# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
#
# 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
# limitations under the License.

from auto_scan_test import MkldnnAutoScanTest
from program_config import TensorConfig, ProgramConfig, OpConfig
import numpy as np
from functools import partial
import unittest
from hypothesis import given
import hypothesis.strategies as st


class TestOneDNNPad3DOp(MkldnnAutoScanTest):

    def sample_program_configs(self, *args, **kwargs):

        def generate_input(*args, **kwargs):
            return np.random.random(kwargs['in_shape']).astype(np.float32)

        def generate_paddings():
            return np.random.randint(0, 4, size=(6)).astype(np.int32)

        pad3d_op = OpConfig(type="pad3d",
                            inputs={
                                "X": ["input_data"],
                                "Paddings": ["paddings_data"]
                            },
                            outputs={"Out": ["output_data"]},
                            attrs={
                                "mode": "constant",
                                "data_format": kwargs['data_format'],
                                "paddings": kwargs['paddings'],
                            })

        program_config = ProgramConfig(
            ops=[pad3d_op],
            weights={},
            inputs={
                "input_data":
                TensorConfig(data_gen=partial(generate_input, *args, **kwargs)),
                "paddings_data":
                TensorConfig(data_gen=generate_paddings)
            },
            outputs=["output_data"])

        yield program_config

    def sample_predictor_configs(self, program_config):
        config = self.create_inference_config(use_mkldnn=True)
        yield config, (1e-5, 1e-5)

    @given(data_format=st.sampled_from(['NCDHW', 'NDHWC']),
           use_paddings_tensor=st.sampled_from([True, False]),
           in_shape=st.sampled_from([[2, 3, 4, 5, 6], [1, 4, 1, 3, 2],
                                     [4, 3, 2, 1, 1], [1, 1, 1, 1, 1]]),
           paddings=st.sampled_from([[0, 0, 0, 0, 0, 0], [1, 2, 0, 1, 2, 1],
                                     [2, 5, 11, 3, 4, 3], [0, 5, 0, 1, 0, 2]]))
    def test(self, *args, **kwargs):
        self.run_test(quant=False, *args, **kwargs)


if __name__ == "__main__":
    unittest.main()
