# cython: embedsignature=True, profile=True, binding=True from cython.operator cimport dereference as deref from cython.operator cimport preincrement as preinc from libc.string cimport memcpy, memset from libcpp.set cimport set as stdset from cymem.cymem cimport Pool from preshed.maps cimport PreshMap cimport cython import re import warnings from .tokens.doc cimport Doc from .strings cimport hash_string from .lexeme cimport EMPTY_LEXEME from .attrs import intify_attrs from .symbols import ORTH, NORM from .errors import Errors, Warnings from . import util from .util import registry, get_words_and_spaces from .attrs import intify_attrs from .symbols import ORTH from .scorer import Scorer from .training import validate_examples from .tokens import Span cdef class Tokenizer: """Segment text, and create Doc objects with the discovered segment boundaries. DOCS: https://spacy.io/api/tokenizer """ def __init__(self, Vocab vocab, rules=None, prefix_search=None, suffix_search=None, infix_finditer=None, token_match=None, url_match=None, faster_heuristics=True): """Create a `Tokenizer`, to create `Doc` objects given unicode text. vocab (Vocab): A storage container for lexical types. rules (dict): Exceptions and special-cases for the tokenizer. prefix_search (callable): A function matching the signature of `re.compile(string).search` to match prefixes. suffix_search (callable): A function matching the signature of `re.compile(string).search` to match suffixes. infix_finditer (callable): A function matching the signature of `re.compile(string).finditer` to find infixes. token_match (callable): A function matching the signature of `re.compile(string).match`, for matching strings to be recognized as tokens. url_match (callable): A function matching the signature of `re.compile(string).match`, for matching strings to be recognized as urls. faster_heuristics (bool): Whether to restrict the final Matcher-based pass for rules to those containing affixes or space. Defaults to True. EXAMPLE: >>> tokenizer = Tokenizer(nlp.vocab) DOCS: https://spacy.io/api/tokenizer#init """ self.mem = Pool() self._cache = PreshMap() self._specials = PreshMap() self.token_match = token_match self.url_match = url_match self.prefix_search = prefix_search self.suffix_search = suffix_search self.infix_finditer = infix_finditer self.vocab = vocab self.faster_heuristics = faster_heuristics self._rules = {} self._special_matcher = PhraseMatcher(self.vocab) self._load_special_cases(rules) property token_match: def __get__(self): return self._token_match def __set__(self, token_match): self._token_match = token_match self._reload_special_cases() property url_match: def __get__(self): return self._url_match def __set__(self, url_match): self._url_match = url_match self._reload_special_cases() property prefix_search: def __get__(self): return self._prefix_search def __set__(self, prefix_search): self._prefix_search = prefix_search self._reload_special_cases() property suffix_search: def __get__(self): return self._suffix_search def __set__(self, suffix_search): self._suffix_search = suffix_search self._reload_special_cases() property infix_finditer: def __get__(self): return self._infix_finditer def __set__(self, infix_finditer): self._infix_finditer = infix_finditer self._reload_special_cases() property rules: def __get__(self): return self._rules def __set__(self, rules): self._rules = {} self._flush_cache() self._flush_specials() self._cache = PreshMap() self._specials = PreshMap() self._load_special_cases(rules) property faster_heuristics: def __get__(self): return bool(self._faster_heuristics) def __set__(self, faster_heuristics): self._faster_heuristics = bool(faster_heuristics) self._reload_special_cases() def __reduce__(self): args = (self.vocab, self.rules, self.prefix_search, self.suffix_search, self.infix_finditer, self.token_match, self.url_match) return (self.__class__, args, None, None) def __call__(self, str string): """Tokenize a string. string (str): The string to tokenize. RETURNS (Doc): A container for linguistic annotations. DOCS: https://spacy.io/api/tokenizer#call """ doc = self._tokenize_affixes(string, True) self._apply_special_cases(doc) return doc @cython.boundscheck(False) cdef Doc _tokenize_affixes(self, str string, bint with_special_cases): """Tokenize according to affix and token_match settings. string (str): The string to tokenize. RETURNS (Doc): A container for linguistic annotations. """ if len(string) >= (2 ** 30): raise ValueError(Errors.E025.format(length=len(string))) cdef int length = len(string) cdef Doc doc = Doc(self.vocab) if length == 0: return doc cdef int i = 0 cdef int start = 0 cdef int has_special = 0 cdef bint in_ws = string[0].isspace() cdef str span # The task here is much like string.split, but not quite # We find spans of whitespace and non-space characters, and ignore # spans that are exactly ' '. So, our sequences will all be separated # by either ' ' or nothing. for uc in string: if uc.isspace() != in_ws: if start < i: # When we want to make this fast, get the data buffer once # with PyUnicode_AS_DATA, and then maintain a start_byte # and end_byte, so we can call hash64 directly. That way # we don't have to create the slice when we hit the cache. span = string[start:i] key = hash_string(span) if not self._try_specials_and_cache(key, doc, &has_special, with_special_cases): self._tokenize(doc, span, key, &has_special, with_special_cases) if uc == ' ': doc.c[doc.length - 1].spacy = True start = i + 1 else: start = i in_ws = not in_ws i += 1 if start < i: span = string[start:] key = hash_string(span) if not self._try_specials_and_cache(key, doc, &has_special, with_special_cases): self._tokenize(doc, span, key, &has_special, with_special_cases) doc.c[doc.length - 1].spacy = string[-1] == " " and not in_ws return doc def pipe(self, texts, batch_size=1000): """Tokenize a stream of texts. texts: A sequence of unicode texts. batch_size (int): Number of texts to accumulate in an internal buffer. Defaults to 1000. YIELDS (Doc): A sequence of Doc objects, in order. DOCS: https://spacy.io/api/tokenizer#pipe """ for text in texts: yield self(text) def _flush_cache(self): self._reset_cache([key for key in self._cache]) def _reset_cache(self, keys): for k in keys: cached = <_Cached*>self._cache.get(k) del self._cache[k] if cached is not NULL: self.mem.free(cached) def _flush_specials(self): self._special_matcher = PhraseMatcher(self.vocab) for k in self._specials: cached = <_Cached*>self._specials.get(k) del self._specials[k] if cached is not NULL: self.mem.free(cached) cdef int _apply_special_cases(self, Doc doc) except -1: """Retokenize doc according to special cases. doc (Doc): Document. """ cdef int i cdef int max_length = 0 cdef bint modify_in_place cdef Pool mem = Pool() cdef vector[SpanC] c_matches cdef vector[SpanC] c_filtered cdef int offset cdef int modified_doc_length # Find matches for special cases self._special_matcher.find_matches(doc, 0, doc.length, &c_matches) # Skip processing if no matches if c_matches.size() == 0: return True self._filter_special_spans(c_matches, c_filtered, doc.length) # Put span info in span.start-indexed dict and calculate maximum # intermediate document size (span_data, max_length, modify_in_place) = self._prepare_special_spans(doc, c_filtered) # If modifications never increase doc length, can modify in place if modify_in_place: tokens = doc.c # Otherwise create a separate array to store modified tokens else: assert max_length > 0 tokens = mem.alloc(max_length, sizeof(TokenC)) # Modify tokenization according to filtered special cases offset = self._retokenize_special_spans(doc, tokens, span_data) # Allocate more memory for doc if needed modified_doc_length = doc.length + offset while modified_doc_length >= doc.max_length: doc._realloc(doc.max_length * 2) # If not modified in place, copy tokens back to doc if not modify_in_place: memcpy(doc.c, tokens, max_length * sizeof(TokenC)) for i in range(doc.length + offset, doc.length): memset(&doc.c[i], 0, sizeof(TokenC)) doc.c[i].lex = &EMPTY_LEXEME doc.length = doc.length + offset return True cdef void _filter_special_spans(self, vector[SpanC] &original, vector[SpanC] &filtered, int doc_len) nogil: cdef int seen_i cdef SpanC span cdef stdset[int] seen_tokens stdsort(original.begin(), original.end(), len_start_cmp) cdef int orig_i = original.size() - 1 while orig_i >= 0: span = original[orig_i] if not seen_tokens.count(span.start) and not seen_tokens.count(span.end - 1): filtered.push_back(span) for seen_i in range(span.start, span.end): seen_tokens.insert(seen_i) orig_i -= 1 stdsort(filtered.begin(), filtered.end(), start_cmp) cdef object _prepare_special_spans(self, Doc doc, vector[SpanC] &filtered): spans = [doc[match.start:match.end] for match in filtered] cdef bint modify_in_place = True cdef int curr_length = doc.length cdef int max_length = 0 cdef int span_length_diff = 0 span_data = {} for span in spans: rule = self._rules.get(span.text, None) span_length_diff = 0 if rule: span_length_diff = len(rule) - (span.end - span.start) if span_length_diff > 0: modify_in_place = False curr_length += span_length_diff if curr_length > max_length: max_length = curr_length span_data[span.start] = (span.text, span.start, span.end, span_length_diff) return (span_data, max_length, modify_in_place) cdef int _retokenize_special_spans(self, Doc doc, TokenC* tokens, object span_data): cdef int i = 0 cdef int j = 0 cdef int offset = 0 cdef _Cached* cached cdef int idx_offset = 0 cdef int orig_final_spacy cdef int orig_idx cdef int span_start cdef int span_end while i < doc.length: if not i in span_data: tokens[i + offset] = doc.c[i] i += 1 else: span = span_data[i] span_start = span[1] span_end = span[2] cached = <_Cached*>self._specials.get(hash_string(span[0])) if cached == NULL: # Copy original tokens if no rule found for j in range(span_end - span_start): tokens[i + offset + j] = doc.c[i + j] i += span_end - span_start else: # Copy special case tokens into doc and adjust token and # character offsets idx_offset = 0 orig_final_spacy = doc.c[span_end - 1].spacy orig_idx = doc.c[i].idx for j in range(cached.length): tokens[i + offset + j] = cached.data.tokens[j] tokens[i + offset + j].idx = orig_idx + idx_offset idx_offset += cached.data.tokens[j].lex.length if cached.data.tokens[j].spacy: idx_offset += 1 tokens[i + offset + cached.length - 1].spacy = orig_final_spacy i += span_end - span_start offset += span[3] return offset cdef int _try_specials_and_cache(self, hash_t key, Doc tokens, int* has_special, bint with_special_cases) except -1: cdef bint specials_hit = 0 cdef bint cache_hit = 0 cdef int i if with_special_cases: cached = <_Cached*>self._specials.get(key) if cached == NULL: specials_hit = False else: for i in range(cached.length): tokens.push_back(&cached.data.tokens[i], False) has_special[0] = 1 specials_hit = True if not specials_hit: cached = <_Cached*>self._cache.get(key) if cached == NULL: cache_hit = False else: if cached.is_lex: for i in range(cached.length): tokens.push_back(cached.data.lexemes[i], False) else: for i in range(cached.length): tokens.push_back(&cached.data.tokens[i], False) cache_hit = True if not specials_hit and not cache_hit: return False return True cdef int _tokenize(self, Doc tokens, str span, hash_t orig_key, int* has_special, bint with_special_cases) except -1: cdef vector[LexemeC*] prefixes cdef vector[LexemeC*] suffixes cdef int orig_size orig_size = tokens.length span = self._split_affixes(tokens.mem, span, &prefixes, &suffixes, has_special, with_special_cases) self._attach_tokens(tokens, span, &prefixes, &suffixes, has_special, with_special_cases) self._save_cached(&tokens.c[orig_size], orig_key, has_special, tokens.length - orig_size) cdef str _split_affixes(self, Pool mem, str string, vector[const LexemeC*] *prefixes, vector[const LexemeC*] *suffixes, int* has_special, bint with_special_cases): cdef size_t i cdef str prefix cdef str suffix cdef str minus_pre cdef str minus_suf cdef size_t last_size = 0 while string and len(string) != last_size: if self.token_match and self.token_match(string): break if with_special_cases and self._specials.get(hash_string(string)) != NULL: break last_size = len(string) pre_len = self.find_prefix(string) if pre_len != 0: prefix = string[:pre_len] minus_pre = string[pre_len:] if minus_pre and with_special_cases and self._specials.get(hash_string(minus_pre)) != NULL: string = minus_pre prefixes.push_back(self.vocab.get(mem, prefix)) break suf_len = self.find_suffix(string[pre_len:]) if suf_len != 0: suffix = string[-suf_len:] minus_suf = string[:-suf_len] if minus_suf and with_special_cases and self._specials.get(hash_string(minus_suf)) != NULL: string = minus_suf suffixes.push_back(self.vocab.get(mem, suffix)) break if pre_len and suf_len and (pre_len + suf_len) <= len(string): string = string[pre_len:-suf_len] prefixes.push_back(self.vocab.get(mem, prefix)) suffixes.push_back(self.vocab.get(mem, suffix)) elif pre_len: string = minus_pre prefixes.push_back(self.vocab.get(mem, prefix)) elif suf_len: string = minus_suf suffixes.push_back(self.vocab.get(mem, suffix)) return string cdef int _attach_tokens(self, Doc tokens, str string, vector[const LexemeC*] *prefixes, vector[const LexemeC*] *suffixes, int* has_special, bint with_special_cases) except -1: cdef bint specials_hit = 0 cdef bint cache_hit = 0 cdef int split, end cdef const LexemeC* const* lexemes cdef const LexemeC* lexeme cdef str span cdef int i if prefixes.size(): for i in range(prefixes.size()): tokens.push_back(prefixes[0][i], False) if string: if self._try_specials_and_cache(hash_string(string), tokens, has_special, with_special_cases): pass elif (self.token_match and self.token_match(string)) or \ (self.url_match and \ self.url_match(string)): # We're always saying 'no' to spaces here -- the caller will # fix up the outermost one, with reference to the original. # See Issue #859 tokens.push_back(self.vocab.get(tokens.mem, string), False) else: matches = self.find_infix(string) if not matches: tokens.push_back(self.vocab.get(tokens.mem, string), False) else: # Let's say we have dyn-o-mite-dave - the regex finds the # start and end positions of the hyphens start = 0 start_before_infixes = start for match in matches: infix_start = match.start() infix_end = match.end() if infix_start == start_before_infixes: continue if infix_start != start: span = string[start:infix_start] tokens.push_back(self.vocab.get(tokens.mem, span), False) if infix_start != infix_end: # If infix_start != infix_end, it means the infix # token is non-empty. Empty infix tokens are useful # for tokenization in some languages (see # https://github.com/explosion/spaCy/issues/768) infix_span = string[infix_start:infix_end] tokens.push_back(self.vocab.get(tokens.mem, infix_span), False) start = infix_end span = string[start:] if span: tokens.push_back(self.vocab.get(tokens.mem, span), False) cdef vector[const LexemeC*].reverse_iterator it = suffixes.rbegin() while it != suffixes.rend(): lexeme = deref(it) preinc(it) tokens.push_back(lexeme, False) cdef int _save_cached(self, const TokenC* tokens, hash_t key, int* has_special, int n) except -1: cdef int i if n <= 0: # avoid mem alloc of zero length return 0 for i in range(n): if self.vocab._by_orth.get(tokens[i].lex.orth) == NULL: return 0 # See #1250 if has_special[0]: return 0 cached = <_Cached*>self.mem.alloc(1, sizeof(_Cached)) cached.length = n cached.is_lex = True lexemes = self.mem.alloc(n, sizeof(LexemeC**)) for i in range(n): lexemes[i] = tokens[i].lex cached.data.lexemes = lexemes self._cache.set(key, cached) def find_infix(self, str string): """Find internal split points of the string, such as hyphens. string (str): The string to segment. RETURNS (list): A list of `re.MatchObject` objects that have `.start()` and `.end()` methods, denoting the placement of internal segment separators, e.g. hyphens. DOCS: https://spacy.io/api/tokenizer#find_infix """ if self.infix_finditer is None: return 0 return list(self.infix_finditer(string)) def find_prefix(self, str string): """Find the length of a prefix that should be segmented from the string, or None if no prefix rules match. string (str): The string to segment. RETURNS (int): The length of the prefix if present, otherwise `None`. DOCS: https://spacy.io/api/tokenizer#find_prefix """ if self.prefix_search is None: return 0 match = self.prefix_search(string) return (match.end() - match.start()) if match is not None else 0 def find_suffix(self, str string): """Find the length of a suffix that should be segmented from the string, or None if no suffix rules match. string (str): The string to segment. Returns (int): The length of the suffix if present, otherwise `None`. DOCS: https://spacy.io/api/tokenizer#find_suffix """ if self.suffix_search is None: return 0 match = self.suffix_search(string) return (match.end() - match.start()) if match is not None else 0 def _load_special_cases(self, special_cases): """Add special-case tokenization rules.""" if special_cases is not None: for chunk, substrings in sorted(special_cases.items()): self.add_special_case(chunk, substrings) def _validate_special_case(self, chunk, substrings): """Check whether the `ORTH` fields match the string. Check that additional features beyond `ORTH` and `NORM` are not set by the exception. chunk (str): The string to specially tokenize. substrings (iterable): A sequence of dicts, where each dict describes a token and its attributes. """ attrs = [intify_attrs(spec, _do_deprecated=True) for spec in substrings] orth = "".join([spec[ORTH] for spec in attrs]) if chunk != orth: raise ValueError(Errors.E997.format(chunk=chunk, orth=orth, token_attrs=substrings)) for substring in attrs: for attr in substring: if attr not in (ORTH, NORM): raise ValueError(Errors.E1005.format(attr=self.vocab.strings[attr], chunk=chunk)) def add_special_case(self, str string, substrings): """Add a special-case tokenization rule. string (str): The string to specially tokenize. substrings (iterable): A sequence of dicts, where each dict describes a token and its attributes. The `ORTH` fields of the attributes must exactly match the string when they are concatenated. DOCS: https://spacy.io/api/tokenizer#add_special_case """ self._validate_special_case(string, substrings) substrings = list(substrings) cached = <_Cached*>self.mem.alloc(1, sizeof(_Cached)) cached.length = len(substrings) cached.is_lex = False cached.data.tokens = self.vocab.make_fused_token(substrings) key = hash_string(string) stale_special = <_Cached*>self._specials.get(key) self._specials.set(key, cached) if stale_special is not NULL: self.mem.free(stale_special) self._rules[string] = substrings self._flush_cache() if not self.faster_heuristics or self.find_prefix(string) or self.find_infix(string) or self.find_suffix(string) or " " in string: self._special_matcher.add(string, None, self._tokenize_affixes(string, False)) def _reload_special_cases(self): self._flush_cache() self._flush_specials() self._load_special_cases(self._rules) def explain(self, text): """A debugging tokenizer that provides information about which tokenizer rule or pattern was matched for each token. The tokens produced are identical to `nlp.tokenizer()` except for whitespace tokens. string (str): The string to tokenize. RETURNS (list): A list of (pattern_string, token_string) tuples DOCS: https://spacy.io/api/tokenizer#explain """ prefix_search = self.prefix_search if prefix_search is None: prefix_search = re.compile("a^").search suffix_search = self.suffix_search if suffix_search is None: suffix_search = re.compile("a^").search infix_finditer = self.infix_finditer if infix_finditer is None: infix_finditer = re.compile("a^").finditer token_match = self.token_match if token_match is None: token_match = re.compile("a^").match url_match = self.url_match if url_match is None: url_match = re.compile("a^").match special_cases = {} for orth, special_tokens in self.rules.items(): special_cases[orth] = [intify_attrs(special_token, strings_map=self.vocab.strings, _do_deprecated=True) for special_token in special_tokens] tokens = [] for substring in text.split(): suffixes = [] while substring: if substring in special_cases: tokens.extend(("SPECIAL-" + str(i + 1), self.vocab.strings[e[ORTH]]) for i, e in enumerate(special_cases[substring])) substring = '' continue while prefix_search(substring) or suffix_search(substring): if token_match(substring): tokens.append(("TOKEN_MATCH", substring)) substring = '' break if substring in special_cases: tokens.extend(("SPECIAL-" + str(i + 1), self.vocab.strings[e[ORTH]]) for i, e in enumerate(special_cases[substring])) substring = '' break if prefix_search(substring): split = prefix_search(substring).end() # break if pattern matches the empty string if split == 0: break tokens.append(("PREFIX", substring[:split])) substring = substring[split:] if substring in special_cases: continue if suffix_search(substring): split = suffix_search(substring).start() # break if pattern matches the empty string if split == len(substring): break suffixes.append(("SUFFIX", substring[split:])) substring = substring[:split] if len(substring) == 0: continue if token_match(substring): tokens.append(("TOKEN_MATCH", substring)) substring = '' elif url_match(substring): tokens.append(("URL_MATCH", substring)) substring = '' elif substring in special_cases: tokens.extend((f"SPECIAL-{i + 1}", self.vocab.strings[e[ORTH]]) for i, e in enumerate(special_cases[substring])) substring = '' elif list(infix_finditer(substring)): infixes = infix_finditer(substring) offset = 0 for match in infixes: if offset == 0 and match.start() == 0: continue if substring[offset : match.start()]: tokens.append(("TOKEN", substring[offset : match.start()])) if substring[match.start() : match.end()]: tokens.append(("INFIX", substring[match.start() : match.end()])) offset = match.end() if substring[offset:]: tokens.append(("TOKEN", substring[offset:])) substring = '' elif substring: tokens.append(("TOKEN", substring)) substring = '' tokens.extend(reversed(suffixes)) # Find matches for special cases handled by special matcher words, spaces = get_words_and_spaces([t[1] for t in tokens], text) t_words = [] t_spaces = [] for word, space in zip(words, spaces): if not word.isspace(): t_words.append(word) t_spaces.append(space) doc = Doc(self.vocab, words=t_words, spaces=t_spaces) matches = self._special_matcher(doc) spans = [Span(doc, s, e, label=m_id) for m_id, s, e in matches] spans = util.filter_spans(spans) # Replace matched tokens with their exceptions i = 0 final_tokens = [] spans_by_start = {s.start: s for s in spans} while i < len(tokens): if i in spans_by_start: span = spans_by_start[i] exc = [d[ORTH] for d in special_cases[span.label_]] for j, orth in enumerate(exc): final_tokens.append((f"SPECIAL-{j + 1}", self.vocab.strings[orth])) i += len(span) else: final_tokens.append(tokens[i]) i += 1 return final_tokens def score(self, examples, **kwargs): validate_examples(examples, "Tokenizer.score") return Scorer.score_tokenization(examples) def to_disk(self, path, **kwargs): """Save the current state to a directory. path (str / Path): A path to a directory, which will be created if it doesn't exist. exclude (list): String names of serialization fields to exclude. DOCS: https://spacy.io/api/tokenizer#to_disk """ path = util.ensure_path(path) with path.open("wb") as file_: file_.write(self.to_bytes(**kwargs)) def from_disk(self, path, *, exclude=tuple()): """Loads state from a directory. Modifies the object in place and returns it. path (str / Path): A path to a directory. exclude (list): String names of serialization fields to exclude. RETURNS (Tokenizer): The modified `Tokenizer` object. DOCS: https://spacy.io/api/tokenizer#from_disk """ path = util.ensure_path(path) with path.open("rb") as file_: bytes_data = file_.read() self.from_bytes(bytes_data, exclude=exclude) return self def to_bytes(self, *, exclude=tuple()): """Serialize the current state to a binary string. exclude (list): String names of serialization fields to exclude. RETURNS (bytes): The serialized form of the `Tokenizer` object. DOCS: https://spacy.io/api/tokenizer#to_bytes """ serializers = { "vocab": lambda: self.vocab.to_bytes(exclude=exclude), "prefix_search": lambda: _get_regex_pattern(self.prefix_search), "suffix_search": lambda: _get_regex_pattern(self.suffix_search), "infix_finditer": lambda: _get_regex_pattern(self.infix_finditer), "token_match": lambda: _get_regex_pattern(self.token_match), "url_match": lambda: _get_regex_pattern(self.url_match), "exceptions": lambda: dict(sorted(self._rules.items())), "faster_heuristics": lambda: self.faster_heuristics, } return util.to_bytes(serializers, exclude) def from_bytes(self, bytes_data, *, exclude=tuple()): """Load state from a binary string. bytes_data (bytes): The data to load from. exclude (list): String names of serialization fields to exclude. RETURNS (Tokenizer): The `Tokenizer` object. DOCS: https://spacy.io/api/tokenizer#from_bytes """ data = {} deserializers = { "vocab": lambda b: self.vocab.from_bytes(b, exclude=exclude), "prefix_search": lambda b: data.setdefault("prefix_search", b), "suffix_search": lambda b: data.setdefault("suffix_search", b), "infix_finditer": lambda b: data.setdefault("infix_finditer", b), "token_match": lambda b: data.setdefault("token_match", b), "url_match": lambda b: data.setdefault("url_match", b), "exceptions": lambda b: data.setdefault("rules", b), "faster_heuristics": lambda b: data.setdefault("faster_heuristics", b), } # reset all properties and flush all caches (through rules), # reset rules first so that _reload_special_cases is trivial/fast as # the other properties are reset self.rules = {} self.prefix_search = None self.suffix_search = None self.infix_finditer = None self.token_match = None self.url_match = None msg = util.from_bytes(bytes_data, deserializers, exclude) if "prefix_search" in data and isinstance(data["prefix_search"], str): self.prefix_search = re.compile(data["prefix_search"]).search if "suffix_search" in data and isinstance(data["suffix_search"], str): self.suffix_search = re.compile(data["suffix_search"]).search if "infix_finditer" in data and isinstance(data["infix_finditer"], str): self.infix_finditer = re.compile(data["infix_finditer"]).finditer if "token_match" in data and isinstance(data["token_match"], str): self.token_match = re.compile(data["token_match"]).match if "url_match" in data and isinstance(data["url_match"], str): self.url_match = re.compile(data["url_match"]).match if "rules" in data and isinstance(data["rules"], dict): self.rules = data["rules"] if "faster_heuristics" in data: self.faster_heuristics = data["faster_heuristics"] return self def _get_regex_pattern(regex): """Get a pattern string for a regex, or None if the pattern is None.""" return None if regex is None else regex.__self__.pattern cdef extern from "" namespace "std" nogil: void stdsort "sort"(vector[SpanC].iterator, vector[SpanC].iterator, bint (*)(SpanC, SpanC)) cdef bint len_start_cmp(SpanC a, SpanC b) nogil: if a.end - a.start == b.end - b.start: return b.start < a.start return a.end - a.start < b.end - b.start cdef bint start_cmp(SpanC a, SpanC b) nogil: return a.start < b.start