Delete configuration_codet5p_matching.py

configuration_codet5p_matching.py

@@ -1,76 +0,0 @@
-# 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 CodeT5pMatchingConfig(PretrainedConfig):
-    model_type = "codet5p_matching"
-    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_decoder_layers=None,
-            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_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.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,
-        )