# 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 unittest
from functools import partial

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


class TestConvMishMkldnnFusePass(PassAutoScanTest):
    def is_program_valid(self, program_config: ProgramConfig) -> bool:
        attrs = [op.attrs for op in program_config.ops]
        # If the problem has been fixed, the judgment
        # needs to be deleted!!!
        if attrs[0]['data_format'] == "NHWC":
            return False

        return True

    def sample_program_config(self, draw):
        data_format = draw(st.sampled_from(["NCHW", "NHWC"]))
        dilations = draw(st.sampled_from([[1, 1], [2, 2], [1, 2]]))
        padding_algorithm = draw(st.sampled_from(["EXPLICIT", "SAME", "VALID"]))
        groups = draw(st.sampled_from([1, 2, 4]))
        paddings = draw(st.sampled_from([[0, 3], [1, 2, 3, 4]]))
        strides = draw(st.sampled_from([[1, 1], [2, 2], [1, 2]]))
        batch_size = draw(st.integers(min_value=1, max_value=4))

        def generate_input():
            if data_format == "NCHW":
                return np.random.random([batch_size, 48, 64, 64]).astype(
                    np.float32
                )
            else:
                return np.random.random([batch_size, 64, 64, 48]).astype(
                    np.float32
                )

        def generate_weight():
            return np.random.random([16, int(48 / groups), 3, 3]).astype(
                np.float32
            )

        ops_config = [
            {
                "op_type": "conv2d",
                "op_inputs": {
                    "Input": ["input_data"],
                    "Filter": ["input_weight"],
                },
                "op_outputs": {"Output": ["conv_output"]},
                "op_attrs": {
                    "data_format": data_format,
                    "dilations": dilations,
                    "padding_algorithm": padding_algorithm,
                    "groups": groups,
                    "paddings": paddings,
                    "strides": strides,
                },
            },
            {
                "op_type": "mish",
                "op_inputs": {"X": ["conv_output"]},
                "op_outputs": {"Out": ["mish_output"]},
                "op_attrs": {},
            },
        ]

        ops = self.generate_op_config(ops_config)

        program_config = ProgramConfig(
            ops=ops,
            weights={
                "input_weight": TensorConfig(data_gen=partial(generate_weight))
            },
            inputs={
                "input_data": TensorConfig(data_gen=partial(generate_input)),
            },
            outputs=["mish_output"],
        )

        return program_config

    def sample_predictor_configs(self, program_config):
        config = self.create_inference_config(use_mkldnn=True)
        yield config, ["fused_conv2d"], (1e-5, 1e-5)

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


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