mismatch for datatypes when providing Features with Array2D and user specified dtype and using with_format("numpy")

[Akhil-CM]

Describe the bug

If the user provides a Features type value to datasets.Dataset with members having Array2D with a value for dtype, it is not respected during with_format("numpy") which should return a np.array with dtype that the user provided for Array2D. It seems for floats, it will be set to float32 and for ints it will be set to int64

Steps to reproduce the bug

import numpy as np
import datasets
from datasets import Dataset, Features, Array2D

print(f"datasets version: {datasets.__version__}")

data_info = {
    "arr_float" : "float64",
    "arr_int" : "int32"
}

sample = {key : [np.zeros([4, 5], dtype=dtype)] for key, dtype in data_info.items()}

features = {key : Array2D(shape=(None, 5), dtype=dtype) for key, dtype in data_info.items()}
features = Features(features)

dataset = Dataset.from_dict(sample, features=features)

ds = dataset.with_format("numpy")
for key in features:
    print(f"{key} feature dtype: ", ds.features[key].dtype)
    print(f"{key} dtype:", ds[key].dtype)

Output:

datasets version: 3.0.2
arr_float feature dtype:  float64
arr_float dtype: float32
arr_int feature dtype:  int32
arr_int dtype: int64

Expected behavior

It should return a np.array with dtype that the user provided for the corresponding member in the Features type value

Environment info

• datasets version: 3.0.2
• Platform: Linux-6.11.5-arch1-1-x86_64-with-glibc2.40
• Python version: 3.12.7
• huggingface_hub version: 0.26.1
• PyArrow version: 16.1.0
• Pandas version: 2.2.2
• fsspec version: 2024.5.0

[Akhil-CM]

It seems that #5517 is exactly the same issue.

It was mentioned there that this would be fixed in version 3.x