Delete push_to_hub.py with huggingface_hub

push_to_hub.py

@@ -1,106 +0,0 @@
-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()
-    
-