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download.py

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+import os
+import glob
+from datasets import load_dataset, get_dataset_config_names
+from dataclasses import dataclass, field
+from transformers import AutoModel, AutoTokenizer, AutoModelForCausalLM, AutoModelForSeq2SeqLM, HfArgumentParser
+from typing import Optional, List
+
+
+@dataclass
+class DownloadArgs:
+    model_cache_dir: str = field(
+        default='/share/LMs',
+        metadata={'help': 'Default path to save language models'}
+    )
+    model_name_or_path: Optional[str] = field(
+        default=None,
+        metadata={'help': 'Path to pretrained model or model identifier from huggingface.co/models'}
+    )
+    use_lm_class: bool = field(
+        default=False,
+        metadata={'help': 'Call .from_pretrained from AutoModelForCausalLM? Useful when downloading remote-code based lms.'}
+    )
+    dataset_cache_dir: str = field(
+        default='/share/peitian/Data/Datasets/huggingface',
+        metadata={'help': 'Default path to save huggingface datasets'}
+    )
+    dataset_name_or_path: Optional[str] = field(
+        default=None,
+        metadata={'help': 'Dataset name'}
+    )
+    dataset_subset: Optional[str] = field(
+        default=None,
+        metadata={'help': 'Dataset subset name'}
+    )
+    dataset_split: Optional[str] = field(
+        default=None,
+        metadata={'help': 'Dataset split'}
+    )
+    
+    revision: str = field(
+        default=None,
+        metadata={'help': 'Remote code revision'}
+    )
+    resume_download: bool = field(
+        default=False,
+        metadata={'help': 'Resume downloading'}
+    )
+    def __post_init__(self):
+        # folder or model not exists
+        if self.model_name_or_path is not None:
+            kwargs = {
+                'revision': self.revision,
+                'resume_download': self.resume_download
+            }
+            AutoTokenizer.from_pretrained(self.model_name_or_path, cache_dir=self.model_cache_dir, trust_remote_code=True, **kwargs)
+            if self.use_lm_class:
+                AutoModelForCausalLM.from_pretrained(self.model_name_or_path, cache_dir=self.model_cache_dir, trust_remote_code=True, **kwargs)
+            else:
+                AutoModel.from_pretrained(self.model_name_or_path, cache_dir=self.model_cache_dir, trust_remote_code=True, **kwargs)
+        if self.dataset_name_or_path is not None:
+            if self.dataset_subset is None:
+                dataset_subsets = get_dataset_config_names(self.dataset_name_or_path)
+                for dataset_subset in dataset_subsets:
+                    load_dataset(self.dataset_name_or_path, name=dataset_subset, split=self.dataset_split, cache_dir=self.dataset_cache_dir)
+            else:
+                load_dataset(self.dataset_name_or_path, name=self.dataset_subset, split=self.dataset_split, cache_dir=self.dataset_cache_dir)
+
+
+if __name__ == "__main__":
+    parser = HfArgumentParser([DownloadArgs])
+    args, = parser.parse_args_into_dataclasses()