# coding=utf-8 """Mitre model configuration""" from transformers.configuration_utils import PretrainedConfig from transformers.utils import logging logger = logging.get_logger(__name__) class MitreConfig(PretrainedConfig): model_type = "mitre" keys_to_ignore_at_inference = ["past_key_values"] attribute_map = {"num_attention_heads": "decoder_attention_heads", "hidden_size": "d_model"} def __init__( self, vocab_size=160025, max_position_embeddings=256, decoder_layers=24, decoder_ffn_dim=4096, decoder_attention_heads=16, use_cache=True, is_encoder_decoder=False, activation_function="relu", d_model=1024, dropout=0.1, attention_dropout=0.1, activation_dropout=0.0, init_std=0.02, decoder_start_token_id=2, scale_embedding=True, pad_token_id=1, bos_token_id=0, eos_token_id=2, **kwargs, ): self.vocab_size = vocab_size self.max_position_embeddings = max_position_embeddings self.d_model = d_model self.decoder_ffn_dim = decoder_ffn_dim self.decoder_layers = decoder_layers self.decoder_attention_heads = decoder_attention_heads self.dropout = dropout self.attention_dropout = attention_dropout self.activation_dropout = activation_dropout self.activation_function = activation_function self.init_std = init_std self.use_cache = use_cache self.num_hidden_layers = decoder_layers self.scale_embedding = scale_embedding # scale factor will be sqrt(d_model) if True self.is_decoder = True self.is_encoder_decoder = False super().__init__( pad_token_id=pad_token_id, bos_token_id=bos_token_id, eos_token_id=eos_token_id, is_encoder_decoder=is_encoder_decoder, decoder_start_token_id=decoder_start_token_id, **kwargs, ) MitreConfig.register_for_auto_class("AutoConfig")