"""
A NumPy sub-namespace that conforms to the Python array API standard.

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.

This submodule will be accompanied with a NEP (not yet written) proposing its
inclusion in NumPy.

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. Note that the array object itself is unchanged, as implementing a
restricted subclass of ndarray seems unnecessarily complex for the purposes of
this namespace, so the API of array methods and other behaviors of the array
object will include things that are not part of the spec.

The 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.

A few notes about the current state of this submodule:

- There is a test suite that tests modules against the array API standard at
  https://github.com/data-apis/array-api-tests. The test suite is still a work
  in progress, but the existing tests pass on this module, with a few
  exceptions:

  - Device support is not yet implemented in NumPy
    (https://data-apis.github.io/array-api/latest/design_topics/device_support.html).
    As a result, the `device` attribute of the array object is missing, and
    array creation functions that take the `device` keyword argument will fail
    with NotImplementedError.

  - DLPack support (see https://github.com/data-apis/array-api/pull/106) is
    not included here, as it requires a full implementation in NumPy proper
    first.

  - np.argmin and np.argmax do not implement the keepdims keyword argument.

  - Some linear algebra functions in the spec are still a work in progress (to
    be added soon). These will be updated once the spec is.

  - Some tests in the test suite are still not fully correct in that they test
    all datatypes whereas certain functions are only defined for a subset of
    datatypes.

  The test suite is yet complete, and even the tests that exist are not
  guaranteed to give a comprehensive coverage of the spec. Therefore, those
  reviewing this submodule should refer to the standard documents themselves.

- There is a custom array object, numpy._array_api.ndarray, which is returned
  by all functions in this module. All functions in the array API namespace
  implicitly assume that they will only receive this object as input. The only
  way to create instances of this object is to use one of the array creation
  functions. It does not have a public constructor on the object itself. The
  object is a small wrapper Python class around numpy.ndarray. The main
  purpose of it is to restrict the namespace of the array object to only those
  dtypes and only those methods that are required by the spec, as well as to
  limit/change certain behavior that differs in the spec. In particular:

  - Indexing: Only a subset of indices supported by NumPy are required by the
    spec. The ndarray object restricts indexing to only allow those types of
    indices that are required by the spec. See the docstring of the
    numpy._array_api.ndarray._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. Note that the
    code to correct the type promotion rules on numpy._array_api.ndarray is
    not yet implemented.

- All functions include type annotations, corresponding to those given in the
  spec (see _types.py for definitions of the types 'array', 'device', and
  'dtype'). These do not currently fully pass mypy due to some limitations in
  mypy.

- The wrapper functions in this module do not do any type checking for things
  that would be impossible without leaving the _array_api namespace.

- All places where the implementations in this submodule are known to deviate
  from their corresponding functions in NumPy are marked with "# Note"
  comments. Reviewers should make note of these comments.

Still TODO in this module are:

- Implement the spec type promotion rules on the ndarray object.

- Disable NumPy warnings in the API functions.

- Implement keepdims on argmin and argmax.

- Device support and DLPack support are not yet implemented. These require
  support in NumPy itself first.

- The a non-default value for the `copy` keyword argument is not yet
  implemented on asarray. This requires support in numpy.asarray() first.

- Some functions are not yet fully tested in the array API test suite, and may
  require updates that are not yet known until the tests are written.

"""

__all__ = []

from ._constants import e, inf, nan, pi

__all__ += ['e', 'inf', 'nan', 'pi']

from ._creation_functions import asarray, arange, empty, empty_like, eye, from_dlpack, full, full_like, linspace, meshgrid, ones, ones_like, zeros, zeros_like

__all__ += ['asarray', 'arange', 'empty', 'empty_like', 'eye', 'from_dlpack', 'full', 'full_like', 'linspace', 'meshgrid', 'ones', 'ones_like', 'zeros', 'zeros_like']

from ._data_type_functions import broadcast_arrays, broadcast_to, can_cast, finfo, iinfo, result_type

__all__ += ['broadcast_arrays', 'broadcast_to', 'can_cast', 'finfo', 'iinfo', 'result_type']

from ._dtypes import int8, int16, int32, int64, uint8, uint16, uint32, uint64, float32, float64, bool

__all__ += ['int8', 'int16', 'int32', 'int64', 'uint8', 'uint16', 'uint32', 'uint64', 'float32', 'float64', 'bool']

from ._elementwise_functions import abs, acos, acosh, add, asin, asinh, atan, atan2, atanh, bitwise_and, bitwise_left_shift, bitwise_invert, bitwise_or, bitwise_right_shift, bitwise_xor, ceil, cos, cosh, divide, equal, exp, expm1, floor, floor_divide, greater, greater_equal, isfinite, isinf, isnan, less, less_equal, log, log1p, log2, log10, logaddexp, logical_and, logical_not, logical_or, logical_xor, multiply, negative, not_equal, positive, pow, remainder, round, sign, sin, sinh, square, sqrt, subtract, tan, tanh, trunc

__all__ += ['abs', 'acos', 'acosh', 'add', 'asin', 'asinh', 'atan', 'atan2', 'atanh', 'bitwise_and', 'bitwise_left_shift', 'bitwise_invert', 'bitwise_or', 'bitwise_right_shift', 'bitwise_xor', 'ceil', 'cos', 'cosh', 'divide', 'equal', 'exp', 'expm1', 'floor', 'floor_divide', 'greater', 'greater_equal', 'isfinite', 'isinf', 'isnan', 'less', 'less_equal', 'log', 'log1p', 'log2', 'log10', 'logaddexp', 'logical_and', 'logical_not', 'logical_or', 'logical_xor', 'multiply', 'negative', 'not_equal', 'positive', 'pow', 'remainder', 'round', 'sign', 'sin', 'sinh', 'square', 'sqrt', 'subtract', 'tan', 'tanh', 'trunc']

from ._linear_algebra_functions import cross, det, diagonal, inv, norm, outer, trace, transpose

__all__ += ['cross', 'det', 'diagonal', 'inv', 'norm', 'outer', 'trace', 'transpose']

# from ._linear_algebra_functions import cholesky, cross, det, diagonal, dot, eig, eigvalsh, einsum, inv, lstsq, matmul, matrix_power, matrix_rank, norm, outer, pinv, qr, slogdet, solve, svd, trace, transpose
#
# __all__ += ['cholesky', 'cross', 'det', 'diagonal', 'dot', 'eig', 'eigvalsh', 'einsum', 'inv', 'lstsq', 'matmul', 'matrix_power', 'matrix_rank', 'norm', 'outer', 'pinv', 'qr', 'slogdet', 'solve', 'svd', 'trace', 'transpose']

from ._manipulation_functions import concat, expand_dims, flip, reshape, roll, squeeze, stack

__all__ += ['concat', 'expand_dims', 'flip', 'reshape', 'roll', 'squeeze', 'stack']

from ._searching_functions import argmax, argmin, nonzero, where

__all__ += ['argmax', 'argmin', 'nonzero', 'where']

from ._set_functions import unique

__all__ += ['unique']

from ._sorting_functions import argsort, sort

__all__ += ['argsort', 'sort']

from ._statistical_functions import max, mean, min, prod, std, sum, var

__all__ += ['max', 'mean', 'min', 'prod', 'std', 'sum', 'var']

from ._utility_functions import all, any

__all__ += ['all', 'any']