mirror of
https://github.com/explosion/spaCy.git
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76 lines
2.1 KiB
Cython
76 lines
2.1 KiB
Cython
# coding: utf-8
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# cython: infer_types=True
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from __future__ import unicode_literals
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from libc.string cimport memcpy, memset
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from libc.stdint cimport uint32_t, uint64_t
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from ..vocab cimport EMPTY_LEXEME
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from ..structs cimport Entity
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from ..lexeme cimport Lexeme
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from ..symbols cimport punct
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from ..attrs cimport IS_SPACE
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from ..attrs cimport attr_id_t
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from ..tokens.token cimport Token
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from ..tokens.doc cimport Doc
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cdef class StateClass:
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def __init__(self, Doc doc=None, int offset=0):
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cdef Pool mem = Pool()
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self.mem = mem
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if doc is not None:
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self.c = new StateC(doc.c, doc.length)
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self.c.offset = offset
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def __dealloc__(self):
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del self.c
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@property
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def stack(self):
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return {self.S(i) for i in range(self.c._s_i)}
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@property
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def queue(self):
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return {self.B(i) for i in range(self.c.buffer_length())}
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@property
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def token_vector_lenth(self):
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return self.doc.tensor.shape[1]
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def is_final(self):
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return self.c.is_final()
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def print_state(self, words):
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words = list(words) + ['_']
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top = words[self.S(0)] + '_%d' % self.S_(0).head
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second = words[self.S(1)] + '_%d' % self.S_(1).head
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third = words[self.S(2)] + '_%d' % self.S_(2).head
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n0 = words[self.B(0)]
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n1 = words[self.B(1)]
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return ' '.join((third, second, top, '|', n0, n1))
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@classmethod
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def nr_context_tokens(cls):
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return 13
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def set_context_tokens(self, int[::1] output):
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output[0] = self.B(0)
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output[1] = self.B(1)
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output[2] = self.S(0)
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output[3] = self.S(1)
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output[4] = self.S(2)
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output[5] = self.L(self.S(0), 1)
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output[6] = self.L(self.S(0), 2)
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output[6] = self.R(self.S(0), 1)
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output[7] = self.L(self.B(0), 1)
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output[8] = self.R(self.S(0), 2)
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output[9] = self.L(self.S(1), 1)
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output[10] = self.L(self.S(1), 2)
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output[11] = self.R(self.S(1), 1)
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output[12] = self.R(self.S(1), 2)
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for i in range(13):
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if output[i] != -1:
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output[i] += self.c.offset
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