diff --git a/spacy/cli/conll17_ud_eval.py b/spacy/cli/conll17_ud_eval.py
new file mode 100644
index 000000000..43fbcf3fa
--- /dev/null
+++ b/spacy/cli/conll17_ud_eval.py
@@ -0,0 +1,570 @@
+#!/usr/bin/env python
+
+# CoNLL 2017 UD Parsing evaluation script.
+#
+# Compatible with Python 2.7 and 3.2+, can be used either as a module
+# or a standalone executable.
+#
+# Copyright 2017 Institute of Formal and Applied Linguistics (UFAL),
+# Faculty of Mathematics and Physics, Charles University, Czech Republic.
+#
+# Changelog:
+# - [02 Jan 2017] Version 0.9: Initial release
+# - [25 Jan 2017] Version 0.9.1: Fix bug in LCS alignment computation
+# - [10 Mar 2017] Version 1.0: Add documentation and test
+#                              Compare HEADs correctly using aligned words
+#                              Allow evaluation with errorneous spaces in forms
+#                              Compare forms in LCS case insensitively
+#                              Detect cycles and multiple root nodes
+#                              Compute AlignedAccuracy
+
+# Command line usage
+# ------------------
+# conll17_ud_eval.py [-v] [-w weights_file] gold_conllu_file system_conllu_file
+#
+# - if no -v is given, only the CoNLL17 UD Shared Task evaluation LAS metrics
+#   is printed
+# - if -v is given, several metrics are printed (as precision, recall, F1 score,
+#   and in case the metric is computed on aligned words also accuracy on these):
+#   - Tokens: how well do the gold tokens match system tokens
+#   - Sentences: how well do the gold sentences match system sentences
+#   - Words: how well can the gold words be aligned to system words
+#   - UPOS: using aligned words, how well does UPOS match
+#   - XPOS: using aligned words, how well does XPOS match
+#   - Feats: using aligned words, how well does FEATS match
+#   - AllTags: using aligned words, how well does UPOS+XPOS+FEATS match
+#   - Lemmas: using aligned words, how well does LEMMA match
+#   - UAS: using aligned words, how well does HEAD match
+#   - LAS: using aligned words, how well does HEAD+DEPREL(ignoring subtypes) match
+# - if weights_file is given (with lines containing deprel-weight pairs),
+#   one more metric is shown:
+#   - WeightedLAS: as LAS, but each deprel (ignoring subtypes) has different weight
+
+# API usage
+# ---------
+# - load_conllu(file)
+#   - loads CoNLL-U file from given file object to an internal representation
+#   - the file object should return str on both Python 2 and Python 3
+#   - raises UDError exception if the given file cannot be loaded
+# - evaluate(gold_ud, system_ud)
+#   - evaluate the given gold and system CoNLL-U files (loaded with load_conllu)
+#   - raises UDError if the concatenated tokens of gold and system file do not match
+#   - returns a dictionary with the metrics described above, each metrics having
+#     three fields: precision, recall and f1
+
+# Description of token matching
+# -----------------------------
+# In order to match tokens of gold file and system file, we consider the text
+# resulting from concatenation of gold tokens and text resulting from
+# concatenation of system tokens. These texts should match -- if they do not,
+# the evaluation fails.
+#
+# If the texts do match, every token is represented as a range in this original
+# text, and tokens are equal only if their range is the same.
+
+# Description of word matching
+# ----------------------------
+# When matching words of gold file and system file, we first match the tokens.
+# The words which are also tokens are matched as tokens, but words in multi-word
+# tokens have to be handled differently.
+#
+# To handle multi-word tokens, we start by finding "multi-word spans".
+# Multi-word span is a span in the original text such that
+# - it contains at least one multi-word token
+# - all multi-word tokens in the span (considering both gold and system ones)
+#   are completely inside the span (i.e., they do not "stick out")
+# - the multi-word span is as small as possible
+#
+# For every multi-word span, we align the gold and system words completely
+# inside this span using LCS on their FORMs. The words not intersecting
+# (even partially) any multi-word span are then aligned as tokens.
+
+
+from __future__ import division
+from __future__ import print_function
+
+import argparse
+import io
+import sys
+import unittest
+
+# CoNLL-U column names
+ID, FORM, LEMMA, UPOS, XPOS, FEATS, HEAD, DEPREL, DEPS, MISC = range(10)
+
+# UD Error is used when raising exceptions in this module
+class UDError(Exception):
+    pass
+
+# Load given CoNLL-U file into internal representation
+def load_conllu(file):
+    # Internal representation classes
+    class UDRepresentation:
+        def __init__(self):
+            # Characters of all the tokens in the whole file.
+            # Whitespace between tokens is not included.
+            self.characters = []
+            # List of UDSpan instances with start&end indices into `characters`.
+            self.tokens = []
+            # List of UDWord instances.
+            self.words = []
+            # List of UDSpan instances with start&end indices into `characters`.
+            self.sentences = []
+    class UDSpan:
+        def __init__(self, start, end, characters):
+            self.start = start
+            # Note that self.end marks the first position **after the end** of span,
+            # so we can use characters[start:end] or range(start, end).
+            self.end = end
+            self.characters = characters
+
+        @property
+        def text(self):
+            return ''.join(self.characters[self.start:self.end])
+
+        def __str__(self):
+            return self.text
+
+        def __repr__(self):
+            return self.text
+    class UDWord:
+        def __init__(self, span, columns, is_multiword):
+            # Span of this word (or MWT, see below) within ud_representation.characters.
+            self.span = span
+            # 10 columns of the CoNLL-U file: ID, FORM, LEMMA,...
+            self.columns = columns
+            # is_multiword==True means that this word is part of a multi-word token.
+            # In that case, self.span marks the span of the whole multi-word token.
+            self.is_multiword = is_multiword
+            # Reference to the UDWord instance representing the HEAD (or None if root).
+            self.parent = None
+            # Let's ignore language-specific deprel subtypes.
+            self.columns[DEPREL] = columns[DEPREL].split(':')[0]
+
+    ud = UDRepresentation()
+
+    # Load the CoNLL-U file
+    index, sentence_start = 0, None
+    linenum = 0
+    while True:
+        line = file.readline()
+        linenum += 1
+        if not line:
+            break
+        line = line.rstrip("\r\n")
+
+        # Handle sentence start boundaries
+        if sentence_start is None:
+            # Skip comments
+            if line.startswith("#"):
+                continue
+            # Start a new sentence
+            ud.sentences.append(UDSpan(index, 0, ud.characters))
+            sentence_start = len(ud.words)
+        if not line:
+            # Add parent UDWord links and check there are no cycles
+            def process_word(word):
+                if word.parent == "remapping":
+                    raise UDError("There is a cycle in a sentence")
+                if word.parent is None:
+                    head = int(word.columns[HEAD])
+                    if head > len(ud.words) - sentence_start:
+                        raise UDError("HEAD '{}' points outside of the sentence".format(word.columns[HEAD]))
+                    if head:
+                        parent = ud.words[sentence_start + head - 1]
+                        word.parent = "remapping"
+                        process_word(parent)
+                        word.parent = parent
+
+            for word in ud.words[sentence_start:]:
+                process_word(word)
+
+            # Check there is a single root node
+            if len([word for word in ud.words[sentence_start:] if word.parent is None]) != 1:
+                raise UDError("There are multiple roots in a sentence")
+
+            # End the sentence
+            ud.sentences[-1].end = index
+            sentence_start = None
+            continue
+
+        # Read next token/word
+        columns = line.split("\t")
+        if len(columns) != 10:
+            raise UDError("The CoNLL-U line {} does not contain 10 tab-separated columns: '{}'".format(linenum, line))
+
+        # Skip empty nodes
+        if "." in columns[ID]:
+            continue
+
+        # Delete spaces from FORM  so gold.characters == system.characters
+        # even if one of them tokenizes the space.
+        columns[FORM] = columns[FORM].replace(" ", "")
+        if not columns[FORM]:
+            raise UDError("There is an empty FORM in the CoNLL-U file -- line %d" % linenum)
+
+        # Save token
+        ud.characters.extend(columns[FORM])
+        ud.tokens.append(UDSpan(index, index + len(columns[FORM]), ud.characters))
+        index += len(columns[FORM])
+
+        # Handle multi-word tokens to save word(s)
+        if "-" in columns[ID]:
+            try:
+                start, end = map(int, columns[ID].split("-"))
+            except:
+                raise UDError("Cannot parse multi-word token ID '{}'".format(columns[ID]))
+            
+            for _ in range(start, end + 1):
+                word_line = file.readline().rstrip("\r\n")
+                word_columns = word_line.split("\t")
+                if len(word_columns) != 10:
+                    print(columns)
+                    raise UDError("The CoNLL-U line {} does not contain 10 tab-separated columns: '{}'".format(linenum, word_line))
+                ud.words.append(UDWord(ud.tokens[-1], word_columns, is_multiword=True))
+        # Basic tokens/words
+        else:
+            try:
+                word_id = int(columns[ID])
+            except:
+                raise UDError("Cannot parse word ID '{}'".format(columns[ID]))
+            if word_id != len(ud.words) - sentence_start + 1:
+                raise UDError("Incorrect word ID '{}' for word '{}', expected '{}'".format(columns[ID], columns[FORM], len(ud.words) - sentence_start + 1))
+
+            try:
+                head_id = int(columns[HEAD])
+            except:
+                raise UDError("Cannot parse HEAD '{}'".format(columns[HEAD]))
+            if head_id < 0:
+                raise UDError("HEAD cannot be negative")
+
+            ud.words.append(UDWord(ud.tokens[-1], columns, is_multiword=False))
+
+    if sentence_start is not None:
+        raise UDError("The CoNLL-U file does not end with empty line")
+
+    return ud
+
+# Evaluate the gold and system treebanks (loaded using load_conllu).
+def evaluate(gold_ud, system_ud, deprel_weights=None):
+    class Score:
+        def __init__(self, gold_total, system_total, correct, aligned_total=None):
+            self.precision = correct / system_total if system_total else 0.0
+            self.recall = correct / gold_total if gold_total else 0.0
+            self.f1 = 2 * correct / (system_total + gold_total) if system_total + gold_total else 0.0
+            self.aligned_accuracy = correct / aligned_total if aligned_total else aligned_total
+    class AlignmentWord:
+        def __init__(self, gold_word, system_word):
+            self.gold_word = gold_word
+            self.system_word = system_word
+            self.gold_parent = None
+            self.system_parent_gold_aligned = None
+    class Alignment:
+        def __init__(self, gold_words, system_words):
+            self.gold_words = gold_words
+            self.system_words = system_words
+            self.matched_words = []
+            self.matched_words_map = {}
+        def append_aligned_words(self, gold_word, system_word):
+            self.matched_words.append(AlignmentWord(gold_word, system_word))
+            self.matched_words_map[system_word] = gold_word
+        def fill_parents(self):
+            # We represent root parents in both gold and system data by '0'.
+            # For gold data, we represent non-root parent by corresponding gold word.
+            # For system data, we represent non-root parent by either gold word aligned
+            # to parent system nodes, or by None if no gold words is aligned to the parent.
+            for words in self.matched_words:
+                words.gold_parent = words.gold_word.parent if words.gold_word.parent is not None else 0
+                words.system_parent_gold_aligned = self.matched_words_map.get(words.system_word.parent, None) \
+                    if words.system_word.parent is not None else 0
+
+    def lower(text):
+        if sys.version_info < (3, 0) and isinstance(text, str):
+            return text.decode("utf-8").lower()
+        return text.lower()
+
+    def spans_score(gold_spans, system_spans):
+        correct, gi, si = 0, 0, 0
+        while gi < len(gold_spans) and si < len(system_spans):
+            if system_spans[si].start < gold_spans[gi].start:
+                si += 1
+            elif gold_spans[gi].start < system_spans[si].start:
+                gi += 1
+            else:
+                correct += gold_spans[gi].end == system_spans[si].end
+                si += 1
+                gi += 1
+
+        return Score(len(gold_spans), len(system_spans), correct)
+
+    def alignment_score(alignment, key_fn, weight_fn=lambda w: 1):
+        gold, system, aligned, correct = 0, 0, 0, 0
+
+        for word in alignment.gold_words:
+            gold += weight_fn(word)
+
+        for word in alignment.system_words:
+            system += weight_fn(word)
+
+        for words in alignment.matched_words:
+            aligned += weight_fn(words.gold_word)
+
+        if key_fn is None:
+            # Return score for whole aligned words
+            return Score(gold, system, aligned)
+
+        for words in alignment.matched_words:
+            if key_fn(words.gold_word, words.gold_parent) == key_fn(words.system_word, words.system_parent_gold_aligned):
+                correct += weight_fn(words.gold_word)
+
+        return Score(gold, system, correct, aligned)
+
+    def beyond_end(words, i, multiword_span_end):
+        if i >= len(words):
+            return True
+        if words[i].is_multiword:
+            return words[i].span.start >= multiword_span_end
+        return words[i].span.end > multiword_span_end
+
+    def extend_end(word, multiword_span_end):
+        if word.is_multiword and word.span.end > multiword_span_end:
+            return word.span.end
+        return multiword_span_end
+
+    def find_multiword_span(gold_words, system_words, gi, si):
+        # We know gold_words[gi].is_multiword or system_words[si].is_multiword.
+        # Find the start of the multiword span (gs, ss), so the multiword span is minimal.
+        # Initialize multiword_span_end characters index.
+        if gold_words[gi].is_multiword:
+            multiword_span_end = gold_words[gi].span.end
+            if not system_words[si].is_multiword and system_words[si].span.start < gold_words[gi].span.start:
+                si += 1
+        else: # if system_words[si].is_multiword
+            multiword_span_end = system_words[si].span.end
+            if not gold_words[gi].is_multiword and gold_words[gi].span.start < system_words[si].span.start:
+                gi += 1
+        gs, ss = gi, si
+
+        # Find the end of the multiword span
+        # (so both gi and si are pointing to the word following the multiword span end).
+        while not beyond_end(gold_words, gi, multiword_span_end) or \
+              not beyond_end(system_words, si, multiword_span_end):
+            if gi < len(gold_words) and (si >= len(system_words) or
+                                         gold_words[gi].span.start <= system_words[si].span.start):
+                multiword_span_end = extend_end(gold_words[gi], multiword_span_end)
+                gi += 1
+            else:
+                multiword_span_end = extend_end(system_words[si], multiword_span_end)
+                si += 1
+        return gs, ss, gi, si
+
+    def compute_lcs(gold_words, system_words, gi, si, gs, ss):
+        lcs = [[0] * (si - ss) for i in range(gi - gs)]
+        for g in reversed(range(gi - gs)):
+            for s in reversed(range(si - ss)):
+                if lower(gold_words[gs + g].columns[FORM]) == lower(system_words[ss + s].columns[FORM]):
+                    lcs[g][s] = 1 + (lcs[g+1][s+1] if g+1 < gi-gs and s+1 < si-ss else 0)
+                lcs[g][s] = max(lcs[g][s], lcs[g+1][s] if g+1 < gi-gs else 0)
+                lcs[g][s] = max(lcs[g][s], lcs[g][s+1] if s+1 < si-ss else 0)
+        return lcs
+
+    def align_words(gold_words, system_words):
+        alignment = Alignment(gold_words, system_words)
+
+        gi, si = 0, 0
+        while gi < len(gold_words) and si < len(system_words):
+            if gold_words[gi].is_multiword or system_words[si].is_multiword:
+                # A: Multi-word tokens => align via LCS within the whole "multiword span".
+                gs, ss, gi, si = find_multiword_span(gold_words, system_words, gi, si)
+
+                if si > ss and gi > gs:
+                    lcs = compute_lcs(gold_words, system_words, gi, si, gs, ss)
+
+                    # Store aligned words
+                    s, g = 0, 0
+                    while g < gi - gs and s < si - ss:
+                        if lower(gold_words[gs + g].columns[FORM]) == lower(system_words[ss + s].columns[FORM]):
+                            alignment.append_aligned_words(gold_words[gs+g], system_words[ss+s])
+                            g += 1
+                            s += 1
+                        elif lcs[g][s] == (lcs[g+1][s] if g+1 < gi-gs else 0):
+                            g += 1
+                        else:
+                            s += 1
+            else:
+                # B: No multi-word token => align according to spans.
+                if (gold_words[gi].span.start, gold_words[gi].span.end) == (system_words[si].span.start, system_words[si].span.end):
+                    alignment.append_aligned_words(gold_words[gi], system_words[si])
+                    gi += 1
+                    si += 1
+                elif gold_words[gi].span.start <= system_words[si].span.start:
+                    gi += 1
+                else:
+                    si += 1
+
+        alignment.fill_parents()
+
+        return alignment
+
+    # Check that underlying character sequences do match
+    if gold_ud.characters != system_ud.characters:
+        index = 0
+        while gold_ud.characters[index] == system_ud.characters[index]:
+            index += 1
+
+        raise UDError(
+            "The concatenation of tokens in gold file and in system file differ!\n" +
+            "First 20 differing characters in gold file: '{}' and system file: '{}'".format(
+                "".join(gold_ud.characters[index:index + 20]),
+                "".join(system_ud.characters[index:index + 20])
+            )
+        )
+
+    # Align words
+    alignment = align_words(gold_ud.words, system_ud.words)
+
+    # Compute the F1-scores
+    result = {
+        "Tokens": spans_score(gold_ud.tokens, system_ud.tokens),
+        "Sentences": spans_score(gold_ud.sentences, system_ud.sentences),
+        "Words": alignment_score(alignment, None),
+        "UPOS": alignment_score(alignment, lambda w, parent: w.columns[UPOS]),
+        "XPOS": alignment_score(alignment, lambda w, parent: w.columns[XPOS]),
+        "Feats": alignment_score(alignment, lambda w, parent: w.columns[FEATS]),
+        "AllTags": alignment_score(alignment, lambda w, parent: (w.columns[UPOS], w.columns[XPOS], w.columns[FEATS])),
+        "Lemmas": alignment_score(alignment, lambda w, parent: w.columns[LEMMA]),
+        "UAS": alignment_score(alignment, lambda w, parent: parent),
+        "LAS": alignment_score(alignment, lambda w, parent: (parent, w.columns[DEPREL])),
+    }
+
+    # Add WeightedLAS if weights are given
+    if deprel_weights is not None:
+        def weighted_las(word):
+            return deprel_weights.get(word.columns[DEPREL], 1.0)
+        result["WeightedLAS"] = alignment_score(alignment, lambda w, parent: (parent, w.columns[DEPREL]), weighted_las)
+
+    return result
+
+def load_deprel_weights(weights_file):
+    if weights_file is None:
+        return None
+
+    deprel_weights = {}
+    for line in weights_file:
+        # Ignore comments and empty lines
+        if line.startswith("#") or not line.strip():
+            continue
+
+        columns = line.rstrip("\r\n").split()
+        if len(columns) != 2:
+            raise ValueError("Expected two columns in the UD Relations weights file on line '{}'".format(line))
+
+        deprel_weights[columns[0]] = float(columns[1])
+
+    return deprel_weights
+
+def load_conllu_file(path):
+    _file = open(path, mode="r", **({"encoding": "utf-8"} if sys.version_info >= (3, 0) else {}))
+    return load_conllu(_file)
+
+def evaluate_wrapper(args):
+    # Load CoNLL-U files
+    gold_ud = load_conllu_file(args.gold_file)
+    system_ud = load_conllu_file(args.system_file)
+
+    # Load weights if requested
+    deprel_weights = load_deprel_weights(args.weights)
+
+    return evaluate(gold_ud, system_ud, deprel_weights)
+
+def main():
+    # Parse arguments
+    parser = argparse.ArgumentParser()
+    parser.add_argument("gold_file", type=str,
+                        help="Name of the CoNLL-U file with the gold data.")
+    parser.add_argument("system_file", type=str,
+                        help="Name of the CoNLL-U file with the predicted data.")
+    parser.add_argument("--weights", "-w", type=argparse.FileType("r"), default=None,
+                        metavar="deprel_weights_file",
+                        help="Compute WeightedLAS using given weights for Universal Dependency Relations.")
+    parser.add_argument("--verbose", "-v", default=0, action="count",
+                        help="Print all metrics.")
+    args = parser.parse_args()
+
+    # Use verbose if weights are supplied
+    if args.weights is not None and not args.verbose:
+        args.verbose = 1
+
+    # Evaluate
+    evaluation = evaluate_wrapper(args)
+
+    # Print the evaluation
+    if not args.verbose:
+        print("LAS F1 Score: {:.2f}".format(100 * evaluation["LAS"].f1))
+    else:
+        metrics = ["Tokens", "Sentences", "Words", "UPOS", "XPOS", "Feats", "AllTags", "Lemmas", "UAS", "LAS"]
+        if args.weights is not None:
+            metrics.append("WeightedLAS")
+
+        print("Metrics    | Precision |    Recall |  F1 Score | AligndAcc")
+        print("-----------+-----------+-----------+-----------+-----------")
+        for metric in metrics:
+            print("{:11}|{:10.2f} |{:10.2f} |{:10.2f} |{}".format(
+                metric,
+                100 * evaluation[metric].precision,
+                100 * evaluation[metric].recall,
+                100 * evaluation[metric].f1,
+                "{:10.2f}".format(100 * evaluation[metric].aligned_accuracy) if evaluation[metric].aligned_accuracy is not None else ""
+            ))
+
+if __name__ == "__main__":
+    main()
+
+# Tests, which can be executed with `python -m unittest conll17_ud_eval`.
+class TestAlignment(unittest.TestCase):
+    @staticmethod
+    def _load_words(words):
+        """Prepare fake CoNLL-U files with fake HEAD to prevent multiple roots errors."""
+        lines, num_words = [], 0
+        for w in words:
+            parts = w.split(" ")
+            if len(parts) == 1:
+                num_words += 1
+                lines.append("{}\t{}\t_\t_\t_\t_\t{}\t_\t_\t_".format(num_words, parts[0], int(num_words>1)))
+            else:
+                lines.append("{}-{}\t{}\t_\t_\t_\t_\t_\t_\t_\t_".format(num_words + 1, num_words + len(parts) - 1, parts[0]))
+                for part in parts[1:]:
+                    num_words += 1
+                    lines.append("{}\t{}\t_\t_\t_\t_\t{}\t_\t_\t_".format(num_words, part, int(num_words>1)))
+        return load_conllu((io.StringIO if sys.version_info >= (3, 0) else io.BytesIO)("\n".join(lines+["\n"])))
+
+    def _test_exception(self, gold, system):
+        self.assertRaises(UDError, evaluate, self._load_words(gold), self._load_words(system))
+
+    def _test_ok(self, gold, system, correct):
+        metrics = evaluate(self._load_words(gold), self._load_words(system))
+        gold_words = sum((max(1, len(word.split(" ")) - 1) for word in gold))
+        system_words = sum((max(1, len(word.split(" ")) - 1) for word in system))
+        self.assertEqual((metrics["Words"].precision, metrics["Words"].recall, metrics["Words"].f1),
+                         (correct / system_words, correct / gold_words, 2 * correct / (gold_words + system_words)))
+
+    def test_exception(self):
+        self._test_exception(["a"], ["b"])
+
+    def test_equal(self):
+        self._test_ok(["a"], ["a"], 1)
+        self._test_ok(["a", "b", "c"], ["a", "b", "c"], 3)
+
+    def test_equal_with_multiword(self):
+        self._test_ok(["abc a b c"], ["a", "b", "c"], 3)
+        self._test_ok(["a", "bc b c", "d"], ["a", "b", "c", "d"], 4)
+        self._test_ok(["abcd a b c d"], ["ab a b", "cd c d"], 4)
+        self._test_ok(["abc a b c", "de d e"], ["a", "bcd b c d", "e"], 5)
+
+    def test_alignment(self):
+        self._test_ok(["abcd"], ["a", "b", "c", "d"], 0)
+        self._test_ok(["abc", "d"], ["a", "b", "c", "d"], 1)
+        self._test_ok(["a", "bc", "d"], ["a", "b", "c", "d"], 2)
+        self._test_ok(["a", "bc b c", "d"], ["a", "b", "cd"], 2)
+        self._test_ok(["abc a BX c", "def d EX f"], ["ab a b", "cd c d", "ef e f"], 4)
+        self._test_ok(["ab a b", "cd bc d"], ["a", "bc", "d"], 2)
+        self._test_ok(["a", "bc b c", "d"], ["ab AX BX", "cd CX a"], 1)
diff --git a/spacy/cli/ud_train.py b/spacy/cli/ud_train.py
new file mode 100644
index 000000000..bc106fb6b
--- /dev/null
+++ b/spacy/cli/ud_train.py
@@ -0,0 +1,394 @@
+'''Train for CONLL 2017 UD treebank evaluation. Takes .conllu files, writes
+.conllu format for development data, allowing the official scorer to be used.
+'''
+from __future__ import unicode_literals
+import plac
+import tqdm
+import attr
+from pathlib import Path
+import re
+import sys
+import json
+
+import spacy
+import spacy.util
+from ..tokens import Token, Doc
+from ..gold import GoldParse
+from ..syntax.nonproj import projectivize
+from ..matcher import Matcher
+from collections import defaultdict, Counter
+from timeit import default_timer as timer
+
+import itertools
+import random
+import numpy.random
+import cytoolz
+
+from . import conll17_ud_eval
+
+from .. import lang
+from .. import lang
+from ..lang import zh
+from ..lang import ja
+
+lang.zh.Chinese.Defaults.use_jieba = False
+lang.ja.Japanese.Defaults.use_janome = False
+
+random.seed(0)
+numpy.random.seed(0)
+
+def minibatch_by_words(items, size=5000):
+    random.shuffle(items)
+    if isinstance(size, int):
+        size_ = itertools.repeat(size)
+    else:
+        size_ = size
+    items = iter(items)
+    while True:
+        batch_size = next(size_)
+        batch = []
+        while batch_size >= 0:
+            try:
+                doc, gold = next(items)
+            except StopIteration:
+                if batch:
+                    yield batch
+                return
+            batch_size -= len(doc)
+            batch.append((doc, gold))
+        if batch:
+            yield batch
+        else:
+            break
+
+################
+# Data reading #
+################
+
+space_re = re.compile('\s+')
+def split_text(text):
+    return [space_re.sub(' ', par.strip()) for par in text.split('\n\n')]
+ 
+
+def read_data(nlp, conllu_file, text_file, raw_text=True, oracle_segments=False,
+              max_doc_length=None, limit=None):
+    '''Read the CONLLU format into (Doc, GoldParse) tuples. If raw_text=True,
+    include Doc objects created using nlp.make_doc and then aligned against
+    the gold-standard sequences. If oracle_segments=True, include Doc objects
+    created from the gold-standard segments. At least one must be True.'''
+    if not raw_text and not oracle_segments:
+        raise ValueError("At least one of raw_text or oracle_segments must be True")
+    paragraphs = split_text(text_file.read())
+    conllu = read_conllu(conllu_file)
+    # sd is spacy doc; cd is conllu doc
+    # cs is conllu sent, ct is conllu token
+    docs = []
+    golds = []
+    for doc_id, (text, cd) in enumerate(zip(paragraphs, conllu)):
+        sent_annots = []
+        for cs in cd:
+            sent = defaultdict(list)
+            for id_, word, lemma, pos, tag, morph, head, dep, _, space_after in cs:
+                if '.' in id_:
+                    continue
+                if '-' in id_:
+                    continue
+                id_ = int(id_)-1
+                head = int(head)-1 if head != '0' else id_
+                sent['words'].append(word)
+                sent['tags'].append(tag)
+                sent['heads'].append(head)
+                sent['deps'].append('ROOT' if dep == 'root' else dep)
+                sent['spaces'].append(space_after == '_')
+            sent['entities'] = ['-'] * len(sent['words'])
+            sent['heads'], sent['deps'] = projectivize(sent['heads'],
+                                                       sent['deps'])
+            if oracle_segments:
+                docs.append(Doc(nlp.vocab, words=sent['words'], spaces=sent['spaces']))
+                golds.append(GoldParse(docs[-1], **sent))
+
+            sent_annots.append(sent)
+            if raw_text and max_doc_length and len(sent_annots) >= max_doc_length:
+                doc, gold = _make_gold(nlp, None, sent_annots)
+                sent_annots = []
+                docs.append(doc)
+                golds.append(gold)
+                if limit and len(docs) >= limit:
+                    return docs, golds
+
+        if raw_text and sent_annots:
+            doc, gold = _make_gold(nlp, None, sent_annots)
+            docs.append(doc)
+            golds.append(gold)
+        if limit and len(docs) >= limit:
+            return docs, golds
+    return docs, golds
+
+
+def read_conllu(file_):
+    docs = []
+    sent = []
+    doc = []
+    for line in file_:
+        if line.startswith('# newdoc'):
+            if doc:
+                docs.append(doc)
+            doc = []
+        elif line.startswith('#'):
+            continue
+        elif not line.strip():
+            if sent:
+                doc.append(sent)
+            sent = []
+        else:
+            sent.append(list(line.strip().split('\t')))
+            if len(sent[-1]) != 10:
+                print(repr(line))
+                raise ValueError
+    if sent:
+        doc.append(sent)
+    if doc:
+        docs.append(doc)
+    return docs
+
+
+def _make_gold(nlp, text, sent_annots):
+    # Flatten the conll annotations, and adjust the head indices
+    flat = defaultdict(list)
+    for sent in sent_annots:
+        flat['heads'].extend(len(flat['words'])+head for head in sent['heads'])
+        for field in ['words', 'tags', 'deps', 'entities', 'spaces']:
+            flat[field].extend(sent[field])
+    # Construct text if necessary
+    assert len(flat['words']) == len(flat['spaces'])
+    if text is None:
+        text = ''.join(word+' '*space for word, space in zip(flat['words'], flat['spaces'])) 
+    doc = nlp.make_doc(text)
+    flat.pop('spaces')
+    gold = GoldParse(doc, **flat)
+    return doc, gold
+
+#############################
+# Data transforms for spaCy #
+#############################
+
+def golds_to_gold_tuples(docs, golds):
+    '''Get out the annoying 'tuples' format used by begin_training, given the
+    GoldParse objects.'''
+    tuples = []
+    for doc, gold in zip(docs, golds):
+        text = doc.text
+        ids, words, tags, heads, labels, iob = zip(*gold.orig_annot)
+        sents = [((ids, words, tags, heads, labels, iob), [])]
+        tuples.append((text, sents))
+    return tuples
+
+
+##############
+# Evaluation #
+##############
+
+def evaluate(nlp, text_loc, gold_loc, sys_loc, limit=None):
+    with text_loc.open('r', encoding='utf8') as text_file:
+        texts = split_text(text_file.read())
+        docs = list(nlp.pipe(texts))
+    with sys_loc.open('w', encoding='utf8') as out_file:
+        write_conllu(docs, out_file)
+    with gold_loc.open('r', encoding='utf8') as gold_file:
+        gold_ud = conll17_ud_eval.load_conllu(gold_file)
+        with sys_loc.open('r', encoding='utf8') as sys_file:
+            sys_ud = conll17_ud_eval.load_conllu(sys_file)
+        scores = conll17_ud_eval.evaluate(gold_ud, sys_ud)
+    return scores
+
+
+def write_conllu(docs, file_):
+    merger = Matcher(docs[0].vocab)
+    merger.add('SUBTOK', None, [{'DEP': 'subtok', 'op': '+'}])
+    for i, doc in enumerate(docs):
+        matches = merger(doc)
+        spans = [doc[start:end+1] for _, start, end in matches]
+        offsets = [(span.start_char, span.end_char) for span in spans]
+        for start_char, end_char in offsets:
+            doc.merge(start_char, end_char)
+        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))
+            file_.write("# text = {text}\n".format(text=sent.text))
+            for k, token in enumerate(sent):
+                file_.write(token._.get_conllu_lines(k) + '\n')
+            file_.write('\n')
+
+
+def print_progress(itn, losses, ud_scores):
+    fields = {
+        'dep_loss': losses.get('parser', 0.0),
+        'tag_loss': losses.get('tagger', 0.0),
+        'words': ud_scores['Words'].f1 * 100,
+        'sents': ud_scores['Sentences'].f1 * 100,
+        'tags': ud_scores['XPOS'].f1 * 100,
+        'uas': ud_scores['UAS'].f1 * 100,
+        'las': ud_scores['LAS'].f1 * 100,
+    }
+    header = ['Epoch', 'Loss', 'LAS', 'UAS', 'TAG', 'SENT', 'WORD']
+    if itn == 0:
+        print('\t'.join(header))
+    tpl = '\t'.join((
+        '{:d}',
+        '{dep_loss:.1f}',
+        '{las:.1f}',
+        '{uas:.1f}',
+        '{tags:.1f}',
+        '{sents:.1f}',
+        '{words:.1f}',
+    ))
+    print(tpl.format(itn, **fields))
+
+#def get_sent_conllu(sent, sent_id):
+#    lines = ["# sent_id = {sent_id}".format(sent_id=sent_id)]
+
+def get_token_conllu(token, i):
+    if token._.begins_fused:
+        n = 1
+        while token.nbor(n)._.inside_fused:
+            n += 1
+        id_ = '%d-%d' % (i, i+n)
+        lines = [id_, token.text, '_', '_', '_', '_', '_', '_', '_', '_']
+    else:
+        lines = []
+    if token.head.i == token.i:
+        head = 0
+    else:
+        head = i + (token.head.i - token.i) + 1
+    fields = [str(i+1), token.text, token.lemma_, token.pos_, token.tag_, '_',
+              str(head), token.dep_.lower(), '_', '_']
+    lines.append('\t'.join(fields))
+    return '\n'.join(lines)
+
+Token.set_extension('get_conllu_lines', method=get_token_conllu)
+Token.set_extension('begins_fused', default=False)
+Token.set_extension('inside_fused', default=False)
+
+
+##################
+# Initialization #
+##################
+
+
+def load_nlp(corpus, config):
+    lang = corpus.split('_')[0]
+    nlp = spacy.blank(lang)
+    if config.vectors:
+        nlp.vocab.from_disk(config.vectors / 'vocab')
+    return nlp
+
+def initialize_pipeline(nlp, docs, golds, config):
+    nlp.add_pipe(nlp.create_pipe('parser'))
+    if config.multitask_tag:
+        nlp.parser.add_multitask_objective('tag')
+    if config.multitask_sent:
+        nlp.parser.add_multitask_objective('sent_start')
+    nlp.parser.moves.add_action(2, 'subtok')
+    nlp.add_pipe(nlp.create_pipe('tagger'))
+    for gold in golds:
+        for tag in gold.tags:
+            if tag is not None:
+                nlp.tagger.add_label(tag)
+    # Replace labels that didn't make the frequency cutoff
+    actions = set(nlp.parser.labels)
+    label_set = set([act.split('-')[1] for act in actions if '-' in act])
+    for gold in golds:
+        for i, label in enumerate(gold.labels):
+            if label is not None and label not in label_set:
+                gold.labels[i] = label.split('||')[0]
+    return nlp.begin_training(lambda: golds_to_gold_tuples(docs, golds))
+
+
+########################
+# Command line helpers #
+########################
+
+@attr.s
+class Config(object):
+    vectors = attr.ib(default=None)
+    max_doc_length = attr.ib(default=10)
+    multitask_tag = attr.ib(default=True)
+    multitask_sent = attr.ib(default=True)
+    nr_epoch = attr.ib(default=30)
+    batch_size = attr.ib(default=1000)
+    dropout = attr.ib(default=0.2)
+
+    @classmethod
+    def load(cls, loc):
+        with Path(loc).open('r', encoding='utf8') as file_:
+            cfg = json.load(file_)
+        return cls(**cfg)
+
+
+class Dataset(object):
+    def __init__(self, path, section):
+        self.path = path
+        self.section = section
+        self.conllu = None
+        self.text = None
+        for file_path in self.path.iterdir():
+            name = file_path.parts[-1]
+            if section in name and name.endswith('conllu'):
+                self.conllu = file_path
+            elif section in name and name.endswith('txt'):
+                self.text = file_path
+        if self.conllu is None:
+            msg = "Could not find .txt file in {path} for {section}"
+            raise IOError(msg.format(section=section, path=path))
+        if self.text is None:
+            msg = "Could not find .txt file in {path} for {section}"
+        self.lang = self.conllu.parts[-1].split('-')[0].split('_')[0]
+
+
+class TreebankPaths(object):
+    def __init__(self, ud_path, treebank, **cfg):
+        self.train = Dataset(ud_path / treebank, 'train')
+        self.dev = Dataset(ud_path / treebank, 'dev')
+        self.lang = self.train.lang
+
+
+@plac.annotations(
+    ud_dir=("Path to Universal Dependencies corpus", "positional", None, Path),
+    corpus=("UD corpus to train and evaluate on, e.g. en, es_ancora, etc",
+            "positional", None, str),
+    parses_dir=("Directory to write the development parses", "positional", None, Path),
+    config=("Path to json formatted config file", "positional", None, Config.load),
+    limit=("Size limit", "option", "n", int)
+)
+def main(ud_dir, parses_dir, config, corpus, limit=0):
+    paths = TreebankPaths(ud_dir, corpus)
+    if not (parses_dir / corpus).exists():
+        (parses_dir / corpus).mkdir()
+    print("Train and evaluate", corpus, "using lang", paths.lang)
+    nlp = load_nlp(paths.lang, config)
+
+    docs, golds = read_data(nlp, paths.train.conllu.open(), paths.train.text.open(),
+                            max_doc_length=config.max_doc_length, limit=limit)
+
+    optimizer = initialize_pipeline(nlp, docs, golds, config)
+
+    for i in range(config.nr_epoch):
+        docs = [nlp.make_doc(doc.text) for doc in docs]
+        batches = minibatch_by_words(list(zip(docs, golds)), size=config.batch_size)
+        losses = {}
+        n_train_words = sum(len(doc) for doc in docs)
+        with tqdm.tqdm(total=n_train_words, leave=False) as pbar:
+            for batch in batches:
+                batch_docs, batch_gold = zip(*batch)
+                pbar.update(sum(len(doc) for doc in batch_docs))
+                nlp.update(batch_docs, batch_gold, sgd=optimizer,
+                           drop=config.dropout, losses=losses)
+        
+        out_path = parses_dir / corpus / 'epoch-{i}.conllu'.format(i=i)
+        with nlp.use_params(optimizer.averages):
+            scores = evaluate(nlp, paths.dev.text, paths.dev.conllu, out_path)
+            print_progress(i, losses, scores)
+
+
+if __name__ == '__main__':
+    plac.call(main)