spaCy/spacy/pipeline/_parser_internals/stateclass.pyx

# cython: infer_types=True
import numpy

from ...tokens.doc cimport Doc


cdef class StateClass:
    def __init__(self, Doc doc=None, int offset=0):
        cdef Pool mem = Pool()
        self.mem = mem
        self._borrowed = 0
        if doc is not None:
            self.c = new StateC(doc.c, doc.length)
            self.c.offset = offset

    def __dealloc__(self):
        if self._borrowed != 1:
            del self.c

    @property
    def stack(self):
        return {self.S(i) for i in range(self.c._s_i)}

    @property
    def queue(self):
        return {self.B(i) for i in range(self.c.buffer_length())}

    @property
    def token_vector_lenth(self):
        return self.doc.tensor.shape[1]

    @property
    def history(self):
        hist = numpy.ndarray((8,), dtype='i')
        for i in range(8):
            hist[i] = self.c.get_hist(i+1)
        return hist

    def is_final(self):
        return self.c.is_final()

    def copy(self):
        cdef StateClass new_state = StateClass.init(self.c._sent, self.c.length)
        new_state.c.clone(self.c)
        return new_state

    def print_state(self, words):
        words = list(words) + ['_']
        top = f"{words[self.S(0)]}_{self.S_(0).head}"
        second = f"{words[self.S(1)]}_{self.S_(1).head}"
        third = f"{words[self.S(2)]}_{self.S_(2).head}"
        n0 = words[self.B(0)]
        n1 = words[self.B(1)]
        return ' '.join((third, second, top, '|', n0, n1))