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chore: import upstream snapshot with attribution
2026-07-13 12:41:19 +08:00

52 lines
1.7 KiB
Python

# Copyright (c) ONNX Project Contributors
# SPDX-License-Identifier: Apache-2.0
from __future__ import annotations
import numpy as np
from onnx.reference.op_run import OpRun
def _specify_int64(indices, inverse_indices, counts):
return (
np.array(indices, dtype=np.int64),
np.array(inverse_indices, dtype=np.int64),
np.array(counts, dtype=np.int64),
)
class Unique(OpRun):
def _run(self, x, axis=None, sorted=None): # type: ignore[override] # noqa: A002
if axis is None or np.isnan(axis):
y, indices, inverse_indices, counts = np.unique(x, True, True, True)
else:
y, indices, inverse_indices, counts = np.unique(
x, True, True, True, axis=axis
)
if len(self.onnx_node.output) == 1:
return (y,)
if not sorted:
argsorted_indices = np.argsort(indices)
inverse_indices_map = dict(
zip(argsorted_indices, np.arange(len(argsorted_indices)), strict=True)
)
indices = indices[argsorted_indices]
y = np.take(x, indices, axis=0)
inverse_indices = np.asarray(
[inverse_indices_map[i] for i in inverse_indices], dtype=np.int64
)
counts = counts[argsorted_indices]
indices, inverse_indices, counts = _specify_int64(
indices, inverse_indices, counts
)
# numpy 2.0 has a different behavior than numpy 1.x.
inverse_indices = inverse_indices.reshape(-1)
if len(self.onnx_node.output) == 2:
return (y, indices)
if len(self.onnx_node.output) == 3:
return (y, indices, inverse_indices)
return (y, indices, inverse_indices, counts)