# 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 PassAutoScanTest
from program_config import TensorConfig, ProgramConfig
import numpy as np
import unittest
import hypothesis.strategies as st


class TestFCMishMkldnnFusePass(PassAutoScanTest):
    def sample_program_config(self, draw):
        x_shape = draw(
            st.lists(
                st.integers(
                    min_value=1, max_value=128), min_size=2, max_size=3))
        in_num_col_dims = len(x_shape) - 1
        w_shape = draw(
            st.lists(
                st.integers(
                    min_value=1, max_value=128), min_size=2, max_size=2))
        w_shape[0] = int(np.prod(x_shape[in_num_col_dims:]))
        fc_bias_shape = [w_shape[1]]

        ops_config = [{
            "op_type": "fc",
            "op_inputs": {
                "Input": ["fc_x"],
                "W": ["fc_w"],
                "Bias": ["fc_bias"]
            },
            "op_outputs": {
                "Out": ["fc_out"]
            },
            "op_attrs": {
                "activation_type": "",
                "padding_weights": False,
                "in_num_col_dims": in_num_col_dims,
                "use_mkldnn": True
            }
        }, {
            "op_type": "mish",
            "op_inputs": {
                "X": ["fc_out"]
            },
            "op_outputs": {
                "Out": ["mish_output"]
            },
            "op_attrs": {},
        }]

        ops = self.generate_op_config(ops_config)

        program_config = ProgramConfig(
            ops=ops,
            weights={
                "fc_w": TensorConfig(shape=w_shape),
                "fc_bias": TensorConfig(shape=fc_bias_shape),
            },
            inputs={"fc_x": TensorConfig(shape=x_shape), },
            outputs=["mish_output"])
        return program_config

    def sample_predictor_configs(self, program_config):
        config = self.create_inference_config(
            use_mkldnn=True, passes=["fc_act_mkldnn_fuse_pass"])
        yield config, ["fc"], (1e-5, 1e-5)

    def test(self):
        self.run_and_statis(quant=False, passes=["fc_act_mkldnn_fuse_pass"])


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