# cython: embedsignature=True
from cython.view cimport array as cvarray

from preshed.maps cimport PreshMap
from preshed.counter cimport PreshCounter

from .vocab cimport EMPTY_LEXEME
from .typedefs cimport attr_id_t, attr_t
from .typedefs cimport LEMMA
from .typedefs cimport ID, SIC, NORM1, NORM2, SHAPE, PREFIX, SUFFIX, LENGTH, CLUSTER
from .typedefs cimport POS, LEMMA

cimport numpy
import numpy

cimport cython


DEF PADDING = 5


cdef int bounds_check(int i, int length, int padding) except -1:
    if (i + padding) < 0:
        raise IndexError
    if (i - padding) >= length:
        raise IndexError


cdef attr_t get_token_attr(const TokenC* token, attr_id_t feat_name) nogil:
    if feat_name == LEMMA:
        return token.lemma
    elif feat_name == POS:
        return token.pos
    else:
        return get_lex_attr(token.lex, feat_name)


cdef attr_t get_lex_attr(const LexemeC* lex, attr_id_t feat_name) nogil:
    if feat_name < (sizeof(flags_t) * 8):
        return check_flag(lex, feat_name)
    elif feat_name == ID:
        return lex.id
    elif feat_name == SIC:
        return lex.sic
    elif feat_name == NORM1:
        return lex.norm1
    elif feat_name == NORM2:
        return lex.norm2
    elif feat_name == SHAPE:
        return lex.shape
    elif feat_name == PREFIX:
        return lex.prefix
    elif feat_name == SUFFIX:
        return lex.suffix
    elif feat_name == LENGTH:
        return lex.length
    elif feat_name == CLUSTER:
        return lex.cluster
    else:
        return 0


cdef class Tokens:
    """Access and set annotations onto some text.
    """
    def __init__(self, Vocab vocab, string_length=0):
        self.vocab = vocab
        if string_length >= 3:
            size = int(string_length / 3.0)
        else:
            size = 5
        self.mem = Pool()
        # Guarantee self.lex[i-x], for any i >= 0 and x < padding is in bounds
        # However, we need to remember the true starting places, so that we can
        # realloc.
        data_start = <TokenC*>self.mem.alloc(size + (PADDING*2), sizeof(TokenC))
        cdef int i
        for i in range(size + (PADDING*2)):
            data_start[i].lex = &EMPTY_LEXEME
        self.data = data_start + PADDING
        self.max_length = size
        self.length = 0

    def sentences(self):
        cdef int i
        sentences = []
        sent = Tokens(self.vocab)
        cdef attr_t period = self.vocab.strings['.']
        cdef attr_t question = self.vocab.strings['?']
        cdef attr_t exclamation = self.vocab.strings['!']
        for i in range(self.length):
            idx = sent.push_back(idx, &self.data[i])
            if self.data[i].lex.sic == period or self.data[i].lex.sic == exclamation or \
              self.data[i].lex.sic == question:
                sentences.append(sent)
                sent = Tokens(self.vocab)
        return sentences

    def __getitem__(self, i):
        """Retrieve a token.
        
        Returns:
            token (Token):
        """
        if i < 0:
            i = self.length - i
        bounds_check(i, self.length, PADDING)
        return Token(self, i)

    def __iter__(self):
        """Iterate over the tokens.

        Yields:
            token (Token):
        """
        for i in range(self.length):
            yield self[i]

    def __len__(self):
        return self.length

    cdef int push_back(self, int idx, LexemeOrToken lex_or_tok) except -1:
        if self.length == self.max_length:
            self._realloc(self.length * 2)
        cdef TokenC* t = &self.data[self.length]
        if LexemeOrToken is TokenC_ptr:
            t[0] = lex_or_tok[0]
        else:
            t.lex = lex_or_tok
        t.idx = idx
        self.length += 1
        return idx + t.lex.length

    @cython.boundscheck(False)
    cpdef long[:,:] to_array(self, object attr_ids):
        """Given a list of M attribute IDs, export the tokens to a numpy ndarray
        of shape N*M, where N is the length of the sentence.

        Arguments:
            attr_ids (list[int]): A list of attribute ID ints.

        Returns:
            feat_array (numpy.ndarray[long, ndim=2]):
              A feature matrix, with one row per word, and one column per attribute
              indicated in the input attr_ids.
        """
        cdef int i, j
        cdef attr_id_t feature
        cdef long[:,:] output = cvarray(shape=(self.length, len(attr_ids)),
                                        itemsize=sizeof(long), format="l")
        for i in range(self.length):
            for j, feature in enumerate(attr_ids):
                output[i, j] = get_token_attr(&self.data[i], feature)
        return output

    def count_by(self, attr_id_t attr_id):
        """Produce a dict of {attribute (int): count (ints)} frequencies, keyed
        by the values of the given attribute ID.

          >>> from spacy.en import English, attrs
          >>> nlp = English()
          >>> tokens = nlp(u'apple apple orange banana')
          >>> tokens.count_by(attrs.SIC)
          {12800L: 1, 11880L: 2, 7561L: 1}
          >>> tokens.to_array([attrs.SIC])
          array([[11880],
                 [11880],
                 [ 7561],
                 [12800]])
        """
        cdef int i
        cdef attr_t attr
        cdef size_t count

        cdef PreshCounter counts = PreshCounter(2 ** 8)
        for i in range(self.length):
            attr = get_token_attr(&self.data[i], attr_id)
            counts.inc(attr, 1)
        return dict(counts)

    def _realloc(self, new_size):
        self.max_length = new_size
        n = new_size + (PADDING * 2)
        # What we're storing is a "padded" array. We've jumped forward PADDING
        # places, and are storing the pointer to that. This way, we can access
        # words out-of-bounds, and get out-of-bounds markers.
        # Now that we want to realloc, we need the address of the true start,
        # so we jump the pointer back PADDING places.
        cdef TokenC* data_start = self.data - PADDING
        data_start = <TokenC*>self.mem.realloc(data_start, n * sizeof(TokenC))
        self.data = data_start + PADDING
        cdef int i
        for i in range(self.length, self.max_length + PADDING):
            self.data[i].lex = &EMPTY_LEXEME


@cython.freelist(64)
cdef class Token:
    """An individual token.

    Internally, the Token is a tuple (i, tokens) --- it delegates to the Tokens
    object.
    """
    def __init__(self, Tokens tokens, int i):
        self._seq = tokens
        self.i = i
        cdef const TokenC* t = &tokens.data[i]
        self.idx = t.idx
        self.cluster = t.lex.cluster
        self.length = t.lex.length
        self.sic = t.lex.sic
        self.norm1 = t.lex.norm1
        self.norm2 = t.lex.norm2
        self.shape = t.lex.shape
        self.prefix = t.lex.prefix
        self.suffix = t.lex.suffix
        self.prob = t.lex.prob
        self.sentiment = t.lex.sentiment
        self.flags = t.lex.flags
        self.lemma = t.lemma
        self.tag = t.tag
        self.dep = t.dep

        self.vec = numpy.ndarray(shape=(300,), dtype=numpy.float32)
        for i in range(300):
            self.vec[i] = t.lex.vec[i]

    def __unicode__(self):
        cdef const TokenC* t = &self._seq.data[self.i]
        cdef int end_idx = t.idx + t.lex.length
        if self.i + 1 == self._seq.length:
            return self.string
        if end_idx == t[1].idx:
            return self.string
        else:
            return self.string + ' '

    def __len__(self):
        """The number of unicode code-points in the original string.

        Returns:
            length (int):
        """
        return self._seq.data[self.i].lex.length

    def check_flag(self, attr_id_t flag):
        return False

    def is_pos(self, univ_tag_t pos):
        return False

    property head:
        """The token predicted by the parser to be the head of the current token."""
        def __get__(self):
            cdef const TokenC* t = &self._seq.data[self.i]
            return Token(self._seq, self.i + t.head)

    property string:
        """The unicode string of the word, with no whitespace padding."""
        def __get__(self):
            cdef const TokenC* t = &self._seq.data[self.i]
            if t.lex.sic == 0:
                return ''
            cdef unicode py_ustr = self._seq.vocab.strings[t.lex.sic]
            return py_ustr

    property sic_:
        def __get__(self):
            return self._seq.vocab.strings[self.sic]

    property norm1_:
        def __get__(self):
            return self._seq.vocab.strings[self.norm1]

    property norm2_:
        def __get__(self):
            return self._seq.vocab.strings[self.norm2]

    property shape_:
        def __get__(self):
            return self._seq.vocab.strings[self.shape]

    property prefix_:
        def __get__(self):
            return self._seq.vocab.strings[self.prefix]

    property suffix_:
        def __get__(self):
            return self._seq.vocab.strings[self.suffix]

    property lemma_:
        def __get__(self):
            cdef const TokenC* t = &self._seq.data[self.i]
            if t.lemma == 0:
                return self.string
            cdef unicode py_ustr = self._seq.vocab.strings[t.lemma]
            return py_ustr

    property tag_:
        def __get__(self):
            return self._seq.tag_names[self.tag]

    property dep_:
        def __get__(self):
            return self._seq.dep_names[self.dep]