# cython: infer_types # coding: utf8 from __future__ import unicode_literals from libc.string cimport memset import ujson as json from .attrs cimport POS, IS_SPACE from .attrs import LEMMA, intify_attrs from .parts_of_speech cimport SPACE from .parts_of_speech import IDS as POS_IDS from .lexeme cimport Lexeme from .errors import Errors def _normalize_props(props): """Transform deprecated string keys to correct names.""" out = {} for key, value in props.items(): if key == POS: if hasattr(value, 'upper'): value = value.upper() if value in POS_IDS: value = POS_IDS[value] out[key] = value elif isinstance(key, int): out[key] = value elif key.lower() == 'pos': out[POS] = POS_IDS[value.upper()] else: out[key] = value return out cdef class Morphology: '''Store the possible morphological analyses for a language, and index them by hash. To save space on each token, tokens only know the hash of their morphological analysis, so queries of morphological attributes are delegated to this class. ''' def __init__(self, StringStore string_store, tag_map, lemmatizer, exc=None): self.mem = Pool() self.strings = string_store self.tags = PreshMap() # Add special space symbol. We prefix with underscore, to make sure it # always sorts to the end. space_attrs = tag_map.get('SP', {POS: SPACE}) if '_SP' not in tag_map: self.strings.add('_SP') tag_map = dict(tag_map) tag_map['_SP'] = space_attrs self.tag_names = tuple(sorted(tag_map.keys())) self.tag_map = {} self.lemmatizer = lemmatizer self.n_tags = len(tag_map) self.reverse_index = {} for i, (tag_str, attrs) in enumerate(sorted(tag_map.items())): print(tag_str, attrs) self.tag_map[tag_str] = dict(attrs) self.reverse_index[self.strings.add(tag_str)] = i self._cache = PreshMapArray(self.n_tags) self.exc = {} if exc is not None: for (tag_str, orth_str), attrs in exc.items(): self.add_special_case(tag_str, orth_str, attrs) def __reduce__(self): return (Morphology, (self.strings, self.tag_map, self.lemmatizer, self.exc), None, None) def add(self, features): """Insert a morphological analysis in the morphology table, if not already present. Returns the hash of the new analysis. """ features = intify_features(self.strings, features) cdef RichTagC tag = create_rich_tag(features) cdef hash_t key = self.insert(tag) return key def lemmatize(self, const univ_pos_t univ_pos, attr_t orth, morphology): if orth not in self.strings: return orth cdef unicode py_string = self.strings[orth] if self.lemmatizer is None: return self.strings.add(py_string.lower()) cdef list lemma_strings cdef unicode lemma_string lemma_strings = self.lemmatizer(py_string, univ_pos, morphology) lemma_string = lemma_strings[0] lemma = self.strings.add(lemma_string) return lemma def add_special_case(self, unicode tag_str, unicode orth_str, attrs, force=False): """Add a special-case rule to the morphological analyser. Tokens whose tag and orth match the rule will receive the specified properties. tag (unicode): The part-of-speech tag to key the exception. orth (unicode): The word-form to key the exception. """ pass ## TODO: Currently we've assumed that we know the number of tags -- ## RichTagC is an array, and _cache is a PreshMapArray ## This is really bad: it makes the morphology typed to the tagger ## classes, which is all wrong. #self.exc[(tag_str, orth_str)] = dict(attrs) #tag = self.strings.add(tag_str) #if tag not in self.reverse_index: # return #tag_id = self.reverse_index[tag] #orth = self.strings[orth_str] #cdef RichTagC rich_tag = self.rich_tags[tag_id] #attrs = intify_attrs(attrs, self.strings, _do_deprecated=True) #cached = self._cache.get(tag_id, orth) #if cached is NULL: # cached = self.mem.alloc(1, sizeof(MorphAnalysisC)) #elif force: # memset(cached, 0, sizeof(cached[0])) #else: # raise ValueError(Errors.E015.format(tag=tag_str, orth=orth_str)) #cached.tag = rich_tag ## TODO: Refactor this to take arbitrary attributes. #for name_id, value_id in attrs.items(): # if name_id == LEMMA: # cached.lemma = value_id # else: # self.assign_feature(&cached.tag.morph, name_id, value_id) #if cached.lemma == 0: # cached.lemma = self.lemmatize(rich_tag.pos, orth, attrs) #self._cache.set(tag_id, orth, cached) cdef hash_t insert(self, RichTagC tag) except 0: cdef hash_t key = hash_tag(tag) if self.tags.get(key) == NULL: tag_ptr = self.mem.alloc(1, sizeof(RichTagC)) tag_ptr[0] = tag self.tags.set(key, tag_ptr) return key cdef int assign_untagged(self, TokenC* token) except -1: """Set morphological attributes on a token without a POS tag. Uses the lemmatizer's lookup() method, which looks up the string in the table provided by the language data as lemma_lookup (if available). """ if token.lemma == 0: orth_str = self.strings[token.lex.orth] lemma = self.lemmatizer.lookup(orth_str) token.lemma = self.strings.add(lemma) cdef int assign_tag(self, TokenC* token, tag_str) except -1: cdef attr_t tag = self.strings.as_int(tag_str) if tag in self.reverse_index: tag_id = self.reverse_index[tag] self.assign_tag_id(token, tag_id) else: token.tag = tag cdef int assign_tag_id(self, TokenC* token, int tag_id) except -1: if tag_id > self.n_tags: raise ValueError(Errors.E014.format(tag=tag_id)) # TODO: It's pretty arbitrary to put this logic here. I guess the # justification is that this is where the specific word and the tag # interact. Still, we should have a better way to enforce this rule, or # figure out why the statistical model fails. Related to Issue #220 if Lexeme.c_check_flag(token.lex, IS_SPACE): tag_id = self.reverse_index[self.strings.add('_SP')] tag_str = self.tag_names[tag_id] features = dict(self.tag_map.get(tag_str, {})) cdef attr_t lemma = self._cache.get(tag_id, token.lex.orth) if lemma == 0 and features: pos = self.strings.as_int(features.pop(POS)) lemma = self.lemmatize(pos, token.lex.orth, features) self._cache.set(tag_id, token.lex.orth, lemma) else: pos = 0 token.lemma = lemma token.pos = pos token.tag = self.strings[tag_str] token.morph = self.add(features) cdef update_morph(self, hash_t morph, features): """Update a morphological analysis with new feature values.""" tag = (self.tags.get(morph))[0] cdef univ_morph_t feature cdef int value for feature_, value in features.items(): feature = self.strings.as_int(feature_) set_feature(&tag, feature, 1) morph = self.insert_tag(tag) return morph def to_bytes(self): json_tags = [] for key in self.tags: tag_ptr = self.tags.get(key) if tag_ptr != NULL: json_tags.append(tag_to_json(tag_ptr[0])) raise json.dumps(json_tags) def from_bytes(self, byte_string): raise NotImplementedError def to_disk(self, path): raise NotImplementedError def from_disk(self, path): raise NotImplementedError cpdef univ_pos_t get_int_tag(pos_): return 0 cpdef intify_features(StringStore strings, features): return {strings.as_int(feature) for feature in features} cdef hash_t hash_tag(RichTagC tag) nogil: return mrmr.hash64(&tag, sizeof(tag), 0) cdef RichTagC create_rich_tag(features): cdef RichTagC tag cdef univ_morph_t feature #for feature in features: # set_feature(&tag, feature, 1) return tag cdef tag_to_json(RichTagC tag): return {} cdef RichTagC tag_from_json(json_tag): cdef RichTagC tag return tag cdef int set_feature(RichTagC* tag, univ_morph_t feature, int value) nogil: if value == True: value_ = feature else: value_ = NIL if feature == NIL: pass if is_abbr_feature(feature): tag.abbr = value_ elif is_adp_type_feature(feature): tag.adp_type = value_ elif is_adv_type_feature(feature): tag.adv_type = value_ elif is_animacy_feature(feature): tag.animacy = value_ elif is_aspect_feature(feature): tag.aspect = value_ elif is_case_feature(feature): tag.case = value_ elif is_conj_type_feature(feature): tag.conj_type = value_ elif is_connegative_feature(feature): tag.connegative = value_ elif is_definite_feature(feature): tag.definite = value_ elif is_degree_feature(feature): tag.degree = value_ elif is_derivation_feature(feature): tag.derivation = value_ elif is_echo_feature(feature): tag.echo = value_ elif is_foreign_feature(feature): tag.foreign = value_ elif is_gender_feature(feature): tag.gender = value_ elif is_hyph_feature(feature): tag.hyph = value_ elif is_inf_form_feature(feature): tag.inf_form = value_ elif is_mood_feature(feature): tag.mood = value_ elif is_negative_feature(feature): tag.negative = value_ elif is_number_feature(feature): tag.number = value_ elif is_name_type_feature(feature): tag.name_type = value_ elif is_num_form_feature(feature): tag.num_form = value_ elif is_num_value_feature(feature): tag.num_value = value_ elif is_part_form_feature(feature): tag.part_form = value_ elif is_part_type_feature(feature): tag.part_type = value_ elif is_person_feature(feature): tag.person = value_ elif is_polite_feature(feature): tag.polite = value_ elif is_polarity_feature(feature): tag.polarity = value_ elif is_poss_feature(feature): tag.poss = value_ elif is_prefix_feature(feature): tag.prefix = value_ elif is_prep_case_feature(feature): tag.prep_case = value_ elif is_pron_type_feature(feature): tag.pron_type = value_ elif is_punct_side_feature(feature): tag.punct_type = value_ elif is_reflex_feature(feature): tag.reflex = value_ elif is_style_feature(feature): tag.style = value_ elif is_style_variant_feature(feature): tag.style_variant = value_ elif is_tense_feature(feature): tag.tense = value_ elif is_verb_form_feature(feature): tag.verb_form = value_ elif is_voice_feature(feature): tag.voice = value_ elif is_verb_type_feature(feature): tag.verb_type = value_ else: with gil: raise ValueError("Unknown feature: %d" % feature) cdef int is_abbr_feature(univ_morph_t abbr) nogil: return 0 cdef int is_adp_type_feature(univ_morph_t feature) nogil: return 0 cdef int is_adv_type_feature(univ_morph_t feature) nogil: return 0 cdef int is_animacy_feature(univ_morph_t feature) nogil: return 0 cdef int is_aspect_feature(univ_morph_t feature) nogil: return 0 cdef int is_case_feature(univ_morph_t feature) nogil: return 0 cdef int is_conj_type_feature(univ_morph_t feature) nogil: return 0 cdef int is_connegative_feature(univ_morph_t feature) nogil: return 0 cdef int is_definite_feature(univ_morph_t feature) nogil: return 0 cdef int is_degree_feature(univ_morph_t feature) nogil: return 0 cdef int is_derivation_feature(univ_morph_t feature) nogil: return 0 cdef int is_echo_feature(univ_morph_t feature) nogil: return 0 cdef int is_foreign_feature(univ_morph_t feature) nogil: return 0 cdef int is_gender_feature(univ_morph_t feature) nogil: return 0 cdef int is_hyph_feature(univ_morph_t feature) nogil: return 0 cdef int is_inf_form_feature(univ_morph_t feature) nogil: return 0 cdef int is_mood_feature(univ_morph_t feature) nogil: return 0 cdef int is_negative_feature(univ_morph_t feature) nogil: return 0 cdef int is_number_feature(univ_morph_t feature) nogil: return 0 cdef int is_name_type_feature(univ_morph_t feature) nogil: return 0 cdef int is_num_form_feature(univ_morph_t feature) nogil: return 0 cdef int is_num_type_feature(univ_morph_t feature) nogil: return 0 cdef int is_num_value_feature(univ_morph_t feature) nogil: return 0 cdef int is_part_form_feature(univ_morph_t feature) nogil: return 0 cdef int is_part_type_feature(univ_morph_t feature) nogil: return 0 cdef int is_person_feature(univ_morph_t feature) nogil: return 0 cdef int is_polite_feature(univ_morph_t feature) nogil: return 0 cdef int is_polarity_feature(univ_morph_t feature) nogil: return 0 cdef int is_poss_feature(univ_morph_t feature) nogil: return 0 cdef int is_prefix_feature(univ_morph_t feature) nogil: return 0 cdef int is_prep_case_feature(univ_morph_t feature) nogil: return 0 cdef int is_pron_type_feature(univ_morph_t feature) nogil: return 0 cdef int is_punct_side_feature(univ_morph_t feature) nogil: return 0 cdef int is_punct_type_feature(univ_morph_t feature) nogil: return 0 cdef int is_reflex_feature(univ_morph_t feature) nogil: return 0 cdef int is_style_feature(univ_morph_t feature) nogil: return 0 cdef int is_style_variant_feature(univ_morph_t feature) nogil: return 0 cdef int is_tense_feature(univ_morph_t feature) nogil: return 0 cdef int is_verb_form_feature(univ_morph_t feature) nogil: return 0 cdef int is_voice_feature(univ_morph_t feature) nogil: return 0 cdef int is_verb_type_feature(univ_morph_t feature) nogil: return 0 IDS = { "Animacy_anim": Animacy_anim, "Animacy_inan": Animacy_inan, "Animacy_hum": Animacy_hum, # U20 "Animacy_nhum": Animacy_nhum, "Aspect_freq": Aspect_freq, "Aspect_imp": Aspect_imp, "Aspect_mod": Aspect_mod, "Aspect_none": Aspect_none, "Aspect_perf": Aspect_perf, "Case_abe": Case_abe, "Case_abl": Case_abl, "Case_abs": Case_abs, "Case_acc": Case_acc, "Case_ade": Case_ade, "Case_all": Case_all, "Case_cau": Case_cau, "Case_com": Case_com, "Case_dat": Case_dat, "Case_del": Case_del, "Case_dis": Case_dis, "Case_ela": Case_ela, "Case_ess": Case_ess, "Case_gen": Case_gen, "Case_ill": Case_ill, "Case_ine": Case_ine, "Case_ins": Case_ins, "Case_loc": Case_loc, "Case_lat": Case_lat, "Case_nom": Case_nom, "Case_par": Case_par, "Case_sub": Case_sub, "Case_sup": Case_sup, "Case_tem": Case_tem, "Case_ter": Case_ter, "Case_tra": Case_tra, "Case_voc": Case_voc, "Definite_two": Definite_two, "Definite_def": Definite_def, "Definite_red": Definite_red, "Definite_cons": Definite_cons, # U20 "Definite_ind": Definite_ind, "Degree_cmp": Degree_cmp, "Degree_comp": Degree_comp, "Degree_none": Degree_none, "Degree_pos": Degree_pos, "Degree_sup": Degree_sup, "Degree_abs": Degree_abs, "Degree_com": Degree_com, "Degree_dim ": Degree_dim, # du "Gender_com": Gender_com, "Gender_fem": Gender_fem, "Gender_masc": Gender_masc, "Gender_neut": Gender_neut, "Mood_cnd": Mood_cnd, "Mood_imp": Mood_imp, "Mood_ind": Mood_ind, "Mood_n": Mood_n, "Mood_pot": Mood_pot, "Mood_sub": Mood_sub, "Mood_opt": Mood_opt, "Negative_neg": Negative_neg, "Negative_pos": Negative_pos, "Negative_yes": Negative_yes, "Polarity_neg": Polarity_neg, # U20 "Polarity_pos": Polarity_pos, # U20 "Number_com": Number_com, "Number_dual": Number_dual, "Number_none": Number_none, "Number_plur": Number_plur, "Number_sing": Number_sing, "Number_ptan ": Number_ptan, # bg "Number_count ": Number_count, # bg "NumType_card": NumType_card, "NumType_dist": NumType_dist, "NumType_frac": NumType_frac, "NumType_gen": NumType_gen, "NumType_mult": NumType_mult, "NumType_none": NumType_none, "NumType_ord": NumType_ord, "NumType_sets": NumType_sets, "Person_one": Person_one, "Person_two": Person_two, "Person_three": Person_three, "Person_none": Person_none, "Poss_yes": Poss_yes, "PronType_advPart": PronType_advPart, "PronType_art": PronType_art, "PronType_default": PronType_default, "PronType_dem": PronType_dem, "PronType_ind": PronType_ind, "PronType_int": PronType_int, "PronType_neg": PronType_neg, "PronType_prs": PronType_prs, "PronType_rcp": PronType_rcp, "PronType_rel": PronType_rel, "PronType_tot": PronType_tot, "PronType_clit": PronType_clit, "PronType_exc ": PronType_exc, # es, ca, it, fa, "Reflex_yes": Reflex_yes, "Tense_fut": Tense_fut, "Tense_imp": Tense_imp, "Tense_past": Tense_past, "Tense_pres": Tense_pres, "VerbForm_fin": VerbForm_fin, "VerbForm_ger": VerbForm_ger, "VerbForm_inf": VerbForm_inf, "VerbForm_none": VerbForm_none, "VerbForm_part": VerbForm_part, "VerbForm_partFut": VerbForm_partFut, "VerbForm_partPast": VerbForm_partPast, "VerbForm_partPres": VerbForm_partPres, "VerbForm_sup": VerbForm_sup, "VerbForm_trans": VerbForm_trans, "VerbForm_conv": VerbForm_conv, # U20 "VerbForm_gdv ": VerbForm_gdv, # la, "Voice_act": Voice_act, "Voice_cau": Voice_cau, "Voice_pass": Voice_pass, "Voice_mid ": Voice_mid, # gkc, "Voice_int ": Voice_int, # hb, "Abbr_yes ": Abbr_yes, # cz, fi, sl, U, "AdpType_prep ": AdpType_prep, # cz, U, "AdpType_post ": AdpType_post, # U, "AdpType_voc ": AdpType_voc, # cz, "AdpType_comprep ": AdpType_comprep, # cz, "AdpType_circ ": AdpType_circ, # U, "AdvType_man": AdvType_man, "AdvType_loc": AdvType_loc, "AdvType_tim": AdvType_tim, "AdvType_deg": AdvType_deg, "AdvType_cau": AdvType_cau, "AdvType_mod": AdvType_mod, "AdvType_sta": AdvType_sta, "AdvType_ex": AdvType_ex, "AdvType_adadj": AdvType_adadj, "ConjType_oper ": ConjType_oper, # cz, U, "ConjType_comp ": ConjType_comp, # cz, U, "Connegative_yes ": Connegative_yes, # fi, "Derivation_minen ": Derivation_minen, # fi, "Derivation_sti ": Derivation_sti, # fi, "Derivation_inen ": Derivation_inen, # fi, "Derivation_lainen ": Derivation_lainen, # fi, "Derivation_ja ": Derivation_ja, # fi, "Derivation_ton ": Derivation_ton, # fi, "Derivation_vs ": Derivation_vs, # fi, "Derivation_ttain ": Derivation_ttain, # fi, "Derivation_ttaa ": Derivation_ttaa, # fi, "Echo_rdp ": Echo_rdp, # U, "Echo_ech ": Echo_ech, # U, "Foreign_foreign ": Foreign_foreign, # cz, fi, U, "Foreign_fscript ": Foreign_fscript, # cz, fi, U, "Foreign_tscript ": Foreign_tscript, # cz, U, "Foreign_yes ": Foreign_yes, # sl, "Gender_dat_masc ": Gender_dat_masc, # bq, U, "Gender_dat_fem ": Gender_dat_fem, # bq, U, "Gender_erg_masc ": Gender_erg_masc, # bq, "Gender_erg_fem ": Gender_erg_fem, # bq, "Gender_psor_masc ": Gender_psor_masc, # cz, sl, U, "Gender_psor_fem ": Gender_psor_fem, # cz, sl, U, "Gender_psor_neut ": Gender_psor_neut, # sl, "Hyph_yes ": Hyph_yes, # cz, U, "InfForm_one ": InfForm_one, # fi, "InfForm_two ": InfForm_two, # fi, "InfForm_three ": InfForm_three, # fi, "NameType_geo ": NameType_geo, # U, cz, "NameType_prs ": NameType_prs, # U, cz, "NameType_giv ": NameType_giv, # U, cz, "NameType_sur ": NameType_sur, # U, cz, "NameType_nat ": NameType_nat, # U, cz, "NameType_com ": NameType_com, # U, cz, "NameType_pro ": NameType_pro, # U, cz, "NameType_oth ": NameType_oth, # U, cz, "NounType_com ": NounType_com, # U, "NounType_prop ": NounType_prop, # U, "NounType_class ": NounType_class, # U, "Number_abs_sing ": Number_abs_sing, # bq, U, "Number_abs_plur ": Number_abs_plur, # bq, U, "Number_dat_sing ": Number_dat_sing, # bq, U, "Number_dat_plur ": Number_dat_plur, # bq, U, "Number_erg_sing ": Number_erg_sing, # bq, U, "Number_erg_plur ": Number_erg_plur, # bq, U, "Number_psee_sing ": Number_psee_sing, # U, "Number_psee_plur ": Number_psee_plur, # U, "Number_psor_sing ": Number_psor_sing, # cz, fi, sl, U, "Number_psor_plur ": Number_psor_plur, # cz, fi, sl, U, "NumForm_digit ": NumForm_digit, # cz, sl, U, "NumForm_roman ": NumForm_roman, # cz, sl, U, "NumForm_word ": NumForm_word, # cz, sl, U, "NumValue_one ": NumValue_one, # cz, U, "NumValue_two ": NumValue_two, # cz, U, "NumValue_three ": NumValue_three, # cz, U, "PartForm_pres ": PartForm_pres, # fi, "PartForm_past ": PartForm_past, # fi, "PartForm_agt ": PartForm_agt, # fi, "PartForm_neg ": PartForm_neg, # fi, "PartType_mod ": PartType_mod, # U, "PartType_emp ": PartType_emp, # U, "PartType_res ": PartType_res, # U, "PartType_inf ": PartType_inf, # U, "PartType_vbp ": PartType_vbp, # U, "Person_abs_one ": Person_abs_one, # bq, U, "Person_abs_two ": Person_abs_two, # bq, U, "Person_abs_three ": Person_abs_three, # bq, U, "Person_dat_one ": Person_dat_one, # bq, U, "Person_dat_two ": Person_dat_two, # bq, U, "Person_dat_three ": Person_dat_three, # bq, U, "Person_erg_one ": Person_erg_one, # bq, U, "Person_erg_two ": Person_erg_two, # bq, U, "Person_erg_three ": Person_erg_three, # bq, U, "Person_psor_one ": Person_psor_one, # fi, U, "Person_psor_two ": Person_psor_two, # fi, U, "Person_psor_three ": Person_psor_three, # fi, U, "Polite_inf ": Polite_inf, # bq, U, "Polite_pol ": Polite_pol, # bq, U, "Polite_abs_inf ": Polite_abs_inf, # bq, U, "Polite_abs_pol ": Polite_abs_pol, # bq, U, "Polite_erg_inf ": Polite_erg_inf, # bq, U, "Polite_erg_pol ": Polite_erg_pol, # bq, U, "Polite_dat_inf ": Polite_dat_inf, # bq, U, "Polite_dat_pol ": Polite_dat_pol, # bq, U, "Prefix_yes ": Prefix_yes, # U, "PrepCase_npr ": PrepCase_npr, # cz, "PrepCase_pre ": PrepCase_pre, # U, "PunctSide_ini ": PunctSide_ini, # U, "PunctSide_fin ": PunctSide_fin, # U, "PunctType_peri ": PunctType_peri, # U, "PunctType_qest ": PunctType_qest, # U, "PunctType_excl ": PunctType_excl, # U, "PunctType_quot ": PunctType_quot, # U, "PunctType_brck ": PunctType_brck, # U, "PunctType_comm ": PunctType_comm, # U, "PunctType_colo ": PunctType_colo, # U, "PunctType_semi ": PunctType_semi, # U, "PunctType_dash ": PunctType_dash, # U, "Style_arch ": Style_arch, # cz, fi, U, "Style_rare ": Style_rare, # cz, fi, U, "Style_poet ": Style_poet, # cz, U, "Style_norm ": Style_norm, # cz, U, "Style_coll ": Style_coll, # cz, U, "Style_vrnc ": Style_vrnc, # cz, U, "Style_sing ": Style_sing, # cz, U, "Style_expr ": Style_expr, # cz, U, "Style_derg ": Style_derg, # cz, U, "Style_vulg ": Style_vulg, # cz, U, "Style_yes ": Style_yes, # fi, U, "StyleVariant_styleShort ": StyleVariant_styleShort, # cz, "StyleVariant_styleBound ": StyleVariant_styleBound, # cz, sl, "VerbType_aux ": VerbType_aux, # U, "VerbType_cop ": VerbType_cop, # U, "VerbType_mod ": VerbType_mod, # U, "VerbType_light ": VerbType_light, # U, } NAMES = [key for key, value in sorted(IDS.items(), key=lambda item: item[1])] # Unfortunate hack here, to work around problem with long cpdef enum # (which is generating an enormous amount of C++ in Cython 0.24+) # We keep the enum cdef, and just make sure the names are available to Python locals().update(IDS)