"""
Contains the core of NumPy: ndarray, ufuncs, dtypes, etc.

Please note that this module is private.  All functions and objects
are available in the main ``numpy`` namespace - use that instead.

"""

import os

from numpy.version import version as __version__

# disables OpenBLAS affinity setting of the main thread that limits
# python threads or processes to one core
env_added = []
for envkey in ['OPENBLAS_MAIN_FREE', 'GOTOBLAS_MAIN_FREE']:
    if envkey not in os.environ:
        os.environ[envkey] = '1'
        env_added.append(envkey)

try:
    from . import multiarray
except ImportError as exc:
    import sys

    # Bypass for the module re-initialization opt-out
    if exc.msg == "cannot load module more than once per process":
        raise

    # Basically always, the problem should be that the C module is wrong/missing...
    if (
        isinstance(exc, ModuleNotFoundError)
        and exc.name == "numpy._core._multiarray_umath"
    ):
        import sys
        candidates = []
        for path in __path__:
            candidates.extend(
                f for f in os.listdir(path) if f.startswith("_multiarray_umath"))
        if len(candidates) == 0:
            bad_c_module_info = (
                "We found no compiled module, did NumPy build successfully?\n")
        else:
            candidate_str = '\n  * '.join(candidates)
            # cache_tag is documented to be possibly None, so just use name if it is
            # this guesses at cache_tag being the same as the extension module scheme
            tag = sys.implementation.cache_tag or sys.implementation.name
            bad_c_module_info = (
                f"The following compiled module files exist, but seem incompatible\n"
                f"with with either python '{tag}' or the "
                f"platform '{sys.platform}':\n\n  * {candidate_str}\n"
            )
    else:
        bad_c_module_info = ""

    major, minor, *_ = sys.version_info
    msg = f"""

IMPORTANT: PLEASE READ THIS FOR ADVICE ON HOW TO SOLVE THIS ISSUE!

Importing the numpy C-extensions failed. This error can happen for
many reasons, often due to issues with your setup or how NumPy was
installed.
{bad_c_module_info}
We have compiled some common reasons and troubleshooting tips at:

    https://numpy.org/devdocs/user/troubleshooting-importerror.html

Please note and check the following:

  * The Python version is: Python {major}.{minor} from "{sys.executable}"
  * The NumPy version is: "{__version__}"

and make sure that they are the versions you expect.

Please carefully study the information and documentation linked above.
This is unlikely to be a NumPy issue but will be caused by a bad install
or environment on your machine.

Original error was: {exc}
"""

    raise ImportError(msg) from exc
finally:
    for envkey in env_added:
        del os.environ[envkey]
del envkey
del env_added
del os

from . import umath

# Check that multiarray,umath are pure python modules wrapping
# _multiarray_umath and not either of the old c-extension modules
if not (hasattr(multiarray, '_multiarray_umath') and
        hasattr(umath, '_multiarray_umath')):
    import sys
    path = sys.modules['numpy'].__path__
    msg = ("Something is wrong with the numpy installation. "
        "While importing we detected an older version of "
        "numpy in {}. One method of fixing this is to repeatedly uninstall "
        "numpy until none is found, then reinstall this version.")
    raise ImportError(msg.format(path))

from . import numerictypes as nt
from .numerictypes import sctypeDict, sctypes

multiarray.set_typeDict(nt.sctypeDict)
from . import (
    _machar,
    einsumfunc,
    fromnumeric,
    function_base,
    getlimits,
    numeric,
    shape_base,
)
from .einsumfunc import *
from .fromnumeric import *
from .function_base import *
from .getlimits import *

# Note: module name memmap is overwritten by a class with same name
from .memmap import *
from .numeric import *
from .records import recarray, record
from .shape_base import *

del nt

# do this after everything else, to minimize the chance of this misleadingly
# appearing in an import-time traceback
# add these for module-freeze analysis (like PyInstaller)
from . import (
    _add_newdocs,
    _add_newdocs_scalars,
    _dtype,
    _dtype_ctypes,
    _internal,
    _methods,
)
from .numeric import absolute as abs

acos = numeric.arccos
acosh = numeric.arccosh
asin = numeric.arcsin
asinh = numeric.arcsinh
atan = numeric.arctan
atanh = numeric.arctanh
atan2 = numeric.arctan2
concat = numeric.concatenate
bitwise_left_shift = numeric.left_shift
bitwise_invert = numeric.invert
bitwise_right_shift = numeric.right_shift
permute_dims = numeric.transpose
pow = numeric.power

__all__ = [
    "abs", "acos", "acosh", "asin", "asinh", "atan", "atanh", "atan2",
    "bitwise_invert", "bitwise_left_shift", "bitwise_right_shift", "concat",
    "pow", "permute_dims", "memmap", "sctypeDict", "record", "recarray"
]
__all__ += numeric.__all__
__all__ += function_base.__all__
__all__ += getlimits.__all__
__all__ += shape_base.__all__
__all__ += einsumfunc.__all__


def _ufunc_reduce(func):
    # Report the `__name__`. pickle will try to find the module. Note that
    # pickle supports for this `__name__` to be a `__qualname__`. It may
    # make sense to add a `__qualname__` to ufuncs, to allow this more
    # explicitly (Numba has ufuncs as attributes).
    # See also: https://github.com/dask/distributed/issues/3450
    return func.__name__


def _DType_reconstruct(scalar_type):
    # This is a work-around to pickle type(np.dtype(np.float64)), etc.
    # and it should eventually be replaced with a better solution, e.g. when
    # DTypes become HeapTypes.
    return type(dtype(scalar_type))


def _DType_reduce(DType):
    # As types/classes, most DTypes can simply be pickled by their name:
    if not DType._legacy or DType.__module__ == "numpy.dtypes":
        return DType.__name__

    # However, user defined legacy dtypes (like rational) do not end up in
    # `numpy.dtypes` as module and do not have a public class at all.
    # For these, we pickle them by reconstructing them from the scalar type:
    scalar_type = DType.type
    return _DType_reconstruct, (scalar_type,)


def __getattr__(name):
    # Deprecated 2022-11-22, NumPy 1.25.
    if name == "MachAr":
        import warnings
        warnings.warn(
            "The `np._core.MachAr` is considered private API (NumPy 1.24)",
            DeprecationWarning, stacklevel=2,
        )
        return _machar.MachAr
    raise AttributeError(f"Module {__name__!r} has no attribute {name!r}")


import copyreg

copyreg.pickle(ufunc, _ufunc_reduce)
copyreg.pickle(type(dtype), _DType_reduce, _DType_reconstruct)

# Unclutter namespace (must keep _*_reconstruct for unpickling)
del copyreg, _ufunc_reduce, _DType_reduce

from numpy._pytesttester import PytestTester

test = PytestTester(__name__)
del PytestTester