whenxuan: update the model type for config

configuration_symtime.py

@@ -1,64 +1,64 @@
-from dataclasses import dataclass
-from typing import List, Literal, Optional, Dict
-from enum import Enum
-
-from transformers.configuration_utils import PretrainedConfig
-
-
-@dataclass
-class SymTimeConfig(PretrainedConfig):
-    """
-    Time series encoder configuration for SymTime Model.
-
-    Parameters
-    -----------
-    patch_size
-        The size of the patch to be used for the input data.
-    num_layers
-        The number of layers to be used for the encoder.
-    d_model
-        The dimension of the model.
-    d_ff
-        The dimension of the feedforward network.
-    num_heads
-        The number of heads to be used for the attention mechanism.
-    norm
-        The normalization to be used for the encoder.
-    attn_dropout
-        The dropout rate to be used for the attention mechanism.
-    dropout
-        The dropout rate to be used for the encoder.
-    act
-        The activation function to be used for the encoder.
-    pre_norm
-        Whether to use pre-norm for the encoder.
-    """
-
-    model_type = "time_series_transformer"
-
-    def __init__(
-        self,
-        patch_size: int = 16,
-        num_layers: int = 6,
-        d_model: int = 512,
-        d_ff: int = 2048,
-        num_heads: int = 8,
-        norm: str = "BatchNorm",
-        dropout: float = 0.1,
-        act: str = "gelu",
-        pre_norm: bool = False,
-        initializer_factor: float = 0.05,
-        **kwargs,
-    ) -> None:
-        self.patch_size = patch_size
-        self.num_layers = num_layers
-        self.d_model = d_model
-        self.num_heads = num_heads
-        self.d_ff = d_ff
-        self.norm = norm
-        self.dropout = dropout
-        self.act = act
-        self.pre_norm = pre_norm
-        self.initializer_factor = initializer_factor
-
-        super().__init__(**kwargs)
+from dataclasses import dataclass
+from typing import List, Literal, Optional, Dict
+from enum import Enum
+
+from transformers.configuration_utils import PretrainedConfig
+
+
+@dataclass
+class SymTimeConfig(PretrainedConfig):
+    """
+    Time series encoder configuration for SymTime Model.
+
+    Parameters
+    -----------
+    patch_size
+        The size of the patch to be used for the input data.
+    num_layers
+        The number of layers to be used for the encoder.
+    d_model
+        The dimension of the model.
+    d_ff
+        The dimension of the feedforward network.
+    num_heads
+        The number of heads to be used for the attention mechanism.
+    norm
+        The normalization to be used for the encoder.
+    attn_dropout
+        The dropout rate to be used for the attention mechanism.
+    dropout
+        The dropout rate to be used for the encoder.
+    act
+        The activation function to be used for the encoder.
+    pre_norm
+        Whether to use pre-norm for the encoder.
+    """
+
+    model_type = "symtime"
+
+    def __init__(
+        self,
+        patch_size: int = 16,
+        num_layers: int = 6,
+        d_model: int = 512,
+        d_ff: int = 2048,
+        num_heads: int = 8,
+        norm: str = "BatchNorm",
+        dropout: float = 0.1,
+        act: str = "gelu",
+        pre_norm: bool = False,
+        initializer_factor: float = 0.05,
+        **kwargs,
+    ) -> None:
+        self.patch_size = patch_size
+        self.num_layers = num_layers
+        self.d_model = d_model
+        self.num_heads = num_heads
+        self.d_ff = d_ff
+        self.norm = norm
+        self.dropout = dropout
+        self.act = act
+        self.pre_norm = pre_norm
+        self.initializer_factor = initializer_factor
+
+        super().__init__(**kwargs)