import pytest

from .util import get_random_doc

from spacy import util
from spacy.util import minibatch_by_words, dot_to_object
from thinc.api import Config, Optimizer

from ..lang.en import English
from ..lang.nl import Dutch
from ..language import DEFAULT_CONFIG_PATH


@pytest.mark.parametrize(
    "doc_sizes, expected_batches",
    [
        ([400, 400, 199], [3]),
        ([400, 400, 199, 3], [4]),
        ([400, 400, 199, 3, 200], [3, 2]),
        ([400, 400, 199, 3, 1], [5]),
        ([400, 400, 199, 3, 1, 1500], [5]),  # 1500 will be discarded
        ([400, 400, 199, 3, 1, 200], [3, 3]),
        ([400, 400, 199, 3, 1, 999], [3, 3]),
        ([400, 400, 199, 3, 1, 999, 999], [3, 2, 1, 1]),
        ([1, 2, 999], [3]),
        ([1, 2, 999, 1], [4]),
        ([1, 200, 999, 1], [2, 2]),
        ([1, 999, 200, 1], [2, 2]),
    ],
)
def test_util_minibatch(doc_sizes, expected_batches):
    docs = [get_random_doc(doc_size) for doc_size in doc_sizes]
    tol = 0.2
    batch_size = 1000
    batches = list(
        minibatch_by_words(docs, size=batch_size, tolerance=tol, discard_oversize=True)
    )
    assert [len(batch) for batch in batches] == expected_batches

    max_size = batch_size + batch_size * tol
    for batch in batches:
        assert sum([len(doc) for doc in batch]) < max_size


@pytest.mark.parametrize(
    "doc_sizes, expected_batches",
    [
        ([400, 4000, 199], [1, 2]),
        ([400, 400, 199, 3000, 200], [1, 4]),
        ([400, 400, 199, 3, 1, 1500], [1, 5]),
        ([400, 400, 199, 3000, 2000, 200, 200], [1, 1, 3, 2]),
        ([1, 2, 9999], [1, 2]),
        ([2000, 1, 2000, 1, 1, 1, 2000], [1, 1, 1, 4]),
    ],
)
def test_util_minibatch_oversize(doc_sizes, expected_batches):
    """ Test that oversized documents are returned in their own batch"""
    docs = [get_random_doc(doc_size) for doc_size in doc_sizes]
    tol = 0.2
    batch_size = 1000
    batches = list(
        minibatch_by_words(docs, size=batch_size, tolerance=tol, discard_oversize=False)
    )
    assert [len(batch) for batch in batches] == expected_batches


def test_util_dot_section():
    cfg_string = """
    [nlp]
    lang = "en"
    pipeline = ["textcat"]
    load_vocab_data = false

    [components]

    [components.textcat]
    factory = "textcat"

    [components.textcat.model]
    @architectures = "spacy.TextCatBOW.v1"
    exclusive_classes = true
    ngram_size = 1
    no_output_layer = false
    """
    nlp_config = Config().from_str(cfg_string)
    en_nlp, en_config = util.load_model_from_config(nlp_config, auto_fill=True)

    default_config = Config().from_disk(DEFAULT_CONFIG_PATH)
    default_config["nlp"]["lang"] = "nl"
    nl_nlp, nl_config = util.load_model_from_config(default_config, auto_fill=True)

    # Test that creation went OK
    assert isinstance(en_nlp, English)
    assert isinstance(nl_nlp, Dutch)
    assert nl_nlp.pipe_names == []
    assert en_nlp.pipe_names == ["textcat"]
    assert en_nlp.get_pipe("textcat").model.attrs["multi_label"] == False   # not exclusive_classes

    # Test that default values got overwritten
    assert not en_config["nlp"]["load_vocab_data"]
    assert nl_config["nlp"]["load_vocab_data"]  # default value True

    # Test proper functioning of 'dot_to_object'
    with pytest.raises(KeyError):
        obj = dot_to_object(en_config, "nlp.pipeline.tagger")
    with pytest.raises(KeyError):
        obj = dot_to_object(en_config, "nlp.unknownattribute")
    assert not dot_to_object(en_config, "nlp.load_vocab_data")
    assert dot_to_object(nl_config, "nlp.load_vocab_data")
    assert isinstance(dot_to_object(nl_config, "training.optimizer"), Optimizer)