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import hashlib
from typing import TYPE_CHECKING, Dict, List, Optional, Union
from llmtuner.extras.logging import get_logger
if TYPE_CHECKING:
from datasets import Dataset, IterableDataset
from transformers import TrainingArguments
from llmtuner.hparams import DataArguments
logger = get_logger(__name__)
EXT2TYPE = {
"csv": "csv",
"json": "json",
"jsonl": "json",
"txt": "text"
}
def checksum(data_files: List[str], file_sha1: Optional[str] = None) -> None:
if file_sha1 is None:
logger.warning("Checksum failed: missing SHA-1 hash value in dataset_info.json.")
return
if len(data_files) != 1:
logger.warning("Checksum failed: too many files.")
return
with open(data_files[0], "rb") as f:
sha1 = hashlib.sha1(f.read()).hexdigest()
if sha1 != file_sha1:
logger.warning("Checksum failed: mismatched SHA-1 hash value at {}.".format(data_files[0]))
def split_dataset(
dataset: Union["Dataset", "IterableDataset"],
data_args: "DataArguments",
training_args: "TrainingArguments"
) -> Dict[str, "Dataset"]:
if training_args.do_train:
if data_args.val_size > 1e-6: # Split the dataset
if data_args.streaming:
val_set = dataset.take(int(data_args.val_size))
train_set = dataset.skip(int(data_args.val_size))
dataset = dataset.shuffle(buffer_size=data_args.buffer_size, seed=training_args.seed)
return {"train_dataset": train_set, "eval_dataset": val_set}
else:
val_size = int(data_args.val_size) if data_args.val_size > 1 else data_args.val_size
dataset = dataset.train_test_split(test_size=val_size, seed=training_args.seed)
return {"train_dataset": dataset["train"], "eval_dataset": dataset["test"]}
else:
if data_args.streaming:
dataset = dataset.shuffle(buffer_size=data_args.buffer_size, seed=training_args.seed)
return {"train_dataset": dataset}
else: # do_eval or do_predict
return {"eval_dataset": dataset}
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