#!/usr/bin/env python3

# Sourced from
# <https://github.com/ufal/conll2018/blob/37bc4d70bf415f188dc82a2e2600a38971d1137c/evaluation_script/conll18_ud_eval.py>
# The modifications include:
#
# - Formatting with black
# - Make function-internal classes module-level classes: since they were not closure classes, there
#   was no point keeping them there
# - Adding typing

# Compatible with Python 3.7+, can be used either as a module
# or a standalone executable.
#
# Copyright 2017, 2018 Institute of Formal and Applied Linguistics (UFAL),
# Faculty of Mathematics and Physics, Charles University, Czech Republic.
#
# This Source Code Form is subject to the terms of the Mozilla Public
# License, v. 2.0. If a copy of the MPL was not distributed with this
# file, You can obtain one at http://mozilla.org/MPL/2.0/.
#
# Authors: Milan Straka, Martin Popel <surname@ufal.mff.cuni.cz>
#
# Changelog:
# - [12 Apr 2018] Version 0.9: Initial release.
# - [19 Apr 2018] Version 1.0: Fix bug in MLAS (duplicate entries in functional_children).
#                              Add --counts option.
# - [02 May 2018] Version 1.1: When removing spaces to match gold and system characters,
#                              consider all Unicode characters of category Zs instead of
#                              just ASCII space.
# - [25 Jun 2018] Version 1.2: Use python3 in the she-bang (instead of python).
#                              In Python2, make the whole computation use `unicode` strings.

# Command line usage
# ------------------
# conll18_ud_eval.py [-v] gold_conllu_file system_conllu_file
#
# - if no -v is given, only the official CoNLL18 UD Shared Task evaluation metrics
#   are printed
# - if -v is given, more 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
#   - UFeats: using aligned words, how well does universal 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
#   - CLAS: using aligned words with content DEPREL, how well does
#       HEAD+DEPREL(ignoring subtypes) match
#   - MLAS: using aligned words with content DEPREL, how well does
#       HEAD+DEPREL(ignoring subtypes)+UPOS+UFEATS+FunctionalChildren(DEPREL+UPOS+UFEATS) match
#   - BLEX: using aligned words with content DEPREL, how well does
#       HEAD+DEPREL(ignoring subtypes)+LEMMAS match
# - if -c is given, raw counts of correct/gold_total/system_total/aligned words are printed
#   instead of precision/recall/F1/AlignedAccuracy for all metrics.

# API usage
# ---------
# - load_conllu(file)
#   - loads CoNLL-U file from given file object to an internal representation
#   - the file object should return str in 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 metric 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 annotations

import argparse
import io
import sys
import unicodedata
import unittest
from typing import Dict, Optional

# CoNLL-U column names
ID, FORM, LEMMA, UPOS, XPOS, FEATS, HEAD, DEPREL, DEPS, MISC = range(10)

# Content and functional relations
CONTENT_DEPRELS = {
    "nsubj",
    "obj",
    "iobj",
    "csubj",
    "ccomp",
    "xcomp",
    "obl",
    "vocative",
    "expl",
    "dislocated",
    "advcl",
    "advmod",
    "discourse",
    "nmod",
    "appos",
    "nummod",
    "acl",
    "amod",
    "conj",
    "fixed",
    "flat",
    "compound",
    "list",
    "parataxis",
    "orphan",
    "goeswith",
    "reparandum",
    "root",
    "dep",
}

FUNCTIONAL_DEPRELS = {"aux", "cop", "mark", "det", "clf", "case", "cc"}

UNIVERSAL_FEATURES = {
    "PronType",
    "NumType",
    "Poss",
    "Reflex",
    "Foreign",
    "Abbr",
    "Gender",
    "Animacy",
    "Number",
    "Case",
    "Definite",
    "Degree",
    "VerbForm",
    "Mood",
    "Tense",
    "Aspect",
    "Voice",
    "Evident",
    "Polarity",
    "Person",
    "Polite",
}

# UD Error is used when raising exceptions in this module
class UDError(Exception):
    pass


# Conversion methods handling `str` <-> `unicode` conversions in Python2
def _decode(text):
    return (
        text
        if sys.version_info[0] >= 3 or not isinstance(text, str)
        else text.decode("utf-8")
    )


def _encode(text):
    return text


# 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):
        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


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
        # List of references to UDWord instances representing functional-deprel children.
        self.functional_children = []
        # Only consider universal FEATS.
        self.columns[FEATS] = "|".join(
            sorted(
                feat
                for feat in columns[FEATS].split("|")
                if feat.split("=", 1)[0] in UNIVERSAL_FEATURES
            )
        )
        # Let's ignore language-specific deprel subtypes.
        self.columns[DEPREL] = columns[DEPREL].split(":")[0]
        # Precompute which deprels are CONTENT_DEPRELS and which FUNCTIONAL_DEPRELS
        self.is_content_deprel = self.columns[DEPREL] in CONTENT_DEPRELS
        self.is_functional_deprel = self.columns[DEPREL] in FUNCTIONAL_DEPRELS


# Load given CoNLL-U file into internal representation
def load_conllu(file):
    ud = UDRepresentation()

    # Load the CoNLL-U file
    index, sentence_start = 0, None
    while True:
        line = file.readline()
        if not line:
            break
        line = _decode(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))
            sentence_start = len(ud.words)
        if not line:
            # Add parent and children 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 < 0 or head > len(ud.words) - sentence_start:
                        raise UDError(
                            "HEAD '{}' points outside of the sentence".format(
                                _encode(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)
            # func_children cannot be assigned within process_word
            # because it is called recursively and may result in adding one child twice.
            for word in ud.words[sentence_start:]:
                if word.parent and word.is_functional_deprel:
                    word.parent.functional_children.append(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(
                    _encode(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. Use any Unicode character
        # with category Zs.
        columns[FORM] = "".join(
            filter(lambda c: unicodedata.category(c) != "Zs", columns[FORM])
        )
        if not columns[FORM]:
            raise UDError("There is an empty FORM in the CoNLL-U file")

        # Save token
        ud.characters.extend(columns[FORM])
        ud.tokens.append(UDSpan(index, index + len(columns[FORM])))
        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(_encode(columns[ID]))
                )

            for _ in range(start, end + 1):
                word_line = _decode(file.readline().rstrip("\r\n"))
                word_columns = word_line.split("\t")
                if len(word_columns) != 10:
                    raise UDError(
                        "The CoNLL-U line does not contain 10 tab-separated columns: '{}'".format(
                            _encode(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(_encode(columns[ID])))
            if word_id != len(ud.words) - sentence_start + 1:
                raise UDError(
                    "Incorrect word ID '{}' for word '{}', expected '{}'".format(
                        _encode(columns[ID]),
                        _encode(columns[FORM]),
                        len(ud.words) - sentence_start + 1,
                    )
                )

            try:
                head_id = int(columns[HEAD])
            except:
                raise UDError("Cannot parse HEAD '{}'".format(_encode(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


class Score:
    def __init__(
        self,
        gold_total: int,
        system_total: int,
        correct: int,
        aligned_total: Optional[int] = None,
    ):
        self.correct = correct
        self.gold_total = gold_total
        self.system_total = system_total
        self.aligned_total = aligned_total
        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


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


# Evaluate the gold and system treebanks (loaded using load_conllu).
def evaluate(
    gold_ud: UDRepresentation, system_ud: UDRepresentation
) -> Dict[str, Score]:
    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=None, filter_fn=None):
        if filter_fn is not None:
            gold = sum(1 for gold in alignment.gold_words if filter_fn(gold))
            system = sum(1 for system in alignment.system_words if filter_fn(system))
            aligned = sum(
                1 for word in alignment.matched_words if filter_fn(word.gold_word)
            )
        else:
            gold = len(alignment.gold_words)
            system = len(alignment.system_words)
            aligned = len(alignment.matched_words)

        if key_fn is None:
            # Return score for whole aligned words
            return Score(gold, system, aligned)

        def gold_aligned_gold(word):
            return word

        def gold_aligned_system(word):
            return (
                alignment.matched_words_map.get(word, "NotAligned")
                if word is not None
                else None
            )

        correct = 0
        for words in alignment.matched_words:
            if filter_fn is None or filter_fn(words.gold_word):
                if key_fn(words.gold_word, gold_aligned_gold) == key_fn(
                    words.system_word, gold_aligned_system
                ):
                    correct += 1

        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 (
                    gold_words[gs + g].columns[FORM].lower()
                    == system_words[ss + s].columns[FORM].lower()
                ):
                    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 (
                            gold_words[gs + g].columns[FORM].lower()
                            == system_words[ss + s].columns[FORM].lower()
                        ):
                            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

        return alignment

    # Check that the underlying character sequences do match.
    if gold_ud.characters != system_ud.characters:
        index = 0
        while (
            index < len(gold_ud.characters)
            and index < len(system_ud.characters)
            and 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(map(_encode, gold_ud.characters[index : index + 20])),
                "".join(map(_encode, system_ud.characters[index : index + 20])),
            )
        )

    # Align words
    alignment = align_words(gold_ud.words, system_ud.words)

    # Compute the F1-scores
    return {
        "Tokens": spans_score(gold_ud.tokens, system_ud.tokens),
        "Sentences": spans_score(gold_ud.sentences, system_ud.sentences),
        "Words": alignment_score(alignment),
        "UPOS": alignment_score(alignment, lambda w, _: w.columns[UPOS]),
        "XPOS": alignment_score(alignment, lambda w, _: w.columns[XPOS]),
        "UFeats": alignment_score(alignment, lambda w, _: w.columns[FEATS]),
        "AllTags": alignment_score(
            alignment, lambda w, _: (w.columns[UPOS], w.columns[XPOS], w.columns[FEATS])
        ),
        "Lemmas": alignment_score(
            alignment,
            lambda w, ga: w.columns[LEMMA] if ga(w).columns[LEMMA] != "_" else "_",
        ),
        "UAS": alignment_score(alignment, lambda w, ga: ga(w.parent)),
        "LAS": alignment_score(
            alignment, lambda w, ga: (ga(w.parent), w.columns[DEPREL])
        ),
        "CLAS": alignment_score(
            alignment,
            lambda w, ga: (ga(w.parent), w.columns[DEPREL]),
            filter_fn=lambda w: w.is_content_deprel,
        ),
        "MLAS": alignment_score(
            alignment,
            lambda w, ga: (
                ga(w.parent),
                w.columns[DEPREL],
                w.columns[UPOS],
                w.columns[FEATS],
                [
                    (ga(c), c.columns[DEPREL], c.columns[UPOS], c.columns[FEATS])
                    for c in w.functional_children
                ],
            ),
            filter_fn=lambda w: w.is_content_deprel,
        ),
        "BLEX": alignment_score(
            alignment,
            lambda w, ga: (
                ga(w.parent),
                w.columns[DEPREL],
                w.columns[LEMMA] if ga(w).columns[LEMMA] != "_" else "_",
            ),
            filter_fn=lambda w: w.is_content_deprel,
        ),
    }


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)
    return evaluate(gold_ud, system_ud)


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(
        "--verbose", "-v", default=False, action="store_true", help="Print all metrics."
    )
    parser.add_argument(
        "--counts",
        "-c",
        default=False,
        action="store_true",
        help="Print raw counts of correct/gold/system/aligned words instead of prec/rec/F1 for all metrics.",
    )
    args = parser.parse_args()

    # Evaluate
    evaluation = evaluate_wrapper(args)

    # Print the evaluation
    if not args.verbose and not args.counts:
        print("LAS F1 Score: {:.2f}".format(100 * evaluation["LAS"].f1))
        print("MLAS Score: {:.2f}".format(100 * evaluation["MLAS"].f1))
        print("BLEX Score: {:.2f}".format(100 * evaluation["BLEX"].f1))
    else:
        if args.counts:
            print("Metric     | Correct   |      Gold | Predicted | Aligned")
        else:
            print("Metric     | Precision |    Recall |  F1 Score | AligndAcc")
        print("-----------+-----------+-----------+-----------+-----------")
        for metric in [
            "Tokens",
            "Sentences",
            "Words",
            "UPOS",
            "XPOS",
            "UFeats",
            "AllTags",
            "Lemmas",
            "UAS",
            "LAS",
            "CLAS",
            "MLAS",
            "BLEX",
        ]:
            if args.counts:
                print(
                    "{:11}|{:10} |{:10} |{:10} |{:10}".format(
                        metric,
                        evaluation[metric].correct,
                        evaluation[metric].gold_total,
                        evaluation[metric].system_total,
                        evaluation[metric].aligned_total
                        or (evaluation[metric].correct if metric == "Words" else ""),
                    )
                )
            else:
                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 conll18_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)
