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# Copyright (c) ONNX Project Contributors
#
# SPDX-License-Identifier: Apache-2.0
import numpy as np # type: ignore
import onnx
from onnx.backend.test.case.base import Base
from onnx.backend.test.case.node import expect
from onnx.numpy_helper import create_random_int
class BitwiseAnd(Base):
@staticmethod
def export() -> None:
node = onnx.helper.make_node(
"BitwiseAnd",
inputs=["x", "y"],
outputs=["bitwiseand"],
)
# 2d
x = create_random_int((3, 4), np.int32)
y = create_random_int((3, 4), np.int32)
z = np.bitwise_and(x, y)
expect(node, inputs=[x, y], outputs=[z], name="test_bitwise_and_i32_2d")
# 3d
x = create_random_int((3, 4, 5), np.int16)
y = create_random_int((3, 4, 5), np.int16)
z = np.bitwise_and(x, y)
expect(node, inputs=[x, y], outputs=[z], name="test_bitwise_and_i16_3d")
@staticmethod
def export_bitwiseand_broadcast() -> None:
node = onnx.helper.make_node(
"BitwiseAnd",
inputs=["x", "y"],
outputs=["bitwiseand"],
)
# 3d vs 1d
x = create_random_int((3, 4, 5), np.uint64)
y = create_random_int((5,), np.uint64)
z = np.bitwise_and(x, y)
expect(
node, inputs=[x, y], outputs=[z], name="test_bitwise_and_ui64_bcast_3v1d"
)
# 4d vs 3d
x = create_random_int((3, 4, 5, 6), np.uint8)
y = create_random_int((4, 5, 6), np.uint8)
z = np.bitwise_and(x, y)
expect(node, inputs=[x, y], outputs=[z], name="test_bitwise_and_ui8_bcast_4v3d")
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