from transformers import PretrainedConfig

class RankingPrompterConfig(PretrainedConfig):
    model_type = "umt5"

    def __init__(
        self,
        vocab_size=250112,
        d_model=512,
        d_kv=64,
        d_ff=1024,
        num_layers=8,
        num_decoder_layers=None,
        num_heads=6,
        relative_attention_num_buckets=32,
        relative_attention_max_distance=128,
        num_labels=1,
        dropout_rate=0.1,
        layer_norm_epsilon=1e-6,
        initializer_factor=1.0,
        feed_forward_proj="gated-gelu",
        is_encoder_decoder=True,
        use_cache=True,
        tokenizer_class="T5Tokenizer",
        tie_word_embeddings=True,
        pad_token_id=0,
        eos_token_id=1,
        decoder_start_token_id=2,
        classifier_dropout=0.1,
        **kwargs,
    ):
        super().__init__(
            is_encoder_decoder=is_encoder_decoder,
            tokenizer_class=tokenizer_class,
            tie_word_embeddings=tie_word_embeddings,
            pad_token_id=pad_token_id,
            eos_token_id=eos_token_id,
            decoder_start_token_id=decoder_start_token_id,
            **kwargs,
        )
        self.vocab_size = vocab_size
        self.d_model = d_model
        self.d_kv = d_kv
        self.d_ff = d_ff
        self.num_layers = num_layers
        self.num_decoder_layers = (
            num_decoder_layers if num_decoder_layers is not None else self.num_layers
        )  # default = symmetry
        self.num_heads = num_heads
        self.relative_attention_num_buckets = relative_attention_num_buckets
        self.relative_attention_max_distance = relative_attention_max_distance
        self.num_labels = num_labels
        self.dropout_rate = dropout_rate
        self.classifier_dropout = classifier_dropout
        self.layer_norm_epsilon = layer_norm_epsilon
        self.initializer_factor = initializer_factor
        self.feed_forward_proj = feed_forward_proj
        self.use_cache = use_cache

        act_info = self.feed_forward_proj.split("-")
        self.dense_act_fn = act_info[-1]
        self.is_gated_act = act_info[0] == "gated"

        if len(act_info) > 1 and act_info[0] != "gated" or len(act_info) > 2:
            raise ValueError(
                f"`feed_forward_proj`: {feed_forward_proj} is not a valid activation function of the dense layer."
                "Please make sure `feed_forward_proj` is of the format `gated-{ACT_FN}` or `{ACT_FN}`, e.g. "
                "'gated-gelu' or 'relu'"
            )

        if feed_forward_proj == "gated-gelu":
            self.dense_act_fn = "gelu_new"

    @property
    def hidden_size(self):
        return self.d_model

    @property
    def num_attention_heads(self):
        return self.num_heads

    @property
    def num_hidden_layers(self):
        return self.num_layers