# coding=utf-8
# Copyright 2023 Salesforce authors, The EleutherAI, and HuggingFace Teams. All rights reserved.

""" CodeT5+ embedding model configuration"""
from transformers.configuration_utils import PretrainedConfig
from transformers.utils import logging

logger = logging.get_logger(__name__)


class CodeT5pEmbeddingConfig(PretrainedConfig):
    model_type = "codet5p_embedding"
    keys_to_ignore_at_inference = ["past_key_values"]
    attribute_map = {"hidden_size": "d_model", "num_attention_heads": "num_heads", "num_hidden_layers": "num_layers"}

    def __init__(
            self,
            vocab_size=32103,
            d_model=768,
            embed_dim=256,
            d_kv=64,
            d_ff=3072,
            num_layers=12,
            num_heads=12,
            relative_attention_num_buckets=32,
            relative_attention_max_distance=128,
            dropout_rate=0.1,
            layer_norm_epsilon=1e-6,
            initializer_factor=1.0,
            feed_forward_proj="relu",
            is_encoder_decoder=False,
            use_cache=True,
            pad_token_id=0,
            eos_token_id=2,
            **kwargs
    ):
        self.vocab_size = vocab_size
        self.d_model = d_model
        self.embed_dim = embed_dim
        self.d_kv = d_kv
        self.d_ff = d_ff
        self.num_layers = num_layers
        self.num_heads = num_heads
        self.relative_attention_num_buckets = relative_attention_num_buckets
        self.relative_attention_max_distance = relative_attention_max_distance
        self.dropout_rate = dropout_rate
        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'"
            )

        # for backwards compatibility
        if feed_forward_proj == "gated-gelu":
            self.dense_act_fn = "gelu_new"

        super().__init__(
            pad_token_id=pad_token_id,
            eos_token_id=eos_token_id,
            is_encoder_decoder=is_encoder_decoder,
            **kwargs,
        )