"""Configuration for BiLSTM-CRF NER."""
from transformers import PretrainedConfig


class BiLSTMConfig(PretrainedConfig):
    model_type = "bilstm-crf-ner"

    def __init__(self, vocab_size=30000, char_vocab_size=100, embed_dim=300,
                 hidden_dim=200, char_emb_dim=30, char_cnn_filters=50,
                 char_cnn_kernel=3, max_word_len=25, dropout=0.3, num_labels=9,
                 use_char_cnn=False, pad_token_id=0, unk_token_id=1,
                 id2label=None, label2id=None, **kwargs):
        self.vocab_size = vocab_size
        self.char_vocab_size = char_vocab_size
        self.embed_dim = embed_dim
        self.hidden_dim = hidden_dim
        self.char_emb_dim = char_emb_dim
        self.char_cnn_filters = char_cnn_filters
        self.char_cnn_kernel = char_cnn_kernel
        self.max_word_len = max_word_len
        self.dropout = dropout
        self.use_char_cnn = use_char_cnn
        self.unk_token_id = unk_token_id
        kwargs.setdefault("num_labels", num_labels)
        kwargs.setdefault("pad_token_id", pad_token_id)
        if id2label is not None: kwargs["id2label"] = id2label
        if label2id is not None: kwargs["label2id"] = label2id
        super().__init__(**kwargs)