# flake8: noqa
"""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
from pathlib import Path
import re
import sys
import json

import spacy
import spacy.util
from bin.ud import conll17_ud_eval
from spacy.tokens import Token, Doc
from spacy.gold import GoldParse
from spacy.util import compounding, minibatch, minibatch_by_words
from spacy.syntax.nonproj import projectivize
from spacy.matcher import Matcher
from spacy import displacy
from collections import defaultdict, Counter
from timeit import default_timer as timer

import itertools
import random
import numpy.random

from spacy import lang
from spacy.lang import zh
from spacy.lang import ja

try:
    import torch
except ImportError:
    torch = None


################
# 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["morphology"].append(_parse_morph_string(morph))
                sent["morphology"][-1].add("POS_%s" % pos)
                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))
                assert golds[-1].morphology is not None

            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)
                assert gold.morphology is not None
                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 _parse_morph_string(morph_string):
    if morph_string == '_':
        return set()
    output = []
    replacements = {'1': 'one', '2': 'two', '3': 'three'}
    for feature in morph_string.split('|'):
        key, value = feature.split('=')
        value = replacements.get(value, value)
        value = value.split(',')[0]
        output.append('%s_%s' % (key, value.lower()))
    return set(output)

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, drop_deps=0.0):
    # Flatten the conll annotations, and adjust the head indices
    flat = defaultdict(list)
    sent_starts = []
    for sent in sent_annots:
        flat["heads"].extend(len(flat["words"])+head for head in sent["heads"])
        for field in ["words", "tags", "deps", "morphology", "entities", "spaces"]:
            flat[field].extend(sent[field])
        sent_starts.append(True)
        sent_starts.extend([False] * (len(sent["words"]) - 1))
    # 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)
    gold.sent_starts = sent_starts
    for i in range(len(gold.heads)):
        if random.random() < drop_deps:
            gold.heads[i] = None
            gold.labels[i] = None

    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):
    if text_loc.parts[-1].endswith(".conllu"):
        docs = []
        with text_loc.open(encoding="utf8") as file_:
            for conllu_doc in read_conllu(file_):
                for conllu_sent in conllu_doc:
                    words = [line[1] for line in conllu_sent]
                    docs.append(Doc(nlp.vocab, words=words))
        for name, component in nlp.pipeline:
            docs = list(component.pipe(docs))
    else:
        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 docs, 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 = []
        if doc.is_parsed:
            matches = merger(doc)
        spans = [doc[start : end + 1] for _, start, end in matches]
        seen_tokens = set()
        with doc.retokenize() as retokenizer:
            for span in spans:
                span_tokens = set(range(span.start, span.end))
                if not span_tokens.intersection(seen_tokens):
                    retokenizer.merge(span)
                    seen_tokens.update(span_tokens)

        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):
                if token.head.i > sent[-1].i or token.head.i < sent[0].i:
                    for word in doc[sent[0].i - 10 : sent[0].i]:
                        print(word.i, word.head.i, word.text, word.dep_)
                    for word in sent:
                        print(word.i, word.head.i, word.text, word.dep_)
                    for word in doc[sent[-1].i : sent[-1].i + 10]:
                        print(word.i, word.head.i, word.text, word.dep_)
                    raise ValueError(
                        "Invalid parse: head outside sentence (%s)" % token.text
                    )
                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),
        "morph_loss": losses.get("morphologizer", 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,
        "morph": ud_scores["Feats"].f1 * 100,
    }
    header = ["Epoch", "P.Loss", "M.Loss", "LAS", "UAS", "TAG", "MORPH", "SENT", "WORD"]
    if itn == 0:
        print("\t".join(header))
    tpl = "\t".join((
        "{:d}",
        "{dep_loss:.1f}",
        "{morph_loss:.1f}",
        "{las:.1f}",
        "{uas:.1f}",
        "{tags:.1f}",
        "{morph:.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
    features = list(token.morph)
    feat_str = []
    replacements = {"one": "1", "two": "2", "three": "3"}
    for feat in features:
        if not feat.startswith("begin") and not feat.startswith("end"):
            key, value = feat.split("_", 1)
            value = replacements.get(value, value)
            feat_str.append("%s=%s" % (key, value.title()))
    if not feat_str:
        feat_str = "_"
    else:
        feat_str = "|".join(feat_str)
    fields = [str(i+1), token.text, token.lemma_, token.pos_, token.tag_, feat_str,
              str(head), token.dep_.lower(), "_", "_"]
    lines.append("\t".join(fields))
    return "\n".join(lines)


Token.set_extension("get_conllu_lines", method=get_token_conllu, force=True)
Token.set_extension("begins_fused", default=False, force=True)
Token.set_extension("inside_fused", default=False, force=True)


##################
# Initialization #
##################


def load_nlp(corpus, config, vectors=None):
    lang = corpus.split("_")[0]
    nlp = spacy.blank(lang)
    if config.vectors:
        if not vectors:
            raise ValueError(
                "config asks for vectors, but no vectors "
                "directory set on command line (use -v)"
            )
        if (Path(vectors) / corpus).exists():
            nlp.vocab.from_disk(Path(vectors) / corpus / "vocab")
    nlp.meta["treebank"] = corpus
    return nlp


def initialize_pipeline(nlp, docs, golds, config, device):
    nlp.add_pipe(nlp.create_pipe("tagger", config={"set_morphology": False}))
    nlp.add_pipe(nlp.create_pipe("morphologizer"))
    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")
    for gold in golds:
        for tag in gold.tags:
            if tag is not None:
                nlp.tagger.add_label(tag)
    if torch is not None and device != -1:
        torch.set_default_tensor_type("torch.cuda.FloatTensor")
    optimizer = nlp.begin_training(
        lambda: golds_to_gold_tuples(docs, golds),
        device=device,
        subword_features=config.subword_features,
        conv_depth=config.conv_depth,
        bilstm_depth=config.bilstm_depth,
    )
    if config.pretrained_tok2vec:
        _load_pretrained_tok2vec(nlp, config.pretrained_tok2vec)
    return optimizer


def _load_pretrained_tok2vec(nlp, loc):
    """Load pretrained weights for the 'token-to-vector' part of the component
    models, which is typically a CNN. See 'spacy pretrain'. Experimental.
    """
    with Path(loc).open("rb", encoding="utf8") as file_:
        weights_data = file_.read()
    loaded = []
    for name, component in nlp.pipeline:
        if hasattr(component, "model") and hasattr(component.model, "tok2vec"):
            component.tok2vec.from_bytes(weights_data)
            loaded.append(name)
    return loaded


########################
# Command line helpers #
########################


class Config(object):
    def __init__(
        self,
        vectors=None,
        max_doc_length=10,
        multitask_tag=False,
        multitask_sent=False,
        multitask_dep=False,
        multitask_vectors=None,
        bilstm_depth=0,
        nr_epoch=30,
        min_batch_size=100,
        max_batch_size=1000,
        batch_by_words=True,
        dropout=0.2,
        conv_depth=4,
        subword_features=True,
        vectors_dir=None,
        pretrained_tok2vec=None,
    ):
        if vectors_dir is not None:
            if vectors is None:
                vectors = True
            if multitask_vectors is None:
                multitask_vectors = True
        for key, value in locals().items():
            setattr(self, key, value)

    @classmethod
    def load(cls, loc, vectors_dir=None):
        with Path(loc).open("r", encoding="utf8") as file_:
            cfg = json.load(file_)
        if vectors_dir is not None:
            cfg["vectors_dir"] = vectors_dir
        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", "option", "C", Path),
    limit=("Size limit", "option", "n", int),
    gpu_device=("Use GPU", "option", "g", int),
    use_oracle_segments=("Use oracle segments", "flag", "G", int),
    vectors_dir=(
        "Path to directory with pretrained vectors, named e.g. en/",
        "option",
        "v",
        Path,
    ),
)
def main(
    ud_dir,
    parses_dir,
    corpus,
    config=None,
    limit=0,
    gpu_device=-1,
    vectors_dir=None,
    use_oracle_segments=False,
):
    # temp fix to avoid import issues cf https://github.com/explosion/spaCy/issues/4200
    import tqdm

    spacy.util.fix_random_seed()
    lang.zh.Chinese.Defaults.use_jieba = False
    lang.ja.Japanese.Defaults.use_janome = False

    if config is not None:
        config = Config.load(config, vectors_dir=vectors_dir)
    else:
        config = Config(vectors_dir=vectors_dir)
    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, vectors=vectors_dir)

    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, gpu_device)

    batch_sizes = compounding(config.min_batch_size, config.max_batch_size, 1.001)
    beam_prob = compounding(0.2, 0.8, 1.001)
    for i in range(config.nr_epoch):
        docs, golds = read_data(
            nlp,
            paths.train.conllu.open(encoding="utf8"),
            paths.train.text.open(encoding="utf8"),
            max_doc_length=config.max_doc_length,
            limit=limit,
            oracle_segments=use_oracle_segments,
            raw_text=not use_oracle_segments,
        )
        Xs = list(zip(docs, golds))
        random.shuffle(Xs)
        if config.batch_by_words:
            batches = minibatch_by_words(Xs, size=batch_sizes)
        else:
            batches = minibatch(Xs, size=batch_sizes)
        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.parser.cfg["beam_update_prob"] = next(beam_prob)
                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):
            if use_oracle_segments:
                parsed_docs, scores = evaluate(nlp, paths.dev.conllu,
                                                paths.dev.conllu, out_path)
            else:
                parsed_docs, scores = evaluate(nlp, paths.dev.text,
                                                paths.dev.conllu, out_path)
        print_progress(i, losses, scores)


def _render_parses(i, to_render):
    to_render[0].user_data["title"] = "Batch %d" % i
    with Path("/tmp/parses.html").open("w", encoding="utf8") as file_:
        html = displacy.render(to_render[:5], style="dep", page=True)
        file_.write(html)


if __name__ == "__main__":
    plac.call(main)