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avoid numpy warning in split
Summary: avoid creating a numpy array of random things just to split it: this can now generate a warning e.g. if the list contains lists of varying lengths. There might also be a performance win here, and we could do more of the same if we care about that. (The vanilla way to avoid the new warning is to replace `np.split(a,` with `np.split(np.array(a, dtype=object), ` btw.) Reviewed By: shapovalov Differential Revision: D40209308 fbshipit-source-id: daae33a23ceb444e8e7241f72ce1525593e2f239
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@ -225,8 +225,8 @@ def _dataclass_list_from_dict_list(dlist, typeannot):
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assert indices[-1] == len(all_keys_res)
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keys = np.split(list(all_keys_res), indices[:-1])
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vals = np.split(list(all_vals_res), indices[:-1])
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return [cls(zip(k, v)) for k, v in zip(keys, vals)]
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all_vals_res_iter = iter(all_vals_res)
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return [cls(zip(k, all_vals_res_iter)) for k in keys]
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elif not dataclasses.is_dataclass(typeannot):
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return dlist
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