"""
A place for internal code

Some things are more easily handled Python.

"""
import ast
import re
import sys
import platform

from .multiarray import dtype, array, ndarray
try:
    import ctypes
except ImportError:
    ctypes = None

IS_PYPY = platform.python_implementation() == 'PyPy'

if (sys.byteorder == 'little'):
    _nbo = '<'
else:
    _nbo = '>'

def _makenames_list(adict, align):
    allfields = []
    fnames = list(adict.keys())
    for fname in fnames:
        obj = adict[fname]
        n = len(obj)
        if not isinstance(obj, tuple) or n not in [2, 3]:
            raise ValueError("entry not a 2- or 3- tuple")
        if (n > 2) and (obj[2] == fname):
            continue
        num = int(obj[1])
        if (num < 0):
            raise ValueError("invalid offset.")
        format = dtype(obj[0], align=align)
        if (n > 2):
            title = obj[2]
        else:
            title = None
        allfields.append((fname, format, num, title))
    # sort by offsets
    allfields.sort(key=lambda x: x[2])
    names = [x[0] for x in allfields]
    formats = [x[1] for x in allfields]
    offsets = [x[2] for x in allfields]
    titles = [x[3] for x in allfields]

    return names, formats, offsets, titles

# Called in PyArray_DescrConverter function when
#  a dictionary without "names" and "formats"
#  fields is used as a data-type descriptor.
def _usefields(adict, align):
    try:
        names = adict[-1]
    except KeyError:
        names = None
    if names is None:
        names, formats, offsets, titles = _makenames_list(adict, align)
    else:
        formats = []
        offsets = []
        titles = []
        for name in names:
            res = adict[name]
            formats.append(res[0])
            offsets.append(res[1])
            if (len(res) > 2):
                titles.append(res[2])
            else:
                titles.append(None)

    return dtype({"names": names,
                  "formats": formats,
                  "offsets": offsets,
                  "titles": titles}, align)


# construct an array_protocol descriptor list
#  from the fields attribute of a descriptor
# This calls itself recursively but should eventually hit
#  a descriptor that has no fields and then return
#  a simple typestring

def _array_descr(descriptor):
    fields = descriptor.fields
    if fields is None:
        subdtype = descriptor.subdtype
        if subdtype is None:
            if descriptor.metadata is None:
                return descriptor.str
            else:
                new = descriptor.metadata.copy()
                if new:
                    return (descriptor.str, new)
                else:
                    return descriptor.str
        else:
            return (_array_descr(subdtype[0]), subdtype[1])

    names = descriptor.names
    ordered_fields = [fields[x] + (x,) for x in names]
    result = []
    offset = 0
    for field in ordered_fields:
        if field[1] > offset:
            num = field[1] - offset
            result.append(('', '|V%d' % num))
            offset += num
        elif field[1] < offset:
            raise ValueError(
                "dtype.descr is not defined for types with overlapping or "
                "out-of-order fields")
        if len(field) > 3:
            name = (field[2], field[3])
        else:
            name = field[2]
        if field[0].subdtype:
            tup = (name, _array_descr(field[0].subdtype[0]),
                   field[0].subdtype[1])
        else:
            tup = (name, _array_descr(field[0]))
        offset += field[0].itemsize
        result.append(tup)

    if descriptor.itemsize > offset:
        num = descriptor.itemsize - offset
        result.append(('', '|V%d' % num))

    return result

# Build a new array from the information in a pickle.
# Note that the name numpy.core._internal._reconstruct is embedded in
# pickles of ndarrays made with NumPy before release 1.0
# so don't remove the name here, or you'll
# break backward compatibility.
def _reconstruct(subtype, shape, dtype):
    return ndarray.__new__(subtype, shape, dtype)


# format_re was originally from numarray by J. Todd Miller

format_re = re.compile(r'(?P<order1>[<>|=]?)'
                       r'(?P<repeats> *[(]?[ ,0-9]*[)]? *)'
                       r'(?P<order2>[<>|=]?)'
                       r'(?P<dtype>[A-Za-z0-9.?]*(?:\[[a-zA-Z0-9,.]+\])?)')
sep_re = re.compile(r'\s*,\s*')
space_re = re.compile(r'\s+$')

# astr is a string (perhaps comma separated)

_convorder = {'=': _nbo}

def _commastring(astr):
    startindex = 0
    result = []
    while startindex < len(astr):
        mo = format_re.match(astr, pos=startindex)
        try:
            (order1, repeats, order2, dtype) = mo.groups()
        except (TypeError, AttributeError):
            raise ValueError('format number %d of "%s" is not recognized' %
                                            (len(result)+1, astr))
        startindex = mo.end()
        # Separator or ending padding
        if startindex < len(astr):
            if space_re.match(astr, pos=startindex):
                startindex = len(astr)
            else:
                mo = sep_re.match(astr, pos=startindex)
                if not mo:
                    raise ValueError(
                        'format number %d of "%s" is not recognized' %
                        (len(result)+1, astr))
                startindex = mo.end()

        if order2 == '':
            order = order1
        elif order1 == '':
            order = order2
        else:
            order1 = _convorder.get(order1, order1)
            order2 = _convorder.get(order2, order2)
            if (order1 != order2):
                raise ValueError(
                    'inconsistent byte-order specification %s and %s' %
                    (order1, order2))
            order = order1

        if order in ['|', '=', _nbo]:
            order = ''
        dtype = order + dtype
        if (repeats == ''):
            newitem = dtype
        else:
            newitem = (dtype, ast.literal_eval(repeats))
        result.append(newitem)

    return result

class dummy_ctype:
    def __init__(self, cls):
        self._cls = cls
    def __mul__(self, other):
        return self
    def __call__(self, *other):
        return self._cls(other)
    def __eq__(self, other):
        return self._cls == other._cls
    def __ne__(self, other):
        return self._cls != other._cls

def _getintp_ctype():
    val = _getintp_ctype.cache
    if val is not None:
        return val
    if ctypes is None:
        import numpy as np
        val = dummy_ctype(np.intp)
    else:
        char = dtype('p').char
        if (char == 'i'):
            val = ctypes.c_int
        elif char == 'l':
            val = ctypes.c_long
        elif char == 'q':
            val = ctypes.c_longlong
        else:
            val = ctypes.c_long
    _getintp_ctype.cache = val
    return val
_getintp_ctype.cache = None

# Used for .ctypes attribute of ndarray

class _missing_ctypes:
    def cast(self, num, obj):
        return num.value

    class c_void_p:
        def __init__(self, ptr):
            self.value = ptr


class _ctypes:
    def __init__(self, array, ptr=None):
        self._arr = array

        if ctypes:
            self._ctypes = ctypes
            self._data = self._ctypes.c_void_p(ptr)
        else:
            # fake a pointer-like object that holds onto the reference
            self._ctypes = _missing_ctypes()
            self._data = self._ctypes.c_void_p(ptr)
            self._data._objects = array

        if self._arr.ndim == 0:
            self._zerod = True
        else:
            self._zerod = False

    def data_as(self, obj):
        """
        Return the data pointer cast to a particular c-types object.
        For example, calling ``self._as_parameter_`` is equivalent to
        ``self.data_as(ctypes.c_void_p)``. Perhaps you want to use the data as a
        pointer to a ctypes array of floating-point data:
        ``self.data_as(ctypes.POINTER(ctypes.c_double))``.

        The returned pointer will keep a reference to the array.
        """
        # _ctypes.cast function causes a circular reference of self._data in
        # self._data._objects. Attributes of self._data cannot be released
        # until gc.collect is called. Make a copy of the pointer first then let
        # it hold the array reference. This is a workaround to circumvent the
        # CPython bug https://bugs.python.org/issue12836
        ptr = self._ctypes.cast(self._data, obj)
        ptr._arr = self._arr
        return ptr

    def shape_as(self, obj):
        """
        Return the shape tuple as an array of some other c-types
        type. For example: ``self.shape_as(ctypes.c_short)``.
        """
        if self._zerod:
            return None
        return (obj*self._arr.ndim)(*self._arr.shape)

    def strides_as(self, obj):
        """
        Return the strides tuple as an array of some other
        c-types type. For example: ``self.strides_as(ctypes.c_longlong)``.
        """
        if self._zerod:
            return None
        return (obj*self._arr.ndim)(*self._arr.strides)

    @property
    def data(self):
        """
        A pointer to the memory area of the array as a Python integer.
        This memory area may contain data that is not aligned, or not in correct
        byte-order. The memory area may not even be writeable. The array
        flags and data-type of this array should be respected when passing this
        attribute to arbitrary C-code to avoid trouble that can include Python
        crashing. User Beware! The value of this attribute is exactly the same
        as ``self._array_interface_['data'][0]``.

        Note that unlike ``data_as``, a reference will not be kept to the array:
        code like ``ctypes.c_void_p((a + b).ctypes.data)`` will result in a
        pointer to a deallocated array, and should be spelt
        ``(a + b).ctypes.data_as(ctypes.c_void_p)``
        """
        return self._data.value

    @property
    def shape(self):
        """
        (c_intp*self.ndim): A ctypes array of length self.ndim where
        the basetype is the C-integer corresponding to ``dtype('p')`` on this
        platform. This base-type could be `ctypes.c_int`, `ctypes.c_long`, or
        `ctypes.c_longlong` depending on the platform.
        The c_intp type is defined accordingly in `numpy.ctypeslib`.
        The ctypes array contains the shape of the underlying array.
        """
        return self.shape_as(_getintp_ctype())

    @property
    def strides(self):
        """
        (c_intp*self.ndim): A ctypes array of length self.ndim where
        the basetype is the same as for the shape attribute. This ctypes array
        contains the strides information from the underlying array. This strides
        information is important for showing how many bytes must be jumped to
        get to the next element in the array.
        """
        return self.strides_as(_getintp_ctype())

    @property
    def _as_parameter_(self):