chore: import upstream snapshot with attribution

2026-07-13 13:34:58 +08:00
commit a203934033
1368 changed files with 175001 additions and 0 deletions
@@ -0,0 +1,39 @@
+# Copyright (c) ModelScope Contributors. All rights reserved.
+import numpy as np
+import pandas as pd
+from typing import Any, Dict, List, Optional, Tuple, Union
+
+
+def transform_jsonl_to_df(dict_list: List[Dict[str, Any]]) -> pd.DataFrame:
+    """Relevant function: `io_utils.read_from_jsonl()`"""
+    data_dict: Dict[str, List[Any]] = {}
+    for i, obj in enumerate(dict_list):
+        for k, v in obj.items():
+            if k not in data_dict:
+                data_dict[k] = [None] * i
+            data_dict[k].append(v)
+        for k in set(data_dict.keys()) - set(obj.keys()):
+            data_dict[k].append(None)
+    return pd.DataFrame.from_dict(data_dict)
+
+
+def get_seed(random_state: Optional[np.random.RandomState] = None) -> int:
+    if random_state is None:
+        random_state = np.random.RandomState()
+    seed_max = np.iinfo(np.int32).max
+    seed = random_state.randint(0, seed_max)
+    return seed
+
+
+def stat_array(array: Union[np.ndarray, List[int], 'torch.Tensor']) -> Tuple[Dict[str, float], str]:
+    if isinstance(array, list):
+        if array and isinstance(array[0], list):
+            array = np.array([sum(sublist) for sublist in array])
+        array = np.array(array)
+    mean = array.mean().item()
+    std = array.std().item()
+    min_ = array.min().item()
+    max_ = array.max().item()
+    size = array.shape[0]
+    string = f'{mean:.6f}±{std:.6f}, min={min_:.6f}, max={max_:.6f}, size={size}'
+    return {'mean': mean, 'std': std, 'min': min_, 'max': max_, 'size': size}, string