#!/usr/bin/env python3
#
# SPDX-FileCopyrightText: Copyright (c) 1993-2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
# SPDX-License-Identifier: Apache-2.0
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
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import onnx_graphsurgeon as gs
import numpy as np
import onnx
import ml_dtypes


def generate(dtype, out_path):
    X = gs.Variable(name="X", dtype=dtype, shape=(1, 3, 224, 224))
    W = gs.Constant(name="W", values=np.ones(shape=(5, 3, 3, 3), dtype=dtype) * 0.5)
    Y = gs.Variable(name="Y", dtype=dtype, shape=(1, 5, 222, 222))
    node = gs.Node(op="Conv", inputs=[X, W], outputs=[Y])

    graph = gs.Graph(nodes=[node], inputs=[X], outputs=[Y], ir_version=10)
    onnx.save(gs.export_onnx(graph), out_path)


generate(ml_dtypes.bfloat16, "test_conv_bf16.onnx")
generate(ml_dtypes.float8_e4m3fn, "test_conv_float8e4m3fn.onnx")
