Update UD bin scripts

* Update imports for `bin/`
* Add all currently supported languages
* Update subtok merger for new Matcher validation
* Modify blinded check to look at tokens instead of lemmas (for corpora
with tokens but not lemmas like Telugu)

bin/ud/run_eval.py

@@ -7,14 +7,16 @@ import datetime
 from pathlib import Path
 import xml.etree.ElementTree as ET
 
-from spacy.cli.ud import conll17_ud_eval
+import conll17_ud_eval
-from spacy.cli.ud.ud_train import write_conllu
+from ud_train import write_conllu
 from spacy.lang.lex_attrs import word_shape
 from spacy.util import get_lang_class
 
 # All languages in spaCy - in UD format (note that Norwegian is 'no' instead of 'nb')
-ALL_LANGUAGES = "ar, ca, da, de, el, en, es, fa, fi, fr, ga, he, hi, hr, hu, id, " \
+ALL_LANGUAGES = ("af, ar, bg, bn, ca, cs, da, de, el, en, es, et, fa, fi, fr,"
-                "it, ja, no, nl, pl, pt, ro, ru, sv, tr, ur, vi, zh"
+                 "ga, he, hi, hr, hu, id, is, it, ja, kn, ko, lt, lv, mr, no,"
+                 "nl, pl, pt, ro, ru, si, sk, sl, sq, sr, sv, ta, te, th, tl,"
+                 "tr, tt, uk, ur, vi, zh")
 
 # Non-parsing tasks that will be evaluated (works for default models)
 EVAL_NO_PARSE = ['Tokens', 'Words', 'Lemmas', 'Sentences', 'Feats']
@@ -73,10 +75,10 @@ def _contains_blinded_text(stats_xml):
     tree = ET.parse(stats_xml)
     root = tree.getroot()
     total_tokens = int(root.find('size/total/tokens').text)
-    unique_lemmas = int(root.find('lemmas').get('unique'))
+    unique_forms = int(root.find('forms').get('unique'))
 
     # assume the corpus is largely blinded when there are less than 1% unique tokens
-    return (unique_lemmas / total_tokens) < 0.01
+    return (unique_forms / total_tokens) < 0.01
 
 
 def fetch_all_treebanks(ud_dir, languages, corpus, best_per_language):
@@ -262,22 +264,26 @@ def main(out_path, ud_dir, check_parse=False, langs=ALL_LANGUAGES, exclude_train
     if not exclude_trained_models:
         if 'de' in models:
             models['de'].append(load_model('de_core_news_sm'))
-        if 'es' in models:
+            models['de'].append(load_model('de_core_news_md'))
-            models['es'].append(load_model('es_core_news_sm'))
+        if 'el' in models:
-            models['es'].append(load_model('es_core_news_md'))
+            models['el'].append(load_model('el_core_news_sm'))
-        if 'pt' in models:
+            models['el'].append(load_model('el_core_news_md'))
-            models['pt'].append(load_model('pt_core_news_sm'))
-        if 'it' in models:
-            models['it'].append(load_model('it_core_news_sm'))
-        if 'nl' in models:
-            models['nl'].append(load_model('nl_core_news_sm'))
         if 'en' in models:
             models['en'].append(load_model('en_core_web_sm'))
             models['en'].append(load_model('en_core_web_md'))
             models['en'].append(load_model('en_core_web_lg'))
+        if 'es' in models:
+            models['es'].append(load_model('es_core_news_sm'))
+            models['es'].append(load_model('es_core_news_md'))
         if 'fr' in models:
             models['fr'].append(load_model('fr_core_news_sm'))
             models['fr'].append(load_model('fr_core_news_md'))
+        if 'it' in models:
+            models['it'].append(load_model('it_core_news_sm'))
+        if 'nl' in models:
+            models['nl'].append(load_model('nl_core_news_sm'))
+        if 'pt' in models:
+            models['pt'].append(load_model('pt_core_news_sm'))
 
     with out_path.open(mode='w', encoding='utf-8') as out_file:
         run_all_evals(models, treebanks, out_file, check_parse, print_freq_tasks)

bin/ud/ud_run_test.py

@@ -109,15 +109,13 @@ def write_conllu(docs, file_):
     merger = Matcher(docs[0].vocab)
     merger.add("SUBTOK", None, [{"DEP": "subtok", "op": "+"}])
     for i, doc in enumerate(docs):
+        matches = []
+        if doc.is_parsed:
             matches = merger(doc)
         spans = [doc[start : end + 1] for _, start, end in matches]
         with doc.retokenize() as retokenizer:
             for span in spans:
                 retokenizer.merge(span)
-        # TODO: This shouldn't be necessary? Should be handled in merge
-        for word in doc:
-            if word.i == word.head.i:
-                word.dep_ = "ROOT"
         file_.write("# newdoc id = {i}\n".format(i=i))
         for j, sent in enumerate(doc.sents):
             file_.write("# sent_id = {i}.{j}\n".format(i=i, j=j))

bin/ud/ud_train.py

@@ -25,7 +25,7 @@ import itertools
 import random
 import numpy.random
 
-from . import conll17_ud_eval
+import conll17_ud_eval
 
 from spacy import lang
 from spacy.lang import zh
@@ -214,6 +214,8 @@ def write_conllu(docs, file_):
     merger = Matcher(docs[0].vocab)
     merger.add("SUBTOK", None, [{"DEP": "subtok", "op": "+"}])
     for i, doc in enumerate(docs):
+        matches = []
+        if doc.is_parsed:
             matches = merger(doc)
         spans = [doc[start : end + 1] for _, start, end in matches]
         with doc.retokenize() as retokenizer:
@@ -298,9 +300,9 @@ def get_token_conllu(token, i):
     return "\n".join(lines)
 
 
-Token.set_extension("get_conllu_lines", method=get_token_conllu)
+Token.set_extension("get_conllu_lines", method=get_token_conllu, force=True)
-Token.set_extension("begins_fused", default=False)
+Token.set_extension("begins_fused", default=False, force=True)
-Token.set_extension("inside_fused", default=False)
+Token.set_extension("inside_fused", default=False, force=True)
 
 
 ##################