# Copyright (c) 2021 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 unittest
from functools import partial

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


class TestMkldnnPreluOp(MkldnnAutoScanTest):
    def is_program_valid(self, program_config: ProgramConfig) -> bool:
        # if mode is channel, and in_shape is 1 rank
        if (
            len(program_config.inputs['input_data'].shape) == 1
            and program_config.ops[0].attrs['mode'] == 'channel'
        ):
            return False
        return True

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

        def generate_alpha(*args, **kwargs):
            if kwargs["mode"] == "all":
                return np.random.random(size=(1)).astype(np.float32)
            elif kwargs["mode"] == "channel":
                if len(kwargs['in_shape']) <= 1:
                    # not valid case, just return 0
                    return np.zeros((1)).astype(np.float32)
                if kwargs['data_format'] == 'NCHW':
                    return np.random.random(kwargs['in_shape'][1]).astype(
                        np.float32
                    )
                else:
                    return np.random.random(kwargs['in_shape'][-1]).astype(
                        np.float32
                    )
            else:
                if len(kwargs['in_shape']) <= 1:
                    # not valid case, just return 0
                    return np.zeros((1)).astype(np.float32)
                return np.random.random(kwargs['in_shape']).astype(np.float32)

        prelu_op = OpConfig(
            type="prelu",
            inputs={"X": ["input_data"], "Alpha": ["alpha_weight"]},
            outputs={"Out": ["output_data"]},
            attrs={
                "mode": kwargs['mode'],
                "data_format": kwargs['data_format'],
            },
        )

        program_config = ProgramConfig(
            ops=[prelu_op],
            weights={
                "alpha_weight": TensorConfig(
                    data_gen=partial(generate_alpha, *args, **kwargs)
                )
            },
            inputs={
                "input_data": TensorConfig(
                    data_gen=partial(generate_input, *args, **kwargs)
                ),
            },
            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)

    def add_skip_pass_case(self):
        pass

    @given(
        mode=st.sampled_from(['all', 'channel', 'element']),
        data_format=st.sampled_from(['NCHW', 'NHWC']),
        in_shape=st.lists(
            st.integers(min_value=1, max_value=32), min_size=1, max_size=4
        ),
    )
    def test(self, *args, **kwargs):
        self.add_skip_pass_case()
        self.run_test(quant=False, *args, **kwargs)


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