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env-llmeval/lib/python3.10/site-packages/numpy/array_api/__init__.py

@@ -0,0 +1,387 @@
+"""
+A NumPy sub-namespace that conforms to the Python array API standard.
+
+This submodule accompanies NEP 47, which proposes its inclusion in NumPy. It
+is still considered experimental, and will issue a warning when imported.
+
+This is a proof-of-concept namespace that wraps the corresponding NumPy
+functions to give a conforming implementation of the Python array API standard
+(https://data-apis.github.io/array-api/latest/). The standard is currently in
+an RFC phase and comments on it are both welcome and encouraged. Comments
+should be made either at https://github.com/data-apis/array-api or at
+https://github.com/data-apis/consortium-feedback/discussions.
+
+NumPy already follows the proposed spec for the most part, so this module
+serves mostly as a thin wrapper around it. However, NumPy also implements a
+lot of behavior that is not included in the spec, so this serves as a
+restricted subset of the API. Only those functions that are part of the spec
+are included in this namespace, and all functions are given with the exact
+signature given in the spec, including the use of position-only arguments, and
+omitting any extra keyword arguments implemented by NumPy but not part of the
+spec. The behavior of some functions is also modified from the NumPy behavior
+to conform to the standard. Note that the underlying array object itself is
+wrapped in a wrapper Array() class, but is otherwise unchanged. This submodule
+is implemented in pure Python with no C extensions.
+
+The array API spec is designed as a "minimal API subset" and explicitly allows
+libraries to include behaviors not specified by it. But users of this module
+that intend to write portable code should be aware that only those behaviors
+that are listed in the spec are guaranteed to be implemented across libraries.
+Consequently, the NumPy implementation was chosen to be both conforming and
+minimal, so that users can use this implementation of the array API namespace
+and be sure that behaviors that it defines will be available in conforming
+namespaces from other libraries.
+
+A few notes about the current state of this submodule:
+
+- There is a test suite that tests modules against the array API standard at
+  https://github.com/data-apis/array-api-tests. The test suite is still a work
+  in progress, but the existing tests pass on this module, with a few
+  exceptions:
+
+  - DLPack support (see https://github.com/data-apis/array-api/pull/106) is
+    not included here, as it requires a full implementation in NumPy proper
+    first.
+
+  The test suite is not yet complete, and even the tests that exist are not
+  guaranteed to give a comprehensive coverage of the spec. Therefore, when
+  reviewing and using this submodule, you should refer to the standard
+  documents themselves. There are some tests in numpy.array_api.tests, but
+  they primarily focus on things that are not tested by the official array API
+  test suite.
+
+- There is a custom array object, numpy.array_api.Array, which is returned by
+  all functions in this module. All functions in the array API namespace
+  implicitly assume that they will only receive this object as input. The only
+  way to create instances of this object is to use one of the array creation
+  functions. It does not have a public constructor on the object itself. The
+  object is a small wrapper class around numpy.ndarray. The main purpose of it
+  is to restrict the namespace of the array object to only those dtypes and
+  only those methods that are required by the spec, as well as to limit/change
+  certain behavior that differs in the spec. In particular:
+
+  - The array API namespace does not have scalar objects, only 0-D arrays.
+    Operations on Array that would create a scalar in NumPy create a 0-D
+    array.
+
+  - Indexing: Only a subset of indices supported by NumPy are required by the
+    spec. The Array object restricts indexing to only allow those types of
+    indices that are required by the spec. See the docstring of the
+    numpy.array_api.Array._validate_indices helper function for more
+    information.
+
+  - Type promotion: Some type promotion rules are different in the spec. In
+    particular, the spec does not have any value-based casting. The spec also
+    does not require cross-kind casting, like integer -> floating-point. Only
+    those promotions that are explicitly required by the array API
+    specification are allowed in this module. See NEP 47 for more info.
+
+  - Functions do not automatically call asarray() on their input, and will not
+    work if the input type is not Array. The exception is array creation
+    functions, and Python operators on the Array object, which accept Python
+    scalars of the same type as the array dtype.
+
+- All functions include type annotations, corresponding to those given in the
+  spec (see _typing.py for definitions of some custom types). These do not
+  currently fully pass mypy due to some limitations in mypy.
+
+- Dtype objects are just the NumPy dtype objects, e.g., float64 =
+  np.dtype('float64'). The spec does not require any behavior on these dtype
+  objects other than that they be accessible by name and be comparable by
+  equality, but it was considered too much extra complexity to create custom
+  objects to represent dtypes.
+
+- All places where the implementations in this submodule are known to deviate
+  from their corresponding functions in NumPy are marked with "# Note:"
+  comments.
+
+Still TODO in this module are:
+
+- DLPack support for numpy.ndarray is still in progress. See
+  https://github.com/numpy/numpy/pull/19083.
+
+- The copy=False keyword argument to asarray() is not yet implemented. This
+  requires support in numpy.asarray() first.
+
+- Some functions are not yet fully tested in the array API test suite, and may
+  require updates that are not yet known until the tests are written.
+
+- The spec is still in an RFC phase and may still have minor updates, which
+  will need to be reflected here.
+
+- Complex number support in array API spec is planned but not yet finalized,
+  as are the fft extension and certain linear algebra functions such as eig
+  that require complex dtypes.
+
+"""
+
+import warnings
+
+warnings.warn(
+    "The numpy.array_api submodule is still experimental. See NEP 47.", stacklevel=2
+)
+
+__array_api_version__ = "2022.12"
+
+__all__ = ["__array_api_version__"]
+
+from ._constants import e, inf, nan, pi, newaxis
+
+__all__ += ["e", "inf", "nan", "pi", "newaxis"]
+
+from ._creation_functions import (
+    asarray,
+    arange,
+    empty,
+    empty_like,
+    eye,
+    from_dlpack,
+    full,
+    full_like,
+    linspace,
+    meshgrid,
+    ones,
+    ones_like,
+    tril,
+    triu,
+    zeros,
+    zeros_like,
+)
+
+__all__ += [
+    "asarray",
+    "arange",
+    "empty",
+    "empty_like",
+    "eye",
+    "from_dlpack",
+    "full",
+    "full_like",
+    "linspace",
+    "meshgrid",
+    "ones",
+    "ones_like",
+    "tril",
+    "triu",
+    "zeros",
+    "zeros_like",
+]
+
+from ._data_type_functions import (
+    astype,
+    broadcast_arrays,
+    broadcast_to,
+    can_cast,
+    finfo,
+    isdtype,
+    iinfo,
+    result_type,
+)
+
+__all__ += [
+    "astype",
+    "broadcast_arrays",
+    "broadcast_to",
+    "can_cast",
+    "finfo",
+    "iinfo",
+    "result_type",
+]
+
+from ._dtypes import (
+    int8,
+    int16,
+    int32,
+    int64,
+    uint8,
+    uint16,
+    uint32,
+    uint64,
+    float32,
+    float64,
+    complex64,
+    complex128,
+    bool,
+)
+
+__all__ += [
+    "int8",
+    "int16",
+    "int32",
+    "int64",
+    "uint8",
+    "uint16",
+    "uint32",
+    "uint64",
+    "float32",
+    "float64",
+    "bool",
+]
+
+from ._elementwise_functions import (
+    abs,
+    acos,
+    acosh,
+    add,
+    asin,
+    asinh,
+    atan,
+    atan2,
+    atanh,
+    bitwise_and,
+    bitwise_left_shift,
+    bitwise_invert,
+    bitwise_or,
+    bitwise_right_shift,
+    bitwise_xor,
+    ceil,
+    conj,
+    cos,
+    cosh,
+    divide,
+    equal,
+    exp,
+    expm1,
+    floor,
+    floor_divide,
+    greater,
+    greater_equal,
+    imag,
+    isfinite,
+    isinf,
+    isnan,
+    less,
+    less_equal,
+    log,
+    log1p,
+    log2,
+    log10,
+    logaddexp,
+    logical_and,
+    logical_not,
+    logical_or,
+    logical_xor,
+    multiply,
+    negative,
+    not_equal,
+    positive,
+    pow,
+    real,
+    remainder,
+    round,
+    sign,
+    sin,
+    sinh,
+    square,
+    sqrt,
+    subtract,
+    tan,
+    tanh,
+    trunc,
+)
+
+__all__ += [
+    "abs",
+    "acos",
+    "acosh",
+    "add",
+    "asin",
+    "asinh",
+    "atan",
+    "atan2",
+    "atanh",
+    "bitwise_and",
+    "bitwise_left_shift",
+    "bitwise_invert",
+    "bitwise_or",
+    "bitwise_right_shift",
+    "bitwise_xor",
+    "ceil",
+    "cos",
+    "cosh",
+    "divide",
+    "equal",
+    "exp",
+    "expm1",
+    "floor",
+    "floor_divide",
+    "greater",
+    "greater_equal",
+    "isfinite",
+    "isinf",
+    "isnan",
+    "less",
+    "less_equal",
+    "log",
+    "log1p",
+    "log2",
+    "log10",
+    "logaddexp",
+    "logical_and",
+    "logical_not",
+    "logical_or",
+    "logical_xor",
+    "multiply",
+    "negative",
+    "not_equal",
+    "positive",
+    "pow",
+    "remainder",
+    "round",
+    "sign",
+    "sin",
+    "sinh",
+    "square",
+    "sqrt",
+    "subtract",
+    "tan",
+    "tanh",
+    "trunc",
+]
+
+from ._indexing_functions import take
+
+__all__ += ["take"]
+
+# linalg is an extension in the array API spec, which is a sub-namespace. Only
+# a subset of functions in it are imported into the top-level namespace.
+from . import linalg
+
+__all__ += ["linalg"]
+
+from .linalg import matmul, tensordot, matrix_transpose, vecdot
+
+__all__ += ["matmul", "tensordot", "matrix_transpose", "vecdot"]
+
+from ._manipulation_functions import (
+    concat,
+    expand_dims,
+    flip,
+    permute_dims,
+    reshape,
+    roll,
+    squeeze,
+    stack,
+)
+
+__all__ += ["concat", "expand_dims", "flip", "permute_dims", "reshape", "roll", "squeeze", "stack"]
+
+from ._searching_functions import argmax, argmin, nonzero, where
+
+__all__ += ["argmax", "argmin", "nonzero", "where"]
+
+from ._set_functions import unique_all, unique_counts, unique_inverse, unique_values
+
+__all__ += ["unique_all", "unique_counts", "unique_inverse", "unique_values"]
+
+from ._sorting_functions import argsort, sort
+
+__all__ += ["argsort", "sort"]
+
+from ._statistical_functions import max, mean, min, prod, std, sum, var
+
+__all__ += ["max", "mean", "min", "prod", "std", "sum", "var"]
+
+from ._utility_functions import all, any
+
+__all__ += ["all", "any"]

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

@@ -0,0 +1,7 @@
+import numpy as np
+
+e = np.e
+inf = np.inf
+nan = np.nan
+pi = np.pi
+newaxis = np.newaxis

env-llmeval/lib/python3.10/site-packages/numpy/array_api/_creation_functions.py

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

env-llmeval/lib/python3.10/site-packages/numpy/array_api/_elementwise_functions.py

@@ -0,0 +1,765 @@
+from __future__ import annotations
+
+from ._dtypes import (
+    _boolean_dtypes,
+    _floating_dtypes,
+    _real_floating_dtypes,
+    _complex_floating_dtypes,
+    _integer_dtypes,
+    _integer_or_boolean_dtypes,
+    _real_numeric_dtypes,
+    _numeric_dtypes,
+    _result_type,
+)
+from ._array_object import Array
+
+import numpy as np
+
+
+def abs(x: Array, /) -> Array:
+    """
+    Array API compatible wrapper for :py:func:`np.abs <numpy.abs>`.
+
+    See its docstring for more information.
+    """
+    if x.dtype not in _numeric_dtypes:
+        raise TypeError("Only numeric dtypes are allowed in abs")
+    return Array._new(np.abs(x._array))
+
+
+# Note: the function name is different here
+def acos(x: Array, /) -> Array:
+    """
+    Array API compatible wrapper for :py:func:`np.arccos <numpy.arccos>`.
+
+    See its docstring for more information.
+    """
+    if x.dtype not in _floating_dtypes:
+        raise TypeError("Only floating-point dtypes are allowed in acos")
+    return Array._new(np.arccos(x._array))
+
+
+# Note: the function name is different here
+def acosh(x: Array, /) -> Array:
+    """
+    Array API compatible wrapper for :py:func:`np.arccosh <numpy.arccosh>`.
+
+    See its docstring for more information.
+    """
+    if x.dtype not in _floating_dtypes:
+        raise TypeError("Only floating-point dtypes are allowed in acosh")
+    return Array._new(np.arccosh(x._array))
+
+
+def add(x1: Array, x2: Array, /) -> Array:
+    """
+    Array API compatible wrapper for :py:func:`np.add <numpy.add>`.
+
+    See its docstring for more information.
+    """
+    if x1.dtype not in _numeric_dtypes or x2.dtype not in _numeric_dtypes:
+        raise TypeError("Only numeric dtypes are allowed in add")
+    # Call result type here just to raise on disallowed type combinations
+    _result_type(x1.dtype, x2.dtype)
+    x1, x2 = Array._normalize_two_args(x1, x2)
+    return Array._new(np.add(x1._array, x2._array))
+
+
+# Note: the function name is different here
+def asin(x: Array, /) -> Array:
+    """
+    Array API compatible wrapper for :py:func:`np.arcsin <numpy.arcsin>`.
+
+    See its docstring for more information.
+    """
+    if x.dtype not in _floating_dtypes:
+        raise TypeError("Only floating-point dtypes are allowed in asin")
+    return Array._new(np.arcsin(x._array))
+
+
+# Note: the function name is different here
+def asinh(x: Array, /) -> Array:
+    """
+    Array API compatible wrapper for :py:func:`np.arcsinh <numpy.arcsinh>`.
+
+    See its docstring for more information.
+    """
+    if x.dtype not in _floating_dtypes:
+        raise TypeError("Only floating-point dtypes are allowed in asinh")
+    return Array._new(np.arcsinh(x._array))
+
+
+# Note: the function name is different here
+def atan(x: Array, /) -> Array:
+    """
+    Array API compatible wrapper for :py:func:`np.arctan <numpy.arctan>`.
+
+    See its docstring for more information.
+    """
+    if x.dtype not in _floating_dtypes:
+        raise TypeError("Only floating-point dtypes are allowed in atan")
+    return Array._new(np.arctan(x._array))
+
+
+# Note: the function name is different here
+def atan2(x1: Array, x2: Array, /) -> Array:
+    """
+    Array API compatible wrapper for :py:func:`np.arctan2 <numpy.arctan2>`.
+
+    See its docstring for more information.
+    """
+    if x1.dtype not in _real_floating_dtypes or x2.dtype not in _real_floating_dtypes:
+        raise TypeError("Only real floating-point dtypes are allowed in atan2")
+    # Call result type here just to raise on disallowed type combinations
+    _result_type(x1.dtype, x2.dtype)
+    x1, x2 = Array._normalize_two_args(x1, x2)
+    return Array._new(np.arctan2(x1._array, x2._array))
+
+
+# Note: the function name is different here
+def atanh(x: Array, /) -> Array:
+    """
+    Array API compatible wrapper for :py:func:`np.arctanh <numpy.arctanh>`.
+
+    See its docstring for more information.
+    """
+    if x.dtype not in _floating_dtypes:
+        raise TypeError("Only floating-point dtypes are allowed in atanh")
+    return Array._new(np.arctanh(x._array))
+
+
+def bitwise_and(x1: Array, x2: Array, /) -> Array:
+    """
+    Array API compatible wrapper for :py:func:`np.bitwise_and <numpy.bitwise_and>`.
+
+    See its docstring for more information.
+    """
+    if (
+        x1.dtype not in _integer_or_boolean_dtypes
+        or x2.dtype not in _integer_or_boolean_dtypes
+    ):
+        raise TypeError("Only integer or boolean dtypes are allowed in bitwise_and")
+    # Call result type here just to raise on disallowed type combinations
+    _result_type(x1.dtype, x2.dtype)
+    x1, x2 = Array._normalize_two_args(x1, x2)
+    return Array._new(np.bitwise_and(x1._array, x2._array))
+
+
+# Note: the function name is different here
+def bitwise_left_shift(x1: Array, x2: Array, /) -> Array:
+    """
+    Array API compatible wrapper for :py:func:`np.left_shift <numpy.left_shift>`.
+
+    See its docstring for more information.
+    """
+    if x1.dtype not in _integer_dtypes or x2.dtype not in _integer_dtypes:
+        raise TypeError("Only integer dtypes are allowed in bitwise_left_shift")
+    # Call result type here just to raise on disallowed type combinations
+    _result_type(x1.dtype, x2.dtype)
+    x1, x2 = Array._normalize_two_args(x1, x2)
+    # Note: bitwise_left_shift is only defined for x2 nonnegative.
+    if np.any(x2._array < 0):
+        raise ValueError("bitwise_left_shift(x1, x2) is only defined for x2 >= 0")
+    return Array._new(np.left_shift(x1._array, x2._array))
+
+
+# Note: the function name is different here
+def bitwise_invert(x: Array, /) -> Array:
+    """
+    Array API compatible wrapper for :py:func:`np.invert <numpy.invert>`.
+
+    See its docstring for more information.
+    """
+    if x.dtype not in _integer_or_boolean_dtypes:
+        raise TypeError("Only integer or boolean dtypes are allowed in bitwise_invert")
+    return Array._new(np.invert(x._array))
+
+
+def bitwise_or(x1: Array, x2: Array, /) -> Array:
+    """
+    Array API compatible wrapper for :py:func:`np.bitwise_or <numpy.bitwise_or>`.
+
+    See its docstring for more information.
+    """
+    if (
+        x1.dtype not in _integer_or_boolean_dtypes
+        or x2.dtype not in _integer_or_boolean_dtypes
+    ):
+        raise TypeError("Only integer or boolean dtypes are allowed in bitwise_or")
+    # Call result type here just to raise on disallowed type combinations
+    _result_type(x1.dtype, x2.dtype)
+    x1, x2 = Array._normalize_two_args(x1, x2)
+    return Array._new(np.bitwise_or(x1._array, x2._array))
+
+
+# Note: the function name is different here
+def bitwise_right_shift(x1: Array, x2: Array, /) -> Array:
+    """
+    Array API compatible wrapper for :py:func:`np.right_shift <numpy.right_shift>`.
+
+    See its docstring for more information.
+    """
+    if x1.dtype not in _integer_dtypes or x2.dtype not in _integer_dtypes:
+        raise TypeError("Only integer dtypes are allowed in bitwise_right_shift")
+    # Call result type here just to raise on disallowed type combinations
+    _result_type(x1.dtype, x2.dtype)
+    x1, x2 = Array._normalize_two_args(x1, x2)
+    # Note: bitwise_right_shift is only defined for x2 nonnegative.
+    if np.any(x2._array < 0):
+        raise ValueError("bitwise_right_shift(x1, x2) is only defined for x2 >= 0")
+    return Array._new(np.right_shift(x1._array, x2._array))
+
+
+def bitwise_xor(x1: Array, x2: Array, /) -> Array:
+    """
+    Array API compatible wrapper for :py:func:`np.bitwise_xor <numpy.bitwise_xor>`.
+
+    See its docstring for more information.
+    """
+    if (
+        x1.dtype not in _integer_or_boolean_dtypes
+        or x2.dtype not in _integer_or_boolean_dtypes
+    ):
+        raise TypeError("Only integer or boolean dtypes are allowed in bitwise_xor")
+    # Call result type here just to raise on disallowed type combinations
+    _result_type(x1.dtype, x2.dtype)
+    x1, x2 = Array._normalize_two_args(x1, x2)
+    return Array._new(np.bitwise_xor(x1._array, x2._array))
+
+
+def ceil(x: Array, /) -> Array:
+    """
+    Array API compatible wrapper for :py:func:`np.ceil <numpy.ceil>`.
+
+    See its docstring for more information.
+    """
+    if x.dtype not in _real_numeric_dtypes:
+        raise TypeError("Only real numeric dtypes are allowed in ceil")
+    if x.dtype in _integer_dtypes:
+        # Note: The return dtype of ceil is the same as the input
+        return x
+    return Array._new(np.ceil(x._array))
+
+
+def conj(x: Array, /) -> Array:
+    """
+    Array API compatible wrapper for :py:func:`np.conj <numpy.conj>`.
+
+    See its docstring for more information.
+    """
+    if x.dtype not in _complex_floating_dtypes:
+        raise TypeError("Only complex floating-point dtypes are allowed in conj")
+    return Array._new(np.conj(x))
+
+
+def cos(x: Array, /) -> Array:
+    """
+    Array API compatible wrapper for :py:func:`np.cos <numpy.cos>`.
+
+    See its docstring for more information.
+    """
+    if x.dtype not in _floating_dtypes:
+        raise TypeError("Only floating-point dtypes are allowed in cos")
+    return Array._new(np.cos(x._array))
+
+
+def cosh(x: Array, /) -> Array:
+    """
+    Array API compatible wrapper for :py:func:`np.cosh <numpy.cosh>`.
+
+    See its docstring for more information.
+    """
+    if x.dtype not in _floating_dtypes:
+        raise TypeError("Only floating-point dtypes are allowed in cosh")
+    return Array._new(np.cosh(x._array))
+
+
+def divide(x1: Array, x2: Array, /) -> Array:
+    """
+    Array API compatible wrapper for :py:func:`np.divide <numpy.divide>`.
+
+    See its docstring for more information.
+    """
+    if x1.dtype not in _floating_dtypes or x2.dtype not in _floating_dtypes:
+        raise TypeError("Only floating-point dtypes are allowed in divide")
+    # Call result type here just to raise on disallowed type combinations
+    _result_type(x1.dtype, x2.dtype)
+    x1, x2 = Array._normalize_two_args(x1, x2)
+    return Array._new(np.divide(x1._array, x2._array))
+
+
+def equal(x1: Array, x2: Array, /) -> Array:
+    """
+    Array API compatible wrapper for :py:func:`np.equal <numpy.equal>`.
+
+    See its docstring for more information.
+    """
+    # Call result type here just to raise on disallowed type combinations
+    _result_type(x1.dtype, x2.dtype)
+    x1, x2 = Array._normalize_two_args(x1, x2)
+    return Array._new(np.equal(x1._array, x2._array))
+
+
+def exp(x: Array, /) -> Array:
+    """
+    Array API compatible wrapper for :py:func:`np.exp <numpy.exp>`.
+
+    See its docstring for more information.
+    """
+    if x.dtype not in _floating_dtypes:
+        raise TypeError("Only floating-point dtypes are allowed in exp")
+    return Array._new(np.exp(x._array))
+
+
+def expm1(x: Array, /) -> Array:
+    """
+    Array API compatible wrapper for :py:func:`np.expm1 <numpy.expm1>`.
+
+    See its docstring for more information.
+    """
+    if x.dtype not in _floating_dtypes:
+        raise TypeError("Only floating-point dtypes are allowed in expm1")
+    return Array._new(np.expm1(x._array))
+
+
+def floor(x: Array, /) -> Array:
+    """
+    Array API compatible wrapper for :py:func:`np.floor <numpy.floor>`.
+
+    See its docstring for more information.
+    """
+    if x.dtype not in _real_numeric_dtypes:
+        raise TypeError("Only real numeric dtypes are allowed in floor")
+    if x.dtype in _integer_dtypes:
+        # Note: The return dtype of floor is the same as the input
+        return x
+    return Array._new(np.floor(x._array))
+
+
+def floor_divide(x1: Array, x2: Array, /) -> Array:
+    """
+    Array API compatible wrapper for :py:func:`np.floor_divide <numpy.floor_divide>`.
+
+    See its docstring for more information.
+    """
+    if x1.dtype not in _real_numeric_dtypes or x2.dtype not in _real_numeric_dtypes:
+        raise TypeError("Only real numeric dtypes are allowed in floor_divide")
+    # Call result type here just to raise on disallowed type combinations
+    _result_type(x1.dtype, x2.dtype)
+    x1, x2 = Array._normalize_two_args(x1, x2)
+    return Array._new(np.floor_divide(x1._array, x2._array))
+
+
+def greater(x1: Array, x2: Array, /) -> Array:
+    """
+    Array API compatible wrapper for :py:func:`np.greater <numpy.greater>`.
+
+    See its docstring for more information.
+    """
+    if x1.dtype not in _real_numeric_dtypes or x2.dtype not in _real_numeric_dtypes:
+        raise TypeError("Only real numeric dtypes are allowed in greater")
+    # Call result type here just to raise on disallowed type combinations
+    _result_type(x1.dtype, x2.dtype)
+    x1, x2 = Array._normalize_two_args(x1, x2)
+    return Array._new(np.greater(x1._array, x2._array))
+
+
+def greater_equal(x1: Array, x2: Array, /) -> Array:
+    """
+    Array API compatible wrapper for :py:func:`np.greater_equal <numpy.greater_equal>`.
+
+    See its docstring for more information.
+    """
+    if x1.dtype not in _real_numeric_dtypes or x2.dtype not in _real_numeric_dtypes:
+        raise TypeError("Only real numeric dtypes are allowed in greater_equal")
+    # Call result type here just to raise on disallowed type combinations
+    _result_type(x1.dtype, x2.dtype)
+    x1, x2 = Array._normalize_two_args(x1, x2)
+    return Array._new(np.greater_equal(x1._array, x2._array))
+
+
+def imag(x: Array, /) -> Array:
+    """
+    Array API compatible wrapper for :py:func:`np.imag <numpy.imag>`.
+
+    See its docstring for more information.
+    """
+    if x.dtype not in _complex_floating_dtypes:
+        raise TypeError("Only complex floating-point dtypes are allowed in imag")
+    return Array._new(np.imag(x))
+
+
+def isfinite(x: Array, /) -> Array:
+    """
+    Array API compatible wrapper for :py:func:`np.isfinite <numpy.isfinite>`.
+
+    See its docstring for more information.
+    """
+    if x.dtype not in _numeric_dtypes:
+        raise TypeError("Only numeric dtypes are allowed in isfinite")
+    return Array._new(np.isfinite(x._array))
+
+
+def isinf(x: Array, /) -> Array:
+    """
+    Array API compatible wrapper for :py:func:`np.isinf <numpy.isinf>`.
+
+    See its docstring for more information.
+    """
+    if x.dtype not in _numeric_dtypes:
+        raise TypeError("Only numeric dtypes are allowed in isinf")
+    return Array._new(np.isinf(x._array))
+
+
+def isnan(x: Array, /) -> Array:
+    """
+    Array API compatible wrapper for :py:func:`np.isnan <numpy.isnan>`.
+
+    See its docstring for more information.
+    """
+    if x.dtype not in _numeric_dtypes:
+        raise TypeError("Only numeric dtypes are allowed in isnan")
+    return Array._new(np.isnan(x._array))
+
+
+def less(x1: Array, x2: Array, /) -> Array:
+    """
+    Array API compatible wrapper for :py:func:`np.less <numpy.less>`.
+
+    See its docstring for more information.
+    """
+    if x1.dtype not in _real_numeric_dtypes or x2.dtype not in _real_numeric_dtypes:
+        raise TypeError("Only real numeric dtypes are allowed in less")
+    # Call result type here just to raise on disallowed type combinations
+    _result_type(x1.dtype, x2.dtype)
+    x1, x2 = Array._normalize_two_args(x1, x2)
+    return Array._new(np.less(x1._array, x2._array))
+
+
+def less_equal(x1: Array, x2: Array, /) -> Array:
+    """
+    Array API compatible wrapper for :py:func:`np.less_equal <numpy.less_equal>`.
+
+    See its docstring for more information.
+    """
+    if x1.dtype not in _real_numeric_dtypes or x2.dtype not in _real_numeric_dtypes:
+        raise TypeError("Only real numeric dtypes are allowed in less_equal")
+    # Call result type here just to raise on disallowed type combinations
+    _result_type(x1.dtype, x2.dtype)
+    x1, x2 = Array._normalize_two_args(x1, x2)
+    return Array._new(np.less_equal(x1._array, x2._array))
+
+
+def log(x: Array, /) -> Array:
+    """
+    Array API compatible wrapper for :py:func:`np.log <numpy.log>`.
+
+    See its docstring for more information.
+    """
+    if x.dtype not in _floating_dtypes:
+        raise TypeError("Only floating-point dtypes are allowed in log")
+    return Array._new(np.log(x._array))
+
+
+def log1p(x: Array, /) -> Array:
+    """
+    Array API compatible wrapper for :py:func:`np.log1p <numpy.log1p>`.
+
+    See its docstring for more information.
+    """
+    if x.dtype not in _floating_dtypes:
+        raise TypeError("Only floating-point dtypes are allowed in log1p")
+    return Array._new(np.log1p(x._array))
+
+
+def log2(x: Array, /) -> Array:
+    """
+    Array API compatible wrapper for :py:func:`np.log2 <numpy.log2>`.
+
+    See its docstring for more information.
+    """
+    if x.dtype not in _floating_dtypes:
+        raise TypeError("Only floating-point dtypes are allowed in log2")
+    return Array._new(np.log2(x._array))
+
+
+def log10(x: Array, /) -> Array:
+    """
+    Array API compatible wrapper for :py:func:`np.log10 <numpy.log10>`.
+
+    See its docstring for more information.
+    """
+    if x.dtype not in _floating_dtypes:
+        raise TypeError("Only floating-point dtypes are allowed in log10")
+    return Array._new(np.log10(x._array))
+
+
+def logaddexp(x1: Array, x2: Array) -> Array:
+    """
+    Array API compatible wrapper for :py:func:`np.logaddexp <numpy.logaddexp>`.
+
+    See its docstring for more information.
+    """
+    if x1.dtype not in _real_floating_dtypes or x2.dtype not in _real_floating_dtypes:
+        raise TypeError("Only real floating-point dtypes are allowed in logaddexp")
+    # Call result type here just to raise on disallowed type combinations
+    _result_type(x1.dtype, x2.dtype)
+    x1, x2 = Array._normalize_two_args(x1, x2)
+    return Array._new(np.logaddexp(x1._array, x2._array))
+
+
+def logical_and(x1: Array, x2: Array, /) -> Array:
+    """
+    Array API compatible wrapper for :py:func:`np.logical_and <numpy.logical_and>`.
+
+    See its docstring for more information.
+    """
+    if x1.dtype not in _boolean_dtypes or x2.dtype not in _boolean_dtypes:
+        raise TypeError("Only boolean dtypes are allowed in logical_and")
+    # Call result type here just to raise on disallowed type combinations
+    _result_type(x1.dtype, x2.dtype)
+    x1, x2 = Array._normalize_two_args(x1, x2)
+    return Array._new(np.logical_and(x1._array, x2._array))
+
+
+def logical_not(x: Array, /) -> Array:
+    """
+    Array API compatible wrapper for :py:func:`np.logical_not <numpy.logical_not>`.
+
+    See its docstring for more information.
+    """
+    if x.dtype not in _boolean_dtypes:
+        raise TypeError("Only boolean dtypes are allowed in logical_not")
+    return Array._new(np.logical_not(x._array))
+
+
+def logical_or(x1: Array, x2: Array, /) -> Array:
+    """
+    Array API compatible wrapper for :py:func:`np.logical_or <numpy.logical_or>`.
+
+    See its docstring for more information.
+    """
+    if x1.dtype not in _boolean_dtypes or x2.dtype not in _boolean_dtypes:
+        raise TypeError("Only boolean dtypes are allowed in logical_or")
+    # Call result type here just to raise on disallowed type combinations
+    _result_type(x1.dtype, x2.dtype)
+    x1, x2 = Array._normalize_two_args(x1, x2)
+    return Array._new(np.logical_or(x1._array, x2._array))
+
+
+def logical_xor(x1: Array, x2: Array, /) -> Array:
+    """
+    Array API compatible wrapper for :py:func:`np.logical_xor <numpy.logical_xor>`.
+
+    See its docstring for more information.
+    """
+    if x1.dtype not in _boolean_dtypes or x2.dtype not in _boolean_dtypes:
+        raise TypeError("Only boolean dtypes are allowed in logical_xor")
+    # Call result type here just to raise on disallowed type combinations
+    _result_type(x1.dtype, x2.dtype)
+    x1, x2 = Array._normalize_two_args(x1, x2)
+    return Array._new(np.logical_xor(x1._array, x2._array))
+
+
+def multiply(x1: Array, x2: Array, /) -> Array:
+    """
+    Array API compatible wrapper for :py:func:`np.multiply <numpy.multiply>`.
+
+    See its docstring for more information.
+    """
+    if x1.dtype not in _numeric_dtypes or x2.dtype not in _numeric_dtypes:
+        raise TypeError("Only numeric dtypes are allowed in multiply")
+    # Call result type here just to raise on disallowed type combinations
+    _result_type(x1.dtype, x2.dtype)
+    x1, x2 = Array._normalize_two_args(x1, x2)
+    return Array._new(np.multiply(x1._array, x2._array))
+
+
+def negative(x: Array, /) -> Array:
+    """
+    Array API compatible wrapper for :py:func:`np.negative <numpy.negative>`.
+
+    See its docstring for more information.
+    """
+    if x.dtype not in _numeric_dtypes:
+        raise TypeError("Only numeric dtypes are allowed in negative")
+    return Array._new(np.negative(x._array))
+
+
+def not_equal(x1: Array, x2: Array, /) -> Array:
+    """
+    Array API compatible wrapper for :py:func:`np.not_equal <numpy.not_equal>`.
+
+    See its docstring for more information.
+    """
+    # Call result type here just to raise on disallowed type combinations
+    _result_type(x1.dtype, x2.dtype)
+    x1, x2 = Array._normalize_two_args(x1, x2)
+    return Array._new(np.not_equal(x1._array, x2._array))
+
+
+def positive(x: Array, /) -> Array:
+    """
+    Array API compatible wrapper for :py:func:`np.positive <numpy.positive>`.
+
+    See its docstring for more information.
+    """
+    if x.dtype not in _numeric_dtypes:
+        raise TypeError("Only numeric dtypes are allowed in positive")
+    return Array._new(np.positive(x._array))
+
+
+# Note: the function name is different here
+def pow(x1: Array, x2: Array, /) -> Array:
+    """
+    Array API compatible wrapper for :py:func:`np.power <numpy.power>`.
+
+    See its docstring for more information.
+    """
+    if x1.dtype not in _numeric_dtypes or x2.dtype not in _numeric_dtypes:
+        raise TypeError("Only numeric dtypes are allowed in pow")
+    # Call result type here just to raise on disallowed type combinations
+    _result_type(x1.dtype, x2.dtype)
+    x1, x2 = Array._normalize_two_args(x1, x2)
+    return Array._new(np.power(x1._array, x2._array))
+
+
+def real(x: Array, /) -> Array:
+    """
+    Array API compatible wrapper for :py:func:`np.real <numpy.real>`.
+
+    See its docstring for more information.
+    """
+    if x.dtype not in _complex_floating_dtypes:
+        raise TypeError("Only complex floating-point dtypes are allowed in real")
+    return Array._new(np.real(x))
+
+
+def remainder(x1: Array, x2: Array, /) -> Array:
+    """
+    Array API compatible wrapper for :py:func:`np.remainder <numpy.remainder>`.
+
+    See its docstring for more information.
+    """
+    if x1.dtype not in _real_numeric_dtypes or x2.dtype not in _real_numeric_dtypes:
+        raise TypeError("Only real numeric dtypes are allowed in remainder")
+    # Call result type here just to raise on disallowed type combinations
+    _result_type(x1.dtype, x2.dtype)
+    x1, x2 = Array._normalize_two_args(x1, x2)
+    return Array._new(np.remainder(x1._array, x2._array))
+
+
+def round(x: Array, /) -> Array:
+    """
+    Array API compatible wrapper for :py:func:`np.round <numpy.round>`.
+
+    See its docstring for more information.
+    """
+    if x.dtype not in _numeric_dtypes:
+        raise TypeError("Only numeric dtypes are allowed in round")
+    return Array._new(np.round(x._array))
+
+
+def sign(x: Array, /) -> Array:
+    """
+    Array API compatible wrapper for :py:func:`np.sign <numpy.sign>`.
+
+    See its docstring for more information.
+    """
+    if x.dtype not in _numeric_dtypes:
+        raise TypeError("Only numeric dtypes are allowed in sign")
+    return Array._new(np.sign(x._array))
+
+
+def sin(x: Array, /) -> Array:
+    """
+    Array API compatible wrapper for :py:func:`np.sin <numpy.sin>`.
+
+    See its docstring for more information.
+    """
+    if x.dtype not in _floating_dtypes:
+        raise TypeError("Only floating-point dtypes are allowed in sin")
+    return Array._new(np.sin(x._array))
+
+
+def sinh(x: Array, /) -> Array:
+    """
+    Array API compatible wrapper for :py:func:`np.sinh <numpy.sinh>`.
+
+    See its docstring for more information.
+    """
+    if x.dtype not in _floating_dtypes:
+        raise TypeError("Only floating-point dtypes are allowed in sinh")
+    return Array._new(np.sinh(x._array))
+
+
+def square(x: Array, /) -> Array:
+    """
+    Array API compatible wrapper for :py:func:`np.square <numpy.square>`.
+
+    See its docstring for more information.
+    """
+    if x.dtype not in _numeric_dtypes:
+        raise TypeError("Only numeric dtypes are allowed in square")
+    return Array._new(np.square(x._array))
+
+
+def sqrt(x: Array, /) -> Array:
+    """
+    Array API compatible wrapper for :py:func:`np.sqrt <numpy.sqrt>`.
+
+    See its docstring for more information.
+    """
+    if x.dtype not in _floating_dtypes:
+        raise TypeError("Only floating-point dtypes are allowed in sqrt")
+    return Array._new(np.sqrt(x._array))
+
+
+def subtract(x1: Array, x2: Array, /) -> Array:
+    """
+    Array API compatible wrapper for :py:func:`np.subtract <numpy.subtract>`.
+
+    See its docstring for more information.
+    """
+    if x1.dtype not in _numeric_dtypes or x2.dtype not in _numeric_dtypes:
+        raise TypeError("Only numeric dtypes are allowed in subtract")
+    # Call result type here just to raise on disallowed type combinations
+    _result_type(x1.dtype, x2.dtype)
+    x1, x2 = Array._normalize_two_args(x1, x2)
+    return Array._new(np.subtract(x1._array, x2._array))
+
+
+def tan(x: Array, /) -> Array:
+    """
+    Array API compatible wrapper for :py:func:`np.tan <numpy.tan>`.
+
+    See its docstring for more information.
+    """
+    if x.dtype not in _floating_dtypes:
+        raise TypeError("Only floating-point dtypes are allowed in tan")
+    return Array._new(np.tan(x._array))
+
+
+def tanh(x: Array, /) -> Array:
+    """
+    Array API compatible wrapper for :py:func:`np.tanh <numpy.tanh>`.
+
+    See its docstring for more information.
+    """
+    if x.dtype not in _floating_dtypes:
+        raise TypeError("Only floating-point dtypes are allowed in tanh")
+    return Array._new(np.tanh(x._array))
+
+
+def trunc(x: Array, /) -> Array:
+    """
+    Array API compatible wrapper for :py:func:`np.trunc <numpy.trunc>`.
+
+    See its docstring for more information.
+    """
+    if x.dtype not in _real_numeric_dtypes:
+        raise TypeError("Only real numeric dtypes are allowed in trunc")
+    if x.dtype in _integer_dtypes:
+        # Note: The return dtype of trunc is the same as the input
+        return x
+    return Array._new(np.trunc(x._array))

env-llmeval/lib/python3.10/site-packages/numpy/array_api/_indexing_functions.py

@@ -0,0 +1,20 @@
+from __future__ import annotations
+
+from ._array_object import Array
+from ._dtypes import _integer_dtypes
+
+import numpy as np
+
+def take(x: Array, indices: Array, /, *, axis: Optional[int] = None) -> Array:
+    """
+    Array API compatible wrapper for :py:func:`np.take <numpy.take>`.
+
+    See its docstring for more information.
+    """
+    if axis is None and x.ndim != 1:
+        raise ValueError("axis must be specified when ndim > 1")
+    if indices.dtype not in _integer_dtypes:
+        raise TypeError("Only integer dtypes are allowed in indexing")
+    if indices.ndim != 1:
+        raise ValueError("Only 1-dim indices array is supported")
+    return Array._new(np.take(x._array, indices._array, axis=axis))

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

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

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

@@ -0,0 +1,122 @@
+from __future__ import annotations
+
+from ._dtypes import (
+    _real_floating_dtypes,
+    _real_numeric_dtypes,
+    _numeric_dtypes,
+)
+from ._array_object import Array
+from ._dtypes import float32, float64, complex64, complex128
+
+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 _real_numeric_dtypes:
+        raise TypeError("Only real 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 _real_floating_dtypes:
+        raise TypeError("Only real 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 _real_numeric_dtypes:
+        raise TypeError("Only real 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 for dtype=None. `np.prod` does that
+    # for integers, but not for float32 or complex64, so we need to
+    # special-case it here
+    if dtype is None:
+        if x.dtype == float32:
+            dtype = float64
+        elif x.dtype == complex64:
+            dtype = complex128
+    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 _real_floating_dtypes:
+        raise TypeError("Only real 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 for dtype=None. `np.sum` does that
+    # for integers, but not for float32 or complex64, so we need to
+    # special-case it here
+    if dtype is None:
+        if x.dtype == float32:
+            dtype = float64
+        elif x.dtype == complex64:
+            dtype = complex128
+    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 _real_floating_dtypes:
+        raise TypeError("Only real floating-point dtypes are allowed in var")
+    return Array._new(np.var(x._array, axis=axis, ddof=correction, keepdims=keepdims))

env-llmeval/lib/python3.10/site-packages/numpy/array_api/linalg.py

@@ -0,0 +1,466 @@
+from __future__ import annotations
+
+from ._dtypes import (
+    _floating_dtypes,
+    _numeric_dtypes,
+    float32,
+    float64,
+    complex64,
+    complex128
+)
+from ._manipulation_functions import reshape
+from ._elementwise_functions import conj
+from ._array_object import Array
+
+from ..core.numeric import normalize_axis_tuple
+
+from typing import TYPE_CHECKING
+if TYPE_CHECKING:
+    from ._typing import Literal, Optional, Sequence, Tuple, Union, Dtype
+
+from typing import NamedTuple
+
+import numpy.linalg
+import numpy as np
+
+class EighResult(NamedTuple):
+    eigenvalues: Array
+    eigenvectors: Array
+
+class QRResult(NamedTuple):
+    Q: Array
+    R: Array
+
+class SlogdetResult(NamedTuple):
+    sign: Array
+    logabsdet: Array
+
+class SVDResult(NamedTuple):
+    U: Array
+    S: Array
+    Vh: Array
+
+# Note: the inclusion of the upper keyword is different from
+# np.linalg.cholesky, which does not have it.
+def cholesky(x: Array, /, *, upper: bool = False) -> Array:
+    """
+    Array API compatible wrapper for :py:func:`np.linalg.cholesky <numpy.linalg.cholesky>`.
+
+    See its docstring for more information.
+    """
+    # Note: the restriction to floating-point dtypes only is different from
+    # np.linalg.cholesky.
+    if x.dtype not in _floating_dtypes:
+        raise TypeError('Only floating-point dtypes are allowed in cholesky')
+    L = np.linalg.cholesky(x._array)
+    if upper:
+        U = Array._new(L).mT
+        if U.dtype in [complex64, complex128]:
+            U = conj(U)
+        return U
+    return Array._new(L)
+
+# Note: cross is the numpy top-level namespace, not np.linalg
+def cross(x1: Array, x2: Array, /, *, axis: int = -1) -> Array:
+    """
+    Array API compatible wrapper for :py:func:`np.cross <numpy.cross>`.
+
+    See its docstring for more information.
+    """
+    if x1.dtype not in _numeric_dtypes or x2.dtype not in _numeric_dtypes:
+        raise TypeError('Only numeric dtypes are allowed in cross')
+    # Note: this is different from np.cross(), which broadcasts
+    if x1.shape != x2.shape:
+        raise ValueError('x1 and x2 must have the same shape')
+    if x1.ndim == 0:
+        raise ValueError('cross() requires arrays of dimension at least 1')
+    # Note: this is different from np.cross(), which allows dimension 2
+    if x1.shape[axis] != 3:
+        raise ValueError('cross() dimension must equal 3')
+    return Array._new(np.cross(x1._array, x2._array, axis=axis))
+
+def det(x: Array, /) -> Array:
+    """
+    Array API compatible wrapper for :py:func:`np.linalg.det <numpy.linalg.det>`.
+
+    See its docstring for more information.
+    """
+    # Note: the restriction to floating-point dtypes only is different from
+    # np.linalg.det.
+    if x.dtype not in _floating_dtypes:
+        raise TypeError('Only floating-point dtypes are allowed in det')
+    return Array._new(np.linalg.det(x._array))
+
+# Note: diagonal is the numpy top-level namespace, not np.linalg
+def diagonal(x: Array, /, *, offset: int = 0) -> Array:
+    """
+    Array API compatible wrapper for :py:func:`np.diagonal <numpy.diagonal>`.
+
+    See its docstring for more information.
+    """
+    # Note: diagonal always operates on the last two axes, whereas np.diagonal
+    # operates on the first two axes by default
+    return Array._new(np.diagonal(x._array, offset=offset, axis1=-2, axis2=-1))
+
+
+def eigh(x: Array, /) -> EighResult:
+    """
+    Array API compatible wrapper for :py:func:`np.linalg.eigh <numpy.linalg.eigh>`.
+
+    See its docstring for more information.
+    """
+    # Note: the restriction to floating-point dtypes only is different from
+    # np.linalg.eigh.
+    if x.dtype not in _floating_dtypes:
+        raise TypeError('Only floating-point dtypes are allowed in eigh')
+
+    # Note: the return type here is a namedtuple, which is different from
+    # np.eigh, which only returns a tuple.
+    return EighResult(*map(Array._new, np.linalg.eigh(x._array)))
+
+
+def eigvalsh(x: Array, /) -> Array:
+    """
+    Array API compatible wrapper for :py:func:`np.linalg.eigvalsh <numpy.linalg.eigvalsh>`.
+
+    See its docstring for more information.
+    """
+    # Note: the restriction to floating-point dtypes only is different from
+    # np.linalg.eigvalsh.
+    if x.dtype not in _floating_dtypes:
+        raise TypeError('Only floating-point dtypes are allowed in eigvalsh')
+
+    return Array._new(np.linalg.eigvalsh(x._array))
+
+def inv(x: Array, /) -> Array:
+    """
+    Array API compatible wrapper for :py:func:`np.linalg.inv <numpy.linalg.inv>`.
+
+    See its docstring for more information.
+    """
+    # Note: the restriction to floating-point dtypes only is different from
+    # np.linalg.inv.
+    if x.dtype not in _floating_dtypes:
+        raise TypeError('Only floating-point dtypes are allowed in inv')
+
+    return Array._new(np.linalg.inv(x._array))
+
+
+# Note: matmul is the numpy top-level namespace but not in np.linalg
+def matmul(x1: Array, x2: Array, /) -> Array:
+    """
+    Array API compatible wrapper for :py:func:`np.matmul <numpy.matmul>`.
+
+    See its docstring for more information.
+    """
+    # Note: the restriction to numeric dtypes only is different from
+    # np.matmul.
+    if x1.dtype not in _numeric_dtypes or x2.dtype not in _numeric_dtypes:
+        raise TypeError('Only numeric dtypes are allowed in matmul')
+
+    return Array._new(np.matmul(x1._array, x2._array))
+
+
+# Note: the name here is different from norm(). The array API norm is split
+# into matrix_norm and vector_norm().
+
+# The type for ord should be Optional[Union[int, float, Literal[np.inf,
+# -np.inf, 'fro', 'nuc']]], but Literal does not support floating-point
+# literals.
+def matrix_norm(x: Array, /, *, keepdims: bool = False, ord: Optional[Union[int, float, Literal['fro', 'nuc']]] = 'fro') -> Array:
+    """
+    Array API compatible wrapper for :py:func:`np.linalg.norm <numpy.linalg.norm>`.
+
+    See its docstring for more information.
+    """
+    # Note: the restriction to floating-point dtypes only is different from
+    # np.linalg.norm.
+    if x.dtype not in _floating_dtypes:
+        raise TypeError('Only floating-point dtypes are allowed in matrix_norm')
+
+    return Array._new(np.linalg.norm(x._array, axis=(-2, -1), keepdims=keepdims, ord=ord))
+
+
+def matrix_power(x: Array, n: int, /) -> Array:
+    """
+    Array API compatible wrapper for :py:func:`np.matrix_power <numpy.matrix_power>`.
+
+    See its docstring for more information.
+    """
+    # Note: the restriction to floating-point dtypes only is different from
+    # np.linalg.matrix_power.
+    if x.dtype not in _floating_dtypes:
+        raise TypeError('Only floating-point dtypes are allowed for the first argument of matrix_power')
+
+    # np.matrix_power already checks if n is an integer
+    return Array._new(np.linalg.matrix_power(x._array, n))
+
+# Note: the keyword argument name rtol is different from np.linalg.matrix_rank
+def matrix_rank(x: Array, /, *, rtol: Optional[Union[float, Array]] = None) -> Array:
+    """
+    Array API compatible wrapper for :py:func:`np.matrix_rank <numpy.matrix_rank>`.
+
+    See its docstring for more information.
+    """
+    # Note: this is different from np.linalg.matrix_rank, which supports 1
+    # dimensional arrays.
+    if x.ndim < 2:
+        raise np.linalg.LinAlgError("1-dimensional array given. Array must be at least two-dimensional")
+    S = np.linalg.svd(x._array, compute_uv=False)
+    if rtol is None:
+        tol = S.max(axis=-1, keepdims=True) * max(x.shape[-2:]) * np.finfo(S.dtype).eps
+    else:
+        if isinstance(rtol, Array):
+            rtol = rtol._array
+        # Note: this is different from np.linalg.matrix_rank, which does not multiply
+        # the tolerance by the largest singular value.
+        tol = S.max(axis=-1, keepdims=True)*np.asarray(rtol)[..., np.newaxis]
+    return Array._new(np.count_nonzero(S > tol, axis=-1))
+
+
+# Note: this function is new in the array API spec. Unlike transpose, it only
+# transposes the last two axes.
+def matrix_transpose(x: Array, /) -> Array:
+    if x.ndim < 2:
+        raise ValueError("x must be at least 2-dimensional for matrix_transpose")
+    return Array._new(np.swapaxes(x._array, -1, -2))
+
+# Note: outer is the numpy top-level namespace, not np.linalg
+def outer(x1: Array, x2: Array, /) -> Array:
+    """
+    Array API compatible wrapper for :py:func:`np.outer <numpy.outer>`.
+
+    See its docstring for more information.
+    """
+    # Note: the restriction to numeric dtypes only is different from
+    # np.outer.
+    if x1.dtype not in _numeric_dtypes or x2.dtype not in _numeric_dtypes:
+        raise TypeError('Only numeric dtypes are allowed in outer')
+
+    # Note: the restriction to only 1-dim arrays is different from np.outer
+    if x1.ndim != 1 or x2.ndim != 1:
+        raise ValueError('The input arrays to outer must be 1-dimensional')
+
+    return Array._new(np.outer(x1._array, x2._array))
+
+# Note: the keyword argument name rtol is different from np.linalg.pinv
+def pinv(x: Array, /, *, rtol: Optional[Union[float, Array]] = None) -> Array:
+    """
+    Array API compatible wrapper for :py:func:`np.linalg.pinv <numpy.linalg.pinv>`.
+
+    See its docstring for more information.
+    """
+    # Note: the restriction to floating-point dtypes only is different from
+    # np.linalg.pinv.
+    if x.dtype not in _floating_dtypes:
+        raise TypeError('Only floating-point dtypes are allowed in pinv')
+
+    # Note: this is different from np.linalg.pinv, which does not multiply the
+    # default tolerance by max(M, N).
+    if rtol is None:
+        rtol = max(x.shape[-2:]) * np.finfo(x.dtype).eps
+    return Array._new(np.linalg.pinv(x._array, rcond=rtol))
+
+def qr(x: Array, /, *, mode: Literal['reduced', 'complete'] = 'reduced') -> QRResult:
+    """
+    Array API compatible wrapper for :py:func:`np.linalg.qr <numpy.linalg.qr>`.
+
+    See its docstring for more information.
+    """
+    # Note: the restriction to floating-point dtypes only is different from
+    # np.linalg.qr.
+    if x.dtype not in _floating_dtypes:
+        raise TypeError('Only floating-point dtypes are allowed in qr')
+
+    # Note: the return type here is a namedtuple, which is different from
+    # np.linalg.qr, which only returns a tuple.
+    return QRResult(*map(Array._new, np.linalg.qr(x._array, mode=mode)))
+
+def slogdet(x: Array, /) -> SlogdetResult:
+    """
+    Array API compatible wrapper for :py:func:`np.linalg.slogdet <numpy.linalg.slogdet>`.
+
+    See its docstring for more information.
+    """
+    # Note: the restriction to floating-point dtypes only is different from
+    # np.linalg.slogdet.
+    if x.dtype not in _floating_dtypes:
+        raise TypeError('Only floating-point dtypes are allowed in slogdet')
+
+    # Note: the return type here is a namedtuple, which is different from
+    # np.linalg.slogdet, which only returns a tuple.
+    return SlogdetResult(*map(Array._new, np.linalg.slogdet(x._array)))
+
+# Note: unlike np.linalg.solve, the array API solve() only accepts x2 as a
+# vector when it is exactly 1-dimensional. All other cases treat x2 as a stack
+# of matrices. The np.linalg.solve behavior of allowing stacks of both
+# matrices and vectors is ambiguous c.f.
+# https://github.com/numpy/numpy/issues/15349 and
+# https://github.com/data-apis/array-api/issues/285.
+
+# To workaround this, the below is the code from np.linalg.solve except
+# only calling solve1 in the exactly 1D case.
+def _solve(a, b):
+    from ..linalg.linalg import (_makearray, _assert_stacked_2d,
+                                 _assert_stacked_square, _commonType,
+                                 isComplexType, get_linalg_error_extobj,
+                                 _raise_linalgerror_singular)
+    from ..linalg import _umath_linalg
+
+    a, _ = _makearray(a)
+    _assert_stacked_2d(a)
+    _assert_stacked_square(a)
+    b, wrap = _makearray(b)
+    t, result_t = _commonType(a, b)
+
+    # This part is different from np.linalg.solve
+    if b.ndim == 1:
+        gufunc = _umath_linalg.solve1
+    else:
+        gufunc = _umath_linalg.solve
+
+    # This does nothing currently but is left in because it will be relevant
+    # when complex dtype support is added to the spec in 2022.
+    signature = 'DD->D' if isComplexType(t) else 'dd->d'
+    with np.errstate(call=_raise_linalgerror_singular, invalid='call',
+                     over='ignore', divide='ignore', under='ignore'):
+        r = gufunc(a, b, signature=signature)
+
+    return wrap(r.astype(result_t, copy=False))
+
+def solve(x1: Array, x2: Array, /) -> Array:
+    """
+    Array API compatible wrapper for :py:func:`np.linalg.solve <numpy.linalg.solve>`.
+
+    See its docstring for more information.
+    """
+    # Note: the restriction to floating-point dtypes only is different from
+    # np.linalg.solve.
+    if x1.dtype not in _floating_dtypes or x2.dtype not in _floating_dtypes:
+        raise TypeError('Only floating-point dtypes are allowed in solve')
+
+    return Array._new(_solve(x1._array, x2._array))
+
+def svd(x: Array, /, *, full_matrices: bool = True) -> SVDResult:
+    """
+    Array API compatible wrapper for :py:func:`np.linalg.svd <numpy.linalg.svd>`.
+
+    See its docstring for more information.
+    """
+    # Note: the restriction to floating-point dtypes only is different from
+    # np.linalg.svd.
+    if x.dtype not in _floating_dtypes:
+        raise TypeError('Only floating-point dtypes are allowed in svd')
+
+    # Note: the return type here is a namedtuple, which is different from
+    # np.svd, which only returns a tuple.
+    return SVDResult(*map(Array._new, np.linalg.svd(x._array, full_matrices=full_matrices)))
+
+# Note: svdvals is not in NumPy (but it is in SciPy). It is equivalent to
+# np.linalg.svd(compute_uv=False).
+def svdvals(x: Array, /) -> Union[Array, Tuple[Array, ...]]:
+    if x.dtype not in _floating_dtypes:
+        raise TypeError('Only floating-point dtypes are allowed in svdvals')
+    return Array._new(np.linalg.svd(x._array, compute_uv=False))
+
+# Note: tensordot is the numpy top-level namespace but not in np.linalg
+
+# Note: axes must be a tuple, unlike np.tensordot where it can be an array or array-like.
+def tensordot(x1: Array, x2: Array, /, *, axes: Union[int, Tuple[Sequence[int], Sequence[int]]] = 2) -> Array:
+    # Note: the restriction to numeric dtypes only is different from
+    # np.tensordot.
+    if x1.dtype not in _numeric_dtypes or x2.dtype not in _numeric_dtypes:
+        raise TypeError('Only numeric dtypes are allowed in tensordot')
+
+    return Array._new(np.tensordot(x1._array, x2._array, axes=axes))
+
+# Note: trace is the numpy top-level namespace, not np.linalg
+def trace(x: Array, /, *, offset: int = 0, dtype: Optional[Dtype] = None) -> Array:
+    """
+    Array API compatible wrapper for :py:func:`np.trace <numpy.trace>`.
+
+    See its docstring for more information.
+    """
+    if x.dtype not in _numeric_dtypes:
+        raise TypeError('Only numeric dtypes are allowed in trace')
+
+    # Note: trace() works the same as sum() and prod() (see
+    # _statistical_functions.py)
+    if dtype is None:
+        if x.dtype == float32:
+            dtype = float64
+        elif x.dtype == complex64:
+            dtype = complex128
+    # Note: trace always operates on the last two axes, whereas np.trace
+    # operates on the first two axes by default
+    return Array._new(np.asarray(np.trace(x._array, offset=offset, axis1=-2, axis2=-1, dtype=dtype)))
+
+# Note: vecdot is not in NumPy
+def vecdot(x1: Array, x2: Array, /, *, axis: int = -1) -> Array:
+    if x1.dtype not in _numeric_dtypes or x2.dtype not in _numeric_dtypes:
+        raise TypeError('Only numeric dtypes are allowed in vecdot')
+    ndim = max(x1.ndim, x2.ndim)
+    x1_shape = (1,)*(ndim - x1.ndim) + tuple(x1.shape)
+    x2_shape = (1,)*(ndim - x2.ndim) + tuple(x2.shape)
+    if x1_shape[axis] != x2_shape[axis]:
+        raise ValueError("x1 and x2 must have the same size along the given axis")
+
+    x1_, x2_ = np.broadcast_arrays(x1._array, x2._array)
+    x1_ = np.moveaxis(x1_, axis, -1)
+    x2_ = np.moveaxis(x2_, axis, -1)
+
+    res = x1_[..., None, :] @ x2_[..., None]
+    return Array._new(res[..., 0, 0])
+
+
+# Note: the name here is different from norm(). The array API norm is split
+# into matrix_norm and vector_norm().
+
+# The type for ord should be Optional[Union[int, float, Literal[np.inf,
+# -np.inf]]] but Literal does not support floating-point literals.
+def vector_norm(x: Array, /, *, axis: Optional[Union[int, Tuple[int, ...]]] = None, keepdims: bool = False, ord: Optional[Union[int, float]] = 2) -> Array:
+    """
+    Array API compatible wrapper for :py:func:`np.linalg.norm <numpy.linalg.norm>`.
+
+    See its docstring for more information.
+    """
+    # Note: the restriction to floating-point dtypes only is different from
+    # np.linalg.norm.
+    if x.dtype not in _floating_dtypes:
+        raise TypeError('Only floating-point dtypes are allowed in norm')
+
+    # np.linalg.norm tries to do a matrix norm whenever axis is a 2-tuple or
+    # when axis=None and the input is 2-D, so to force a vector norm, we make
+    # it so the input is 1-D (for axis=None), or reshape so that norm is done
+    # on a single dimension.
+    a = x._array
+    if axis is None:
+        # Note: np.linalg.norm() doesn't handle 0-D arrays
+        a = a.ravel()
+        _axis = 0
+    elif isinstance(axis, tuple):
+        # Note: The axis argument supports any number of axes, whereas
+        # np.linalg.norm() only supports a single axis for vector norm.
+        normalized_axis = normalize_axis_tuple(axis, x.ndim)
+        rest = tuple(i for i in range(a.ndim) if i not in normalized_axis)
+        newshape = axis + rest
+        a = np.transpose(a, newshape).reshape(
+            (np.prod([a.shape[i] for i in axis], dtype=int), *[a.shape[i] for i in rest]))
+        _axis = 0
+    else:
+        _axis = axis
+
+    res = Array._new(np.linalg.norm(a, axis=_axis, ord=ord))
+
+    if keepdims:
+        # We can't reuse np.linalg.norm(keepdims) because of the reshape hacks
+        # above to avoid matrix norm logic.
+        shape = list(x.shape)
+        _axis = normalize_axis_tuple(range(x.ndim) if axis is None else axis, x.ndim)
+        for i in _axis:
+            shape[i] = 1
+        res = reshape(res, tuple(shape))
+
+    return res
+
+__all__ = ['cholesky', 'cross', 'det', 'diagonal', 'eigh', 'eigvalsh', 'inv', 'matmul', 'matrix_norm', 'matrix_power', 'matrix_rank', 'matrix_transpose', 'outer', 'pinv', 'qr', 'slogdet', 'solve', 'svd', 'svdvals', 'tensordot', 'trace', 'vecdot', 'vector_norm']

env-llmeval/lib/python3.10/site-packages/numpy/array_api/setup.py

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

env-llmeval/lib/python3.10/site-packages/numpy/f2py/_backends/__init__.py

@@ -0,0 +1,9 @@
+def f2py_build_generator(name):
+    if name == "meson":
+        from ._meson import MesonBackend
+        return MesonBackend
+    elif name == "distutils":
+        from ._distutils import DistutilsBackend
+        return DistutilsBackend
+    else:
+        raise ValueError(f"Unknown backend: {name}")

env-llmeval/lib/python3.10/site-packages/numpy/f2py/_backends/_backend.py

@@ -0,0 +1,46 @@
+from __future__ import annotations
+
+from abc import ABC, abstractmethod
+
+
+class Backend(ABC):
+    def __init__(
+        self,
+        modulename,
+        sources,
+        extra_objects,
+        build_dir,
+        include_dirs,
+        library_dirs,
+        libraries,
+        define_macros,
+        undef_macros,
+        f2py_flags,
+        sysinfo_flags,
+        fc_flags,
+        flib_flags,
+        setup_flags,
+        remove_build_dir,
+        extra_dat,
+    ):
+        self.modulename = modulename
+        self.sources = sources
+        self.extra_objects = extra_objects
+        self.build_dir = build_dir
+        self.include_dirs = include_dirs
+        self.library_dirs = library_dirs
+        self.libraries = libraries
+        self.define_macros = define_macros
+        self.undef_macros = undef_macros
+        self.f2py_flags = f2py_flags
+        self.sysinfo_flags = sysinfo_flags
+        self.fc_flags = fc_flags
+        self.flib_flags = flib_flags
+        self.setup_flags = setup_flags
+        self.remove_build_dir = remove_build_dir
+        self.extra_dat = extra_dat
+
+    @abstractmethod
+    def compile(self) -> None:
+        """Compile the wrapper."""
+        pass

env-llmeval/lib/python3.10/site-packages/numpy/f2py/_backends/_distutils.py

@@ -0,0 +1,75 @@
+from ._backend import Backend
+
+from numpy.distutils.core import setup, Extension
+from numpy.distutils.system_info import get_info
+from numpy.distutils.misc_util import dict_append
+from numpy.exceptions import VisibleDeprecationWarning
+import os
+import sys
+import shutil
+import warnings
+
+
+class DistutilsBackend(Backend):
+    def __init__(sef, *args, **kwargs):
+        warnings.warn(
+            "distutils has been deprecated since NumPy 1.26.x"
+            "Use the Meson backend instead, or generate wrappers"
+            "without -c and use a custom build script",
+            VisibleDeprecationWarning,
+            stacklevel=2,
+        )
+        super().__init__(*args, **kwargs)
+
+    def compile(self):
+        num_info = {}
+        if num_info:
+            self.include_dirs.extend(num_info.get("include_dirs", []))
+        ext_args = {
+            "name": self.modulename,
+            "sources": self.sources,
+            "include_dirs": self.include_dirs,
+            "library_dirs": self.library_dirs,
+            "libraries": self.libraries,
+            "define_macros": self.define_macros,
+            "undef_macros": self.undef_macros,
+            "extra_objects": self.extra_objects,
+            "f2py_options": self.f2py_flags,
+        }
+
+        if self.sysinfo_flags:
+            for n in self.sysinfo_flags:
+                i = get_info(n)
+                if not i:
+                    print(
+                        f"No {repr(n)} resources found"
+                        "in system (try `f2py --help-link`)"
+                    )
+                dict_append(ext_args, **i)
+
+        ext = Extension(**ext_args)
+
+        sys.argv = [sys.argv[0]] + self.setup_flags
+        sys.argv.extend(
+            [
+                "build",
+                "--build-temp",
+                self.build_dir,
+                "--build-base",
+                self.build_dir,
+                "--build-platlib",
+                ".",
+                "--disable-optimization",
+            ]
+        )
+
+        if self.fc_flags:
+            sys.argv.extend(["config_fc"] + self.fc_flags)
+        if self.flib_flags:
+            sys.argv.extend(["build_ext"] + self.flib_flags)
+
+        setup(ext_modules=[ext])
+
+        if self.remove_build_dir and os.path.exists(self.build_dir):
+            print(f"Removing build directory {self.build_dir}")
+            shutil.rmtree(self.build_dir)

env-llmeval/lib/python3.10/site-packages/numpy/f2py/_backends/_meson.py

@@ -0,0 +1,205 @@
+from __future__ import annotations
+
+import os
+import errno
+import shutil
+import subprocess
+import sys
+from pathlib import Path
+
+from ._backend import Backend
+from string import Template
+from itertools import chain
+
+import warnings
+
+
+class MesonTemplate:
+    """Template meson build file generation class."""
+
+    def __init__(
+        self,
+        modulename: str,
+        sources: list[Path],
+        deps: list[str],
+        libraries: list[str],
+        library_dirs: list[Path],
+        include_dirs: list[Path],
+        object_files: list[Path],
+        linker_args: list[str],
+        c_args: list[str],
+        build_type: str,
+        python_exe: str,
+    ):
+        self.modulename = modulename
+        self.build_template_path = (
+            Path(__file__).parent.absolute() / "meson.build.template"
+        )
+        self.sources = sources
+        self.deps = deps
+        self.libraries = libraries
+        self.library_dirs = library_dirs
+        if include_dirs is not None:
+            self.include_dirs = include_dirs
+        else:
+            self.include_dirs = []
+        self.substitutions = {}
+        self.objects = object_files
+        self.pipeline = [
+            self.initialize_template,
+            self.sources_substitution,
+            self.deps_substitution,
+            self.include_substitution,
+            self.libraries_substitution,
+        ]
+        self.build_type = build_type
+        self.python_exe = python_exe
+
+    def meson_build_template(self) -> str:
+        if not self.build_template_path.is_file():
+            raise FileNotFoundError(
+                errno.ENOENT,
+                "Meson build template"
+                f" {self.build_template_path.absolute()}"
+                " does not exist.",
+            )
+        return self.build_template_path.read_text()
+
+    def initialize_template(self) -> None:
+        self.substitutions["modulename"] = self.modulename
+        self.substitutions["buildtype"] = self.build_type
+        self.substitutions["python"] = self.python_exe
+
+    def sources_substitution(self) -> None:
+        indent = " " * 21
+        self.substitutions["source_list"] = f",\n{indent}".join(
+            [f"{indent}'{source}'" for source in self.sources]
+        )
+
+    def deps_substitution(self) -> None:
+        indent = " " * 21
+        self.substitutions["dep_list"] = f",\n{indent}".join(
+            [f"{indent}dependency('{dep}')" for dep in self.deps]
+        )
+
+    def libraries_substitution(self) -> None:
+        self.substitutions["lib_dir_declarations"] = "\n".join(
+            [
+                f"lib_dir_{i} = declare_dependency(link_args : ['-L{lib_dir}'])"
+                for i, lib_dir in enumerate(self.library_dirs)
+            ]
+        )
+
+        self.substitutions["lib_declarations"] = "\n".join(
+            [
+                f"{lib} = declare_dependency(link_args : ['-l{lib}'])"
+                for lib in self.libraries
+            ]
+        )
+
+        indent = " " * 21
+        self.substitutions["lib_list"] = f"\n{indent}".join(
+            [f"{indent}{lib}," for lib in self.libraries]
+        )
+        self.substitutions["lib_dir_list"] = f"\n{indent}".join(
+            [f"{indent}lib_dir_{i}," for i in range(len(self.library_dirs))]
+        )
+
+    def include_substitution(self) -> None:
+        indent = " " * 21
+        self.substitutions["inc_list"] = f",\n{indent}".join(
+            [f"{indent}'{inc}'" for inc in self.include_dirs]
+        )
+
+    def generate_meson_build(self):
+        for node in self.pipeline:
+            node()
+        template = Template(self.meson_build_template())
+        return template.substitute(self.substitutions)
+
+
+class MesonBackend(Backend):
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+        self.dependencies = self.extra_dat.get("dependencies", [])
+        self.meson_build_dir = "bbdir"
+        self.build_type = (
+            "debug" if any("debug" in flag for flag in self.fc_flags) else "release"
+        )
+
+    def _move_exec_to_root(self, build_dir: Path):
+        walk_dir = Path(build_dir) / self.meson_build_dir
+        path_objects = chain(
+            walk_dir.glob(f"{self.modulename}*.so"),
+            walk_dir.glob(f"{self.modulename}*.pyd"),
+        )
+        # Same behavior as distutils
+        # https://github.com/numpy/numpy/issues/24874#issuecomment-1835632293
+        for path_object in path_objects:
+            dest_path = Path.cwd() / path_object.name
+            if dest_path.exists():
+                dest_path.unlink()
+            shutil.copy2(path_object, dest_path)
+            os.remove(path_object)
+
+    def write_meson_build(self, build_dir: Path) -> None:
+        """Writes the meson build file at specified location"""
+        meson_template = MesonTemplate(
+            self.modulename,
+            self.sources,
+            self.dependencies,
+            self.libraries,
+            self.library_dirs,
+            self.include_dirs,
+            self.extra_objects,
+            self.flib_flags,
+            self.fc_flags,
+            self.build_type,
+            sys.executable,
+        )
+        src = meson_template.generate_meson_build()
+        Path(build_dir).mkdir(parents=True, exist_ok=True)
+        meson_build_file = Path(build_dir) / "meson.build"
+        meson_build_file.write_text(src)
+        return meson_build_file
+
+    def _run_subprocess_command(self, command, cwd):
+        subprocess.run(command, cwd=cwd, check=True)
+
+    def run_meson(self, build_dir: Path):
+        setup_command = ["meson", "setup", self.meson_build_dir]
+        self._run_subprocess_command(setup_command, build_dir)
+        compile_command = ["meson", "compile", "-C", self.meson_build_dir]
+        self._run_subprocess_command(compile_command, build_dir)
+
+    def compile(self) -> None:
+        self.sources = _prepare_sources(self.modulename, self.sources, self.build_dir)
+        self.write_meson_build(self.build_dir)
+        self.run_meson(self.build_dir)
+        self._move_exec_to_root(self.build_dir)
+
+
+def _prepare_sources(mname, sources, bdir):
+    extended_sources = sources.copy()
+    Path(bdir).mkdir(parents=True, exist_ok=True)
+    # Copy sources
+    for source in sources:
+        if Path(source).exists() and Path(source).is_file():
+            shutil.copy(source, bdir)
+    generated_sources = [
+        Path(f"{mname}module.c"),
+        Path(f"{mname}-f2pywrappers2.f90"),
+        Path(f"{mname}-f2pywrappers.f"),
+    ]
+    bdir = Path(bdir)
+    for generated_source in generated_sources:
+        if generated_source.exists():
+            shutil.copy(generated_source, bdir / generated_source.name)
+            extended_sources.append(generated_source.name)
+            generated_source.unlink()
+    extended_sources = [
+        Path(source).name
+        for source in extended_sources
+        if not Path(source).suffix == ".pyf"
+    ]
+    return extended_sources

env-llmeval/lib/python3.10/site-packages/numpy/f2py/_backends/meson.build.template

@@ -0,0 +1,54 @@
+project('${modulename}',
+        ['c', 'fortran'],
+        version : '0.1',
+        meson_version: '>= 1.1.0',
+        default_options : [
+                            'warning_level=1',
+                            'buildtype=${buildtype}'
+                          ])
+fc = meson.get_compiler('fortran')
+
+py = import('python').find_installation('${python}', pure: false)
+py_dep = py.dependency()
+
+incdir_numpy = run_command(py,
+  ['-c', 'import os; os.chdir(".."); import numpy; print(numpy.get_include())'],
+  check : true
+).stdout().strip()
+
+incdir_f2py = run_command(py,
+    ['-c', 'import os; os.chdir(".."); import numpy.f2py; print(numpy.f2py.get_include())'],
+    check : true
+).stdout().strip()
+
+inc_np = include_directories(incdir_numpy)
+np_dep = declare_dependency(include_directories: inc_np)
+
+incdir_f2py = incdir_numpy / '..' / '..' / 'f2py' / 'src'
+inc_f2py = include_directories(incdir_f2py)
+fortranobject_c = incdir_f2py / 'fortranobject.c'
+
+inc_np = include_directories(incdir_numpy, incdir_f2py)
+# gh-25000
+quadmath_dep = fc.find_library('quadmath', required: false)
+
+${lib_declarations}
+${lib_dir_declarations}
+
+py.extension_module('${modulename}',
+                     [
+${source_list},
+                     fortranobject_c
+                     ],
+                     include_directories: [
+                     inc_np,
+${inc_list}
+                     ],
+                     dependencies : [
+                     py_dep,
+                     quadmath_dep,
+${dep_list}
+${lib_list}
+${lib_dir_list}
+                     ],
+                     install : true)

env-llmeval/lib/python3.10/site-packages/numpy/f2py/src/fortranobject.c

@@ -0,0 +1,1423 @@
+#define FORTRANOBJECT_C
+#include "fortranobject.h"
+
+#ifdef __cplusplus
+extern "C" {
+#endif
+
+#include <stdarg.h>
+#include <stdlib.h>
+#include <string.h>
+
+/*
+  This file implements: FortranObject, array_from_pyobj, copy_ND_array
+
+  Author: Pearu Peterson <[email protected]>
+  $Revision: 1.52 $
+  $Date: 2005/07/11 07:44:20 $
+*/
+
+int
+F2PyDict_SetItemString(PyObject *dict, char *name, PyObject *obj)
+{
+    if (obj == NULL) {
+        fprintf(stderr, "Error loading %s\n", name);
+        if (PyErr_Occurred()) {
+            PyErr_Print();
+            PyErr_Clear();
+        }
+        return -1;
+    }
+    return PyDict_SetItemString(dict, name, obj);
+}
+
+/*
+ * Python-only fallback for thread-local callback pointers
+ */
+void *
+F2PySwapThreadLocalCallbackPtr(char *key, void *ptr)
+{
+    PyObject *local_dict, *value;
+    void *prev;
+
+    local_dict = PyThreadState_GetDict();
+    if (local_dict == NULL) {
+        Py_FatalError(
+                "F2PySwapThreadLocalCallbackPtr: PyThreadState_GetDict "
+                "failed");
+    }
+
+    value = PyDict_GetItemString(local_dict, key);
+    if (value != NULL) {
+        prev = PyLong_AsVoidPtr(value);
+        if (PyErr_Occurred()) {
+            Py_FatalError(
+                    "F2PySwapThreadLocalCallbackPtr: PyLong_AsVoidPtr failed");
+        }
+    }
+    else {
+        prev = NULL;
+    }
+
+    value = PyLong_FromVoidPtr((void *)ptr);
+    if (value == NULL) {
+        Py_FatalError(
+                "F2PySwapThreadLocalCallbackPtr: PyLong_FromVoidPtr failed");
+    }
+
+    if (PyDict_SetItemString(local_dict, key, value) != 0) {
+        Py_FatalError(
+                "F2PySwapThreadLocalCallbackPtr: PyDict_SetItemString failed");
+    }
+
+    Py_DECREF(value);
+
+    return prev;
+}
+
+void *
+F2PyGetThreadLocalCallbackPtr(char *key)
+{
+    PyObject *local_dict, *value;
+    void *prev;
+
+    local_dict = PyThreadState_GetDict();
+    if (local_dict == NULL) {
+        Py_FatalError(
+                "F2PyGetThreadLocalCallbackPtr: PyThreadState_GetDict failed");
+    }
+
+    value = PyDict_GetItemString(local_dict, key);
+    if (value != NULL) {
+        prev = PyLong_AsVoidPtr(value);
+        if (PyErr_Occurred()) {
+            Py_FatalError(
+                    "F2PyGetThreadLocalCallbackPtr: PyLong_AsVoidPtr failed");
+        }
+    }
+    else {
+        prev = NULL;
+    }
+
+    return prev;
+}
+
+static PyArray_Descr *
+get_descr_from_type_and_elsize(const int type_num, const int elsize)  {
+  PyArray_Descr * descr = PyArray_DescrFromType(type_num);
+  if (type_num == NPY_STRING) {
+    // PyArray_DescrFromType returns descr with elsize = 0.
+    PyArray_DESCR_REPLACE(descr);
+    if (descr == NULL) {
+      return NULL;
+    }
+    descr->elsize = elsize;
+  }
+  return descr;
+}
+
+/************************* FortranObject *******************************/
+
+typedef PyObject *(*fortranfunc)(PyObject *, PyObject *, PyObject *, void *);
+
+PyObject *
+PyFortranObject_New(FortranDataDef *defs, f2py_void_func init)
+{
+    int i;
+    PyFortranObject *fp = NULL;
+    PyObject *v = NULL;
+    if (init != NULL) { /* Initialize F90 module objects */
+        (*(init))();
+    }
+    fp = PyObject_New(PyFortranObject, &PyFortran_Type);
+    if (fp == NULL) {
+        return NULL;
+    }
+    if ((fp->dict = PyDict_New()) == NULL) {
+        Py_DECREF(fp);
+        return NULL;
+    }
+    fp->len = 0;
+    while (defs[fp->len].name != NULL) {
+        fp->len++;
+    }
+    if (fp->len == 0) {
+        goto fail;
+    }
+    fp->defs = defs;
+    for (i = 0; i < fp->len; i++) {
+        if (fp->defs[i].rank == -1) { /* Is Fortran routine */
+            v = PyFortranObject_NewAsAttr(&(fp->defs[i]));
+            if (v == NULL) {
+                goto fail;
+            }
+            PyDict_SetItemString(fp->dict, fp->defs[i].name, v);
+            Py_XDECREF(v);
+        }
+        else if ((fp->defs[i].data) !=
+                 NULL) { /* Is Fortran variable or array (not allocatable) */
+            PyArray_Descr *
+            descr = get_descr_from_type_and_elsize(fp->defs[i].type,
+                                                   fp->defs[i].elsize);
+            if (descr == NULL) {
+                goto fail;
+            }
+            v = PyArray_NewFromDescr(&PyArray_Type, descr, fp->defs[i].rank,
+                                     fp->defs[i].dims.d, NULL, fp->defs[i].data,
+                                     NPY_ARRAY_FARRAY, NULL);
+            if (v == NULL) {
+                Py_DECREF(descr);
+                goto fail;
+            }
+            PyDict_SetItemString(fp->dict, fp->defs[i].name, v);
+            Py_XDECREF(v);
+        }
+    }
+    return (PyObject *)fp;
+fail:
+    Py_XDECREF(fp);
+    return NULL;
+}
+
+PyObject *
+PyFortranObject_NewAsAttr(FortranDataDef *defs)
+{ /* used for calling F90 module routines */
+    PyFortranObject *fp = NULL;
+    fp = PyObject_New(PyFortranObject, &PyFortran_Type);
+    if (fp == NULL)
+        return NULL;
+    if ((fp->dict = PyDict_New()) == NULL) {
+        PyObject_Del(fp);
+        return NULL;
+    }
+    fp->len = 1;
+    fp->defs = defs;
+    if (defs->rank == -1) {
+      PyDict_SetItemString(fp->dict, "__name__", PyUnicode_FromFormat("function %s", defs->name));
+    } else if (defs->rank == 0) {
+      PyDict_SetItemString(fp->dict, "__name__", PyUnicode_FromFormat("scalar %s", defs->name));
+    } else {
+      PyDict_SetItemString(fp->dict, "__name__", PyUnicode_FromFormat("array %s", defs->name));
+    }
+    return (PyObject *)fp;
+}
+
+/* Fortran methods */
+
+static void
+fortran_dealloc(PyFortranObject *fp)
+{
+    Py_XDECREF(fp->dict);
+    PyObject_Del(fp);
+}
+
+/* Returns number of bytes consumed from buf, or -1 on error. */
+static Py_ssize_t
+format_def(char *buf, Py_ssize_t size, FortranDataDef def)
+{
+    char *p = buf;
+    int i;
+    npy_intp n;
+
+    n = PyOS_snprintf(p, size, "array(%" NPY_INTP_FMT, def.dims.d[0]);
+    if (n < 0 || n >= size) {
+        return -1;
+    }
+    p += n;
+    size -= n;
+
+    for (i = 1; i < def.rank; i++) {
+        n = PyOS_snprintf(p, size, ",%" NPY_INTP_FMT, def.dims.d[i]);
+        if (n < 0 || n >= size) {
+            return -1;
+        }
+        p += n;
+        size -= n;
+    }
+
+    if (size <= 0) {
+        return -1;
+    }
+
+    *p++ = ')';
+    size--;
+
+    if (def.data == NULL) {
+        static const char notalloc[] = ", not allocated";
+        if ((size_t)size < sizeof(notalloc)) {
+            return -1;
+        }
+        memcpy(p, notalloc, sizeof(notalloc));
+        p += sizeof(notalloc);
+        size -= sizeof(notalloc);
+    }
+
+    return p - buf;
+}
+
+static PyObject *
+fortran_doc(FortranDataDef def)
+{
+    char *buf, *p;
+    PyObject *s = NULL;
+    Py_ssize_t n, origsize, size = 100;
+
+    if (def.doc != NULL) {
+        size += strlen(def.doc);
+    }
+    origsize = size;
+    buf = p = (char *)PyMem_Malloc(size);
+    if (buf == NULL) {
+        return PyErr_NoMemory();
+    }
+
+    if (def.rank == -1) {
+        if (def.doc) {
+            n = strlen(def.doc);
+            if (n > size) {
+                goto fail;
+            }
+            memcpy(p, def.doc, n);
+            p += n;
+            size -= n;
+        }
+        else {
+            n = PyOS_snprintf(p, size, "%s - no docs available", def.name);
+            if (n < 0 || n >= size) {
+                goto fail;
+            }
+            p += n;
+            size -= n;
+        }
+    }
+    else {
+        PyArray_Descr *d = PyArray_DescrFromType(def.type);
+        n = PyOS_snprintf(p, size, "%s : '%c'-", def.name, d->type);
+        Py_DECREF(d);
+        if (n < 0 || n >= size) {
+            goto fail;
+        }
+        p += n;
+        size -= n;
+
+        if (def.data == NULL) {
+            n = format_def(p, size, def);
+            if (n < 0) {
+                goto fail;
+            }
+            p += n;
+            size -= n;
+        }
+        else if (def.rank > 0) {
+            n = format_def(p, size, def);
+            if (n < 0) {
+                goto fail;
+            }
+            p += n;
+            size -= n;
+        }
+        else {
+            n = strlen("scalar");
+            if (size < n) {
+                goto fail;
+            }
+            memcpy(p, "scalar", n);
+            p += n;
+            size -= n;
+        }
+    }
+    if (size <= 1) {
+        goto fail;
+    }
+    *p++ = '\n';
+    size--;
+
+    /* p now points one beyond the last character of the string in buf */
+    s = PyUnicode_FromStringAndSize(buf, p - buf);
+
+    PyMem_Free(buf);
+    return s;
+
+fail:
+    fprintf(stderr,
+            "fortranobject.c: fortran_doc: len(p)=%zd>%zd=size:"
+            " too long docstring required, increase size\n",
+            p - buf, origsize);
+    PyMem_Free(buf);
+    return NULL;
+}
+
+static FortranDataDef *save_def; /* save pointer of an allocatable array */
+static void
+set_data(char *d, npy_intp *f)
+{           /* callback from Fortran */
+    if (*f) /* In fortran f=allocated(d) */
+        save_def->data = d;
+    else
+        save_def->data = NULL;
+    /* printf("set_data: d=%p,f=%d\n",d,*f); */
+}
+
+static PyObject *
+fortran_getattr(PyFortranObject *fp, char *name)
+{
+    int i, j, k, flag;
+    if (fp->dict != NULL) {
+        PyObject *v = _PyDict_GetItemStringWithError(fp->dict, name);
+        if (v == NULL && PyErr_Occurred()) {
+            return NULL;
+        }
+        else if (v != NULL) {
+            Py_INCREF(v);
+            return v;
+        }
+    }
+    for (i = 0, j = 1; i < fp->len && (j = strcmp(name, fp->defs[i].name));
+         i++)
+        ;
+    if (j == 0)
+        if (fp->defs[i].rank != -1) { /* F90 allocatable array */
+            if (fp->defs[i].func == NULL)
+                return NULL;
+            for (k = 0; k < fp->defs[i].rank; ++k) fp->defs[i].dims.d[k] = -1;
+            save_def = &fp->defs[i];
+            (*(fp->defs[i].func))(&fp->defs[i].rank, fp->defs[i].dims.d,
+                                  set_data, &flag);
+            if (flag == 2)
+                k = fp->defs[i].rank + 1;
+            else
+                k = fp->defs[i].rank;
+            if (fp->defs[i].data != NULL) { /* array is allocated */
+                PyObject *v = PyArray_New(
+                        &PyArray_Type, k, fp->defs[i].dims.d, fp->defs[i].type,
+                        NULL, fp->defs[i].data, 0, NPY_ARRAY_FARRAY, NULL);
+                if (v == NULL)
+                    return NULL;
+                /* Py_INCREF(v); */
+                return v;
+            }
+            else { /* array is not allocated */
+                Py_RETURN_NONE;
+            }
+        }
+    if (strcmp(name, "__dict__") == 0) {
+        Py_INCREF(fp->dict);
+        return fp->dict;
+    }
+    if (strcmp(name, "__doc__") == 0) {
+        PyObject *s = PyUnicode_FromString(""), *s2, *s3;
+        for (i = 0; i < fp->len; i++) {
+            s2 = fortran_doc(fp->defs[i]);
+            s3 = PyUnicode_Concat(s, s2);
+            Py_DECREF(s2);
+            Py_DECREF(s);
+            s = s3;
+        }
+        if (PyDict_SetItemString(fp->dict, name, s))
+            return NULL;
+        return s;
+    }
+    if ((strcmp(name, "_cpointer") == 0) && (fp->len == 1)) {
+        PyObject *cobj =
+                F2PyCapsule_FromVoidPtr((void *)(fp->defs[0].data), NULL);
+        if (PyDict_SetItemString(fp->dict, name, cobj))
+            return NULL;
+        return cobj;
+    }
+    PyObject *str, *ret;
+    str = PyUnicode_FromString(name);
+    ret = PyObject_GenericGetAttr((PyObject *)fp, str);
+    Py_DECREF(str);
+    return ret;
+}
+
+static int
+fortran_setattr(PyFortranObject *fp, char *name, PyObject *v)
+{
+    int i, j, flag;
+    PyArrayObject *arr = NULL;
+    for (i = 0, j = 1; i < fp->len && (j = strcmp(name, fp->defs[i].name));
+         i++)
+        ;
+    if (j == 0) {
+        if (fp->defs[i].rank == -1) {
+            PyErr_SetString(PyExc_AttributeError,
+                            "over-writing fortran routine");
+            return -1;
+        }
+        if (fp->defs[i].func != NULL) { /* is allocatable array */
+            npy_intp dims[F2PY_MAX_DIMS];
+            int k;
+            save_def = &fp->defs[i];
+            if (v != Py_None) { /* set new value (reallocate if needed --
+                                   see f2py generated code for more
+                                   details ) */
+                for (k = 0; k < fp->defs[i].rank; k++) dims[k] = -1;
+                if ((arr = array_from_pyobj(fp->defs[i].type, dims,
+                                            fp->defs[i].rank, F2PY_INTENT_IN,
+                                            v)) == NULL)
+                    return -1;
+                (*(fp->defs[i].func))(&fp->defs[i].rank, PyArray_DIMS(arr),
+                                      set_data, &flag);
+            }
+            else { /* deallocate */
+                for (k = 0; k < fp->defs[i].rank; k++) dims[k] = 0;
+                (*(fp->defs[i].func))(&fp->defs[i].rank, dims, set_data,
+                                      &flag);
+                for (k = 0; k < fp->defs[i].rank; k++) dims[k] = -1;
+            }
+            memcpy(fp->defs[i].dims.d, dims,
+                   fp->defs[i].rank * sizeof(npy_intp));
+        }
+        else { /* not allocatable array */
+            if ((arr = array_from_pyobj(fp->defs[i].type, fp->defs[i].dims.d,
+                                        fp->defs[i].rank, F2PY_INTENT_IN,
+                                        v)) == NULL)
+                return -1;
+        }
+        if (fp->defs[i].data !=
+            NULL) { /* copy Python object to Fortran array */
+            npy_intp s = PyArray_MultiplyList(fp->defs[i].dims.d,
+                                              PyArray_NDIM(arr));
+            if (s == -1)
+                s = PyArray_MultiplyList(PyArray_DIMS(arr), PyArray_NDIM(arr));
+            if (s < 0 || (memcpy(fp->defs[i].data, PyArray_DATA(arr),
+                                 s * PyArray_ITEMSIZE(arr))) == NULL) {
+                if ((PyObject *)arr != v) {
+                    Py_DECREF(arr);
+                }
+                return -1;
+            }
+            if ((PyObject *)arr != v) {
+                Py_DECREF(arr);
+            }
+        }
+        else
+            return (fp->defs[i].func == NULL ? -1 : 0);
+        return 0; /* successful */
+    }
+    if (fp->dict == NULL) {
+        fp->dict = PyDict_New();
+        if (fp->dict == NULL)
+            return -1;
+    }
+    if (v == NULL) {
+        int rv = PyDict_DelItemString(fp->dict, name);
+        if (rv < 0)
+            PyErr_SetString(PyExc_AttributeError,
+                            "delete non-existing fortran attribute");
+        return rv;
+    }
+    else
+        return PyDict_SetItemString(fp->dict, name, v);
+}
+
+static PyObject *
+fortran_call(PyFortranObject *fp, PyObject *arg, PyObject *kw)
+{
+    int i = 0;
+    /*  printf("fortran call
+        name=%s,func=%p,data=%p,%p\n",fp->defs[i].name,
+        fp->defs[i].func,fp->defs[i].data,&fp->defs[i].data); */
+    if (fp->defs[i].rank == -1) { /* is Fortran routine */
+        if (fp->defs[i].func == NULL) {
+            PyErr_Format(PyExc_RuntimeError, "no function to call");
+            return NULL;
+        }
+        else if (fp->defs[i].data == NULL)
+            /* dummy routine */
+            return (*((fortranfunc)(fp->defs[i].func)))((PyObject *)fp, arg,
+                                                        kw, NULL);
+        else
+            return (*((fortranfunc)(fp->defs[i].func)))(
+                    (PyObject *)fp, arg, kw, (void *)fp->defs[i].data);
+    }
+    PyErr_Format(PyExc_TypeError, "this fortran object is not callable");
+    return NULL;
+}
+
+static PyObject *
+fortran_repr(PyFortranObject *fp)
+{
+    PyObject *name = NULL, *repr = NULL;
+    name = PyObject_GetAttrString((PyObject *)fp, "__name__");
+    PyErr_Clear();
+    if (name != NULL && PyUnicode_Check(name)) {
+        repr = PyUnicode_FromFormat("<fortran %U>", name);
+    }
+    else {
+        repr = PyUnicode_FromString("<fortran object>");
+    }
+    Py_XDECREF(name);
+    return repr;
+}
+
+PyTypeObject PyFortran_Type = {
+        PyVarObject_HEAD_INIT(NULL, 0).tp_name = "fortran",
+        .tp_basicsize = sizeof(PyFortranObject),
+        .tp_dealloc = (destructor)fortran_dealloc,
+        .tp_getattr = (getattrfunc)fortran_getattr,
+        .tp_setattr = (setattrfunc)fortran_setattr,
+        .tp_repr = (reprfunc)fortran_repr,
+        .tp_call = (ternaryfunc)fortran_call,
+};
+
+/************************* f2py_report_atexit *******************************/
+
+#ifdef F2PY_REPORT_ATEXIT
+static int passed_time = 0;
+static int passed_counter = 0;
+static int passed_call_time = 0;
+static struct timeb start_time;
+static struct timeb stop_time;
+static struct timeb start_call_time;
+static struct timeb stop_call_time;
+static int cb_passed_time = 0;
+static int cb_passed_counter = 0;
+static int cb_passed_call_time = 0;
+static struct timeb cb_start_time;
+static struct timeb cb_stop_time;
+static struct timeb cb_start_call_time;
+static struct timeb cb_stop_call_time;
+
+extern void
+f2py_start_clock(void)
+{
+    ftime(&start_time);
+}
+extern void
+f2py_start_call_clock(void)
+{
+    f2py_stop_clock();
+    ftime(&start_call_time);
+}
+extern void
+f2py_stop_clock(void)
+{
+    ftime(&stop_time);
+    passed_time += 1000 * (stop_time.time - start_time.time);
+    passed_time += stop_time.millitm - start_time.millitm;
+}
+extern void
+f2py_stop_call_clock(void)
+{
+    ftime(&stop_call_time);
+    passed_call_time += 1000 * (stop_call_time.time - start_call_time.time);
+    passed_call_time += stop_call_time.millitm - start_call_time.millitm;
+    passed_counter += 1;
+    f2py_start_clock();
+}
+
+extern void
+f2py_cb_start_clock(void)
+{
+    ftime(&cb_start_time);
+}
+extern void
+f2py_cb_start_call_clock(void)
+{
+    f2py_cb_stop_clock();
+    ftime(&cb_start_call_time);
+}
+extern void
+f2py_cb_stop_clock(void)
+{
+    ftime(&cb_stop_time);
+    cb_passed_time += 1000 * (cb_stop_time.time - cb_start_time.time);
+    cb_passed_time += cb_stop_time.millitm - cb_start_time.millitm;
+}
+extern void
+f2py_cb_stop_call_clock(void)
+{
+    ftime(&cb_stop_call_time);
+    cb_passed_call_time +=
+            1000 * (cb_stop_call_time.time - cb_start_call_time.time);
+    cb_passed_call_time +=
+            cb_stop_call_time.millitm - cb_start_call_time.millitm;
+    cb_passed_counter += 1;
+    f2py_cb_start_clock();
+}
+
+static int f2py_report_on_exit_been_here = 0;
+extern void
+f2py_report_on_exit(int exit_flag, void *name)
+{
+    if (f2py_report_on_exit_been_here) {
+        fprintf(stderr, "             %s\n", (char *)name);
+        return;
+    }
+    f2py_report_on_exit_been_here = 1;
+    fprintf(stderr, "                      /-----------------------\\\n");
+    fprintf(stderr, "                     < F2PY performance report >\n");
+    fprintf(stderr, "                      \\-----------------------/\n");
+    fprintf(stderr, "Overall time spent in ...\n");
+    fprintf(stderr, "(a) wrapped (Fortran/C) functions           : %8d msec\n",
+            passed_call_time);
+    fprintf(stderr, "(b) f2py interface,           %6d calls  : %8d msec\n",
+            passed_counter, passed_time);
+    fprintf(stderr, "(c) call-back (Python) functions            : %8d msec\n",
+            cb_passed_call_time);
+    fprintf(stderr, "(d) f2py call-back interface, %6d calls  : %8d msec\n",
+            cb_passed_counter, cb_passed_time);
+
+    fprintf(stderr,
+            "(e) wrapped (Fortran/C) functions (actual) : %8d msec\n\n",
+            passed_call_time - cb_passed_call_time - cb_passed_time);
+    fprintf(stderr,
+            "Use -DF2PY_REPORT_ATEXIT_DISABLE to disable this message.\n");
+    fprintf(stderr, "Exit status: %d\n", exit_flag);
+    fprintf(stderr, "Modules    : %s\n", (char *)name);
+}
+#endif
+
+/********************** report on array copy ****************************/
+
+#ifdef F2PY_REPORT_ON_ARRAY_COPY
+static void
+f2py_report_on_array_copy(PyArrayObject *arr)
+{
+    const npy_intp arr_size = PyArray_Size((PyObject *)arr);
+    if (arr_size > F2PY_REPORT_ON_ARRAY_COPY) {
+        fprintf(stderr,
+                "copied an array: size=%ld, elsize=%" NPY_INTP_FMT "\n",
+                arr_size, (npy_intp)PyArray_ITEMSIZE(arr));
+    }
+}
+static void
+f2py_report_on_array_copy_fromany(void)
+{
+    fprintf(stderr, "created an array from object\n");
+}
+
+#define F2PY_REPORT_ON_ARRAY_COPY_FROMARR \
+    f2py_report_on_array_copy((PyArrayObject *)arr)
+#define F2PY_REPORT_ON_ARRAY_COPY_FROMANY f2py_report_on_array_copy_fromany()
+#else
+#define F2PY_REPORT_ON_ARRAY_COPY_FROMARR
+#define F2PY_REPORT_ON_ARRAY_COPY_FROMANY
+#endif
+
+/************************* array_from_obj *******************************/
+
+/*
+ * File: array_from_pyobj.c
+ *
+ * Description:
+ * ------------
+ * Provides array_from_pyobj function that returns a contiguous array
+ * object with the given dimensions and required storage order, either
+ * in row-major (C) or column-major (Fortran) order. The function
+ * array_from_pyobj is very flexible about its Python object argument
+ * that can be any number, list, tuple, or array.
+ *
+ * array_from_pyobj is used in f2py generated Python extension
+ * modules.
+ *
+ * Author: Pearu Peterson <[email protected]>
+ * Created: 13-16 January 2002
+ * $Id: fortranobject.c,v 1.52 2005/07/11 07:44:20 pearu Exp $
+ */
+
+static int check_and_fix_dimensions(const PyArrayObject* arr,
+                                    const int rank,
+                                    npy_intp *dims,
+                                    const char *errmess);
+
+static int
+find_first_negative_dimension(const int rank, const npy_intp *dims)
+{
+    int i;
+    for (i = 0; i < rank; ++i) {
+        if (dims[i] < 0) {
+            return i;
+        }
+    }
+    return -1;
+}
+
+#ifdef DEBUG_COPY_ND_ARRAY
+void
+dump_dims(int rank, npy_intp const *dims)
+{
+    int i;
+    printf("[");
+    for (i = 0; i < rank; ++i) {
+        printf("%3" NPY_INTP_FMT, dims[i]);
+    }
+    printf("]\n");
+}
+void
+dump_attrs(const PyArrayObject *obj)
+{
+    const PyArrayObject_fields *arr = (const PyArrayObject_fields *)obj;
+    int rank = PyArray_NDIM(arr);
+    npy_intp size = PyArray_Size((PyObject *)arr);
+    printf("\trank = %d, flags = %d, size = %" NPY_INTP_FMT "\n", rank,
+           arr->flags, size);
+    printf("\tstrides = ");
+    dump_dims(rank, arr->strides);
+    printf("\tdimensions = ");
+    dump_dims(rank, arr->dimensions);
+}
+#endif
+
+#define SWAPTYPE(a, b, t) \
+    {                     \
+        t c;              \
+        c = (a);          \
+        (a) = (b);        \
+        (b) = c;          \
+    }
+
+static int
+swap_arrays(PyArrayObject *obj1, PyArrayObject *obj2)
+{
+    PyArrayObject_fields *arr1 = (PyArrayObject_fields *)obj1,
+                         *arr2 = (PyArrayObject_fields *)obj2;
+    SWAPTYPE(arr1->data, arr2->data, char *);
+    SWAPTYPE(arr1->nd, arr2->nd, int);
+    SWAPTYPE(arr1->dimensions, arr2->dimensions, npy_intp *);
+    SWAPTYPE(arr1->strides, arr2->strides, npy_intp *);
+    SWAPTYPE(arr1->base, arr2->base, PyObject *);
+    SWAPTYPE(arr1->descr, arr2->descr, PyArray_Descr *);
+    SWAPTYPE(arr1->flags, arr2->flags, int);
+    /* SWAPTYPE(arr1->weakreflist,arr2->weakreflist,PyObject*); */
+    return 0;
+}
+
+#define ARRAY_ISCOMPATIBLE(arr,type_num)                                \
+    ((PyArray_ISINTEGER(arr) && PyTypeNum_ISINTEGER(type_num)) ||     \
+     (PyArray_ISFLOAT(arr) && PyTypeNum_ISFLOAT(type_num)) ||         \
+     (PyArray_ISCOMPLEX(arr) && PyTypeNum_ISCOMPLEX(type_num)) ||     \
+     (PyArray_ISBOOL(arr) && PyTypeNum_ISBOOL(type_num)) ||           \
+     (PyArray_ISSTRING(arr) && PyTypeNum_ISSTRING(type_num)))
+
+static int
+get_elsize(PyObject *obj) {
+  /*
+    get_elsize determines array itemsize from a Python object.  Returns
+    elsize if successful, -1 otherwise.
+
+    Supported types of the input are: numpy.ndarray, bytes, str, tuple,
+    list.
+  */
+
+  if (PyArray_Check(obj)) {
+    return PyArray_DESCR((PyArrayObject *)obj)->elsize;
+  } else if (PyBytes_Check(obj)) {
+    return PyBytes_GET_SIZE(obj);
+  } else if (PyUnicode_Check(obj)) {
+    return PyUnicode_GET_LENGTH(obj);
+  } else if (PySequence_Check(obj)) {
+    PyObject* fast = PySequence_Fast(obj, "f2py:fortranobject.c:get_elsize");
+    if (fast != NULL) {
+      Py_ssize_t i, n = PySequence_Fast_GET_SIZE(fast);
+      int sz, elsize = 0;
+      for (i=0; i<n; i++) {
+        sz = get_elsize(PySequence_Fast_GET_ITEM(fast, i) /* borrowed */);
+        if (sz > elsize) {
+          elsize = sz;
+        }
+      }
+      Py_DECREF(fast);
+      return elsize;
+    }
+  }
+  return -1;
+}
+
+extern PyArrayObject *
+ndarray_from_pyobj(const int type_num,
+                   const int elsize_,
+                   npy_intp *dims,
+                   const int rank,
+                   const int intent,
+                   PyObject *obj,
+                   const char *errmess) {
+    /*
+     * Return an array with given element type and shape from a Python
+     * object while taking into account the usage intent of the array.
+     *
+     * - element type is defined by type_num and elsize
+     * - shape is defined by dims and rank
+     *
+     * ndarray_from_pyobj is used to convert Python object arguments
+     * to numpy ndarrays with given type and shape that data is passed
+     * to interfaced Fortran or C functions.
+     *
+     * errmess (if not NULL), contains a prefix of an error message
+     * for an exception to be triggered within this function.
+     *
+     * Negative elsize value means that elsize is to be determined
+     * from the Python object in runtime.
+     *
+     * Note on strings
+     * ---------------
+     *
+     * String type (type_num == NPY_STRING) does not have fixed
+     * element size and, by default, the type object sets it to
+     * 0. Therefore, for string types, one has to use elsize
+     * argument. For other types, elsize value is ignored.
+     *
+     * NumPy defines the type of a fixed-width string as
+     * dtype('S<width>'). In addition, there is also dtype('c'), that
+     * appears as dtype('S1') (these have the same type_num value),
+     * but is actually different (.char attribute is either 'S' or
+     * 'c', respecitely).
+     *
+     * In Fortran, character arrays and strings are different
+     * concepts.  The relation between Fortran types, NumPy dtypes,
+     * and type_num-elsize pairs, is defined as follows:
+     *
+     * character*5 foo     | dtype('S5')  | elsize=5, shape=()
+     * character(5) foo    | dtype('S1')  | elsize=1, shape=(5)
+     * character*5 foo(n)  | dtype('S5')  | elsize=5, shape=(n,)
+     * character(5) foo(n) | dtype('S1')  | elsize=1, shape=(5, n)
+     * character*(*) foo   | dtype('S')   | elsize=-1, shape=()
+     *
+     * Note about reference counting
+     * -----------------------------
+     *
+     * If the caller returns the array to Python, it must be done with
+     * Py_BuildValue("N",arr).  Otherwise, if obj!=arr then the caller
+     * must call Py_DECREF(arr).
+     *
+     * Note on intent(cache,out,..)
+     * ----------------------------
+     * Don't expect correct data when returning intent(cache) array.
+     *
+     */
+    char mess[F2PY_MESSAGE_BUFFER_SIZE];
+    PyArrayObject *arr = NULL;
+    int elsize = (elsize_ < 0 ? get_elsize(obj) : elsize_);
+    if (elsize < 0) {
+      if (errmess != NULL) {
+        strcpy(mess, errmess);
+      }
+      sprintf(mess + strlen(mess),
+              " -- failed to determine element size from %s",
+              Py_TYPE(obj)->tp_name);
+      PyErr_SetString(PyExc_SystemError, mess);
+      return NULL;
+    }
+    PyArray_Descr * descr = get_descr_from_type_and_elsize(type_num, elsize);  // new reference
+    if (descr == NULL) {
+      return NULL;
+    }
+    elsize = descr->elsize;
+    if ((intent & F2PY_INTENT_HIDE)
+        || ((intent & F2PY_INTENT_CACHE) && (obj == Py_None))
+        || ((intent & F2PY_OPTIONAL) && (obj == Py_None))
+        ) {
+        /* intent(cache), optional, intent(hide) */
+        int ineg = find_first_negative_dimension(rank, dims);
+        if (ineg >= 0) {
+            int i;
+            strcpy(mess, "failed to create intent(cache|hide)|optional array"
+                   "-- must have defined dimensions but got (");
+            for(i = 0; i < rank; ++i)
+                sprintf(mess + strlen(mess), "%" NPY_INTP_FMT ",", dims[i]);
+            strcat(mess, ")");
+            PyErr_SetString(PyExc_ValueError, mess);
+            Py_DECREF(descr);
+            return NULL;
+        }
+        arr = (PyArrayObject *)                                      \
+          PyArray_NewFromDescr(&PyArray_Type, descr, rank, dims,
+                               NULL, NULL, !(intent & F2PY_INTENT_C), NULL);
+        if (arr == NULL) {
+          Py_DECREF(descr);
+          return NULL;
+        }
+        if (PyArray_ITEMSIZE(arr) != elsize) {
+          strcpy(mess, "failed to create intent(cache|hide)|optional array");
+          sprintf(mess+strlen(mess)," -- expected elsize=%d got %" NPY_INTP_FMT, elsize, (npy_intp)PyArray_ITEMSIZE(arr));
+          PyErr_SetString(PyExc_ValueError,mess);
+          Py_DECREF(arr);
+          return NULL;
+        }
+        if (!(intent & F2PY_INTENT_CACHE)) {
+          PyArray_FILLWBYTE(arr, 0);
+        }
+        return arr;
+    }
+
+    if (PyArray_Check(obj)) {
+        arr = (PyArrayObject *)obj;
+        if (intent & F2PY_INTENT_CACHE) {
+            /* intent(cache) */
+            if (PyArray_ISONESEGMENT(arr)
+                && PyArray_ITEMSIZE(arr) >= elsize) {
+                if (check_and_fix_dimensions(arr, rank, dims, errmess)) {
+                  Py_DECREF(descr);
+                  return NULL;
+                }
+                if (intent & F2PY_INTENT_OUT)
+                  Py_INCREF(arr);
+                Py_DECREF(descr);
+                return arr;
+            }
+            strcpy(mess, "failed to initialize intent(cache) array");
+            if (!PyArray_ISONESEGMENT(arr))
+                strcat(mess, " -- input must be in one segment");
+            if (PyArray_ITEMSIZE(arr) < elsize)
+                sprintf(mess + strlen(mess),
+                        " -- expected at least elsize=%d but got "
+                        "%" NPY_INTP_FMT,
+                        elsize, (npy_intp)PyArray_ITEMSIZE(arr));
+            PyErr_SetString(PyExc_ValueError, mess);
+            Py_DECREF(descr);
+            return NULL;
+        }
+
+        /* here we have always intent(in) or intent(inout) or intent(inplace)
+         */
+
+        if (check_and_fix_dimensions(arr, rank, dims, errmess)) {
+          Py_DECREF(descr);
+          return NULL;
+        }
+        /*
+        printf("intent alignment=%d\n", F2PY_GET_ALIGNMENT(intent));
+        printf("alignment check=%d\n", F2PY_CHECK_ALIGNMENT(arr, intent));
+        int i;
+        for (i=1;i<=16;i++)
+          printf("i=%d isaligned=%d\n", i, ARRAY_ISALIGNED(arr, i));
+        */
+        if ((! (intent & F2PY_INTENT_COPY)) &&
+            PyArray_ITEMSIZE(arr) == elsize &&
+            ARRAY_ISCOMPATIBLE(arr,type_num) &&
+            F2PY_CHECK_ALIGNMENT(arr, intent)) {
+            if ((intent & F2PY_INTENT_INOUT || intent & F2PY_INTENT_INPLACE)
+              ? ((intent & F2PY_INTENT_C) ? PyArray_ISCARRAY(arr) : PyArray_ISFARRAY(arr))
+              : ((intent & F2PY_INTENT_C) ? PyArray_ISCARRAY_RO(arr) : PyArray_ISFARRAY_RO(arr))) {
+                if ((intent & F2PY_INTENT_OUT)) {
+                    Py_INCREF(arr);
+                }
+                /* Returning input array */
+                Py_DECREF(descr);
+                return arr;
+            }
+        }
+        if (intent & F2PY_INTENT_INOUT) {
+            strcpy(mess, "failed to initialize intent(inout) array");
+            /* Must use PyArray_IS*ARRAY because intent(inout) requires
+             * writable input */
+            if ((intent & F2PY_INTENT_C) && !PyArray_ISCARRAY(arr))
+                strcat(mess, " -- input not contiguous");
+            if (!(intent & F2PY_INTENT_C) && !PyArray_ISFARRAY(arr))
+                strcat(mess, " -- input not fortran contiguous");
+            if (PyArray_ITEMSIZE(arr) != elsize)
+                sprintf(mess + strlen(mess),
+                        " -- expected elsize=%d but got %" NPY_INTP_FMT,
+                        elsize,
+                        (npy_intp)PyArray_ITEMSIZE(arr)
+                        );
+            if (!(ARRAY_ISCOMPATIBLE(arr, type_num))) {
+                sprintf(mess + strlen(mess),
+                        " -- input '%c' not compatible to '%c'",
+                        PyArray_DESCR(arr)->type, descr->type);
+            }
+            if (!(F2PY_CHECK_ALIGNMENT(arr, intent)))
+                sprintf(mess + strlen(mess), " -- input not %d-aligned",
+                        F2PY_GET_ALIGNMENT(intent));
+            PyErr_SetString(PyExc_ValueError, mess);
+            Py_DECREF(descr);
+            return NULL;
+        }
+
+        /* here we have always intent(in) or intent(inplace) */
+
+        {
+          PyArrayObject * retarr = (PyArrayObject *)                    \
+            PyArray_NewFromDescr(&PyArray_Type, descr, PyArray_NDIM(arr), PyArray_DIMS(arr),
+                                 NULL, NULL, !(intent & F2PY_INTENT_C), NULL);
+          if (retarr==NULL) {
+            Py_DECREF(descr);
+            return NULL;
+          }
+          F2PY_REPORT_ON_ARRAY_COPY_FROMARR;
+          if (PyArray_CopyInto(retarr, arr)) {
+            Py_DECREF(retarr);
+            return NULL;
+          }
+          if (intent & F2PY_INTENT_INPLACE) {
+            if (swap_arrays(arr,retarr)) {
+              Py_DECREF(retarr);
+              return NULL; /* XXX: set exception */
+            }
+            Py_XDECREF(retarr);
+            if (intent & F2PY_INTENT_OUT)
+              Py_INCREF(arr);
+          } else {
+            arr = retarr;
+          }
+        }
+        return arr;
+    }
+
+    if ((intent & F2PY_INTENT_INOUT) || (intent & F2PY_INTENT_INPLACE) ||
+        (intent & F2PY_INTENT_CACHE)) {
+        PyErr_Format(PyExc_TypeError,
+                     "failed to initialize intent(inout|inplace|cache) "
+                     "array, input '%s' object is not an array",
+                     Py_TYPE(obj)->tp_name);
+        Py_DECREF(descr);
+        return NULL;
+    }
+
+    {
+        F2PY_REPORT_ON_ARRAY_COPY_FROMANY;
+        arr = (PyArrayObject *)PyArray_FromAny(
+                obj, descr, 0, 0,
+                ((intent & F2PY_INTENT_C) ? NPY_ARRAY_CARRAY
+                                          : NPY_ARRAY_FARRAY) |
+                        NPY_ARRAY_FORCECAST,
+                NULL);
+        // Warning: in the case of NPY_STRING, PyArray_FromAny may
+        // reset descr->elsize, e.g. dtype('S0') becomes dtype('S1').
+        if (arr == NULL) {
+          Py_DECREF(descr);
+          return NULL;
+        }
+        if (type_num != NPY_STRING && PyArray_ITEMSIZE(arr) != elsize) {
+          // This is internal sanity tests: elsize has been set to
+          // descr->elsize in the beginning of this function.
+          strcpy(mess, "failed to initialize intent(in) array");
+          sprintf(mess + strlen(mess),
+                  " -- expected elsize=%d got %" NPY_INTP_FMT, elsize,
+                  (npy_intp)PyArray_ITEMSIZE(arr));
+          PyErr_SetString(PyExc_ValueError, mess);
+          Py_DECREF(arr);
+          return NULL;
+        }
+        if (check_and_fix_dimensions(arr, rank, dims, errmess)) {
+          Py_DECREF(arr);
+          return NULL;
+        }
+        return arr;
+    }
+}
+
+extern PyArrayObject *
+array_from_pyobj(const int type_num,
+                                npy_intp *dims,
+                                const int rank,
+                                const int intent,
+                                PyObject *obj) {
+  /*
+    Same as ndarray_from_pyobj but with elsize determined from type,
+    if possible. Provided for backward compatibility.
+   */
+  PyArray_Descr* descr = PyArray_DescrFromType(type_num);
+  int elsize = descr->elsize;
+  Py_DECREF(descr);
+  return ndarray_from_pyobj(type_num, elsize, dims, rank, intent, obj, NULL);
+}
+
+/*****************************************/
+/* Helper functions for array_from_pyobj */
+/*****************************************/
+
+static int
+check_and_fix_dimensions(const PyArrayObject* arr, const int rank,
+                         npy_intp *dims, const char *errmess)
+{
+    /*
+     * This function fills in blanks (that are -1's) in dims list using
+     * the dimensions from arr. It also checks that non-blank dims will
+     * match with the corresponding values in arr dimensions.
+     *
+     * Returns 0 if the function is successful.
+     *
+     * If an error condition is detected, an exception is set and 1 is
+     * returned.
+     */
+    char mess[F2PY_MESSAGE_BUFFER_SIZE];
+    const npy_intp arr_size =
+            (PyArray_NDIM(arr)) ? PyArray_Size((PyObject *)arr) : 1;
+#ifdef DEBUG_COPY_ND_ARRAY
+    dump_attrs(arr);
+    printf("check_and_fix_dimensions:init: dims=");
+    dump_dims(rank, dims);
+#endif
+    if (rank > PyArray_NDIM(arr)) { /* [1,2] -> [[1],[2]]; 1 -> [[1]]  */
+        npy_intp new_size = 1;
+        int free_axe = -1;
+        int i;
+        npy_intp d;
+        /* Fill dims where -1 or 0; check dimensions; calc new_size; */
+        for (i = 0; i < PyArray_NDIM(arr); ++i) {
+            d = PyArray_DIM(arr, i);
+            if (dims[i] >= 0) {
+                if (d > 1 && dims[i] != d) {
+                    PyErr_Format(
+                            PyExc_ValueError,
+                            "%d-th dimension must be fixed to %" NPY_INTP_FMT
+                            " but got %" NPY_INTP_FMT "\n",
+                            i, dims[i], d);
+                    return 1;
+                }
+                if (!dims[i])
+                    dims[i] = 1;
+            }
+            else {
+                dims[i] = d ? d : 1;
+            }
+            new_size *= dims[i];
+        }
+        for (i = PyArray_NDIM(arr); i < rank; ++i)
+            if (dims[i] > 1) {
+                PyErr_Format(PyExc_ValueError,
+                             "%d-th dimension must be %" NPY_INTP_FMT
+                             " but got 0 (not defined).\n",
+                             i, dims[i]);
+                return 1;
+            }
+            else if (free_axe < 0)
+                free_axe = i;
+            else
+                dims[i] = 1;
+        if (free_axe >= 0) {
+            dims[free_axe] = arr_size / new_size;
+            new_size *= dims[free_axe];
+        }
+        if (new_size != arr_size) {
+            PyErr_Format(PyExc_ValueError,
+                         "unexpected array size: new_size=%" NPY_INTP_FMT
+                         ", got array with arr_size=%" NPY_INTP_FMT
+                         " (maybe too many free indices)\n",
+                         new_size, arr_size);
+            return 1;
+        }
+    }
+    else if (rank == PyArray_NDIM(arr)) {
+        npy_intp new_size = 1;
+        int i;
+        npy_intp d;
+        for (i = 0; i < rank; ++i) {
+            d = PyArray_DIM(arr, i);
+            if (dims[i] >= 0) {
+                if (d > 1 && d != dims[i]) {
+                    if (errmess != NULL) {
+                        strcpy(mess, errmess);
+                    }
+                    sprintf(mess + strlen(mess),
+                            " -- %d-th dimension must be fixed to %"
+                            NPY_INTP_FMT " but got %" NPY_INTP_FMT,
+                            i, dims[i], d);
+                    PyErr_SetString(PyExc_ValueError, mess);
+                    return 1;
+                }
+                if (!dims[i])
+                    dims[i] = 1;
+            }
+            else
+                dims[i] = d;
+            new_size *= dims[i];
+        }
+        if (new_size != arr_size) {
+            PyErr_Format(PyExc_ValueError,
+                         "unexpected array size: new_size=%" NPY_INTP_FMT
+                         ", got array with arr_size=%" NPY_INTP_FMT "\n",
+                         new_size, arr_size);
+            return 1;
+        }
+    }
+    else { /* [[1,2]] -> [[1],[2]] */
+        int i, j;
+        npy_intp d;
+        int effrank;
+        npy_intp size;
+        for (i = 0, effrank = 0; i < PyArray_NDIM(arr); ++i)
+            if (PyArray_DIM(arr, i) > 1)
+                ++effrank;
+        if (dims[rank - 1] >= 0)
+            if (effrank > rank) {
+                PyErr_Format(PyExc_ValueError,
+                             "too many axes: %d (effrank=%d), "
+                             "expected rank=%d\n",
+                             PyArray_NDIM(arr), effrank, rank);
+                return 1;
+            }
+
+        for (i = 0, j = 0; i < rank; ++i) {
+            while (j < PyArray_NDIM(arr) && PyArray_DIM(arr, j) < 2) ++j;
+            if (j >= PyArray_NDIM(arr))
+                d = 1;
+            else
+                d = PyArray_DIM(arr, j++);
+            if (dims[i] >= 0) {
+                if (d > 1 && d != dims[i]) {
+                    if (errmess != NULL) {
+                        strcpy(mess, errmess);
+                    }
+                    sprintf(mess + strlen(mess),
+                            " -- %d-th dimension must be fixed to %"
+                            NPY_INTP_FMT " but got %" NPY_INTP_FMT
+                            " (real index=%d)\n",
+                            i, dims[i], d, j-1);
+                    PyErr_SetString(PyExc_ValueError, mess);
+                    return 1;
+                }
+                if (!dims[i])
+                    dims[i] = 1;
+            }
+            else
+                dims[i] = d;
+        }
+
+        for (i = rank; i < PyArray_NDIM(arr);
+             ++i) { /* [[1,2],[3,4]] -> [1,2,3,4] */
+            while (j < PyArray_NDIM(arr) && PyArray_DIM(arr, j) < 2) ++j;
+            if (j >= PyArray_NDIM(arr))
+                d = 1;
+            else
+                d = PyArray_DIM(arr, j++);
+            dims[rank - 1] *= d;
+        }
+        for (i = 0, size = 1; i < rank; ++i) size *= dims[i];
+        if (size != arr_size) {
+            char msg[200];
+            int len;
+            snprintf(msg, sizeof(msg),
+                     "unexpected array size: size=%" NPY_INTP_FMT
+                     ", arr_size=%" NPY_INTP_FMT
+                     ", rank=%d, effrank=%d, arr.nd=%d, dims=[",
+                     size, arr_size, rank, effrank, PyArray_NDIM(arr));
+            for (i = 0; i < rank; ++i) {
+                len = strlen(msg);
+                snprintf(msg + len, sizeof(msg) - len, " %" NPY_INTP_FMT,
+                         dims[i]);
+            }
+            len = strlen(msg);
+            snprintf(msg + len, sizeof(msg) - len, " ], arr.dims=[");
+            for (i = 0; i < PyArray_NDIM(arr); ++i) {
+                len = strlen(msg);
+                snprintf(msg + len, sizeof(msg) - len, " %" NPY_INTP_FMT,
+                         PyArray_DIM(arr, i));
+            }
+            len = strlen(msg);
+            snprintf(msg + len, sizeof(msg) - len, " ]\n");
+            PyErr_SetString(PyExc_ValueError, msg);
+            return 1;
+        }
+    }
+#ifdef DEBUG_COPY_ND_ARRAY
+    printf("check_and_fix_dimensions:end: dims=");
+    dump_dims(rank, dims);
+#endif
+    return 0;
+}
+
+/* End of file: array_from_pyobj.c */
+
+/************************* copy_ND_array *******************************/
+
+extern int
+copy_ND_array(const PyArrayObject *arr, PyArrayObject *out)
+{
+    F2PY_REPORT_ON_ARRAY_COPY_FROMARR;
+    return PyArray_CopyInto(out, (PyArrayObject *)arr);
+}
+
+/********************* Various utility functions ***********************/
+
+extern int
+f2py_describe(PyObject *obj, char *buf) {
+  /*
+    Write the description of a Python object to buf. The caller must
+    provide buffer with size sufficient to write the description.
+
+    Return 1 on success.
+  */
+  char localbuf[F2PY_MESSAGE_BUFFER_SIZE];
+  if (PyBytes_Check(obj)) {
+    sprintf(localbuf, "%d-%s", (npy_int)PyBytes_GET_SIZE(obj), Py_TYPE(obj)->tp_name);
+  } else if (PyUnicode_Check(obj)) {
+    sprintf(localbuf, "%d-%s", (npy_int)PyUnicode_GET_LENGTH(obj), Py_TYPE(obj)->tp_name);
+  } else if (PyArray_CheckScalar(obj)) {
+    PyArrayObject* arr = (PyArrayObject*)obj;
+    sprintf(localbuf, "%c%" NPY_INTP_FMT "-%s-scalar", PyArray_DESCR(arr)->kind, PyArray_ITEMSIZE(arr), Py_TYPE(obj)->tp_name);
+  } else if (PyArray_Check(obj)) {
+    int i;
+    PyArrayObject* arr = (PyArrayObject*)obj;
+    strcpy(localbuf, "(");
+    for (i=0; i<PyArray_NDIM(arr); i++) {
+      if (i) {
+        strcat(localbuf, " ");
+      }
+      sprintf(localbuf + strlen(localbuf), "%" NPY_INTP_FMT ",", PyArray_DIM(arr, i));
+    }
+    sprintf(localbuf + strlen(localbuf), ")-%c%" NPY_INTP_FMT "-%s", PyArray_DESCR(arr)->kind, PyArray_ITEMSIZE(arr), Py_TYPE(obj)->tp_name);
+  } else if (PySequence_Check(obj)) {
+    sprintf(localbuf, "%d-%s", (npy_int)PySequence_Length(obj), Py_TYPE(obj)->tp_name);
+  } else {
+    sprintf(localbuf, "%s instance", Py_TYPE(obj)->tp_name);
+  }
+  // TODO: detect the size of buf and make sure that size(buf) >= size(localbuf).
+  strcpy(buf, localbuf);
+  return 1;
+}
+
+extern npy_intp
+f2py_size_impl(PyArrayObject* var, ...)
+{
+  npy_intp sz = 0;
+  npy_intp dim;
+  npy_intp rank;
+  va_list argp;
+  va_start(argp, var);
+  dim = va_arg(argp, npy_int);
+  if (dim==-1)
+    {
+      sz = PyArray_SIZE(var);
+    }
+  else
+    {
+      rank = PyArray_NDIM(var);
+      if (dim>=1 && dim<=rank)
+        sz = PyArray_DIM(var, dim-1);
+      else
+        fprintf(stderr, "f2py_size: 2nd argument value=%" NPY_INTP_FMT
+                " fails to satisfy 1<=value<=%" NPY_INTP_FMT
+                ". Result will be 0.\n", dim, rank);
+    }
+  va_end(argp);
+  return sz;
+}
+
+/*********************************************/
+/* Compatibility functions for Python >= 3.0 */
+/*********************************************/
+
+PyObject *
+F2PyCapsule_FromVoidPtr(void *ptr, void (*dtor)(PyObject *))
+{
+    PyObject *ret = PyCapsule_New(ptr, NULL, dtor);
+    if (ret == NULL) {
+        PyErr_Clear();
+    }
+    return ret;
+}
+
+void *
+F2PyCapsule_AsVoidPtr(PyObject *obj)
+{
+    void *ret = PyCapsule_GetPointer(obj, NULL);
+    if (ret == NULL) {
+        PyErr_Clear();
+    }
+    return ret;
+}
+
+int
+F2PyCapsule_Check(PyObject *ptr)
+{
+    return PyCapsule_CheckExact(ptr);
+}
+
+#ifdef __cplusplus
+}
+#endif
+/************************* EOF fortranobject.c *******************************/

env-llmeval/lib/python3.10/site-packages/numpy/f2py/src/fortranobject.h

@@ -0,0 +1,173 @@
+#ifndef Py_FORTRANOBJECT_H
+#define Py_FORTRANOBJECT_H
+#ifdef __cplusplus
+extern "C" {
+#endif
+
+#include <Python.h>
+
+#ifndef NPY_NO_DEPRECATED_API
+#define NPY_NO_DEPRECATED_API NPY_API_VERSION
+#endif
+#ifdef FORTRANOBJECT_C
+#define NO_IMPORT_ARRAY
+#endif
+#define PY_ARRAY_UNIQUE_SYMBOL _npy_f2py_ARRAY_API
+#include "numpy/arrayobject.h"
+#include "numpy/npy_3kcompat.h"
+
+#ifdef F2PY_REPORT_ATEXIT
+#include <sys/timeb.h>
+// clang-format off
+extern void f2py_start_clock(void);
+extern void f2py_stop_clock(void);
+extern void f2py_start_call_clock(void);
+extern void f2py_stop_call_clock(void);
+extern void f2py_cb_start_clock(void);
+extern void f2py_cb_stop_clock(void);
+extern void f2py_cb_start_call_clock(void);
+extern void f2py_cb_stop_call_clock(void);
+extern void f2py_report_on_exit(int, void *);
+// clang-format on
+#endif
+
+#ifdef DMALLOC
+#include "dmalloc.h"
+#endif
+
+/* Fortran object interface */
+
+/*
+123456789-123456789-123456789-123456789-123456789-123456789-123456789-12
+
+PyFortranObject represents various Fortran objects:
+Fortran (module) routines, COMMON blocks, module data.
+
+Author: Pearu Peterson <[email protected]>
+*/
+
+#define F2PY_MAX_DIMS 40
+#define F2PY_MESSAGE_BUFFER_SIZE 300  // Increase on "stack smashing detected"
+
+typedef void (*f2py_set_data_func)(char *, npy_intp *);
+typedef void (*f2py_void_func)(void);
+typedef void (*f2py_init_func)(int *, npy_intp *, f2py_set_data_func, int *);
+
+/*typedef void* (*f2py_c_func)(void*,...);*/
+
+typedef void *(*f2pycfunc)(void);
+
+typedef struct {
+    char *name; /* attribute (array||routine) name */
+    int rank;   /* array rank, 0 for scalar, max is F2PY_MAX_DIMS,
+                   || rank=-1 for Fortran routine */
+    struct {
+        npy_intp d[F2PY_MAX_DIMS];
+    } dims;              /* dimensions of the array, || not used */
+    int type;            /* PyArray_<type> || not used */
+    int elsize;                /* Element size || not used */
+    char *data;          /* pointer to array || Fortran routine */
+    f2py_init_func func; /* initialization function for
+                            allocatable arrays:
+                            func(&rank,dims,set_ptr_func,name,len(name))
+                            || C/API wrapper for Fortran routine */
+    char *doc;           /* documentation string; only recommended
+                            for routines. */
+} FortranDataDef;
+
+typedef struct {
+    PyObject_HEAD
+    int len;              /* Number of attributes */
+    FortranDataDef *defs; /* An array of FortranDataDef's */
+    PyObject *dict;       /* Fortran object attribute dictionary */
+} PyFortranObject;
+
+#define PyFortran_Check(op) (Py_TYPE(op) == &PyFortran_Type)
+#define PyFortran_Check1(op) (0 == strcmp(Py_TYPE(op)->tp_name, "fortran"))
+
+extern PyTypeObject PyFortran_Type;
+extern int
+F2PyDict_SetItemString(PyObject *dict, char *name, PyObject *obj);
+extern PyObject *
+PyFortranObject_New(FortranDataDef *defs, f2py_void_func init);
+extern PyObject *
+PyFortranObject_NewAsAttr(FortranDataDef *defs);
+
+PyObject *
+F2PyCapsule_FromVoidPtr(void *ptr, void (*dtor)(PyObject *));
+void *
+F2PyCapsule_AsVoidPtr(PyObject *obj);
+int
+F2PyCapsule_Check(PyObject *ptr);
+
+extern void *
+F2PySwapThreadLocalCallbackPtr(char *key, void *ptr);
+extern void *
+F2PyGetThreadLocalCallbackPtr(char *key);
+
+#define ISCONTIGUOUS(m) (PyArray_FLAGS(m) & NPY_ARRAY_C_CONTIGUOUS)
+#define F2PY_INTENT_IN 1
+#define F2PY_INTENT_INOUT 2
+#define F2PY_INTENT_OUT 4
+#define F2PY_INTENT_HIDE 8
+#define F2PY_INTENT_CACHE 16
+#define F2PY_INTENT_COPY 32
+#define F2PY_INTENT_C 64
+#define F2PY_OPTIONAL 128
+#define F2PY_INTENT_INPLACE 256
+#define F2PY_INTENT_ALIGNED4 512
+#define F2PY_INTENT_ALIGNED8 1024
+#define F2PY_INTENT_ALIGNED16 2048
+
+#define ARRAY_ISALIGNED(ARR, SIZE) ((size_t)(PyArray_DATA(ARR)) % (SIZE) == 0)
+#define F2PY_ALIGN4(intent) (intent & F2PY_INTENT_ALIGNED4)
+#define F2PY_ALIGN8(intent) (intent & F2PY_INTENT_ALIGNED8)
+#define F2PY_ALIGN16(intent) (intent & F2PY_INTENT_ALIGNED16)
+
+#define F2PY_GET_ALIGNMENT(intent) \
+    (F2PY_ALIGN4(intent)           \
+             ? 4                   \
+             : (F2PY_ALIGN8(intent) ? 8 : (F2PY_ALIGN16(intent) ? 16 : 1)))
+#define F2PY_CHECK_ALIGNMENT(arr, intent) \
+    ARRAY_ISALIGNED(arr, F2PY_GET_ALIGNMENT(intent))
+#define F2PY_ARRAY_IS_CHARACTER_COMPATIBLE(arr) ((PyArray_DESCR(arr)->type_num == NPY_STRING && PyArray_DESCR(arr)->elsize >= 1) \
+                                                 || PyArray_DESCR(arr)->type_num == NPY_UINT8)
+#define F2PY_IS_UNICODE_ARRAY(arr) (PyArray_DESCR(arr)->type_num == NPY_UNICODE)
+
+extern PyArrayObject *
+ndarray_from_pyobj(const int type_num, const int elsize_, npy_intp *dims,
+                   const int rank, const int intent, PyObject *obj,
+                   const char *errmess);
+
+extern PyArrayObject *
+array_from_pyobj(const int type_num, npy_intp *dims, const int rank,
+                 const int intent, PyObject *obj);
+extern int
+copy_ND_array(const PyArrayObject *in, PyArrayObject *out);
+
+#ifdef DEBUG_COPY_ND_ARRAY
+extern void
+dump_attrs(const PyArrayObject *arr);
+#endif
+
+  extern int f2py_describe(PyObject *obj, char *buf);
+
+  /* Utility CPP macros and functions that can be used in signature file
+     expressions. See signature-file.rst for documentation.
+  */
+
+#define f2py_itemsize(var) (PyArray_DESCR((capi_ ## var ## _as_array))->elsize)
+#define f2py_size(var, ...) f2py_size_impl((PyArrayObject *)(capi_ ## var ## _as_array), ## __VA_ARGS__, -1)
+#define f2py_rank(var) var ## _Rank
+#define f2py_shape(var,dim) var ## _Dims[dim]
+#define f2py_len(var) f2py_shape(var,0)
+#define f2py_fshape(var,dim) f2py_shape(var,rank(var)-dim-1)
+#define f2py_flen(var) f2py_fshape(var,0)
+#define f2py_slen(var) capi_ ## var ## _len
+
+  extern npy_intp f2py_size_impl(PyArrayObject* var, ...);
+
+#ifdef __cplusplus
+}
+#endif
+#endif /* !Py_FORTRANOBJECT_H */