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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()
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