"""
This file is separate from ``_add_newdocs.py`` so that it can be mocked out by
our sphinx ``conf.py`` during doc builds, where we want to avoid showing
platform-dependent information.
"""
import os
import sys

from numpy._core import dtype, numerictypes as _numerictypes
from numpy._core.function_base import add_newdoc

##############################################################################
#
# Documentation for concrete scalar classes
#
##############################################################################

def numeric_type_aliases(aliases):
    def type_aliases_gen():
        for alias, doc in aliases:
            try:
                alias_type = getattr(_numerictypes, alias)
            except AttributeError:
                # The set of aliases that actually exist varies between platforms
                pass
            else:
                yield (alias_type, alias, doc)
    return list(type_aliases_gen())


possible_aliases = numeric_type_aliases([
    ('int8', '8-bit signed integer (``-128`` to ``127``)'),
    ('int16', '16-bit signed integer (``-32_768`` to ``32_767``)'),
    ('int32', '32-bit signed integer (``-2_147_483_648`` to ``2_147_483_647``)'),
    ('int64', '64-bit signed integer (``-9_223_372_036_854_775_808`` to ``9_223_372_036_854_775_807``)'),
    ('intp', 'Signed integer large enough to fit pointer, compatible with C ``intptr_t``'),
    ('uint8', '8-bit unsigned integer (``0`` to ``255``)'),
    ('uint16', '16-bit unsigned integer (``0`` to ``65_535``)'),
    ('uint32', '32-bit unsigned integer (``0`` to ``4_294_967_295``)'),
    ('uint64', '64-bit unsigned integer (``0`` to ``18_446_744_073_709_551_615``)'),
    ('uintp', 'Unsigned integer large enough to fit pointer, compatible with C ``uintptr_t``'),
    ('float16', '16-bit-precision floating-point number type: sign bit, 5 bits exponent, 10 bits mantissa'),
    ('float32', '32-bit-precision floating-point number type: sign bit, 8 bits exponent, 23 bits mantissa'),
    ('float64', '64-bit precision floating-point number type: sign bit, 11 bits exponent, 52 bits mantissa'),
    ('float96', '96-bit extended-precision floating-point number type'),
    ('float128', '128-bit extended-precision floating-point number type'),
    ('complex64', 'Complex number type composed of 2 32-bit-precision floating-point numbers'),
    ('complex128', 'Complex number type composed of 2 64-bit-precision floating-point numbers'),
    ('complex192', 'Complex number type composed of 2 96-bit extended-precision floating-point numbers'),
    ('complex256', 'Complex number type composed of 2 128-bit extended-precision floating-point numbers'),
])


def _get_platform_and_machine():
    try:
        system, _, _, _, machine = os.uname()
    except AttributeError:
        system = sys.platform
        if system == 'win32':
            machine = os.environ.get('PROCESSOR_ARCHITEW6432', '') \
                    or os.environ.get('PROCESSOR_ARCHITECTURE', '')
        else:
            machine = 'unknown'
    return system, machine


_system, _machine = _get_platform_and_machine()
_doc_alias_string = f":Alias on this platform ({_system} {_machine}):"

# docstring prefix that cpython uses to populate `__text_signature__`
_ARGUMENT_CLINIC_TEMPLATE = """{name}{signature}
--

{docstring}"""

def add_newdoc_for_scalar_type(name: str, text_signature: str, doc: str) -> None:
    # note: `:field: value` is rST syntax which renders as field lists.
    cls = getattr(_numerictypes, name)
    module = cls.__module__

    lines_extra = [
        "",  # blank line after main doc
        f":Character code: ``{dtype(cls).char!r}``",
    ]

    if name != cls.__name__:
        lines_extra.append(f":Canonical name: `{module}.{name}`")

    lines_extra.extend(
        f"{_doc_alias_string} `{module}.{alias}`: {doc}."
        for alias_type, alias, doc in possible_aliases
        if alias_type is cls
    )

    docstring = _ARGUMENT_CLINIC_TEMPLATE.format(
        name=cls.__name__,  # must match the class name
        signature=text_signature,
        docstring="\n".join([doc.strip(), *lines_extra]),
    )
    add_newdoc('numpy._core.numerictypes', name, docstring)


for bool_name in ('bool', 'bool_'):
    add_newdoc_for_scalar_type(bool_name, '(value=False, /)', """
Boolean type (True or False), stored as a byte.

.. warning::

    The :class:`bool` type is not a subclass of the :class:`int_` type
    (the :class:`bool` is not even a number type). This is different
    than Python's default implementation of :class:`bool` as a
    sub-class of :class:`int`.
""")

add_newdoc_for_scalar_type('byte', '(value=0, /)', """
Signed integer type, compatible with C ``char``.
""")

add_newdoc_for_scalar_type('short', '(value=0, /)', """
Signed integer type, compatible with C ``short``.
""")

add_newdoc_for_scalar_type('intc', '(value=0, /)', """
Signed integer type, compatible with C ``int``.
""")

add_newdoc_for_scalar_type('long', '(value=0, /)', """
Signed integer type, compatible with C ``long``.
""")

# TODO: These docs probably need an if to highlight the default rather than
#       the C-types (and be correct).
add_newdoc_for_scalar_type('int_', '(value=0, /)', """
Default signed integer type, 64bit on 64bit systems and 32bit on 32bit systems.
""")

add_newdoc_for_scalar_type('longlong', '(value=0, /)', """
Signed integer type, compatible with C ``long long``.
""")

add_newdoc_for_scalar_type('ubyte', '(value=0, /)', """
Unsigned integer type, compatible with C ``unsigned char``.
""")

add_newdoc_for_scalar_type('ushort', '(value=0, /)', """
Unsigned integer type, compatible with C ``unsigned short``.
""")

add_newdoc_for_scalar_type('uintc', '(value=0, /)', """
Unsigned integer type, compatible with C ``unsigned int``.
""")

add_newdoc_for_scalar_type('uint', '(value=0, /)', """
Unsigned signed integer type, 64bit on 64bit systems and 32bit on 32bit systems.
""")

add_newdoc_for_scalar_type('ulong', '(value=0, /)', """
Unsigned integer type, compatible with C ``unsigned long``.
""")

add_newdoc_for_scalar_type('ulonglong', '(value=0, /)', """
Unsigned integer type, compatible with C ``unsigned long long``.
""")

add_newdoc_for_scalar_type('half', '(value=0, /)', """
Half-precision floating-point number type.
""")

add_newdoc_for_scalar_type('single', '(value=0, /)', """
Single-precision floating-point number type, compatible with C ``float``.
""")

add_newdoc_for_scalar_type('double', '(value=0, /)', """
Double-precision floating-point number type, compatible with Python :class:`float` and C ``double``.
""")

add_newdoc_for_scalar_type('longdouble', '(value=0, /)', """
Extended-precision floating-point number type, compatible with C ``long double``
but not necessarily with IEEE 754 quadruple-precision.
""")

add_newdoc_for_scalar_type('csingle', '(real=0, imag=0, /)', """
Complex number type composed of two single-precision floating-point numbers.
""")

add_newdoc_for_scalar_type('cdouble', '(real=0, imag=0, /)', """
Complex number type composed of two double-precision floating-point numbers,
compatible with Python :class:`complex`.
""")

add_newdoc_for_scalar_type('clongdouble', '(real=0, imag=0, /)', """
Complex number type composed of two extended-precision floating-point numbers.
""")

add_newdoc_for_scalar_type('object_', '(value=None, /)', """
Any Python object.
""")

add_newdoc_for_scalar_type('str_', '(value="", /, *args, **kwargs)', r"""
A unicode string.

This type strips trailing null codepoints.

>>> s = np.str_("abc\x00")
>>> s
'abc'

Unlike the builtin :class:`str`, this supports the
:ref:`python:bufferobjects`, exposing its contents as UCS4:

>>> m = memoryview(np.str_("abc"))
>>> m.format
'3w'
>>> m.tobytes()
b'a\x00\x00\x00b\x00\x00\x00c\x00\x00\x00'
""")

add_newdoc_for_scalar_type('bytes_', '(value="", /, *args, **kwargs)', r"""
A byte string.

When used in arrays, this type strips trailing null bytes.
""")

add_newdoc_for_scalar_type('void', '(length_or_data, /, dtype=None)', r"""
np.void(length_or_data, /, dtype=None)

Create a new structured or unstructured void scalar.

Parameters
----------
length_or_data : int, array-like, bytes-like, object
    One of multiple meanings (see notes).  The length or
    bytes data of an unstructured void.  Or alternatively,
    the data to be stored in the new scalar when `dtype`
    is provided.
    This can be an array-like, in which case an array may
    be returned.
dtype : dtype, optional
    If provided the dtype of the new scalar.  This dtype must
    be "void" dtype (i.e. a structured or unstructured void,
    see also :ref:`defining-structured-types`).

    .. versionadded:: 1.24

Notes
-----
For historical reasons and because void scalars can represent both
arbitrary byte data and structured dtypes, the void constructor
has three calling conventions:

1. ``np.void(5)`` creates a ``dtype="V5"`` scalar filled with five
    ``\0`` bytes.  The 5 can be a Python or NumPy integer.
2. ``np.void(b"bytes-like")`` creates a void scalar from the byte string.
    The dtype itemsize will match the byte string length, here ``"V10"``.
3. When a ``dtype=`` is passed the call is roughly the same as an
    array creation.  However, a void scalar rather than array is returned.

Please see the examples which show all three different conventions.

Examples
--------
>>> np.void(5)
np.void(b'\x00\x00\x00\x00\x00')
>>> np.void(b'abcd')
np.void(b'\x61\x62\x63\x64')
>>> np.void((3.2, b'eggs'), dtype="d,S5")
np.void((3.2, b'eggs'), dtype=[('f0', '<f8'), ('f1', 'S5')])
>>> np.void(3, dtype=[('x', np.int8), ('y', np.int8)])
np.void((3, 3), dtype=[('x', 'i1'), ('y', 'i1')])
""")

add_newdoc_for_scalar_type('datetime64', '(value=None, /, *args)', """
If created from a 64-bit integer, it represents an offset from ``1970-01-01T00:00:00``.
If created from string, the string can be in ISO 8601 date or datetime format.

When parsing a string to create a datetime object, if the string contains
a trailing timezone (A 'Z' or a timezone offset), the timezone will be
dropped and a User Warning is given.

Datetime64 objects should be considered to be UTC and therefore have an
offset of +0000.

>>> np.datetime64(10, 'Y')
np.datetime64('1980')
>>> np.datetime64('1980', 'Y')
np.datetime64('1980')
>>> np.datetime64(10, 'D')
np.datetime64('1970-01-11')

See :ref:`arrays.datetime` for more information.
""")

add_newdoc_for_scalar_type('timedelta64', '(value=0, /, *args)', """
A timedelta stored as a 64-bit integer.

See :ref:`arrays.datetime` for more information.
""")

add_newdoc('numpy._core.numerictypes', "integer", ('is_integer',
    """
    is_integer($self, /)
    --

    integer.is_integer() -> bool

    Return ``True`` if the number is finite with integral value.

    .. versionadded:: 1.22

    Examples
    --------
    >>> import numpy as np
    >>> np.int64(-2).is_integer()
    True
    >>> np.uint32(5).is_integer()
    True
    """))

# TODO: work out how to put this on the base class, np.floating
for float_name in ('half', 'single', 'double', 'longdouble'):
    add_newdoc('numpy._core.numerictypes', float_name, ('as_integer_ratio',
        f"""
        as_integer_ratio($self, /)
        --

        {float_name}.as_integer_ratio() -> (int, int)

        Return a pair of integers, whose ratio is exactly equal to the original
        floating point number, and with a positive denominator.
        Raise `OverflowError` on infinities and a `ValueError` on NaNs.

        >>> np.{float_name}(10.0).as_integer_ratio()
        (10, 1)
        >>> np.{float_name}(0.0).as_integer_ratio()
        (0, 1)
        >>> np.{float_name}(-.25).as_integer_ratio()
        (-1, 4)
        """))

    add_newdoc('numpy._core.numerictypes', float_name, ('is_integer',
        f"""
        is_integer($self, /)
        --

        {float_name}.is_integer() -> bool

        Return ``True`` if the floating point number is finite with integral
        value, and ``False`` otherwise.

        .. versionadded:: 1.22

        Examples
        --------
        >>> np.{float_name}(-2.0).is_integer()
        True
        >>> np.{float_name}(3.2).is_integer()
        False
        """))

for int_name in ('int8', 'uint8', 'int16', 'uint16', 'int32', 'uint32',
                 'int64', 'uint64', 'int64', 'uint64', 'int64', 'uint64',
                 'longlong', 'ulonglong'):
    # Add negative examples for signed cases by checking typecode
    add_newdoc('numpy._core.numerictypes', int_name, ('bit_count',
        f"""
        bit_count($self, /)
        --

        {int_name}.bit_count() -> int

        Computes the number of 1-bits in the absolute value of the input.
        Analogous to the builtin `int.bit_count` or ``popcount`` in C++.

        Examples
        --------
        >>> np.{int_name}(127).bit_count()
        7""" +
        (f"""
        >>> np.{int_name}(-127).bit_count()
        7
        """ if dtype(int_name).char.islower() else "")))