# coding: utf-8
from __future__ import unicode_literals

import numpy
import tempfile
import shutil
import contextlib
import msgpack
from pathlib import Path
from spacy.tokens import Doc, Span
from spacy.attrs import POS, HEAD, DEP
from spacy.compat import path2str


@contextlib.contextmanager
def make_tempfile(mode="r"):
    f = tempfile.TemporaryFile(mode=mode)
    yield f
    f.close()


@contextlib.contextmanager
def make_tempdir():
    d = Path(tempfile.mkdtemp())
    yield d
    shutil.rmtree(path2str(d))


def get_doc(vocab, words=[], pos=None, heads=None, deps=None, tags=None, ents=None):
    """Create Doc object from given vocab, words and annotations."""
    pos = pos or [""] * len(words)
    tags = tags or [""] * len(words)
    heads = heads or [0] * len(words)
    deps = deps or [""] * len(words)
    for value in deps + tags + pos:
        vocab.strings.add(value)

    doc = Doc(vocab, words=words)
    attrs = doc.to_array([POS, HEAD, DEP])
    for i, (p, head, dep) in enumerate(zip(pos, heads, deps)):
        attrs[i, 0] = doc.vocab.strings[p]
        attrs[i, 1] = head
        attrs[i, 2] = doc.vocab.strings[dep]
    doc.from_array([POS, HEAD, DEP], attrs)
    if ents:
        doc.ents = [
            Span(doc, start, end, label=doc.vocab.strings[label])
            for start, end, label in ents
        ]
    if tags:
        for token in doc:
            token.tag_ = tags[token.i]
    return doc


def apply_transition_sequence(parser, doc, sequence):
    """Perform a series of pre-specified transitions, to put the parser in a
    desired state."""
    for action_name in sequence:
        if "-" in action_name:
            move, label = action_name.split("-")
            parser.add_label(label)
    with parser.step_through(doc) as stepwise:
        for transition in sequence:
            stepwise.transition(transition)


def add_vecs_to_vocab(vocab, vectors):
    """Add list of vector tuples to given vocab. All vectors need to have the
    same length. Format: [("text", [1, 2, 3])]"""
    length = len(vectors[0][1])
    vocab.reset_vectors(width=length)
    for word, vec in vectors:
        vocab.set_vector(word, vector=vec)
    return vocab


def get_cosine(vec1, vec2):
    """Get cosine for two given vectors"""
    return numpy.dot(vec1, vec2) / (numpy.linalg.norm(vec1) * numpy.linalg.norm(vec2))


def assert_docs_equal(doc1, doc2):
    """Compare two Doc objects and assert that they're equal. Tests for tokens,
    tags, dependencies and entities."""
    assert [t.orth for t in doc1] == [t.orth for t in doc2]

    assert [t.pos for t in doc1] == [t.pos for t in doc2]
    assert [t.tag for t in doc1] == [t.tag for t in doc2]

    assert [t.head.i for t in doc1] == [t.head.i for t in doc2]
    assert [t.dep for t in doc1] == [t.dep for t in doc2]
    if doc1.is_parsed and doc2.is_parsed:
        assert [s for s in doc1.sents] == [s for s in doc2.sents]

    assert [t.ent_type for t in doc1] == [t.ent_type for t in doc2]
    assert [t.ent_iob for t in doc1] == [t.ent_iob for t in doc2]
    assert [ent for ent in doc1.ents] == [ent for ent in doc2.ents]


def assert_packed_msg_equal(b1, b2):
    """Assert that two packed msgpack messages are equal."""
    msg1 = msgpack.loads(b1, encoding="utf8")
    msg2 = msgpack.loads(b2, encoding="utf8")
    assert sorted(msg1.keys()) == sorted(msg2.keys())
    for (k1, v1), (k2, v2) in zip(sorted(msg1.items()), sorted(msg2.items())):
        assert k1 == k2
        assert v1 == v2