# 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 PassAutoScanTest
from program_config import OpConfig, ProgramConfig, TensorConfig


class TestRepeatedFcReluFusePass(PassAutoScanTest):
    def is_program_valid(self, program_config: ProgramConfig) -> bool:
        return True

    def sample_program_config(self, draw):
        x_col = draw(st.sampled_from([1]))
        y_col = draw(st.sampled_from([1]))
        axis = draw(st.sampled_from([-1, 1]))
        batch_size = draw(st.integers(min_value=1, max_value=4))
        dim = draw(st.sampled_from([32, 64, 128]))

        def generate_input():
            return np.random.random([batch_size, dim]).astype(np.float32)

        def generate_weight(shape):
            return np.random.random(shape).astype(np.float32)

        attrs = [
            {"x_col": x_col, "y_col": y_col},
            {"axis": axis},
            {'batch_size': batch_size, 'dim': dim},
        ]

        mul_op1 = OpConfig(
            type="mul",
            inputs={"X": ["input_data"], "Y": ["mul1_weight"]},
            outputs={"Out": ["mul1_output"]},
            attrs={"x_num_col_dims": x_col, "y_num_col_dims": y_col},
        )

        elt_op1 = OpConfig(
            type="elementwise_add",
            inputs={"X": ["mul1_output"], "Y": ["elementwise1_weight"]},
            outputs={"Out": ["elementwise1_output"]},
            attrs={"axis": axis},
        )

        relu_op1 = OpConfig(
            type="relu",
            inputs={"X": ["elementwise1_output"]},
            outputs={"Out": ["relu1_output"]},
            attrs={},
        )

        mul_op2 = OpConfig(
            type="mul",
            inputs={"X": ["relu1_output"], "Y": ["mul2_weight"]},
            outputs={"Out": ["mul2_output"]},
            attrs={"x_num_col_dims": x_col, "y_num_col_dims": y_col},
        )

        elt_op2 = OpConfig(
            type="elementwise_add",
            inputs={"X": ["mul2_output"], "Y": ["elementwise2_weight"]},
            outputs={"Out": ["elementwise2_output"]},
            attrs={"axis": axis},
        )

        relu_op2 = OpConfig(
            type="relu",
            inputs={"X": ["elementwise2_output"]},
            outputs={"Out": ["relu2_output"]},
            attrs={},
        )

        model_net = [mul_op1, elt_op1, relu_op1, mul_op2, elt_op2, relu_op2]

        program_config = ProgramConfig(
            ops=model_net,
            weights={
                "mul1_weight": TensorConfig(
                    data_gen=partial(generate_weight, [dim, 32])
                ),
                "mul2_weight": TensorConfig(
                    data_gen=partial(generate_weight, [32, 128])
                ),
                "elementwise1_weight": TensorConfig(
                    data_gen=partial(generate_weight, [32])
                ),
                "elementwise2_weight": TensorConfig(
                    data_gen=partial(generate_weight, [128])
                ),
            },
            inputs={
                "input_data": TensorConfig(data_gen=partial(generate_input)),
            },
            outputs=["relu2_output"],
        )

        return program_config

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

    def test(self):
        self.run_and_statis(passes=["repeated_fc_relu_fuse_pass"])


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