# 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.

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


class TestFCElementwiseAddOneDNNFusePass(PassAutoScanTest):
    def sample_program_config(self, draw):
        axis = draw(st.sampled_from([-1, 0, 1]))
        fc_as_x = draw(st.booleans())
        fc_in = draw(st.sampled_from([32, 64]))
        fc_wei = draw(st.sampled_from([32, 64]))

        def generate_data(shape):
            return np.random.random(shape).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, 'use_mkldnn': True},
        )

        model_net = [relu_op, fc_op, elt_add_op]

        program_config = ProgramConfig(
            ops=model_net,
            weights={
                'fc_weight': TensorConfig(
                    data_gen=partial(generate_data, [fc_wei, fc_wei])
                ),
                'fc_bias': TensorConfig(
                    data_gen=partial(generate_data, [fc_wei])
                ),
            },
            inputs={
                'input_data': TensorConfig(
                    data_gen=partial(generate_data, [fc_in, fc_wei])
                )
            },
            outputs=['elementwise_output'],
        )

        return program_config

    def sample_predictor_configs(self, program_config):
        config = self.create_inference_config(
            use_mkldnn=True, passes=['fc_elementwise_add_mkldnn_fuse_pass']
        )
        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()