# coding: utf-8
from __future__ import unicode_literals


import pytest
import numpy
from spacy.tokens import Doc, Span
from spacy.vocab import Vocab
from spacy.errors import ModelsWarning
from spacy.attrs import ENT_TYPE, ENT_IOB

from ..util import get_doc


@pytest.mark.parametrize("text", [["one", "two", "three"]])
def test_doc_api_compare_by_string_position(en_vocab, text):
    doc = Doc(en_vocab, words=text)
    # Get the tokens in this order, so their ID ordering doesn't match the idx
    token3 = doc[-1]
    token2 = doc[-2]
    token1 = doc[-1]
    token1, token2, token3 = doc
    assert token1 < token2 < token3
    assert not token1 > token2
    assert token2 > token1
    assert token2 <= token3
    assert token3 >= token1


def test_doc_api_getitem(en_tokenizer):
    text = "Give it back! He pleaded."
    tokens = en_tokenizer(text)
    assert tokens[0].text == "Give"
    assert tokens[-1].text == "."
    with pytest.raises(IndexError):
        tokens[len(tokens)]

    def to_str(span):
        return "/".join(token.text for token in span)

    span = tokens[1:1]
    assert not to_str(span)
    span = tokens[1:4]
    assert to_str(span) == "it/back/!"
    span = tokens[1:4:1]
    assert to_str(span) == "it/back/!"
    with pytest.raises(ValueError):
        tokens[1:4:2]
    with pytest.raises(ValueError):
        tokens[1:4:-1]

    span = tokens[-3:6]
    assert to_str(span) == "He/pleaded"
    span = tokens[4:-1]
    assert to_str(span) == "He/pleaded"
    span = tokens[-5:-3]
    assert to_str(span) == "back/!"
    span = tokens[5:4]
    assert span.start == span.end == 5 and not to_str(span)
    span = tokens[4:-3]
    assert span.start == span.end == 4 and not to_str(span)

    span = tokens[:]
    assert to_str(span) == "Give/it/back/!/He/pleaded/."
    span = tokens[4:]
    assert to_str(span) == "He/pleaded/."
    span = tokens[:4]
    assert to_str(span) == "Give/it/back/!"
    span = tokens[:-3]
    assert to_str(span) == "Give/it/back/!"
    span = tokens[-3:]
    assert to_str(span) == "He/pleaded/."

    span = tokens[4:50]
    assert to_str(span) == "He/pleaded/."
    span = tokens[-50:4]
    assert to_str(span) == "Give/it/back/!"
    span = tokens[-50:-40]
    assert span.start == span.end == 0 and not to_str(span)
    span = tokens[40:50]
    assert span.start == span.end == 7 and not to_str(span)

    span = tokens[1:4]
    assert span[0].orth_ == "it"
    subspan = span[:]
    assert to_str(subspan) == "it/back/!"
    subspan = span[:2]
    assert to_str(subspan) == "it/back"
    subspan = span[1:]
    assert to_str(subspan) == "back/!"
    subspan = span[:-1]
    assert to_str(subspan) == "it/back"
    subspan = span[-2:]
    assert to_str(subspan) == "back/!"
    subspan = span[1:2]
    assert to_str(subspan) == "back"
    subspan = span[-2:-1]
    assert to_str(subspan) == "back"
    subspan = span[-50:50]
    assert to_str(subspan) == "it/back/!"
    subspan = span[50:-50]
    assert subspan.start == subspan.end == 4 and not to_str(subspan)


@pytest.mark.parametrize(
    "text", ["Give it back! He pleaded.", " Give it back! He pleaded. "]
)
def test_doc_api_serialize(en_tokenizer, text):
    tokens = en_tokenizer(text)
    new_tokens = Doc(tokens.vocab).from_bytes(tokens.to_bytes())
    assert tokens.text == new_tokens.text
    assert [t.text for t in tokens] == [t.text for t in new_tokens]
    assert [t.orth for t in tokens] == [t.orth for t in new_tokens]

    new_tokens = Doc(tokens.vocab).from_bytes(
        tokens.to_bytes(exclude=["tensor"]), exclude=["tensor"]
    )
    assert tokens.text == new_tokens.text
    assert [t.text for t in tokens] == [t.text for t in new_tokens]
    assert [t.orth for t in tokens] == [t.orth for t in new_tokens]

    new_tokens = Doc(tokens.vocab).from_bytes(
        tokens.to_bytes(exclude=["sentiment"]), exclude=["sentiment"]
    )
    assert tokens.text == new_tokens.text
    assert [t.text for t in tokens] == [t.text for t in new_tokens]
    assert [t.orth for t in tokens] == [t.orth for t in new_tokens]


def test_doc_api_set_ents(en_tokenizer):
    text = "I use goggle chrone to surf the web"
    tokens = en_tokenizer(text)
    assert len(tokens.ents) == 0
    tokens.ents = [(tokens.vocab.strings["PRODUCT"], 2, 4)]
    assert len(list(tokens.ents)) == 1
    assert [t.ent_iob for t in tokens] == [0, 0, 3, 1, 0, 0, 0, 0]
    assert tokens.ents[0].label_ == "PRODUCT"
    assert tokens.ents[0].start == 2
    assert tokens.ents[0].end == 4


def test_doc_api_sents_empty_string(en_tokenizer):
    doc = en_tokenizer("")
    doc.is_parsed = True
    sents = list(doc.sents)
    assert len(sents) == 0


def test_doc_api_runtime_error(en_tokenizer):
    # Example that caused run-time error while parsing Reddit
    # fmt: off
    text = "67% of black households are single parent \n\n72% of all black babies born out of wedlock \n\n50% of all black kids don\u2019t finish high school"
    deps = ["nsubj", "prep", "amod", "pobj", "ROOT", "amod", "attr", "",
            "nummod", "prep", "det", "amod", "pobj", "acl", "prep", "prep",
            "pobj", "", "nummod", "prep", "det", "amod", "pobj", "aux", "neg",
            "ROOT", "amod", "dobj"]
    # fmt: on
    tokens = en_tokenizer(text)
    doc = get_doc(tokens.vocab, words=[t.text for t in tokens], deps=deps)
    nps = []
    for np in doc.noun_chunks:
        while len(np) > 1 and np[0].dep_ not in ("advmod", "amod", "compound"):
            np = np[1:]
        if len(np) > 1:
            nps.append(np)
    with doc.retokenize() as retokenizer:
        for np in nps:
            attrs = {
                "tag": np.root.tag_,
                "lemma": np.text,
                "ent_type": np.root.ent_type_,
            }
            retokenizer.merge(np, attrs=attrs)


def test_doc_api_right_edge(en_tokenizer):
    """Test for bug occurring from Unshift action, causing incorrect right edge"""
    # fmt: off
    text = "I have proposed to myself, for the sake of such as live under the government of the Romans, to translate those books into the Greek tongue."
    heads = [2, 1, 0, -1, -1, -3, 15, 1, -2, -1, 1, -3, -1, -1, 1, -2, -1, 1,
             -2, -7, 1, -19, 1, -2, -3, 2, 1, -3, -26]
    # fmt: on

    tokens = en_tokenizer(text)
    doc = get_doc(tokens.vocab, words=[t.text for t in tokens], heads=heads)
    assert doc[6].text == "for"
    subtree = [w.text for w in doc[6].subtree]
    assert subtree == [
        "for",
        "the",
        "sake",
        "of",
        "such",
        "as",
        "live",
        "under",
        "the",
        "government",
        "of",
        "the",
        "Romans",
        ",",
    ]
    assert doc[6].right_edge.text == ","


def test_doc_api_has_vector():
    vocab = Vocab()
    vocab.reset_vectors(width=2)
    vocab.set_vector("kitten", vector=numpy.asarray([0.0, 2.0], dtype="f"))
    doc = Doc(vocab, words=["kitten"])
    assert doc.has_vector


def test_doc_api_similarity_match():
    doc = Doc(Vocab(), words=["a"])
    assert doc.similarity(doc[0]) == 1.0
    assert doc.similarity(doc.vocab["a"]) == 1.0
    doc2 = Doc(doc.vocab, words=["a", "b", "c"])
    with pytest.warns(ModelsWarning):
        assert doc.similarity(doc2[:1]) == 1.0
        assert doc.similarity(doc2) == 0.0


@pytest.mark.parametrize(
    "sentence,heads,lca_matrix",
    [
        (
            "the lazy dog slept",
            [2, 1, 1, 0],
            numpy.array([[0, 2, 2, 3], [2, 1, 2, 3], [2, 2, 2, 3], [3, 3, 3, 3]]),
        ),
        (
            "The lazy dog slept. The quick fox jumped",
            [2, 1, 1, 0, -1, 2, 1, 1, 0],
            numpy.array(
                [
                    [0, 2, 2, 3, 3, -1, -1, -1, -1],
                    [2, 1, 2, 3, 3, -1, -1, -1, -1],
                    [2, 2, 2, 3, 3, -1, -1, -1, -1],
                    [3, 3, 3, 3, 3, -1, -1, -1, -1],
                    [3, 3, 3, 3, 4, -1, -1, -1, -1],
                    [-1, -1, -1, -1, -1, 5, 7, 7, 8],
                    [-1, -1, -1, -1, -1, 7, 6, 7, 8],
                    [-1, -1, -1, -1, -1, 7, 7, 7, 8],
                    [-1, -1, -1, -1, -1, 8, 8, 8, 8],
                ]
            ),
        ),
    ],
)
def test_lowest_common_ancestor(en_tokenizer, sentence, heads, lca_matrix):
    tokens = en_tokenizer(sentence)
    doc = get_doc(tokens.vocab, [t.text for t in tokens], heads=heads)
    lca = doc.get_lca_matrix()
    assert (lca == lca_matrix).all()
    assert lca[1, 1] == 1
    assert lca[0, 1] == 2
    assert lca[1, 2] == 2


def test_doc_is_nered(en_vocab):
    words = ["I", "live", "in", "New", "York"]
    doc = Doc(en_vocab, words=words)
    assert not doc.is_nered
    doc.ents = [Span(doc, 3, 5, label="GPE")]
    assert doc.is_nered
    # Test creating doc from array with unknown values
    arr = numpy.array([[0, 0], [0, 0], [0, 0], [384, 3], [384, 1]], dtype="uint64")
    doc = Doc(en_vocab, words=words).from_array([ENT_TYPE, ENT_IOB], arr)
    assert doc.is_nered
    # Test serialization
    new_doc = Doc(en_vocab).from_bytes(doc.to_bytes())
    assert new_doc.is_nered