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# Copyright (c) ONNX Project Contributors
#
# SPDX-License-Identifier: Apache-2.0
import numpy as np
import onnx
from onnx.backend.test.case.base import Base
from onnx.backend.test.case.node import expect
def bernoulli_reference_implementation(x, dtype): # type: ignore
# binomial n = 1 equal bernoulli
# This example and test-case is for informational purpose. The generator operator is
# non-deterministic and may not produce the same values in different implementations
# even if a seed is specified.
return np.random.binomial(1, p=x).astype(dtype)
class Bernoulli(Base):
@staticmethod
def export_bernoulli_without_dtype() -> None:
node = onnx.helper.make_node(
"Bernoulli",
inputs=["x"],
outputs=["y"],
)
x = np.random.uniform(0.0, 1.0, 10).astype(float)
y = bernoulli_reference_implementation(x, float)
expect(node, inputs=[x], outputs=[y], name="test_bernoulli")
@staticmethod
def export_bernoulli_with_dtype() -> None:
node = onnx.helper.make_node(
"Bernoulli",
inputs=["x"],
outputs=["y"],
dtype=onnx.TensorProto.DOUBLE,
)
x = np.random.uniform(0.0, 1.0, 10).astype(np.float32)
y = bernoulli_reference_implementation(x, float)
expect(node, inputs=[x], outputs=[y], name="test_bernoulli_double")
@staticmethod
def export_bernoulli_with_seed() -> None:
seed = float(0)
node = onnx.helper.make_node(
"Bernoulli",
inputs=["x"],
outputs=["y"],
seed=seed,
)
x = np.random.uniform(0.0, 1.0, 10).astype(np.float32)
y = bernoulli_reference_implementation(x, np.float32)
expect(node, inputs=[x], outputs=[y], name="test_bernoulli_seed")
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