# coding: utf-8
from __future__ import unicode_literals

from ..util import get_doc
from ...vocab import Vocab
from ...tokens import Doc
from ...tokens import Span

import pytest


def test_spans_merge_tokens(en_tokenizer):
    text = "Los Angeles start."
    heads = [1, 1, 0, -1]
    tokens = en_tokenizer(text)
    doc = get_doc(tokens.vocab, [t.text for t in tokens], heads=heads)
    assert len(doc) == 4
    assert doc[0].head.text == 'Angeles'
    assert doc[1].head.text == 'start'
    with doc.retokenize() as retokenizer:
        retokenizer.merge(doc[0 : 2], attrs={'tag':'NNP', 'lemma':'Los Angeles', 'ent_type':'GPE'})
    assert len(doc) == 3
    assert doc[0].text == 'Los Angeles'
    assert doc[0].head.text == 'start'
    assert doc[0].ent_type_ == 'GPE'

def test_spans_merge_heads(en_tokenizer):
    text = "I found a pilates class near work."
    heads = [1, 0, 2, 1, -3, -1, -1, -6]
    tokens = en_tokenizer(text)
    doc = get_doc(tokens.vocab, [t.text for t in tokens], heads=heads)

    assert len(doc) == 8
    with doc.retokenize() as retokenizer:
        retokenizer.merge(doc[3 : 5], attrs={'tag':doc[4].tag_, 'lemma':'pilates class', 'ent_type':'O'})
    assert len(doc) == 7
    assert doc[0].head.i == 1
    assert doc[1].head.i == 1
    assert doc[2].head.i == 3
    assert doc[3].head.i == 1
    assert doc[4].head.i in [1, 3]
    assert doc[5].head.i == 4

def test_spans_merge_non_disjoint(en_tokenizer):
    text = "Los Angeles start."
    tokens = en_tokenizer(text)
    doc = get_doc(tokens.vocab, [t.text for t in tokens])
    with pytest.raises(ValueError):
        with doc.retokenize() as retokenizer:
            retokenizer.merge(doc[0: 2], attrs={'tag': 'NNP', 'lemma': 'Los Angeles', 'ent_type': 'GPE'})
            retokenizer.merge(doc[0: 1], attrs={'tag': 'NNP', 'lemma': 'Los Angeles', 'ent_type': 'GPE'})

def test_span_np_merges(en_tokenizer):
    text = "displaCy is a parse tool built with Javascript"
    heads = [1, 0, 2, 1, -3, -1, -1, -1]
    tokens = en_tokenizer(text)
    doc = get_doc(tokens.vocab, [t.text for t in tokens], heads=heads)

    assert doc[4].head.i == 1
    doc.merge(doc[2].idx, doc[4].idx + len(doc[4]), tag='NP', lemma='tool',
              ent_type='O')
    assert doc[2].head.i == 1

    text = "displaCy is a lightweight and modern dependency parse tree visualization tool built with CSS3 and JavaScript."
    heads = [1, 0, 8, 3, -1, -2, 4, 3, 1, 1, -9, -1, -1, -1, -1, -2, -15]
    tokens = en_tokenizer(text)
    doc = get_doc(tokens.vocab, [t.text for t in tokens], heads=heads)

    ents = [(e[0].idx, e[-1].idx + len(e[-1]), e.label_, e.lemma_) for e in doc.ents]
    for start, end, label, lemma in ents:
        merged = doc.merge(start, end, tag=label, lemma=lemma, ent_type=label)
        assert merged != None, (start, end, label, lemma)


    text = "One test with entities like New York City so the ents list is not void"
    heads = [1, 11, -1, -1, -1, 1, 1, -3, 4, 2, 1, 1, 0, -1, -2]
    tokens = en_tokenizer(text)
    doc = get_doc(tokens.vocab, [t.text for t in tokens], heads=heads)

    for span in doc.ents:
        merged = doc.merge()
        assert merged != None, (span.start, span.end, span.label_, span.lemma_)


def test_spans_entity_merge(en_tokenizer):
    text = "Stewart Lee is a stand up comedian who lives in England and loves Joe Pasquale.\n"
    heads = [1, 1, 0, 1, 2, -1, -4, 1, -2, -1, -1, -3, -10, 1, -2, -13, -1]
    tags = ['NNP', 'NNP', 'VBZ', 'DT', 'VB', 'RP', 'NN', 'WP', 'VBZ', 'IN', 'NNP', 'CC', 'VBZ', 'NNP', 'NNP', '.', 'SP']
    ents = [('Stewart Lee', 'PERSON', 0, 2), ('England', 'GPE', 10, 11), ('Joe Pasquale', 'PERSON', 13, 15)]

    tokens = en_tokenizer(text)
    doc = get_doc(tokens.vocab, [t.text for t in tokens], heads=heads, tags=tags, ents=ents)
    assert len(doc) == 17
    for ent in doc.ents:
        label, lemma, type_ = (ent.root.tag_, ent.root.lemma_, max(w.ent_type_ for w in ent))
        ent.merge(label=label, lemma=lemma, ent_type=type_)
    # check looping is ok
    assert len(doc) == 15


def test_spans_entity_merge_iob():
    # Test entity IOB stays consistent after merging
    words = ["a", "b", "c", "d", "e"]
    doc = Doc(Vocab(), words=words)
    doc.ents = [(doc.vocab.strings.add('ent-abc'), 0, 3),
                (doc.vocab.strings.add('ent-d'), 3, 4)]
    assert doc[0].ent_iob_ == "B"
    assert doc[1].ent_iob_ == "I"
    assert doc[2].ent_iob_ == "I"
    assert doc[3].ent_iob_ == "B"
    doc[0:1].merge()
    assert doc[0].ent_iob_ == "B"
    assert doc[1].ent_iob_ == "I"

    words = ["a", "b", "c", "d", "e", "f", "g", "h", "i"]
    doc = Doc(Vocab(), words=words)
    doc.ents = [(doc.vocab.strings.add('ent-de'), 3, 5),
                (doc.vocab.strings.add('ent-fg'), 5, 7)]
    assert doc[3].ent_iob_ == "B"
    assert doc[4].ent_iob_ == "I"
    assert doc[5].ent_iob_ == "B"
    assert doc[6].ent_iob_ == "I"
    with doc.retokenize() as retokenizer:
        retokenizer.merge(doc[2 : 4])
        retokenizer.merge(doc[4 : 6])
        retokenizer.merge(doc[7 : 9])
    for token in doc:
        print(token)
        print(token.ent_iob)
    assert len(doc) == 6
    assert doc[3].ent_iob_ == "B"
    assert doc[4].ent_iob_ == "I"


def test_spans_sentence_update_after_merge(en_tokenizer):
    text = "Stewart Lee is a stand up comedian. He lives in England and loves Joe Pasquale."
    heads = [1, 1, 0, 1, 2, -1, -4, -5, 1, 0, -1, -1, -3, -4, 1, -2, -7]
    deps = ['compound', 'nsubj', 'ROOT', 'det', 'amod', 'prt', 'attr',
            'punct', 'nsubj', 'ROOT', 'prep', 'pobj', 'cc', 'conj',
            'compound', 'dobj', 'punct']

    tokens = en_tokenizer(text)
    doc = get_doc(tokens.vocab, [t.text for t in tokens], heads=heads, deps=deps)
    sent1, sent2 = list(doc.sents)
    init_len = len(sent1)
    init_len2 = len(sent2)
    doc[0:2].merge(label='none', lemma='none', ent_type='none')
    doc[-2:].merge(label='none', lemma='none', ent_type='none')
    assert len(sent1) == init_len - 1
    assert len(sent2) == init_len2 - 1


def test_spans_subtree_size_check(en_tokenizer):
    text = "Stewart Lee is a stand up comedian who lives in England and loves Joe Pasquale"
    heads = [1, 1, 0, 1, 2, -1, -4, 1, -2, -1, -1, -3, -10, 1, -2]
    deps = ['compound', 'nsubj', 'ROOT', 'det', 'amod', 'prt', 'attr',
            'nsubj', 'relcl', 'prep', 'pobj', 'cc', 'conj', 'compound',
            'dobj']

    tokens = en_tokenizer(text)
    doc = get_doc(tokens.vocab, [t.text for t in tokens], heads=heads, deps=deps)
    sent1 = list(doc.sents)[0]
    init_len = len(list(sent1.root.subtree))
    doc[0:2].merge(label='none', lemma='none', ent_type='none')
    assert len(list(sent1.root.subtree)) == init_len - 1