Upload push_to_hub.py with huggingface_hub

push_to_hub.py

@@ -0,0 +1,106 @@
+from pathlib import Path
+from datasets import load_dataset, load_from_disk
+from dataclasses import dataclass, field
+from huggingface_hub import HfApi
+from transformers import AutoModel, AutoTokenizer, 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'}
+    )
+    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'}
+    )
+    data_files: Optional[dict] = field(
+        default=None,
+        metadata={'help': 'Data files for json dataset.'}
+    )
+    dataset_from_disk: bool = field(
+        default=False,
+        metadata={'help': 'Load dataset from disk?'}
+    )
+    
+    file: Optional[str] = field(
+        default=None,
+        metadata={'help': 'File to upload.'}
+    )
+    file_in_repo: Optional[str] = field(
+        default=None,
+        metadata={'help': 'File name in repository.'}
+    )
+    
+    hub_name: Optional[str] = field(
+        default=None,
+        metadata={'help': 'Name of the huggingface repo.'}
+    )
+    
+    revision: str = field(
+        default=None,
+        metadata={'help': 'Remote code revision'}
+    )
+    resume_download: bool = field(
+        default=True,
+        metadata={'help': 'Resume downloading'}
+    )
+    def __post_init__(self):
+        # folder or model not exists
+        if self.model_name_or_path is not None:
+            tokenizer = AutoTokenizer.from_pretrained(self.model_name_or_path, cache_dir=self.model_cache_dir, trust_remote_code=True)
+            model = AutoModel.from_pretrained(self.model_name_or_path, cache_dir=self.model_cache_dir, trust_remote_code=True)
+            # use loop to force success upload
+            while 1:
+                try:
+                    tokenizer.push_to_hub(self.hub_name)
+                    break
+                except:
+                    pass
+            while 1:
+                try:
+                    model.push_to_hub(self.hub_name)
+                    break
+                except:
+                    pass
+            
+        if self.dataset_name_or_path is not None:
+            if self.dataset_from_disk:
+                dataset = load_from_disk(self.dataset_name_or_path)
+            else:
+                dataset = load_dataset(self.dataset_name_or_path, data_files=self.data_files, cache_dir=self.dataset_cache_dir)
+            # use loop to force success upload
+            while 1:
+                try:
+                    dataset.push_to_hub(self.hub_name)
+                    break
+                except:
+                    pass
+        
+        if self.file is not None:
+            api = HfApi()
+            if self.file_in_repo is None:
+                self.file_in_repo = Path(self.file).name
+            api.upload_file(
+                path_or_fileobj=self.file,
+                path_in_repo=self.file_in_repo,
+                repo_id=self.hub_name,
+                repo_type="dataset",
+            )
+
+
+if __name__ == "__main__":
+    parser = HfArgumentParser([DownloadArgs])
+    args, = parser.parse_args_into_dataclasses()
+    
+