mirror of https://github.com/explosion/spaCy.git
325 lines
13 KiB
Cython
325 lines
13 KiB
Cython
# cython: embedsignature=True
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from __future__ import unicode_literals
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import re
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import pathlib
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from cython.operator cimport dereference as deref
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from cython.operator cimport preincrement as preinc
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from cpython cimport Py_UNICODE_ISSPACE
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try:
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import ujson as json
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except ImportError:
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import json
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from cymem.cymem cimport Pool
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from preshed.maps cimport PreshMap
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from .strings cimport hash_string
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cimport cython
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from . import util
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from .tokens.doc cimport Doc
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cdef class Tokenizer:
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@classmethod
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def load(cls, path, Vocab vocab, rules=None, prefix_search=None, suffix_search=None,
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infix_finditer=None):
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'''Load a Tokenizer, reading unsupplied components from the path.
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Arguments:
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path pathlib.Path (or string, or Path-like)
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vocab Vocab
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rules dict
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prefix_search callable -- Signature of re.compile(string).search
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suffix_search callable -- Signature of re.compile(string).search
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infix_finditer callable -- Signature of re.compile(string).finditer
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'''
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if isinstance(path, basestring):
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path = pathlib.Path(path)
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if rules is None:
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with (path / 'tokenizer' / 'specials.json').open() as file_:
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rules = json.load(file_)
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if prefix_search is None:
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prefix_search = util.read_prefix_regex(path / 'tokenizer' / 'prefix.txt').search
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if suffix_search is None:
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suffix_search = util.read_suffix_regex(path / 'tokenizer' / 'suffix.txt').search
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if infix_finditer is None:
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infix_finditer = util.read_infix_regex(path / 'tokenizer' / 'infix.txt').finditer
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return cls(vocab, rules, prefix_search, suffix_search, infix_finditer)
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def __init__(self, Vocab vocab, rules, prefix_search, suffix_search, infix_finditer):
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'''Create a Tokenizer, to create Doc objects given unicode text.
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Arguments:
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vocab Vocab
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rules dict
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prefix_search callable -- Signature of re.compile(string).search
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suffix_search callable -- Signature of re.compile(string).search
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infix_finditer callable -- Signature of re.compile(string).finditer
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'''
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self.mem = Pool()
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self._cache = PreshMap()
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self._specials = PreshMap()
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self.prefix_search = prefix_search
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self.suffix_search = suffix_search
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self.infix_finditer = infix_finditer
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self.vocab = vocab
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self._rules = {}
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for chunk, substrings in sorted(rules.items()):
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self.add_special_case(chunk, substrings)
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def __reduce__(self):
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args = (self.vocab,
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self._rules,
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self._prefix_re,
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self._suffix_re,
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self._infix_re)
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return (self.__class__, args, None, None)
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cpdef Doc tokens_from_list(self, list strings):
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cdef Doc tokens = Doc(self.vocab)
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if sum([len(s) for s in strings]) == 0:
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return tokens
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cdef unicode py_string
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cdef int idx = 0
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for i, py_string in enumerate(strings):
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# Note that we pass tokens.mem here --- the Doc object has ownership
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tokens.push_back(
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<const LexemeC*>self.vocab.get(tokens.mem, py_string), True)
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idx += len(py_string) + 1
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return tokens
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@cython.boundscheck(False)
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def __call__(self, unicode string):
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"""Tokenize a string.
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The tokenization rules are defined in three places:
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* The data/<lang>/tokenization table, which handles special cases like contractions;
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* The data/<lang>/prefix file, used to build a regex to split off prefixes;
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* The data/<lang>/suffix file, used to build a regex to split off suffixes.
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The string is first split on whitespace. To tokenize a whitespace-delimited
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chunk, we first try to look it up in the special-cases. If it's not found,
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we split off a prefix, and then try again. If it's still not found, we
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split off a suffix, and repeat.
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Args:
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string (unicode): The string to be tokenized.
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Returns:
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tokens (Doc): A Doc object, giving access to a sequence of LexemeCs.
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"""
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if len(string) >= (2 ** 30):
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raise ValueError(
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"String is too long: %d characters. Max is 2**30." % len(string)
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)
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cdef int length = len(string)
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cdef Doc tokens = Doc(self.vocab)
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if length == 0:
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return tokens
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cdef int i = 0
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cdef int start = 0
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cdef bint cache_hit
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cdef bint in_ws = string[0].isspace()
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cdef unicode span
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# The task here is much like string.split, but not quite
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# We find spans of whitespace and non-space characters, and ignore
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# spans that are exactly ' '. So, our sequences will all be separated
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# by either ' ' or nothing.
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for uc in string:
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if uc.isspace() != in_ws:
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if start < i:
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# When we want to make this fast, get the data buffer once
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# with PyUnicode_AS_DATA, and then maintain a start_byte
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# and end_byte, so we can call hash64 directly. That way
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# we don't have to create the slice when we hit the cache.
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span = string[start:i]
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key = hash_string(span)
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cache_hit = self._try_cache(key, tokens)
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if not cache_hit:
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self._tokenize(tokens, span, key)
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if uc == ' ':
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tokens.c[tokens.length - 1].spacy = True
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start = i + 1
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else:
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start = i
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in_ws = not in_ws
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i += 1
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i += 1
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if start < i:
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span = string[start:]
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key = hash_string(span)
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cache_hit = self._try_cache(key, tokens)
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if not cache_hit:
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self._tokenize(tokens, span, key)
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tokens.c[tokens.length - 1].spacy = string[-1] == ' ' and not in_ws
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return tokens
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def pipe(self, texts, batch_size=1000, n_threads=2):
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for text in texts:
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yield self(text)
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cdef int _try_cache(self, hash_t key, Doc tokens) except -1:
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cached = <_Cached*>self._cache.get(key)
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if cached == NULL:
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return False
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cdef int i
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if cached.is_lex:
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for i in range(cached.length):
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tokens.push_back(cached.data.lexemes[i], False)
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else:
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for i in range(cached.length):
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tokens.push_back(&cached.data.tokens[i], False)
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return True
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cdef int _tokenize(self, Doc tokens, unicode span, hash_t orig_key) except -1:
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cdef vector[LexemeC*] prefixes
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cdef vector[LexemeC*] suffixes
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cdef int orig_size
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orig_size = tokens.length
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span = self._split_affixes(tokens.mem, span, &prefixes, &suffixes)
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self._attach_tokens(tokens, span, &prefixes, &suffixes)
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self._save_cached(&tokens.c[orig_size], orig_key, tokens.length - orig_size)
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cdef unicode _split_affixes(self, Pool mem, unicode string,
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vector[const LexemeC*] *prefixes,
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vector[const LexemeC*] *suffixes):
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cdef size_t i
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cdef unicode prefix
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cdef unicode suffix
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cdef unicode minus_pre
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cdef unicode minus_suf
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cdef size_t last_size = 0
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while string and len(string) != last_size:
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last_size = len(string)
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pre_len = self.find_prefix(string)
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if pre_len != 0:
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prefix = string[:pre_len]
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minus_pre = string[pre_len:]
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# Check whether we've hit a special-case
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if minus_pre and self._specials.get(hash_string(minus_pre)) != NULL:
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string = minus_pre
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prefixes.push_back(self.vocab.get(mem, prefix))
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break
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suf_len = self.find_suffix(string)
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if suf_len != 0:
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suffix = string[-suf_len:]
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minus_suf = string[:-suf_len]
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# Check whether we've hit a special-case
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if minus_suf and (self._specials.get(hash_string(minus_suf)) != NULL):
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string = minus_suf
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suffixes.push_back(self.vocab.get(mem, suffix))
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break
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if pre_len and suf_len and (pre_len + suf_len) <= len(string):
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string = string[pre_len:-suf_len]
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prefixes.push_back(self.vocab.get(mem, prefix))
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suffixes.push_back(self.vocab.get(mem, suffix))
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elif pre_len:
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string = minus_pre
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prefixes.push_back(self.vocab.get(mem, prefix))
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elif suf_len:
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string = minus_suf
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suffixes.push_back(self.vocab.get(mem, suffix))
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if string and (self._specials.get(hash_string(string)) != NULL):
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break
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return string
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cdef int _attach_tokens(self, Doc tokens, unicode string,
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vector[const LexemeC*] *prefixes,
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vector[const LexemeC*] *suffixes) except -1:
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cdef bint cache_hit
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cdef int split, end
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cdef const LexemeC* const* lexemes
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cdef const LexemeC* lexeme
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cdef unicode span
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cdef int i
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if prefixes.size():
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for i in range(prefixes.size()):
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tokens.push_back(prefixes[0][i], False)
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if string:
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cache_hit = self._try_cache(hash_string(string), tokens)
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if not cache_hit:
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matches = self.find_infix(string)
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if not matches:
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tokens.push_back(self.vocab.get(tokens.mem, string), False)
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else:
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# let's say we have dyn-o-mite-dave
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# the regex finds the start and end positions of the hyphens
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start = 0
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for match in matches:
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infix_start = match.start()
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infix_end = match.end()
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if infix_start == start:
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continue
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span = string[start:infix_start]
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tokens.push_back(self.vocab.get(tokens.mem, span), False)
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infix_span = string[infix_start:infix_end]
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tokens.push_back(self.vocab.get(tokens.mem, infix_span), False)
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start = infix_end
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span = string[start:]
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tokens.push_back(self.vocab.get(tokens.mem, span), False)
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cdef vector[const LexemeC*].reverse_iterator it = suffixes.rbegin()
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while it != suffixes.rend():
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lexeme = deref(it)
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preinc(it)
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tokens.push_back(lexeme, False)
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cdef int _save_cached(self, const TokenC* tokens, hash_t key, int n) except -1:
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cdef int i
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for i in range(n):
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if tokens[i].lex.id == 0:
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return 0
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cached = <_Cached*>self.mem.alloc(1, sizeof(_Cached))
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cached.length = n
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cached.is_lex = True
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lexemes = <const LexemeC**>self.mem.alloc(n, sizeof(LexemeC**))
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for i in range(n):
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lexemes[i] = tokens[i].lex
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cached.data.lexemes = <const LexemeC* const*>lexemes
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self._cache.set(key, cached)
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def find_infix(self, unicode string):
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return list(self.infix_finditer(string))
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def find_prefix(self, unicode string):
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match = self.prefix_search(string)
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return (match.end() - match.start()) if match is not None else 0
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def find_suffix(self, unicode string):
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match = self.suffix_search(string)
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print("Suffix", match, string)
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return (match.end() - match.start()) if match is not None else 0
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def _load_special_tokenization(self, special_cases):
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'''Add special-case tokenization rules.
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'''
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for chunk, substrings in sorted(special_cases.items()):
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self.add_special_case(chunk, substrings)
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def add_special_case(self, unicode chunk, substrings):
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'''Add a special-case tokenization rule.
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For instance, "don't" is special-cased to tokenize into
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["do", "n't"]. The split tokens can have lemmas and part-of-speech
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tags.
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'''
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substrings = list(substrings)
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cached = <_Cached*>self.mem.alloc(1, sizeof(_Cached))
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cached.length = len(substrings)
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cached.is_lex = False
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cached.data.tokens = self.vocab.make_fused_token(substrings)
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key = hash_string(chunk)
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self._specials.set(key, cached)
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self._cache.set(key, cached)
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self._rules[chunk] = substrings
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