import numpy as np

from immuneML.encodings.EncoderParams import EncoderParams
from immuneML.encodings.word2vec.Word2VecEncoder import Word2VecEncoder
from immuneML.environment.SequenceType import SequenceType
from immuneML.util.KmerHelper import KmerHelper


class W2VSequenceEncoder(Word2VecEncoder):

    def _encode_labels(self, dataset, params: EncoderParams):

        labels = {name: [] for name in params.label_config.get_labels_by_name()}

        for sequence in dataset.get_data():
            for label_name in params.label_config.get_labels_by_name():
                label = sequence.metadata[label_name]
                labels[label_name].append(label)

        return np.array([labels[name] for name in labels.keys()])

    def _encode_item(self, item, vectors, sequence_type: SequenceType):
        kmers = KmerHelper.create_kmers_from_sequence(sequence=item, k=self.k, sequence_type=sequence_type)
        sequence_vector = np.zeros(vectors.vector_size)
        for kmer in kmers:
            try:
                word_vector = vectors.get_vector(kmer)
                sequence_vector = np.add(sequence_vector, word_vector)
            except KeyError:
                pass
        return sequence_vector
