Fixes type annotation for numpy arrays

Corrects type annotations for numpy arrays to allow for a broader range of numpy dtypes and resolves type checking errors.

Removes NumPy DTypeLike type hint

Updates type hints for NumPy compatibility by removing DTypeLike.

Ensures alignment with NumPy's typing system, preventing potential
type-related issues.

Author: robbiemu

gguf-py/gguf/quants.py

@@ -3,8 +3,6 @@
 from typing import Any, Callable, Sequence
 from math import log2, ceil
 
-from numpy.typing import DTypeLike
-
 from .constants import GGML_QUANT_SIZES, GGMLQuantizationType, QK_K
 from .lazy import LazyNumpyTensor
 
@@ -26,7 +24,7 @@ def quant_shape_from_byte_shape(shape: Sequence[int], quant_type: GGMLQuantizati
 
 
 # This is faster than np.vectorize and np.apply_along_axis because it works on more than one row at a time
-def _apply_over_grouped_rows(func: Callable[[np.ndarray], np.ndarray], arr: np.ndarray, otype: DTypeLike, oshape: tuple[int, ...]) -> np.ndarray:
+def _apply_over_grouped_rows(func: Callable[[np.ndarray], np.ndarray], arr: np.ndarray, otype: np.dtype[Any], oshape: tuple[int, ...]) -> np.ndarray:
     rows = arr.reshape((-1, arr.shape[-1]))
     osize = 1
     for dim in oshape:
@@ -80,7 +78,7 @@ class __Quant(ABC):
     block_size: int
     type_size: int
 
-    grid: np.ndarray[Any, np.dtype[np.float32]] | None = None
+    grid: np.ndarray | None = None # np.float32
     grid_shape: tuple[int, int] = (0, 0)
     grid_map: tuple[int | float, ...] = ()
     grid_hex: bytes | None = None