# 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, OpConfig
import numpy as np
from functools import partial
import unittest

import hypothesis.strategies as st


class TestFCElementwiseAddMkldnnFusePass(PassAutoScanTest):

    def sample_program_config(self, draw):
        axis = draw(st.sampled_from([-1, 0, 1]))
        fc_as_x = draw(st.sampled_from([True, False]))
        fc_in = draw(st.sampled_from([32, 64]))
        fc_wei = draw(st.sampled_from([32, 64]))

        def generate_input():
            return np.random.random([fc_in, fc_wei]).astype(np.float32)

        def generate_fc_weight():
            return np.random.random([fc_wei, fc_wei]).astype(np.float32)

        def generate_fc_bias():
            return np.random.random([fc_wei]).astype(np.float32)

        relu_op = OpConfig(type="relu",
                           inputs={"X": ["input_data"]},
                           outputs={"Out": ["relu_out"]},
                           attrs={})

        fc_op = OpConfig(type="fc",
                         inputs={
                             "Input": ["relu_out"],
                             "W": ["fc_weight"],
                             "Bias": ["fc_bias"]
                         },
                         outputs={"Out": ["fc_output"]},
                         attrs={
                             "use_mkldnn": True,
                             "padding_weights": False,
                             "activation_type": "",
                             "in_num_col_dims": 1,
                         })

        if fc_as_x:
            inputs = {"X": ["fc_output"], "Y": ["input_data"]}
        else:
            inputs = {"X": ["input_data"], "Y": ["fc_output"]}

        elt_add_op = OpConfig(type="elementwise_add",
                              inputs=inputs,
                              outputs={"Out": ["elementwise_output"]},
                              attrs={'axis': axis})

        model_net = [relu_op, fc_op, elt_add_op]

        program_config = ProgramConfig(
            ops=model_net,
            weights={
                "fc_weight": TensorConfig(data_gen=partial(generate_fc_weight)),
                "fc_bias": TensorConfig(data_gen=partial(generate_fc_bias)),
            },
            inputs={
                "input_data": TensorConfig(data_gen=partial(generate_input))
            },
            outputs=["elementwise_output"])

        return program_config

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

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


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