初始化项目，由ModelHub XC社区提供模型

Model: flax-community/gpt2-medium-persian
Source: Original Platform

src/create_config.py

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+import ast
+import logging
+import os
+import sys
+from dataclasses import dataclass, field
+from typing import Dict, List, Optional, Tuple
+
+from transformers import (
+    HfArgumentParser,
+    AutoConfig
+)
+
+logger = logging.getLogger(__name__)
+
+
+@dataclass
+class ConfigArguments:
+    """
+    Arguments to which config we are going to set up.
+    """
+    output_dir: str = field(
+        default=".",
+        metadata={"help": "The output directory where the config will be written."},
+    )
+    name_or_path: Optional[str] = field(
+        default=None,
+        metadata={
+            "help": "The model checkpoint for weights initialization."
+                    "Don't set if you want to train a model from scratch."
+        },
+    )
+    params: Optional[str] = field(
+        default=None,
+        metadata={"help": "Custom configuration for the specific `name_or_path`"}
+    )
+
+    def __post_init__(self):
+        if self.params:
+            try:
+                self.params = ast.literal_eval(self.params)
+            except Exception as e:
+                print(f"Your custom parameters do not acceptable due to {e}")
+
+
+def main():
+    parser = HfArgumentParser([ConfigArguments])
+    if len(sys.argv) == 2 and sys.argv[1].endswith(".json"):
+        # If we pass only one argument to the script and it's the path to a json file,
+        # let's parse it to get our arguments.
+        config_args = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]))[0]
+    else:
+        config_args = parser.parse_args_into_dataclasses()[0]
+
+    # Setup logging
+    logging.basicConfig(
+        format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
+        datefmt="%m/%d/%Y %H:%M:%S",
+        handlers=[logging.StreamHandler(sys.stdout)],
+    )
+    logger.setLevel(logging.INFO)
+
+    logger.info(f"Setting up configuration {config_args.name_or_path} with extra params {config_args.params}")
+
+    if config_args.params and isinstance(config_args.params, dict):
+        config = AutoConfig.from_pretrained(config_args.name_or_path, **config_args.params)
+    else:
+        config = AutoConfig.from_pretrained(config_args.name_or_path)
+
+    logger.info(f"Your configuration saved here {config_args.output_dir}/config.json")
+    config.save_pretrained(config_args.output_dir)
+
+
+if __name__ == '__main__':
+    main()