Delete download.py with huggingface_hub

download.py

@@ -1,71 +0,0 @@
-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()