update preprocessor config

preprocessor_config.json

@@ -1,5 +1,8 @@
 {
   "processor_type": "TimeRCDProcessor",
+  "auto_map": {
+    "AutoProcessor": "processing_time_rcd.TimeRCDProcessor"
+  },
   "win_size": 5000,
   "stride": 5000,
   "normalize": true,

processing_time_rcd.py

@@ -17,9 +17,10 @@ Usage:
 import numpy as np
 import torch
 from typing import Optional, Dict, Any
+from transformers import ProcessorMixin
 
 
-class TimeRCDProcessor:
+class TimeRCDProcessor(ProcessorMixin):
     """
     Processor for preparing time series data for Time_RCD model.
     
@@ -47,11 +48,21 @@ class TimeRCDProcessor:
         stride: Optional[int] = None,
         normalize: bool = True,
         pad_to_multiple: bool = True,
+        **kwargs
     ):
+        super().__init__(**kwargs)
         self.win_size = win_size
         self.stride = stride if stride is not None else win_size
         self.normalize = normalize
         self.pad_to_multiple = pad_to_multiple
+        
+        # Required attributes for ProcessorMixin
+        self.model_input_names = ["time_series", "attention_mask"]
+    
+    @property
+    def attributes(self):
+        """Return list of attribute names for serialization."""
+        return ["win_size", "stride", "normalize", "pad_to_multiple"]
     
     def __call__(
         self,
@@ -166,6 +177,9 @@ class TimeRCDProcessor:
         
         config = {
             "processor_type": "TimeRCDProcessor",
+            "auto_map": {
+                "AutoProcessor": "processing_time_rcd.TimeRCDProcessor"
+            },
             "win_size": self.win_size,
             "stride": self.stride,
             "normalize": self.normalize,
@@ -176,18 +190,34 @@ class TimeRCDProcessor:
             json.dump(config, f, indent=2)
     
     @classmethod
-    def from_pretrained(cls, pretrained_model_name_or_path: str):
+    def from_pretrained(cls, pretrained_model_name_or_path: str, **kwargs):
         """Load processor from pretrained configuration."""
         import json
         import os
+        from huggingface_hub import hf_hub_download
         
+        # Try to load from local path first
         config_file = os.path.join(pretrained_model_name_or_path, "preprocessor_config.json")
         
-        if not os.path.exists(config_file):
-            raise FileNotFoundError(f"Preprocessor config not found at {config_file}")
-        
-        with open(config_file, "r") as f:
-            config = json.load(f)
+        if os.path.exists(config_file):
+            # Load from local path
+            with open(config_file, "r") as f:
+                config = json.load(f)
+        else:
+            # Try to download from HuggingFace Hub
+            try:
+                config_file = hf_hub_download(
+                    repo_id=pretrained_model_name_or_path,
+                    filename="preprocessor_config.json",
+                    **kwargs
+                )
+                with open(config_file, "r") as f:
+                    config = json.load(f)
+            except Exception as e:
+                raise FileNotFoundError(
+                    f"Could not load preprocessor config from {pretrained_model_name_or_path}. "
+                    f"Error: {e}"
+                )
         
         # Remove processor_type from config
         config.pop("processor_type", None)