"""
A place for code to be called from core C-code.

Some things are more easily handled Python.

"""
from __future__ import division, absolute_import, print_function

import re
import sys

from numpy.compat import basestring
from .multiarray import dtype, array, ndarray
try:
    import ctypes
except ImportError:
    ctypes = None
from .numerictypes import object_

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

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

# astr is a string (perhaps comma separated)

_convorder = {b'=': _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 == b'':
            order = order1
        elif order1 == b'':
            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 [b'|', b'=', _nbo]:
            order = b''
        dtype = order + dtype
        if (repeats == b''):
            newitem = dtype
        else:
            newitem = (dtype, eval(repeats))
        result.append(newitem)

    return result

class dummy_ctype(object):
    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 _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(object):
    def cast(self, num, obj):
        return num

    def c_void_p(self, num):
        return num

class _ctypes(object):
    def __init__(self, array, ptr=None):
        if ctypes:
            self._ctypes = ctypes
        else:
            self._ctypes = _missing_ctypes()
        self._arr = array
        self._data = ptr
        if self._arr.ndim == 0:
            self._zerod = True
        else:
            self._zerod = False

    def data_as(self, obj):
        return self._ctypes.cast(self._data, obj)

    def shape_as(self, obj):
        if self._zerod:
            return None
        return (obj*self._arr.ndim)(*self._arr.shape)

    def strides_as(self, obj):
        if self._zerod:
            return None
        return (obj*self._arr.ndim)(*self._arr.strides)

    def get_data(self):
        return self._data

    def get_shape(self):
        return self.shape_as(_getintp_ctype())

    def get_strides(self):
        return self.strides_as(_getintp_ctype())

    def get_as_parameter(self):
        return self._ctypes.c_void_p(self._data)

    data = property(get_data, None, doc="c-types data")
    shape = property(get_shape, None, doc="c-types shape")
    strides = property(get_strides, None, doc="c-types strides")
    _as_parameter_ = property(get_as_parameter, None, doc="_as parameter_")


# Given a datatype and an order object
#  return a new names tuple
#  with the order indicated
def _newnames(datatype, order):
    oldnames = datatype.names
    nameslist = list(oldnames)
    if isinstance(order, str):
        order = [order]
    if isinstance(order, (list, tuple)):
        for name in order:
            try:
                nameslist.remove(name)
            except ValueError:
                raise ValueError("unknown field name: %s" % (name,))
        return tuple(list(order) + nameslist)
    raise ValueError("unsupported order value: %s" % (order,))

def _copy_fields(ary):
    """Return copy of structured array with padding between fields removed.

    Parameters
    ----------
    ary : ndarray
       Structured array from which to remove padding bytes

    Returns
    -------
    ary_copy : ndarray
       Copy of ary with padding bytes removed
    """
    dt = ary.dtype
    copy_dtype = {'names': dt.names,
                  'formats': [dt.fields[name][0] for name in dt.names]}
    return array(ary, dtype=copy_dtype, copy=True)

def _getfield_is_safe(oldtype, newtype, offset):
    """ Checks safety of getfield for object arrays.

    As in _view_is_safe, we need to check that memory containing objects is not
    reinterpreted as a non-object datatype and vice versa.

    Parameters
    ----------
    oldtype : data-type
        Data type of the original ndarray.
    newtype : data-type
        Data type of the field being accessed by ndarray.getfield
    offset : int
        Offset of the field being accessed by ndarray.getfield

    Raises
    ------
    TypeError
        If the field access is invalid

    """
    if newtype.hasobject or oldtype.hasobject:
        if offset == 0 and newtype == oldtype:
            return
        if oldtype.names:
            for name in oldtype.names:
                if (oldtype.fields[name][1] == offset and
                        oldtype.fields[name][0] == newtype):
                    return
        raise TypeError("Cannot get/set field of an object array")
    return

def _view_is_safe(oldtype, newtype):
    """ Checks safety of a view involving object arrays, for example when
    doing::

        np.zeros(10, dtype=oldtype).view(newtype)

    Parameters
    ----------
    oldtype : data-type
        Data type of original ndarray
    newtype : data-type
        Data type of the view

    Raises
    ------
    TypeError
        If the new type is incompatible with the old type.

    """

    # if the types are equivalent, there is no problem.
    # for example: dtype((np.record, 'i4,i4')) == dtype((np.void, 'i4,i4'))
    if oldtype == newtype:
        return

    if newtype.hasobject or oldtype.hasobject:
        raise TypeError("Cannot change data-type for object array.")
    return

# Given a string containing a PEP 3118 format specifier,
# construct a NumPy dtype

_pep3118_native_map = {
    '?': '?',
    'c': 'S1',
    'b': 'b',
    'B': 'B',
    'h': 'h',
    'H': 'H',
    'i': 'i',
    'I': 'I',
    'l': 'l',
    'L': 'L',
    'q': 'q',
    'Q': 'Q',
    'e': 'e',
    'f': 'f',
    'd': 'd',
    'g': 'g',
    'Zf': 'F',
    'Zd': 'D',
    'Zg': 'G',
    's': 'S',
    'w': 'U',
    'O': 'O',
    'x': 'V',  # padding
}
_pep3118_native_typechars = ''.join(_pep3118_native_map.keys())

_pep3118_standard_map = {
    '?': '?',
    'c': 'S1',
    'b': 'b',
    'B': 'B',
    'h': 'i2',
    'H': 'u2',
    'i': 'i4',
    'I': 'u4',
    'l': 'i4',
    'L': 'u4',
    'q': 'i8',
    'Q': 'u8',
    'e': 'f2',
    'f': 'f',
    'd': 'd',
    'Zf': 'F',
    'Zd': 'D',
    's': 'S',
    'w': 'U',
    'O': 'O',
    'x': 'V',  # padding
}
_pep3118_standard_typechars = ''.join(_pep3118_standard_map.keys())

def _dtype_from_pep3118(spec):

    class Stream(object):
        def __init__(self, s):
            self.s = s
            self.byteorder = '@'

        def advance(self, n):
            res = self.s[:n]
            self.s = self.s[n:]
            return res

        def consume(self, c):
            if self.s[:len(c)] == c:
                self.advance(len(c))
                return True
            return False

        def consume_until(self, c):
            if callable(c):
                i = 0
                while i < len(self.s) and not c(self.s[i]):
                    i = i + 1
                return self.advance(i)
            else:
                i = self.s.index(c)
                res = self.advance(i)
                self.advance(len(c))
                return res

        @property
        def next(self):
            return self.s[0]

        def __bool__(self):
            return bool(self.s)
        __nonzero__ = __bool__

    stream = Stream(spec)

    dtype, align = __dtype_from_pep3118(stream, is_subdtype=False)
    return dtype

def __dtype_from_pep3118(stream, is_subdtype):
    field_spec = dict(
        names=[],
        formats=[],
        offsets=[],
        itemsize=0
    )
    offset = 0
    common_alignment = 1
    is_padding = False

    # Parse spec
    while stream:
        value = None

        # End of structure, bail out to upper level
        if stream.consume('}'):
            break

        # Sub-arrays (1)
        shape = None
        if stream.consume('('):
            shape = stream.consume_until(')')
            shape = tuple(map(int, shape.split(',')))

        # Byte order
        if stream.next in ('@', '=', '<', '>', '^', '!'):
            byteorder = stream.advance(1)
            if byteorder == '!':
                byteorder = '>'
            stream.byteorder = byteorder

        # Byte order characters also control native vs. standard type sizes
        if stream.byteorder in ('@', '^'):
            type_map = _pep3118_native_map
            type_map_chars = _pep3118_native_typechars
        else:
            type_map = _pep3118_standard_map
            type_map_chars = _pep3118_standard_typechars

        # Item sizes
        itemsize_str = stream.consume_until(lambda c: not c.isdigit())
        if itemsize_str:
            itemsize = int(itemsize_str)
        else:
            itemsize = 1

        # Data types
        is_padding = False

        if stream.consume('T{'):
            value, align = __dtype_from_pep3118(
                stream, is_subdtype=True)
        elif stream.next in type_map_chars:
            if stream.next == 'Z':
                typechar = stream.advance(2)
            else:
                typechar = stream.advance(1)

            is_padding = (typechar == 'x')
            dtypechar = type_map[typechar]
            if dtypechar in 'USV':
                dtypechar += '%d' % itemsize
                itemsize = 1
            numpy_byteorder = {'@': '=', '^': '='}.get(
                stream.byteorder, stream.byteorder)
            value = dtype(numpy_byteorder + dtypechar)
            align = value.alignment
        else:
            raise ValueError("Unknown PEP 3118 data type specifier %r" % stream.s)

        #
        # Native alignment may require padding
        #
        # Here we assume that the presence of a '@' character implicitly implies
        # that the start of the array is *already* aligned.
        #
        extra_offset = 0
        if stream.byteorder == '@':
            start_padding = (-offset) % align
            intra_padding = (-value.itemsize) % align

            offset += start_padding

            if intra_padding != 0:
                if itemsize > 1 or (shape is not None and _prod(shape) > 1):
                    # Inject internal padding to the end of the sub-item
                    value = _add_trailing_padding(value, intra_padding)
                else:
                    # We can postpone the injection of internal padding,
                    # as the item appears at most once
                    extra_offset += intra_padding

            # Update common alignment
            common_alignment = _lcm(align, common_alignment)

        # Convert itemsize to sub-array
        if itemsize != 1:
            value = dtype((value, (itemsize,)))

        # Sub-arrays (2)
        if shape is not None:
            value = dtype((value, shape))

        # Field name
        if stream.consume(':'):
            name = stream.consume_until(':')
        else:
            name = None

        if not (is_padding and name is None):
            if name is not None and name in field_spec['names']:
                raise RuntimeError("Duplicate field name '%s' in PEP3118 format"
                                   % name)
            field_spec['names'].append(name)
            field_spec['formats'].append(value)
            field_spec['offsets'].append(offset)

        offset += value.itemsize
        offset += extra_offset

        field_spec['itemsize'] = offset

    # extra final padding for aligned types
    if stream.byteorder == '@':
        field_spec['itemsize'] += (-offset) % common_alignment

    # Check if this was a simple 1-item type, and unwrap it
    if (field_spec['names'] == [None]
            and field_spec['offsets'][0] == 0
            and field_spec['itemsize'] == field_spec['formats'][0].itemsize
            and not is_subdtype):
        ret = field_spec['formats'][0]
    else:
        _fix_names(field_spec)
        ret = dtype(field_spec)

    # Finished
    return ret, common_alignment

def _fix_names(field_spec):
    """ Replace names which are None with the next unused f%d name """
    names = field_spec['names']
    for i, name in enumerate(names):
        if name is not None:
            continue

        j = 0
        while True:
            name = 'f{}'.format(j)
            if name not in names:
                break
            j = j + 1
        names[i] = name

def _add_trailing_padding(value, padding):
    """Inject the specified number of padding bytes at the end of a dtype"""
    if value.fields is None:
        field_spec = dict(
            names=['f0'],
            formats=[value],
            offsets=[0],
            itemsize=value.itemsize
        )
    else:
        fields = value.fields
        names = value.names
        field_spec = dict(
            names=names,
            formats=[fields[name][0] for name in names],
            offsets=[fields[name][1] for name in names],
            itemsize=value.itemsize
        )

    field_spec['itemsize'] += padding
    return dtype(field_spec)

def _prod(a):
    p = 1
    for x in a:
        p *= x
    return p

def _gcd(a, b):
    """Calculate the greatest common divisor of a and b"""
    while b:
        a, b = b, a % b
    return a

def _lcm(a, b):
    return a // _gcd(a, b) * b

# Exception used in shares_memory()
class TooHardError(RuntimeError):
    pass

class AxisError(ValueError, IndexError):
    """ Axis supplied was invalid. """
    def __init__(self, axis, ndim=None, msg_prefix=None):
        # single-argument form just delegates to base class
        if ndim is None and msg_prefix is None:
            msg = axis

        # do the string formatting here, to save work in the C code
        else:
            msg = ("axis {} is out of bounds for array of dimension {}"
                   .format(axis, ndim))
            if msg_prefix is not None:
                msg = "{}: {}".format(msg_prefix, msg)

        super(AxisError, self).__init__(msg)


def array_ufunc_errmsg_formatter(dummy, ufunc, method, *inputs, **kwargs):
    """ Format the error message for when __array_ufunc__ gives up. """
    args_string = ', '.join(['{!r}'.format(arg) for arg in inputs] +
                            ['{}={!r}'.format(k, v)
                             for k, v in kwargs.items()])
    args = inputs + kwargs.get('out', ())
    types_string = ', '.join(repr(type(arg).__name__) for arg in args)
    return ('operand type(s) all returned NotImplemented from '
            '__array_ufunc__({!r}, {!r}, {}): {}'
            .format(ufunc, method, args_string, types_string))


def _ufunc_doc_signature_formatter(ufunc):
    """
    Builds a signature string which resembles PEP 457

    This is used to construct the first line of the docstring
    """

    # input arguments are simple
    if ufunc.nin == 1:
        in_args = 'x'
    else:
        in_args = ', '.join('x{}'.format(i+1) for i in range(ufunc.nin))

    # output arguments are both keyword or positional
    if ufunc.nout == 0:
        out_args = ', /, out=()'
    elif ufunc.nout == 1:
        out_args = ', /, out=None'
    else:
        out_args = '[, {positional}], / [, out={default}]'.format(
            positional=', '.join(
                'out{}'.format(i+1) for i in range(ufunc.nout)),
            default=repr((None,)*ufunc.nout)
        )

    # keyword only args depend on whether this is a gufunc
    kwargs = (
        ", casting='same_kind'"
        ", order='K'"
        ", dtype=None"
        ", subok=True"
        "[, signature"
        ", extobj]"
    )
    if ufunc.signature is None:
        kwargs = ", where=True" + kwargs

    # join all the parts together
    return '{name}({in_args}{out_args}, *{kwargs})'.format(
        name=ufunc.__name__,
        in_args=in_args,
        out_args=out_args,
        kwargs=kwargs
    )
