 @@
+import pytest
+from hypothesis import given
+from hypothesis.extra.array_api import make_strategies_namespace
+
+from numpy import array_api as xp
+
+xps = make_strategies_namespace(xp)
+
+
+@pytest.mark.parametrize("func", [xp.unique_all, xp.unique_inverse])
+@given(xps.arrays(dtype=xps.scalar_dtypes(), shape=xps.array_shapes()))
+def test_inverse_indices_shape(func, x):
+    """
+    Inverse indices share shape of input array
+
+    See https://github.com/numpy/numpy/issues/20638
+    """
+    out = func(x)
+    assert out.inverse_indices.shape == x.shape

wemm/lib/python3.10/site-packages/numpy/array_api/tests/test_sorting_functions.py

@@ -0,0 +1,23 @@
+import pytest
+
+from numpy import array_api as xp
+
+
+@pytest.mark.parametrize(
+    "obj, axis, expected",
+    [
+        ([0, 0], -1, [0, 1]),
+        ([0, 1, 0], -1, [1, 0, 2]),
+        ([[0, 1], [1, 1]], 0, [[1, 0], [0, 1]]),
+        ([[0, 1], [1, 1]], 1, [[1, 0], [0, 1]]),
+    ],
+)
+def test_stable_desc_argsort(obj, axis, expected):
+    """
+    Indices respect relative order of a descending stable-sort
+
+    See https://github.com/numpy/numpy/issues/20778
+    """
+    x = xp.asarray(obj)
+    out = xp.argsort(x, axis=axis, stable=True, descending=True)
+    assert xp.all(out == xp.asarray(expected))

wemm/lib/python3.10/site-packages/numpy/array_api/tests/test_validation.py

@@ -0,0 +1,27 @@
+from typing import Callable
+
+import pytest
+
+from numpy import array_api as xp
+
+
+def p(func: Callable, *args, **kwargs):
+    f_sig = ", ".join(
+        [str(a) for a in args] + [f"{k}={v}" for k, v in kwargs.items()]
+    )
+    id_ = f"{func.__name__}({f_sig})"
+    return pytest.param(func, args, kwargs, id=id_)
+
+
+@pytest.mark.parametrize(
+    "func, args, kwargs",
+    [
+        p(xp.can_cast, 42, xp.int8),
+        p(xp.can_cast, xp.int8, 42),
+        p(xp.result_type, 42),
+    ],
+)
+def test_raises_on_invalid_types(func, args, kwargs):
+    """Function raises TypeError when passed invalidly-typed inputs"""
+    with pytest.raises(TypeError):
+        func(*args, **kwargs)

wemm/lib/python3.10/site-packages/numpy/random/__init__.pxd

@@ -0,0 +1,14 @@
+cimport numpy as np
+from libc.stdint cimport uint32_t, uint64_t
+
+cdef extern from "numpy/random/bitgen.h":
+    struct bitgen:
+        void *state
+        uint64_t (*next_uint64)(void *st) nogil
+        uint32_t (*next_uint32)(void *st) nogil
+        double (*next_double)(void *st) nogil
+        uint64_t (*next_raw)(void *st) nogil
+
+    ctypedef bitgen bitgen_t
+
+from numpy.random.bit_generator cimport BitGenerator, SeedSequence

wemm/lib/python3.10/site-packages/numpy/random/__init__.py

@@ -0,0 +1,215 @@
+"""
+========================
+Random Number Generation
+========================
+
+Use ``default_rng()`` to create a `Generator` and call its methods.
+
+=============== =========================================================
+Generator
+--------------- ---------------------------------------------------------
+Generator       Class implementing all of the random number distributions
+default_rng     Default constructor for ``Generator``
+=============== =========================================================
+
+============================================= ===
+BitGenerator Streams that work with Generator
+--------------------------------------------- ---
+MT19937
+PCG64
+PCG64DXSM
+Philox
+SFC64
+============================================= ===
+
+============================================= ===
+Getting entropy to initialize a BitGenerator
+--------------------------------------------- ---
+SeedSequence
+============================================= ===
+
+
+Legacy
+------
+
+For backwards compatibility with previous versions of numpy before 1.17, the
+various aliases to the global `RandomState` methods are left alone and do not
+use the new `Generator` API.
+
+==================== =========================================================
+Utility functions
+-------------------- ---------------------------------------------------------
+random               Uniformly distributed floats over ``[0, 1)``
+bytes                Uniformly distributed random bytes.
+permutation          Randomly permute a sequence / generate a random sequence.
+shuffle              Randomly permute a sequence in place.
+choice               Random sample from 1-D array.
+==================== =========================================================
+
+==================== =========================================================
+Compatibility
+functions - removed
+in the new API
+-------------------- ---------------------------------------------------------
+rand                 Uniformly distributed values.
+randn                Normally distributed values.
+ranf                 Uniformly distributed floating point numbers.
+random_integers      Uniformly distributed integers in a given range.
+                     (deprecated, use ``integers(..., closed=True)`` instead)
+random_sample        Alias for `random_sample`
+randint              Uniformly distributed integers in a given range
+seed                 Seed the legacy random number generator.
+==================== =========================================================
+
+==================== =========================================================
+Univariate
+distributions
+-------------------- ---------------------------------------------------------
+beta                 Beta distribution over ``[0, 1]``.
+binomial             Binomial distribution.
+chisquare            :math:`\\chi^2` distribution.
+exponential          Exponential distribution.
+f                    F (Fisher-Snedecor) distribution.
+gamma                Gamma distribution.
+geometric            Geometric distribution.
+gumbel               Gumbel distribution.
+hypergeometric       Hypergeometric distribution.
+laplace              Laplace distribution.
+logistic             Logistic distribution.
+lognormal            Log-normal distribution.
+logseries            Logarithmic series distribution.
+negative_binomial    Negative binomial distribution.
+noncentral_chisquare Non-central chi-square distribution.
+noncentral_f         Non-central F distribution.
+normal               Normal / Gaussian distribution.
+pareto               Pareto distribution.
+poisson              Poisson distribution.
+power                Power distribution.
+rayleigh             Rayleigh distribution.
+triangular           Triangular distribution.
+uniform              Uniform distribution.
+vonmises             Von Mises circular distribution.
+wald                 Wald (inverse Gaussian) distribution.
+weibull              Weibull distribution.
+zipf                 Zipf's distribution over ranked data.
+==================== =========================================================
+
+==================== ==========================================================
+Multivariate
+distributions
+-------------------- ----------------------------------------------------------
+dirichlet            Multivariate generalization of Beta distribution.
+multinomial          Multivariate generalization of the binomial distribution.
+multivariate_normal  Multivariate generalization of the normal distribution.
+==================== ==========================================================
+
+==================== =========================================================
+Standard
+distributions
+-------------------- ---------------------------------------------------------
+standard_cauchy      Standard Cauchy-Lorentz distribution.
+standard_exponential Standard exponential distribution.
+standard_gamma       Standard Gamma distribution.
+standard_normal      Standard normal distribution.
+standard_t           Standard Student's t-distribution.
+==================== =========================================================
+
+==================== =========================================================
+Internal functions
+-------------------- ---------------------------------------------------------
+get_state            Get tuple representing internal state of generator.
+set_state            Set state of generator.
+==================== =========================================================
+
+
+"""
+__all__ = [
+    'beta',
+    'binomial',
+    'bytes',
+    'chisquare',
+    'choice',
+    'dirichlet',
+    'exponential',
+    'f',
+    'gamma',
+    'geometric',
+    'get_state',
+    'gumbel',
+    'hypergeometric',
+    'laplace',
+    'logistic',
+    'lognormal',
+    'logseries',
+    'multinomial',
+    'multivariate_normal',
+    'negative_binomial',
+    'noncentral_chisquare',
+    'noncentral_f',
+    'normal',
+    'pareto',
+    'permutation',
+    'poisson',
+    'power',
+    'rand',
+    'randint',
+    'randn',
+    'random',
+    'random_integers',
+    'random_sample',
+    'ranf',
+    'rayleigh',
+    'sample',
+    'seed',
+    'set_state',
+    'shuffle',
+    'standard_cauchy',
+    'standard_exponential',
+    'standard_gamma',
+    'standard_normal',
+    'standard_t',
+    'triangular',
+    'uniform',
+    'vonmises',
+    'wald',
+    'weibull',
+    'zipf',
+]
+
+# add these for module-freeze analysis (like PyInstaller)
+from . import _pickle
+from . import _common
+from . import _bounded_integers
+
+from ._generator import Generator, default_rng
+from .bit_generator import SeedSequence, BitGenerator
+from ._mt19937 import MT19937
+from ._pcg64 import PCG64, PCG64DXSM
+from ._philox import Philox
+from ._sfc64 import SFC64
+from .mtrand import *
+
+__all__ += ['Generator', 'RandomState', 'SeedSequence', 'MT19937',
+            'Philox', 'PCG64', 'PCG64DXSM', 'SFC64', 'default_rng',
+            'BitGenerator']
+
+
+def __RandomState_ctor():
+    """Return a RandomState instance.
+
+    This function exists solely to assist (un)pickling.
+
+    Note that the state of the RandomState returned here is irrelevant, as this
+    function's entire purpose is to return a newly allocated RandomState whose
+    state pickle can set.  Consequently the RandomState returned by this function
+    is a freshly allocated copy with a seed=0.
+
+    See https://github.com/numpy/numpy/issues/4763 for a detailed discussion
+
+    """
+    return RandomState(seed=0)
+
+
+from numpy._pytesttester import PytestTester
+test = PytestTester(__name__)
+del PytestTester

wemm/lib/python3.10/site-packages/numpy/random/__init__.pyi

@@ -0,0 +1,70 @@
+from numpy._pytesttester import PytestTester
+
+from numpy.random._generator import Generator as Generator
+from numpy.random._generator import default_rng as default_rng
+from numpy.random._mt19937 import MT19937 as MT19937
+from numpy.random._pcg64 import (
+    PCG64 as PCG64,
+    PCG64DXSM as PCG64DXSM,
+)
+from numpy.random._philox import Philox as Philox
+from numpy.random._sfc64 import SFC64 as SFC64
+from numpy.random.bit_generator import BitGenerator as BitGenerator
+from numpy.random.bit_generator import SeedSequence as SeedSequence
+from numpy.random.mtrand import (
+    RandomState as RandomState,
+    beta as beta,
+    binomial as binomial,
+    bytes as bytes,
+    chisquare as chisquare,
+    choice as choice,
+    dirichlet as dirichlet,
+    exponential as exponential,
+    f as f,
+    gamma as gamma,
+    geometric as geometric,
+    get_state as get_state,
+    gumbel as gumbel,
+    hypergeometric as hypergeometric,
+    laplace as laplace,
+    logistic as logistic,
+    lognormal as lognormal,
+    logseries as logseries,
+    multinomial as multinomial,
+    multivariate_normal as multivariate_normal,
+    negative_binomial as negative_binomial,
+    noncentral_chisquare as noncentral_chisquare,
+    noncentral_f as noncentral_f,
+    normal as normal,
+    pareto as pareto,
+    permutation as permutation,
+    poisson as poisson,
+    power as power,
+    rand as rand,
+    randint as randint,
+    randn as randn,
+    random as random,
+    random_integers as random_integers,
+    random_sample as random_sample,
+    ranf as ranf,
+    rayleigh as rayleigh,
+    sample as sample,
+    seed as seed,
+    set_state as set_state,
+    shuffle as shuffle,
+    standard_cauchy as standard_cauchy,
+    standard_exponential as standard_exponential,
+    standard_gamma as standard_gamma,
+    standard_normal as standard_normal,
+    standard_t as standard_t,
+    triangular as triangular,
+    uniform as uniform,
+    vonmises as vonmises,
+    wald as wald,
+    weibull as weibull,
+    zipf as zipf,
+)
+
+__all__: list[str]
+__path__: list[str]
+test: PytestTester

wemm/lib/python3.10/site-packages/numpy/random/_bounded_integers.pxd

@@ -0,0 +1,29 @@
+from libc.stdint cimport (uint8_t, uint16_t, uint32_t, uint64_t,
+                          int8_t, int16_t, int32_t, int64_t, intptr_t)
+import numpy as np
+cimport numpy as np
+ctypedef np.npy_bool bool_t
+
+from numpy.random cimport bitgen_t
+
+cdef inline uint64_t _gen_mask(uint64_t max_val) nogil:
+    """Mask generator for use in bounded random numbers"""
+    # Smallest bit mask >= max
+    cdef uint64_t mask = max_val
+    mask |= mask >> 1
+    mask |= mask >> 2
+    mask |= mask >> 4
+    mask |= mask >> 8
+    mask |= mask >> 16
+    mask |= mask >> 32
+    return mask
+
+cdef object _rand_uint64(object low, object high, object size, bint use_masked, bint closed, bitgen_t *state, object lock)
+cdef object _rand_uint32(object low, object high, object size, bint use_masked, bint closed, bitgen_t *state, object lock)
+cdef object _rand_uint16(object low, object high, object size, bint use_masked, bint closed, bitgen_t *state, object lock)
+cdef object _rand_uint8(object low, object high, object size, bint use_masked, bint closed, bitgen_t *state, object lock)
+cdef object _rand_bool(object low, object high, object size, bint use_masked, bint closed, bitgen_t *state, object lock)
+cdef object _rand_int64(object low, object high, object size, bint use_masked, bint closed, bitgen_t *state, object lock)
+cdef object _rand_int32(object low, object high, object size, bint use_masked, bint closed, bitgen_t *state, object lock)
+cdef object _rand_int16(object low, object high, object size, bint use_masked, bint closed, bitgen_t *state, object lock)
+cdef object _rand_int8(object low, object high, object size, bint use_masked, bint closed, bitgen_t *state, object lock)

wemm/lib/python3.10/site-packages/numpy/random/_common.pxd

@@ -0,0 +1,106 @@
+#cython: language_level=3
+
+from libc.stdint cimport uint32_t, uint64_t, int32_t, int64_t
+
+import numpy as np
+cimport numpy as np
+
+from numpy.random cimport bitgen_t
+
+cdef double POISSON_LAM_MAX
+cdef double LEGACY_POISSON_LAM_MAX
+cdef uint64_t MAXSIZE
+
+cdef enum ConstraintType:
+    CONS_NONE
+    CONS_NON_NEGATIVE
+    CONS_POSITIVE
+    CONS_POSITIVE_NOT_NAN
+    CONS_BOUNDED_0_1
+    CONS_BOUNDED_GT_0_1
+    CONS_BOUNDED_LT_0_1
+    CONS_GT_1
+    CONS_GTE_1
+    CONS_POISSON
+    LEGACY_CONS_POISSON
+
+ctypedef ConstraintType constraint_type
+
+cdef object benchmark(bitgen_t *bitgen, object lock, Py_ssize_t cnt, object method)
+cdef object random_raw(bitgen_t *bitgen, object lock, object size, object output)
+cdef object prepare_cffi(bitgen_t *bitgen)
+cdef object prepare_ctypes(bitgen_t *bitgen)
+cdef int check_constraint(double val, object name, constraint_type cons) except -1
+cdef int check_array_constraint(np.ndarray val, object name, constraint_type cons) except -1
+
+cdef extern from "include/aligned_malloc.h":
+    cdef void *PyArray_realloc_aligned(void *p, size_t n)
+    cdef void *PyArray_malloc_aligned(size_t n)
+    cdef void *PyArray_calloc_aligned(size_t n, size_t s)
+    cdef void PyArray_free_aligned(void *p)
+
+ctypedef void (*random_double_fill)(bitgen_t *state, np.npy_intp count, double* out) nogil
+ctypedef double (*random_double_0)(void *state) nogil
+ctypedef double (*random_double_1)(void *state, double a) nogil
+ctypedef double (*random_double_2)(void *state, double a, double b) nogil
+ctypedef double (*random_double_3)(void *state, double a, double b, double c) nogil
+
+ctypedef void (*random_float_fill)(bitgen_t *state, np.npy_intp count, float* out) nogil
+ctypedef float (*random_float_0)(bitgen_t *state) nogil
+ctypedef float (*random_float_1)(bitgen_t *state, float a) nogil
+
+ctypedef int64_t (*random_uint_0)(void *state) nogil
+ctypedef int64_t (*random_uint_d)(void *state, double a) nogil
+ctypedef int64_t (*random_uint_dd)(void *state, double a, double b) nogil
+ctypedef int64_t (*random_uint_di)(void *state, double a, uint64_t b) nogil
+ctypedef int64_t (*random_uint_i)(void *state, int64_t a) nogil
+ctypedef int64_t (*random_uint_iii)(void *state, int64_t a, int64_t b, int64_t c) nogil
+
+ctypedef uint32_t (*random_uint_0_32)(bitgen_t *state) nogil
+ctypedef uint32_t (*random_uint_1_i_32)(bitgen_t *state, uint32_t a) nogil
+
+ctypedef int32_t (*random_int_2_i_32)(bitgen_t *state, int32_t a, int32_t b) nogil
+ctypedef int64_t (*random_int_2_i)(bitgen_t *state, int64_t a, int64_t b) nogil
+
+cdef double kahan_sum(double *darr, np.npy_intp n)
+
+cdef inline double uint64_to_double(uint64_t rnd) nogil:
+    return (rnd >> 11) * (1.0 / 9007199254740992.0)
+
+cdef object double_fill(void *func, bitgen_t *state, object size, object lock, object out)
+
+cdef object float_fill(void *func, bitgen_t *state, object size, object lock, object out)
+
+cdef object float_fill_from_double(void *func, bitgen_t *state, object size, object lock, object out)
+
+cdef object wrap_int(object val, object bits)
+
+cdef np.ndarray int_to_array(object value, object name, object bits, object uint_size)
+
+cdef validate_output_shape(iter_shape, np.ndarray output)
+
+cdef object cont(void *func, void *state, object size, object lock, int narg,
+                 object a, object a_name, constraint_type a_constraint,
+                 object b, object b_name, constraint_type b_constraint,
+                 object c, object c_name, constraint_type c_constraint,
+                 object out)
+
+cdef object disc(void *func, void *state, object size, object lock,
+                 int narg_double, int narg_int64,
+                 object a, object a_name, constraint_type a_constraint,
+                 object b, object b_name, constraint_type b_constraint,
+                 object c, object c_name, constraint_type c_constraint)
+
+cdef object cont_f(void *func, bitgen_t *state, object size, object lock,
+                   object a, object a_name, constraint_type a_constraint,
+                   object out)
+
+cdef object cont_broadcast_3(void *func, void *state, object size, object lock,
+                             np.ndarray a_arr, object a_name, constraint_type a_constraint,
+                             np.ndarray b_arr, object b_name, constraint_type b_constraint,
+                             np.ndarray c_arr, object c_name, constraint_type c_constraint)
+
+cdef object discrete_broadcast_iii(void *func, void *state, object size, object lock,
+                                   np.ndarray a_arr, object a_name, constraint_type a_constraint,
+                                   np.ndarray b_arr, object b_name, constraint_type b_constraint,
+                                   np.ndarray c_arr, object c_name, constraint_type c_constraint)

wemm/lib/python3.10/site-packages/numpy/random/_examples/cffi/extending.py

@@ -0,0 +1,40 @@
+"""
+Use cffi to access any of the underlying C functions from distributions.h
+"""
+import os
+import numpy as np
+import cffi
+from .parse import parse_distributions_h
+ffi = cffi.FFI()
+
+inc_dir = os.path.join(np.get_include(), 'numpy')
+
+# Basic numpy types
+ffi.cdef('''
+    typedef intptr_t npy_intp;
+    typedef unsigned char npy_bool;
+
+''')
+
+parse_distributions_h(ffi, inc_dir)
+
+lib = ffi.dlopen(np.random._generator.__file__)
+
+# Compare the distributions.h random_standard_normal_fill to
+# Generator.standard_random
+bit_gen = np.random.PCG64()
+rng = np.random.Generator(bit_gen)
+state = bit_gen.state
+
+interface = rng.bit_generator.cffi
+n = 100
+vals_cffi = ffi.new('double[%d]' % n)
+lib.random_standard_normal_fill(interface.bit_generator, n, vals_cffi)
+
+# reset the state
+bit_gen.state = state
+
+vals = rng.standard_normal(n)
+
+for i in range(n):
+    assert vals[i] == vals_cffi[i]

wemm/lib/python3.10/site-packages/numpy/random/_examples/cffi/parse.py

@@ -0,0 +1,55 @@
+import os
+
+
+def parse_distributions_h(ffi, inc_dir):
+    """
+    Parse distributions.h located in inc_dir for CFFI, filling in the ffi.cdef
+
+    Read the function declarations without the "#define ..." macros that will
+    be filled in when loading the library.
+    """
+
+    with open(os.path.join(inc_dir, 'random', 'bitgen.h')) as fid:
+        s = []
+        for line in fid:
+            # massage the include file
+            if line.strip().startswith('#'):
+                continue
+            s.append(line)
+        ffi.cdef('\n'.join(s))
+
+    with open(os.path.join(inc_dir, 'random', 'distributions.h')) as fid:
+        s = []
+        in_skip = 0
+        ignoring = False
+        for line in fid:
+            # check for and remove extern "C" guards
+            if ignoring:
+                if line.strip().startswith('#endif'):
+                    ignoring = False
+                continue
+            if line.strip().startswith('#ifdef __cplusplus'):
+                ignoring = True
+            
+            # massage the include file
+            if line.strip().startswith('#'):
+                continue
+    
+            # skip any inlined function definition
+            # which starts with 'static NPY_INLINE xxx(...) {'
+            # and ends with a closing '}'
+            if line.strip().startswith('static NPY_INLINE'):
+                in_skip += line.count('{')
+                continue
+            elif in_skip > 0:
+                in_skip += line.count('{')
+                in_skip -= line.count('}')
+                continue
+    
+            # replace defines with their value or remove them
+            line = line.replace('DECLDIR', '')
+            line = line.replace('NPY_INLINE', '')
+            line = line.replace('RAND_INT_TYPE', 'int64_t')
+            s.append(line)
+        ffi.cdef('\n'.join(s))
+

wemm/lib/python3.10/site-packages/numpy/random/_examples/cython/extending_distributions.pyx

@@ -0,0 +1,117 @@
+#!/usr/bin/env python3
+#cython: language_level=3
+"""
+This file shows how the to use a BitGenerator to create a distribution.
+"""
+import numpy as np
+cimport numpy as np
+cimport cython
+from cpython.pycapsule cimport PyCapsule_IsValid, PyCapsule_GetPointer
+from libc.stdint cimport uint16_t, uint64_t
+from numpy.random cimport bitgen_t
+from numpy.random import PCG64
+from numpy.random.c_distributions cimport (
+      random_standard_uniform_fill, random_standard_uniform_fill_f)
+
+
+@cython.boundscheck(False)
+@cython.wraparound(False)
+def uniforms(Py_ssize_t n):
+    """
+    Create an array of `n` uniformly distributed doubles.
+    A 'real' distribution would want to process the values into
+    some non-uniform distribution
+    """
+    cdef Py_ssize_t i
+    cdef bitgen_t *rng
+    cdef const char *capsule_name = "BitGenerator"
+    cdef double[::1] random_values
+
+    x = PCG64()
+    capsule = x.capsule
+    # Optional check that the capsule if from a BitGenerator
+    if not PyCapsule_IsValid(capsule, capsule_name):
+        raise ValueError("Invalid pointer to anon_func_state")
+    # Cast the pointer
+    rng = <bitgen_t *> PyCapsule_GetPointer(capsule, capsule_name)
+    random_values = np.empty(n, dtype='float64')
+    with x.lock, nogil:
+        for i in range(n):
+            # Call the function
+            random_values[i] = rng.next_double(rng.state)
+    randoms = np.asarray(random_values)
+
+    return randoms
+
+# cython example 2
+@cython.boundscheck(False)
+@cython.wraparound(False)
+def uint10_uniforms(Py_ssize_t n):
+    """Uniform 10 bit integers stored as 16-bit unsigned integers"""
+    cdef Py_ssize_t i
+    cdef bitgen_t *rng
+    cdef const char *capsule_name = "BitGenerator"
+    cdef uint16_t[::1] random_values
+    cdef int bits_remaining
+    cdef int width = 10
+    cdef uint64_t buff, mask = 0x3FF
+
+    x = PCG64()
+    capsule = x.capsule
+    if not PyCapsule_IsValid(capsule, capsule_name):
+        raise ValueError("Invalid pointer to anon_func_state")
+    rng = <bitgen_t *> PyCapsule_GetPointer(capsule, capsule_name)
+    random_values = np.empty(n, dtype='uint16')
+    # Best practice is to release GIL and acquire the lock
+    bits_remaining = 0
+    with x.lock, nogil:
+        for i in range(n):
+            if bits_remaining < width:
+                buff = rng.next_uint64(rng.state)
+            random_values[i] = buff & mask
+            buff >>= width
+
+    randoms = np.asarray(random_values)
+    return randoms
+
+# cython example 3
+def uniforms_ex(bit_generator, Py_ssize_t n, dtype=np.float64):
+    """
+    Create an array of `n` uniformly distributed doubles via a "fill" function.
+
+    A 'real' distribution would want to process the values into
+    some non-uniform distribution
+
+    Parameters
+    ----------
+    bit_generator: BitGenerator instance
+    n: int
+        Output vector length
+    dtype: {str, dtype}, optional
+        Desired dtype, either 'd' (or 'float64') or 'f' (or 'float32'). The
+        default dtype value is 'd'
+    """
+    cdef Py_ssize_t i
+    cdef bitgen_t *rng
+    cdef const char *capsule_name = "BitGenerator"
+    cdef np.ndarray randoms
+
+    capsule = bit_generator.capsule
+    # Optional check that the capsule if from a BitGenerator
+    if not PyCapsule_IsValid(capsule, capsule_name):
+        raise ValueError("Invalid pointer to anon_func_state")
+    # Cast the pointer
+    rng = <bitgen_t *> PyCapsule_GetPointer(capsule, capsule_name)
+
+    _dtype = np.dtype(dtype)
+    randoms = np.empty(n, dtype=_dtype)
+    if _dtype == np.float32:
+        with bit_generator.lock:
+            random_standard_uniform_fill_f(rng, n, <float*>np.PyArray_DATA(randoms))
+    elif _dtype == np.float64:
+        with bit_generator.lock:
+            random_standard_uniform_fill(rng, n, <double*>np.PyArray_DATA(randoms))
+    else:
+        raise TypeError('Unsupported dtype %r for random' % _dtype)
+    return randoms
+

wemm/lib/python3.10/site-packages/numpy/random/_examples/numba/extending.py

@@ -0,0 +1,84 @@
+import numpy as np
+import numba as nb
+
+from numpy.random import PCG64
+from timeit import timeit
+
+bit_gen = PCG64()
+next_d = bit_gen.cffi.next_double
+state_addr = bit_gen.cffi.state_address
+
+def normals(n, state):
+    out = np.empty(n)
+    for i in range((n + 1) // 2):
+        x1 = 2.0 * next_d(state) - 1.0
+        x2 = 2.0 * next_d(state) - 1.0
+        r2 = x1 * x1 + x2 * x2
+        while r2 >= 1.0 or r2 == 0.0:
+            x1 = 2.0 * next_d(state) - 1.0
+            x2 = 2.0 * next_d(state) - 1.0
+            r2 = x1 * x1 + x2 * x2
+        f = np.sqrt(-2.0 * np.log(r2) / r2)
+        out[2 * i] = f * x1
+        if 2 * i + 1 < n:
+            out[2 * i + 1] = f * x2
+    return out
+
+# Compile using Numba
+normalsj = nb.jit(normals, nopython=True)
+# Must use state address not state with numba
+n = 10000
+
+def numbacall():
+    return normalsj(n, state_addr)
+
+rg = np.random.Generator(PCG64())
+
+def numpycall():
+    return rg.normal(size=n)
+
+# Check that the functions work
+r1 = numbacall()
+r2 = numpycall()
+assert r1.shape == (n,)
+assert r1.shape == r2.shape
+
+t1 = timeit(numbacall, number=1000)
+print(f'{t1:.2f} secs for {n} PCG64 (Numba/PCG64) gaussian randoms')
+t2 = timeit(numpycall, number=1000)
+print(f'{t2:.2f} secs for {n} PCG64 (NumPy/PCG64) gaussian randoms')
+
+# example 2
+
+next_u32 = bit_gen.ctypes.next_uint32
+ctypes_state = bit_gen.ctypes.state
+
+@nb.jit(nopython=True)
+def bounded_uint(lb, ub, state):
+    mask = delta = ub - lb
+    mask |= mask >> 1
+    mask |= mask >> 2
+    mask |= mask >> 4
+    mask |= mask >> 8
+    mask |= mask >> 16
+
+    val = next_u32(state) & mask
+    while val > delta:
+        val = next_u32(state) & mask
+
+    return lb + val
+
+
+print(bounded_uint(323, 2394691, ctypes_state.value))
+
+
+@nb.jit(nopython=True)
+def bounded_uints(lb, ub, n, state):
+    out = np.empty(n, dtype=np.uint32)
+    for i in range(n):
+        out[i] = bounded_uint(lb, ub, state)
+
+
+bounded_uints(323, 2394691, 10000000, ctypes_state.value)
+
+

wemm/lib/python3.10/site-packages/numpy/random/_generator.pyi

@@ -0,0 +1,638 @@
+from collections.abc import Callable
+from typing import Any, Union, overload, TypeVar, Literal
+
+from numpy import (
+    bool_,
+    dtype,
+    float32,
+    float64,
+    int8,
+    int16,
+    int32,
+    int64,
+    int_,
+    ndarray,
+    uint,
+    uint8,
+    uint16,
+    uint32,
+    uint64,
+)
+from numpy.random import BitGenerator, SeedSequence
+from numpy._typing import (
+    ArrayLike,
+    _ArrayLikeFloat_co,
+    _ArrayLikeInt_co,
+    _DoubleCodes,
+    _DTypeLikeBool,
+    _DTypeLikeInt,
+    _DTypeLikeUInt,
+    _Float32Codes,
+    _Float64Codes,
+    _Int8Codes,
+    _Int16Codes,
+    _Int32Codes,
+    _Int64Codes,
+    _IntCodes,
+    _ShapeLike,
+    _SingleCodes,
+    _SupportsDType,
+    _UInt8Codes,
+    _UInt16Codes,
+    _UInt32Codes,
+    _UInt64Codes,
+    _UIntCodes,
+)
+
+_ArrayType = TypeVar("_ArrayType", bound=ndarray[Any, Any])
+
+_DTypeLikeFloat32 = Union[
+    dtype[float32],
+    _SupportsDType[dtype[float32]],
+    type[float32],
+    _Float32Codes,
+    _SingleCodes,
+]
+
+_DTypeLikeFloat64 = Union[
+    dtype[float64],
+    _SupportsDType[dtype[float64]],
+    type[float],
+    type[float64],
+    _Float64Codes,
+    _DoubleCodes,
+]
+
+class Generator:
+    def __init__(self, bit_generator: BitGenerator) -> None: ...
+    def __repr__(self) -> str: ...
+    def __str__(self) -> str: ...
+    def __getstate__(self) -> dict[str, Any]: ...
+    def __setstate__(self, state: dict[str, Any]) -> None: ...
+    def __reduce__(self) -> tuple[Callable[[str], Generator], tuple[str], dict[str, Any]]: ...
+    @property
+    def bit_generator(self) -> BitGenerator: ...
+    def bytes(self, length: int) -> bytes: ...
+    @overload
+    def standard_normal(  # type: ignore[misc]
+        self,
+        size: None = ...,
+        dtype: _DTypeLikeFloat32 | _DTypeLikeFloat64 = ...,
+        out: None = ...,
+    ) -> float: ...
+    @overload
+    def standard_normal(  # type: ignore[misc]
+        self,
+        size: _ShapeLike = ...,
+    ) -> ndarray[Any, dtype[float64]]: ...
+    @overload
+    def standard_normal(  # type: ignore[misc]
+        self,
+        *,
+        out: ndarray[Any, dtype[float64]] = ...,
+    ) -> ndarray[Any, dtype[float64]]: ...
+    @overload
+    def standard_normal(  # type: ignore[misc]
+        self,
+        size: _ShapeLike = ...,
+        dtype: _DTypeLikeFloat32 = ...,
+        out: None | ndarray[Any, dtype[float32]] = ...,
+    ) -> ndarray[Any, dtype[float32]]: ...
+    @overload
+    def standard_normal(  # type: ignore[misc]
+        self,
+        size: _ShapeLike = ...,
+        dtype: _DTypeLikeFloat64 = ...,
+        out: None | ndarray[Any, dtype[float64]] = ...,
+    ) -> ndarray[Any, dtype[float64]]: ...
+    @overload
+    def permutation(self, x: int, axis: int = ...) -> ndarray[Any, dtype[int64]]: ...
+    @overload
+    def permutation(self, x: ArrayLike, axis: int = ...) -> ndarray[Any, Any]: ...
+    @overload
+    def standard_exponential(  # type: ignore[misc]
+        self,
+        size: None = ...,
+        dtype: _DTypeLikeFloat32 | _DTypeLikeFloat64 = ...,
+        method: Literal["zig", "inv"] = ...,
+        out: None = ...,
+    ) -> float: ...
+    @overload
+    def standard_exponential(
+        self,
+        size: _ShapeLike = ...,
+    ) -> ndarray[Any, dtype[float64]]: ...
+    @overload
+    def standard_exponential(
+        self,
+        *,
+        out: ndarray[Any, dtype[float64]] = ...,
+    ) -> ndarray[Any, dtype[float64]]: ...
+    @overload
+    def standard_exponential(
+        self,
+        size: _ShapeLike = ...,
+        *,
+        method: Literal["zig", "inv"] = ...,
+        out: None | ndarray[Any, dtype[float64]] = ...,
+    ) -> ndarray[Any, dtype[float64]]: ...
+    @overload
+    def standard_exponential(
+        self,
+        size: _ShapeLike = ...,
+        dtype: _DTypeLikeFloat32 = ...,
+        method: Literal["zig", "inv"] = ...,
+        out: None | ndarray[Any, dtype[float32]] = ...,
+    ) -> ndarray[Any, dtype[float32]]: ...
+    @overload
+    def standard_exponential(
+        self,
+        size: _ShapeLike = ...,
+        dtype: _DTypeLikeFloat64 = ...,
+        method: Literal["zig", "inv"] = ...,
+        out: None | ndarray[Any, dtype[float64]] = ...,
+    ) -> ndarray[Any, dtype[float64]]: ...
+    @overload
+    def random(  # type: ignore[misc]
+        self,
+        size: None = ...,
+        dtype: _DTypeLikeFloat32 | _DTypeLikeFloat64 = ...,
+        out: None = ...,
+    ) -> float: ...
+    @overload
+    def random(
+        self,
+        *,
+        out: ndarray[Any, dtype[float64]] = ...,
+    ) -> ndarray[Any, dtype[float64]]: ...
+    @overload
+    def random(
+        self,
+        size: _ShapeLike = ...,
+        *,
+        out: None | ndarray[Any, dtype[float64]] = ...,
+    ) -> ndarray[Any, dtype[float64]]: ...
+    @overload
+    def random(
+        self,
+        size: _ShapeLike = ...,
+        dtype: _DTypeLikeFloat32 = ...,
+        out: None | ndarray[Any, dtype[float32]] = ...,
+    ) -> ndarray[Any, dtype[float32]]: ...
+    @overload
+    def random(
+        self,
+        size: _ShapeLike = ...,
+        dtype: _DTypeLikeFloat64 = ...,
+        out: None | ndarray[Any, dtype[float64]] = ...,
+    ) -> ndarray[Any, dtype[float64]]: ...
+    @overload
+    def beta(self, a: float, b: float, size: None = ...) -> float: ...  # type: ignore[misc]
+    @overload
+    def beta(
+        self, a: _ArrayLikeFloat_co, b: _ArrayLikeFloat_co, size: None | _ShapeLike = ...
+    ) -> ndarray[Any, dtype[float64]]: ...
+    @overload
+    def exponential(self, scale: float = ..., size: None = ...) -> float: ...  # type: ignore[misc]
+    @overload
+    def exponential(
+        self, scale: _ArrayLikeFloat_co = ..., size: None | _ShapeLike = ...
+    ) -> ndarray[Any, dtype[float64]]: ...
+    @overload
+    def integers(  # type: ignore[misc]
+        self,
+        low: int,
+        high: None | int = ...,
+    ) -> int: ...
+    @overload
+    def integers(  # type: ignore[misc]
+        self,
+        low: int,
+        high: None | int = ...,
+        size: None = ...,
+        dtype: _DTypeLikeBool = ...,
+        endpoint: bool = ...,
+    ) -> bool: ...
+    @overload
+    def integers(  # type: ignore[misc]
+        self,
+        low: int,
+        high: None | int = ...,
+        size: None = ...,
+        dtype: _DTypeLikeInt | _DTypeLikeUInt = ...,
+        endpoint: bool = ...,
+    ) -> int: ...
+    @overload
+    def integers(  # type: ignore[misc]
+        self,
+        low: _ArrayLikeInt_co,
+        high: None | _ArrayLikeInt_co = ...,
+        size: None | _ShapeLike = ...,
+    ) -> ndarray[Any, dtype[int64]]: ...
+    @overload
+    def integers(  # type: ignore[misc]
+        self,
+        low: _ArrayLikeInt_co,
+        high: None | _ArrayLikeInt_co = ...,
+        size: None | _ShapeLike = ...,
+        dtype: _DTypeLikeBool = ...,
+        endpoint: bool = ...,
+    ) -> ndarray[Any, dtype[bool_]]: ...
+    @overload
+    def integers(  # type: ignore[misc]
+        self,
+        low: _ArrayLikeInt_co,
+        high: None | _ArrayLikeInt_co = ...,
+        size: None | _ShapeLike = ...,
+        dtype: dtype[int8] | type[int8] | _Int8Codes | _SupportsDType[dtype[int8]] = ...,
+        endpoint: bool = ...,
+    ) -> ndarray[Any, dtype[int8]]: ...
+    @overload
+    def integers(  # type: ignore[misc]
+        self,
+        low: _ArrayLikeInt_co,
+        high: None | _ArrayLikeInt_co = ...,
+        size: None | _ShapeLike = ...,
+        dtype: dtype[int16] | type[int16] | _Int16Codes | _SupportsDType[dtype[int16]] = ...,
+        endpoint: bool = ...,
+    ) -> ndarray[Any, dtype[int16]]: ...
+    @overload
+    def integers(  # type: ignore[misc]
+        self,
+        low: _ArrayLikeInt_co,
+        high: None | _ArrayLikeInt_co = ...,
+        size: None | _ShapeLike = ...,
+        dtype: dtype[int32] | type[int32] | _Int32Codes | _SupportsDType[dtype[int32]] = ...,
+        endpoint: bool = ...,
+    ) -> ndarray[Any, dtype[int32]]: ...
+    @overload
+    def integers(  # type: ignore[misc]
+        self,
+        low: _ArrayLikeInt_co,
+        high: None | _ArrayLikeInt_co = ...,
+        size: None | _ShapeLike = ...,
+        dtype: None | dtype[int64] | type[int64] | _Int64Codes | _SupportsDType[dtype[int64]] = ...,
+        endpoint: bool = ...,
+    ) -> ndarray[Any, dtype[int64]]: ...
+    @overload
+    def integers(  # type: ignore[misc]
+        self,
+        low: _ArrayLikeInt_co,
+        high: None | _ArrayLikeInt_co = ...,
+        size: None | _ShapeLike = ...,
+        dtype: dtype[uint8] | type[uint8] | _UInt8Codes | _SupportsDType[dtype[uint8]] = ...,
+        endpoint: bool = ...,
+    ) -> ndarray[Any, dtype[uint8]]: ...
+    @overload
+    def integers(  # type: ignore[misc]
+        self,
+        low: _ArrayLikeInt_co,
+        high: None | _ArrayLikeInt_co = ...,
+        size: None | _ShapeLike = ...,
+        dtype: dtype[uint16] | type[uint16] | _UInt16Codes | _SupportsDType[dtype[uint16]] = ...,
+        endpoint: bool = ...,
+    ) -> ndarray[Any, dtype[uint16]]: ...
+    @overload
+    def integers(  # type: ignore[misc]
+        self,
+        low: _ArrayLikeInt_co,
+        high: None | _ArrayLikeInt_co = ...,
+        size: None | _ShapeLike = ...,
+        dtype: dtype[uint32] | type[uint32] | _UInt32Codes | _SupportsDType[dtype[uint32]] = ...,
+        endpoint: bool = ...,
+    ) -> ndarray[Any, dtype[uint32]]: ...
+    @overload
+    def integers(  # type: ignore[misc]
+        self,
+        low: _ArrayLikeInt_co,
+        high: None | _ArrayLikeInt_co = ...,
+        size: None | _ShapeLike = ...,
+        dtype: dtype[uint64] | type[uint64] | _UInt64Codes | _SupportsDType[dtype[uint64]] = ...,
+        endpoint: bool = ...,
+    ) -> ndarray[Any, dtype[uint64]]: ...
+    @overload
+    def integers(  # type: ignore[misc]
+        self,
+        low: _ArrayLikeInt_co,
+        high: None | _ArrayLikeInt_co = ...,
+        size: None | _ShapeLike = ...,
+        dtype: dtype[int_] | type[int] | type[int_] | _IntCodes | _SupportsDType[dtype[int_]] = ...,
+        endpoint: bool = ...,
+    ) -> ndarray[Any, dtype[int_]]: ...
+    @overload
+    def integers(  # type: ignore[misc]
+        self,
+        low: _ArrayLikeInt_co,
+        high: None | _ArrayLikeInt_co = ...,
+        size: None | _ShapeLike = ...,
+        dtype: dtype[uint] | type[uint] | _UIntCodes | _SupportsDType[dtype[uint]] = ...,
+        endpoint: bool = ...,
+    ) -> ndarray[Any, dtype[uint]]: ...
+    # TODO: Use a TypeVar _T here to get away from Any output?  Should be int->ndarray[Any,dtype[int64]], ArrayLike[_T] -> _T | ndarray[Any,Any]
+    @overload
+    def choice(
+        self,
+        a: int,
+        size: None = ...,
+        replace: bool = ...,
+        p: None | _ArrayLikeFloat_co = ...,
+        axis: int = ...,
+        shuffle: bool = ...,
+    ) -> int: ...
+    @overload
+    def choice(
+        self,
+        a: int,
+        size: _ShapeLike = ...,
+        replace: bool = ...,
+        p: None | _ArrayLikeFloat_co = ...,
+        axis: int = ...,
+        shuffle: bool = ...,
+    ) -> ndarray[Any, dtype[int64]]: ...
+    @overload
+    def choice(
+        self,
+        a: ArrayLike,
+        size: None = ...,
+        replace: bool = ...,
+        p: None | _ArrayLikeFloat_co = ...,
+        axis: int = ...,
+        shuffle: bool = ...,
+    ) -> Any: ...
+    @overload
+    def choice(
+        self,
+        a: ArrayLike,
+        size: _ShapeLike = ...,
+        replace: bool = ...,
+        p: None | _ArrayLikeFloat_co = ...,
+        axis: int = ...,
+        shuffle: bool = ...,
+    ) -> ndarray[Any, Any]: ...
+    @overload
+    def uniform(self, low: float = ..., high: float = ..., size: None = ...) -> float: ...  # type: ignore[misc]
+    @overload
+    def uniform(
+        self,
+        low: _ArrayLikeFloat_co = ...,
+        high: _ArrayLikeFloat_co = ...,
+        size: None | _ShapeLike = ...,
+    ) -> ndarray[Any, dtype[float64]]: ...
+    @overload
+    def normal(self, loc: float = ..., scale: float = ..., size: None = ...) -> float: ...  # type: ignore[misc]
+    @overload
+    def normal(
+        self,
+        loc: _ArrayLikeFloat_co = ...,
+        scale: _ArrayLikeFloat_co = ...,
+        size: None | _ShapeLike = ...,
+    ) -> ndarray[Any, dtype[float64]]: ...
+    @overload
+    def standard_gamma(  # type: ignore[misc]
+        self,
+        shape: float,
+        size: None = ...,
+        dtype: _DTypeLikeFloat32 | _DTypeLikeFloat64 = ...,
+        out: None = ...,
+    ) -> float: ...
+    @overload
+    def standard_gamma(
+        self,
+        shape: _ArrayLikeFloat_co,
+        size: None | _ShapeLike = ...,
+    ) -> ndarray[Any, dtype[float64]]: ...
+    @overload
+    def standard_gamma(
+        self,
+        shape: _ArrayLikeFloat_co,
+        *,
+        out: ndarray[Any, dtype[float64]] = ...,
+    ) -> ndarray[Any, dtype[float64]]: ...
+    @overload
+    def standard_gamma(
+        self,
+        shape: _ArrayLikeFloat_co,
+        size: None | _ShapeLike = ...,
+        dtype: _DTypeLikeFloat32 = ...,
+        out: None | ndarray[Any, dtype[float32]] = ...,
+    ) -> ndarray[Any, dtype[float32]]: ...
+    @overload
+    def standard_gamma(
+        self,
+        shape: _ArrayLikeFloat_co,
+        size: None | _ShapeLike = ...,
+        dtype: _DTypeLikeFloat64 = ...,
+        out: None | ndarray[Any, dtype[float64]] = ...,
+    ) -> ndarray[Any, dtype[float64]]: ...
+    @overload
+    def gamma(self, shape: float, scale: float = ..., size: None = ...) -> float: ...  # type: ignore[misc]
+    @overload
+    def gamma(
+        self,
+        shape: _ArrayLikeFloat_co,
+        scale: _ArrayLikeFloat_co = ...,
+        size: None | _ShapeLike = ...,
+    ) -> ndarray[Any, dtype[float64]]: ...
+    @overload
+    def f(self, dfnum: float, dfden: float, size: None = ...) -> float: ...  # type: ignore[misc]
+    @overload
+    def f(
+        self, dfnum: _ArrayLikeFloat_co, dfden: _ArrayLikeFloat_co, size: None | _ShapeLike = ...
+    ) -> ndarray[Any, dtype[float64]]: ...
+    @overload
+    def noncentral_f(self, dfnum: float, dfden: float, nonc: float, size: None = ...) -> float: ...  # type: ignore[misc]
+    @overload
+    def noncentral_f(
+        self,
+        dfnum: _ArrayLikeFloat_co,
+        dfden: _ArrayLikeFloat_co,
+        nonc: _ArrayLikeFloat_co,
+        size: None | _ShapeLike = ...,
+    ) -> ndarray[Any, dtype[float64]]: ...
+    @overload
+    def chisquare(self, df: float, size: None = ...) -> float: ...  # type: ignore[misc]
+    @overload
+    def chisquare(
+        self, df: _ArrayLikeFloat_co, size: None | _ShapeLike = ...
+    ) -> ndarray[Any, dtype[float64]]: ...
+    @overload
+    def noncentral_chisquare(self, df: float, nonc: float, size: None = ...) -> float: ...  # type: ignore[misc]
+    @overload
+    def noncentral_chisquare(
+        self, df: _ArrayLikeFloat_co, nonc: _ArrayLikeFloat_co, size: None | _ShapeLike = ...
+    ) -> ndarray[Any, dtype[float64]]: ...
+    @overload
+    def standard_t(self, df: float, size: None = ...) -> float: ...  # type: ignore[misc]
+    @overload
+    def standard_t(
+        self, df: _ArrayLikeFloat_co, size: None = ...
+    ) -> ndarray[Any, dtype[float64]]: ...
+    @overload
+    def standard_t(
+        self, df: _ArrayLikeFloat_co, size: _ShapeLike = ...
+    ) -> ndarray[Any, dtype[float64]]: ...
+    @overload
+    def vonmises(self, mu: float, kappa: float, size: None = ...) -> float: ...  # type: ignore[misc]
+    @overload
+    def vonmises(
+        self, mu: _ArrayLikeFloat_co, kappa: _ArrayLikeFloat_co, size: None | _ShapeLike = ...
+    ) -> ndarray[Any, dtype[float64]]: ...
+    @overload
+    def pareto(self, a: float, size: None = ...) -> float: ...  # type: ignore[misc]
+    @overload
+    def pareto(
+        self, a: _ArrayLikeFloat_co, size: None | _ShapeLike = ...
+    ) -> ndarray[Any, dtype[float64]]: ...
+    @overload
+    def weibull(self, a: float, size: None = ...) -> float: ...  # type: ignore[misc]
+    @overload
+    def weibull(
+        self, a: _ArrayLikeFloat_co, size: None | _ShapeLike = ...
+    ) -> ndarray[Any, dtype[float64]]: ...
+    @overload
+    def power(self, a: float, size: None = ...) -> float: ...  # type: ignore[misc]
+    @overload
+    def power(
+        self, a: _ArrayLikeFloat_co, size: None | _ShapeLike = ...
+    ) -> ndarray[Any, dtype[float64]]: ...
+    @overload
+    def standard_cauchy(self, size: None = ...) -> float: ...  # type: ignore[misc]
+    @overload
+    def standard_cauchy(self, size: _ShapeLike = ...) -> ndarray[Any, dtype[float64]]: ...
+    @overload
+    def laplace(self, loc: float = ..., scale: float = ..., size: None = ...) -> float: ...  # type: ignore[misc]
+    @overload
+    def laplace(
+        self,
+        loc: _ArrayLikeFloat_co = ...,
+        scale: _ArrayLikeFloat_co = ...,
+        size: None | _ShapeLike = ...,
+    ) -> ndarray[Any, dtype[float64]]: ...
+    @overload
+    def gumbel(self, loc: float = ..., scale: float = ..., size: None = ...) -> float: ...  # type: ignore[misc]
+    @overload
+    def gumbel(
+        self,
+        loc: _ArrayLikeFloat_co = ...,
+        scale: _ArrayLikeFloat_co = ...,
+        size: None | _ShapeLike = ...,
+    ) -> ndarray[Any, dtype[float64]]: ...
+    @overload
+    def logistic(self, loc: float = ..., scale: float = ..., size: None = ...) -> float: ...  # type: ignore[misc]
+    @overload
+    def logistic(
+        self,
+        loc: _ArrayLikeFloat_co = ...,
+        scale: _ArrayLikeFloat_co = ...,
+        size: None | _ShapeLike = ...,
+    ) -> ndarray[Any, dtype[float64]]: ...
+    @overload
+    def lognormal(self, mean: float = ..., sigma: float = ..., size: None = ...) -> float: ...  # type: ignore[misc]
+    @overload
+    def lognormal(
+        self,
+        mean: _ArrayLikeFloat_co = ...,
+        sigma: _ArrayLikeFloat_co = ...,
+        size: None | _ShapeLike = ...,
+    ) -> ndarray[Any, dtype[float64]]: ...
+    @overload
+    def rayleigh(self, scale: float = ..., size: None = ...) -> float: ...  # type: ignore[misc]
+    @overload
+    def rayleigh(
+        self, scale: _ArrayLikeFloat_co = ..., size: None | _ShapeLike = ...
+    ) -> ndarray[Any, dtype[float64]]: ...
+    @overload
+    def wald(self, mean: float, scale: float, size: None = ...) -> float: ...  # type: ignore[misc]
+    @overload
+    def wald(
+        self, mean: _ArrayLikeFloat_co, scale: _ArrayLikeFloat_co, size: None | _ShapeLike = ...
+    ) -> ndarray[Any, dtype[float64]]: ...
+    @overload
+    def triangular(self, left: float, mode: float, right: float, size: None = ...) -> float: ...  # type: ignore[misc]
+    @overload
+    def triangular(
+        self,
+        left: _ArrayLikeFloat_co,
+        mode: _ArrayLikeFloat_co,
+        right: _ArrayLikeFloat_co,
+        size: None | _ShapeLike = ...,
+    ) -> ndarray[Any, dtype[float64]]: ...
+    @overload
+    def binomial(self, n: int, p: float, size: None = ...) -> int: ...  # type: ignore[misc]
+    @overload
+    def binomial(
+        self, n: _ArrayLikeInt_co, p: _ArrayLikeFloat_co, size: None | _ShapeLike = ...
+    ) -> ndarray[Any, dtype[int64]]: ...
+    @overload
+    def negative_binomial(self, n: float, p: float, size: None = ...) -> int: ...  # type: ignore[misc]
+    @overload
+    def negative_binomial(
+        self, n: _ArrayLikeFloat_co, p: _ArrayLikeFloat_co, size: None | _ShapeLike = ...
+    ) -> ndarray[Any, dtype[int64]]: ...
+    @overload
+    def poisson(self, lam: float = ..., size: None = ...) -> int: ...  # type: ignore[misc]
+    @overload
+    def poisson(
+        self, lam: _ArrayLikeFloat_co = ..., size: None | _ShapeLike = ...
+    ) -> ndarray[Any, dtype[int64]]: ...
+    @overload
+    def zipf(self, a: float, size: None = ...) -> int: ...  # type: ignore[misc]
+    @overload
+    def zipf(
+        self, a: _ArrayLikeFloat_co, size: None | _ShapeLike = ...
+    ) -> ndarray[Any, dtype[int64]]: ...
+    @overload
+    def geometric(self, p: float, size: None = ...) -> int: ...  # type: ignore[misc]
+    @overload
+    def geometric(
+        self, p: _ArrayLikeFloat_co, size: None | _ShapeLike = ...
+    ) -> ndarray[Any, dtype[int64]]: ...
+    @overload
+    def hypergeometric(self, ngood: int, nbad: int, nsample: int, size: None = ...) -> int: ...  # type: ignore[misc]
+    @overload
+    def hypergeometric(
+        self,
+        ngood: _ArrayLikeInt_co,
+        nbad: _ArrayLikeInt_co,
+        nsample: _ArrayLikeInt_co,
+        size: None | _ShapeLike = ...,
+    ) -> ndarray[Any, dtype[int64]]: ...
+    @overload
+    def logseries(self, p: float, size: None = ...) -> int: ...  # type: ignore[misc]
+    @overload
+    def logseries(
+        self, p: _ArrayLikeFloat_co, size: None | _ShapeLike = ...
+    ) -> ndarray[Any, dtype[int64]]: ...
+    def multivariate_normal(
+        self,
+        mean: _ArrayLikeFloat_co,
+        cov: _ArrayLikeFloat_co,
+        size: None | _ShapeLike = ...,
+        check_valid: Literal["warn", "raise", "ignore"] = ...,
+        tol: float = ...,
+        *,
+        method: Literal["svd", "eigh", "cholesky"] = ...,
+    ) -> ndarray[Any, dtype[float64]]: ...
+    def multinomial(
+        self, n: _ArrayLikeInt_co,
+            pvals: _ArrayLikeFloat_co,
+            size: None | _ShapeLike = ...
+    ) -> ndarray[Any, dtype[int64]]: ...
+    def multivariate_hypergeometric(
+        self,
+        colors: _ArrayLikeInt_co,
+        nsample: int,
+        size: None | _ShapeLike = ...,
+        method: Literal["marginals", "count"] = ...,
+    ) -> ndarray[Any, dtype[int64]]: ...
+    def dirichlet(
+        self, alpha: _ArrayLikeFloat_co, size: None | _ShapeLike = ...
+    ) -> ndarray[Any, dtype[float64]]: ...
+    def permuted(
+        self, x: ArrayLike, *, axis: None | int = ..., out: None | ndarray[Any, Any] = ...
+    ) -> ndarray[Any, Any]: ...
+    def shuffle(self, x: ArrayLike, axis: int = ...) -> None: ...
+
+def default_rng(
+    seed: None | _ArrayLikeInt_co | SeedSequence | BitGenerator | Generator = ...
+) -> Generator: ...

wemm/lib/python3.10/site-packages/numpy/random/_mt19937.pyi

@@ -0,0 +1,22 @@
+from typing import Any, TypedDict
+
+from numpy import dtype, ndarray, uint32
+from numpy.random.bit_generator import BitGenerator, SeedSequence
+from numpy._typing import _ArrayLikeInt_co
+
+class _MT19937Internal(TypedDict):
+    key: ndarray[Any, dtype[uint32]]
+    pos: int
+
+class _MT19937State(TypedDict):
+    bit_generator: str
+    state: _MT19937Internal
+
+class MT19937(BitGenerator):
+    def __init__(self, seed: None | _ArrayLikeInt_co | SeedSequence = ...) -> None: ...
+    def _legacy_seeding(self, seed: _ArrayLikeInt_co) -> None: ...
+    def jumped(self, jumps: int = ...) -> MT19937: ...
+    @property
+    def state(self) -> _MT19937State: ...
+    @state.setter
+    def state(self, value: _MT19937State) -> None: ...

wemm/lib/python3.10/site-packages/numpy/random/_pcg64.pyi

@@ -0,0 +1,42 @@
+from typing import TypedDict
+
+from numpy.random.bit_generator import BitGenerator, SeedSequence
+from numpy._typing import _ArrayLikeInt_co
+
+class _PCG64Internal(TypedDict):
+    state: int
+    inc: int
+
+class _PCG64State(TypedDict):
+    bit_generator: str
+    state: _PCG64Internal
+    has_uint32: int
+    uinteger: int
+
+class PCG64(BitGenerator):
+    def __init__(self, seed: None | _ArrayLikeInt_co | SeedSequence = ...) -> None: ...
+    def jumped(self, jumps: int = ...) -> PCG64: ...
+    @property
+    def state(
+        self,
+    ) -> _PCG64State: ...
+    @state.setter
+    def state(
+        self,
+        value: _PCG64State,
+    ) -> None: ...
+    def advance(self, delta: int) -> PCG64: ...
+
+class PCG64DXSM(BitGenerator):
+    def __init__(self, seed: None | _ArrayLikeInt_co | SeedSequence = ...) -> None: ...
+    def jumped(self, jumps: int = ...) -> PCG64DXSM: ...
+    @property
+    def state(
+        self,
+    ) -> _PCG64State: ...
+    @state.setter
+    def state(
+        self,
+        value: _PCG64State,
+    ) -> None: ...
+    def advance(self, delta: int) -> PCG64DXSM: ...

wemm/lib/python3.10/site-packages/numpy/random/_philox.pyi

@@ -0,0 +1,36 @@
+from typing import Any, TypedDict
+
+from numpy import dtype, ndarray, uint64
+from numpy.random.bit_generator import BitGenerator, SeedSequence
+from numpy._typing import _ArrayLikeInt_co
+
+class _PhiloxInternal(TypedDict):
+    counter: ndarray[Any, dtype[uint64]]
+    key: ndarray[Any, dtype[uint64]]
+
+class _PhiloxState(TypedDict):
+    bit_generator: str
+    state: _PhiloxInternal
+    buffer: ndarray[Any, dtype[uint64]]
+    buffer_pos: int
+    has_uint32: int
+    uinteger: int
+
+class Philox(BitGenerator):
+    def __init__(
+        self,
+        seed: None | _ArrayLikeInt_co | SeedSequence = ...,
+        counter: None | _ArrayLikeInt_co = ...,
+        key: None | _ArrayLikeInt_co = ...,
+    ) -> None: ...
+    @property
+    def state(
+        self,
+    ) -> _PhiloxState: ...
+    @state.setter
+    def state(
+        self,
+        value: _PhiloxState,
+    ) -> None: ...
+    def jumped(self, jumps: int = ...) -> Philox: ...
+    def advance(self, delta: int) -> Philox: ...

wemm/lib/python3.10/site-packages/numpy/random/_pickle.py

@@ -0,0 +1,83 @@
+from .mtrand import RandomState
+from ._philox import Philox
+from ._pcg64 import PCG64, PCG64DXSM
+from ._sfc64 import SFC64
+
+from ._generator import Generator
+from ._mt19937 import MT19937
+
+BitGenerators = {'MT19937': MT19937,
+                 'PCG64': PCG64,
+                 'PCG64DXSM': PCG64DXSM,
+                 'Philox': Philox,
+                 'SFC64': SFC64,
+                 }
+
+
+def __generator_ctor(bit_generator_name='MT19937'):
+    """
+    Pickling helper function that returns a Generator object
+
+    Parameters
+    ----------
+    bit_generator_name : str
+        String containing the core BitGenerator
+
+    Returns
+    -------
+    rg : Generator
+        Generator using the named core BitGenerator
+    """
+    if bit_generator_name in BitGenerators:
+        bit_generator = BitGenerators[bit_generator_name]
+    else:
+        raise ValueError(str(bit_generator_name) + ' is not a known '
+                                                   'BitGenerator module.')
+
+    return Generator(bit_generator())
+
+
+def __bit_generator_ctor(bit_generator_name='MT19937'):
+    """
+    Pickling helper function that returns a bit generator object
+
+    Parameters
+    ----------
+    bit_generator_name : str
+        String containing the name of the BitGenerator
+
+    Returns
+    -------
+    bit_generator : BitGenerator
+        BitGenerator instance
+    """
+    if bit_generator_name in BitGenerators:
+        bit_generator = BitGenerators[bit_generator_name]
+    else:
+        raise ValueError(str(bit_generator_name) + ' is not a known '
+                                                   'BitGenerator module.')
+
+    return bit_generator()
+
+
+def __randomstate_ctor(bit_generator_name='MT19937'):
+    """
+    Pickling helper function that returns a legacy RandomState-like object
+
+    Parameters
+    ----------
+    bit_generator_name : str
+        String containing the core BitGenerator
+
+    Returns
+    -------
+    rs : RandomState
+        Legacy RandomState using the named core BitGenerator
+    """
+    if bit_generator_name in BitGenerators:
+        bit_generator = BitGenerators[bit_generator_name]
+    else:
+        raise ValueError(str(bit_generator_name) + ' is not a known '
+                                                   'BitGenerator module.')
+
+    return RandomState(bit_generator())