378 lines
10 KiB
Python
378 lines
10 KiB
Python
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"""
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A NumPy sub-namespace that conforms to the Python array API standard.
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This submodule accompanies NEP 47, which proposes its inclusion in NumPy. It
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is still considered experimental, and will issue a warning when imported.
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This is a proof-of-concept namespace that wraps the corresponding NumPy
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functions to give a conforming implementation of the Python array API standard
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(https://data-apis.github.io/array-api/latest/). The standard is currently in
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an RFC phase and comments on it are both welcome and encouraged. Comments
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should be made either at https://github.com/data-apis/array-api or at
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https://github.com/data-apis/consortium-feedback/discussions.
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NumPy already follows the proposed spec for the most part, so this module
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serves mostly as a thin wrapper around it. However, NumPy also implements a
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lot of behavior that is not included in the spec, so this serves as a
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restricted subset of the API. Only those functions that are part of the spec
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are included in this namespace, and all functions are given with the exact
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signature given in the spec, including the use of position-only arguments, and
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omitting any extra keyword arguments implemented by NumPy but not part of the
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spec. The behavior of some functions is also modified from the NumPy behavior
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to conform to the standard. Note that the underlying array object itself is
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wrapped in a wrapper Array() class, but is otherwise unchanged. This submodule
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is implemented in pure Python with no C extensions.
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The array API spec is designed as a "minimal API subset" and explicitly allows
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libraries to include behaviors not specified by it. But users of this module
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that intend to write portable code should be aware that only those behaviors
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that are listed in the spec are guaranteed to be implemented across libraries.
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Consequently, the NumPy implementation was chosen to be both conforming and
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minimal, so that users can use this implementation of the array API namespace
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and be sure that behaviors that it defines will be available in conforming
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namespaces from other libraries.
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A few notes about the current state of this submodule:
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- There is a test suite that tests modules against the array API standard at
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https://github.com/data-apis/array-api-tests. The test suite is still a work
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in progress, but the existing tests pass on this module, with a few
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exceptions:
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- DLPack support (see https://github.com/data-apis/array-api/pull/106) is
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not included here, as it requires a full implementation in NumPy proper
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first.
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The test suite is not yet complete, and even the tests that exist are not
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guaranteed to give a comprehensive coverage of the spec. Therefore, when
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reviewing and using this submodule, you should refer to the standard
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documents themselves. There are some tests in numpy.array_api.tests, but
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they primarily focus on things that are not tested by the official array API
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test suite.
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- There is a custom array object, numpy.array_api.Array, which is returned by
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all functions in this module. All functions in the array API namespace
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implicitly assume that they will only receive this object as input. The only
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way to create instances of this object is to use one of the array creation
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functions. It does not have a public constructor on the object itself. The
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object is a small wrapper class around numpy.ndarray. The main purpose of it
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is to restrict the namespace of the array object to only those dtypes and
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only those methods that are required by the spec, as well as to limit/change
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certain behavior that differs in the spec. In particular:
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- The array API namespace does not have scalar objects, only 0-D arrays.
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Operations on Array that would create a scalar in NumPy create a 0-D
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array.
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- Indexing: Only a subset of indices supported by NumPy are required by the
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spec. The Array object restricts indexing to only allow those types of
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indices that are required by the spec. See the docstring of the
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numpy.array_api.Array._validate_indices helper function for more
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information.
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- Type promotion: Some type promotion rules are different in the spec. In
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particular, the spec does not have any value-based casting. The spec also
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does not require cross-kind casting, like integer -> floating-point. Only
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those promotions that are explicitly required by the array API
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specification are allowed in this module. See NEP 47 for more info.
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- Functions do not automatically call asarray() on their input, and will not
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work if the input type is not Array. The exception is array creation
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functions, and Python operators on the Array object, which accept Python
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scalars of the same type as the array dtype.
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- All functions include type annotations, corresponding to those given in the
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spec (see _typing.py for definitions of some custom types). These do not
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currently fully pass mypy due to some limitations in mypy.
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- Dtype objects are just the NumPy dtype objects, e.g., float64 =
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np.dtype('float64'). The spec does not require any behavior on these dtype
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objects other than that they be accessible by name and be comparable by
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equality, but it was considered too much extra complexity to create custom
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objects to represent dtypes.
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- All places where the implementations in this submodule are known to deviate
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from their corresponding functions in NumPy are marked with "# Note:"
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comments.
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Still TODO in this module are:
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- DLPack support for numpy.ndarray is still in progress. See
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https://github.com/numpy/numpy/pull/19083.
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- The copy=False keyword argument to asarray() is not yet implemented. This
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requires support in numpy.asarray() first.
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- Some functions are not yet fully tested in the array API test suite, and may
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require updates that are not yet known until the tests are written.
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- The spec is still in an RFC phase and may still have minor updates, which
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will need to be reflected here.
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- Complex number support in array API spec is planned but not yet finalized,
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as are the fft extension and certain linear algebra functions such as eig
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that require complex dtypes.
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"""
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import warnings
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warnings.warn(
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"The numpy.array_api submodule is still experimental. See NEP 47.", stacklevel=2
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)
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__array_api_version__ = "2021.12"
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__all__ = ["__array_api_version__"]
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from ._constants import e, inf, nan, pi
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__all__ += ["e", "inf", "nan", "pi"]
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from ._creation_functions import (
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asarray,
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arange,
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empty,
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empty_like,
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eye,
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from_dlpack,
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full,
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full_like,
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linspace,
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meshgrid,
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ones,
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ones_like,
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tril,
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triu,
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zeros,
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zeros_like,
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)
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__all__ += [
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"asarray",
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"arange",
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"empty",
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"empty_like",
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"eye",
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"from_dlpack",
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"full",
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"full_like",
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"linspace",
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"meshgrid",
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"ones",
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"ones_like",
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"tril",
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"triu",
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"zeros",
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"zeros_like",
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]
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from ._data_type_functions import (
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astype,
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broadcast_arrays,
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broadcast_to,
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can_cast,
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finfo,
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iinfo,
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result_type,
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)
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__all__ += [
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"astype",
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"broadcast_arrays",
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"broadcast_to",
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"can_cast",
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"finfo",
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"iinfo",
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"result_type",
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]
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from ._dtypes import (
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int8,
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int16,
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int32,
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int64,
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uint8,
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uint16,
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uint32,
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uint64,
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float32,
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float64,
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bool,
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)
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__all__ += [
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"int8",
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"int16",
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"int32",
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"int64",
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"uint8",
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"uint16",
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"uint32",
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"uint64",
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"float32",
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"float64",
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"bool",
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]
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from ._elementwise_functions import (
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abs,
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acos,
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acosh,
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add,
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asin,
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asinh,
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atan,
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atan2,
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atanh,
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bitwise_and,
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bitwise_left_shift,
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bitwise_invert,
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bitwise_or,
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bitwise_right_shift,
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bitwise_xor,
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ceil,
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cos,
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cosh,
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divide,
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equal,
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exp,
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expm1,
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floor,
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floor_divide,
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greater,
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greater_equal,
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isfinite,
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isinf,
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isnan,
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less,
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less_equal,
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log,
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log1p,
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log2,
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log10,
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logaddexp,
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logical_and,
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logical_not,
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logical_or,
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logical_xor,
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multiply,
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negative,
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not_equal,
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positive,
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pow,
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remainder,
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round,
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sign,
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sin,
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sinh,
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square,
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sqrt,
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subtract,
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tan,
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tanh,
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trunc,
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)
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__all__ += [
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"abs",
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"acos",
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"acosh",
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"add",
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"asin",
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"asinh",
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"atan",
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"atan2",
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"atanh",
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"bitwise_and",
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"bitwise_left_shift",
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"bitwise_invert",
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"bitwise_or",
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"bitwise_right_shift",
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"bitwise_xor",
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"ceil",
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"cos",
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"cosh",
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"divide",
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"equal",
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"exp",
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"expm1",
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"floor",
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"floor_divide",
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"greater",
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"greater_equal",
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"isfinite",
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"isinf",
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"isnan",
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"less",
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"less_equal",
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"log",
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"log1p",
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"log2",
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"log10",
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"logaddexp",
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"logical_and",
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"logical_not",
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"logical_or",
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"logical_xor",
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"multiply",
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"negative",
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"not_equal",
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"positive",
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"pow",
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"remainder",
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"round",
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"sign",
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"sin",
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"sinh",
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"square",
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"sqrt",
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"subtract",
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"tan",
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"tanh",
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"trunc",
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]
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# linalg is an extension in the array API spec, which is a sub-namespace. Only
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# a subset of functions in it are imported into the top-level namespace.
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from . import linalg
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__all__ += ["linalg"]
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from .linalg import matmul, tensordot, matrix_transpose, vecdot
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__all__ += ["matmul", "tensordot", "matrix_transpose", "vecdot"]
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from ._manipulation_functions import (
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concat,
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expand_dims,
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flip,
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permute_dims,
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reshape,
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roll,
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squeeze,
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stack,
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)
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__all__ += ["concat", "expand_dims", "flip", "permute_dims", "reshape", "roll", "squeeze", "stack"]
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from ._searching_functions import argmax, argmin, nonzero, where
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__all__ += ["argmax", "argmin", "nonzero", "where"]
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from ._set_functions import unique_all, unique_counts, unique_inverse, unique_values
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__all__ += ["unique_all", "unique_counts", "unique_inverse", "unique_values"]
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from ._sorting_functions import argsort, sort
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__all__ += ["argsort", "sort"]
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from ._statistical_functions import max, mean, min, prod, std, sum, var
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__all__ += ["max", "mean", "min", "prod", "std", "sum", "var"]
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from ._utility_functions import all, any
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__all__ += ["all", "any"]
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