from thinc.api import Model, reduce_mean, Linear, list2ragged, Logistic, ParametricAttention
from thinc.api import chain, concatenate, clone, Dropout
from thinc.api import SparseLinear, Softmax, softmax_activation, Maxout, reduce_sum, Relu, residual, expand_window
from thinc.api import HashEmbed, with_ragged, with_array, with_cpu, uniqued, FeatureExtractor

from ..spacy_vectors import SpacyVectors
from ... import util
from ...attrs import ID, ORTH, NORM, PREFIX, SUFFIX, SHAPE, LOWER
from ...util import registry
from ..extract_ngrams import extract_ngrams


@registry.architectures.register("spacy.TextCatCNN.v1")
def build_simple_cnn_text_classifier(tok2vec, exclusive_classes, nO=None):
    """
    Build a simple CNN text classifier, given a token-to-vector model as inputs.
    If exclusive_classes=True, a softmax non-linearity is applied, so that the
    outputs sum to 1. If exclusive_classes=False, a logistic non-linearity
    is applied instead, so that outputs are in the range [0, 1].
    """
    with Model.define_operators({">>": chain}):
        if exclusive_classes:
            output_layer = Softmax(nO=nO, nI=tok2vec.get_dim("nO"))
            model = tok2vec >> list2ragged() >> reduce_mean() >> output_layer
            model.set_ref("output_layer", output_layer)
        else:
            linear_layer = Linear(nO=nO, nI=tok2vec.get_dim("nO"))
            model = (
                tok2vec >> list2ragged() >> reduce_mean() >> linear_layer >> Logistic()
            )
            model.set_ref("output_layer", linear_layer)
    model.set_ref("tok2vec", tok2vec)
    model.set_dim("nO", nO)
    model.attrs["multi_label"] = not exclusive_classes
    return model


@registry.architectures.register("spacy.TextCatBOW.v1")
def build_bow_text_classifier(exclusive_classes, ngram_size, no_output_layer, nO=None):
    with Model.define_operators({">>": chain}):
        sparse_linear = SparseLinear(nO)
        model = extract_ngrams(ngram_size, attr=ORTH) >> sparse_linear
        model = with_cpu(model, model.ops)
        if not no_output_layer:
            output_layer = softmax_activation() if exclusive_classes else Logistic()
            model = model >> with_cpu(output_layer, output_layer.ops)
    model.set_ref("output_layer", sparse_linear)
    model.attrs["multi_label"] = not exclusive_classes
    return model


@registry.architectures.register("spacy.TextCat.v1")
def build_text_classifier(width, embed_size, pretrained_vectors, exclusive_classes, ngram_size,
                          window_size, conv_depth, dropout, nO=None):
    cols = [ORTH, LOWER, PREFIX, SUFFIX, SHAPE, ID]
    with Model.define_operators({">>": chain, "|": concatenate, "**": clone}):
        lower = HashEmbed(nO=width, nV=embed_size, column=cols.index(LOWER), dropout=dropout)
        prefix = HashEmbed(nO=width // 2, nV=embed_size, column=cols.index(PREFIX), dropout=dropout)
        suffix = HashEmbed(nO=width // 2, nV=embed_size, column=cols.index(SUFFIX), dropout=dropout)
        shape = HashEmbed(nO=width // 2, nV=embed_size, column=cols.index(SHAPE), dropout=dropout)

        width_nI = sum(layer.get_dim("nO") for layer in [lower, prefix, suffix, shape])
        trained_vectors = FeatureExtractor(cols) >> with_array(
            uniqued(
                (lower | prefix | suffix | shape)
                >> Maxout(nO=width, nI=width_nI, normalize=True),
                column=cols.index(ORTH),
            )
        )

        if pretrained_vectors:
            nlp = util.load_model(pretrained_vectors)
            vectors = nlp.vocab.vectors
            vector_dim = vectors.data.shape[1]

            static_vectors = SpacyVectors(vectors) >> with_array(
                Linear(width, vector_dim)
            )
            vector_layer = trained_vectors | static_vectors
            vectors_width = width * 2
        else:
            vector_layer = trained_vectors
            vectors_width = width
        tok2vec = vector_layer >> with_array(
            Maxout(width, vectors_width, normalize=True)
            >> residual((expand_window(window_size=window_size)
                         >> Maxout(nO=width, nI=width * ((window_size * 2) + 1), normalize=True))) ** conv_depth,
            pad=conv_depth,
        )
        cnn_model = (
                tok2vec
                >> list2ragged()
                >> ParametricAttention(width)
                >> reduce_sum()
                >> residual(Maxout(nO=width, nI=width))
                >> Linear(nO=nO, nI=width)
                >> Dropout(0.0)
        )

        linear_model = build_bow_text_classifier(
            nO=nO, ngram_size=ngram_size, exclusive_classes=exclusive_classes, no_output_layer=False
        )
        nO_double = nO*2 if nO else None
        if exclusive_classes:
            output_layer = Softmax(nO=nO, nI=nO_double)
        else:
            output_layer = (
                    Linear(nO=nO, nI=nO_double) >> Dropout(0.0) >> Logistic()
            )
        model = (linear_model | cnn_model) >> output_layer
        model.set_ref("tok2vec", tok2vec)
    if model.has_dim("nO") is not False:
        model.set_dim("nO", nO)
    model.set_ref("output_layer", linear_model.get_ref("output_layer"))
    model.attrs["multi_label"] = not exclusive_classes
    return model


@registry.architectures.register("spacy.TextCatLowData.v1")
def build_text_classifier_lowdata(width, pretrained_vectors, dropout, nO=None):
    nlp = util.load_model(pretrained_vectors)
    vectors = nlp.vocab.vectors
    vector_dim = vectors.data.shape[1]

    # Note, before v.3, this was the default if setting "low_data" and "pretrained_dims"
    with Model.define_operators({">>": chain, "**": clone}):
        model = (
            SpacyVectors(vectors)
            >> list2ragged()
            >> with_ragged(0, Linear(width, vector_dim))
            >> ParametricAttention(width)
            >> reduce_sum()
            >> residual(Relu(width, width)) ** 2
            >> Linear(nO, width)
        )
        if dropout:
            model = model >> Dropout(dropout)
        model = model >> Logistic()
    return model