# 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

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

import paddle.inference as paddle_infer


class TestAdaptivePool2dConvertGlobalPass(PassAutoScanTest):
    def is_program_valid(self, program_config: ProgramConfig) -> bool:
        return True

    def sample_program_config(self, draw):
        x_shape = draw(
            st.lists(
                st.integers(min_value=1, max_value=4), min_size=4, max_size=4
            )
        )
        pooling_type = draw(st.sampled_from(["max", "avg"]))

        data_format = "NCHW"  # trt support this format only
        strides = draw(
            st.lists(
                st.integers(min_value=1, max_value=4), min_size=2, max_size=2
            )
        )

        paddings = draw(
            st.lists(
                st.integers(min_value=1, max_value=4), min_size=2, max_size=2
            )
        )

        ceil_mode = draw(st.booleans())
        exclusive = draw(st.booleans())
        global_pooling = draw(st.booleans())
        padding_algorithm = draw(st.sampled_from(["EXPLICIT", "SAME", "VAILD"]))

        pool_op = OpConfig(
            "pool2d",
            inputs={"X": ["input_data"]},
            outputs={"Out": ["pool_output"]},
            ksize=[1, 1],
            adaptive=True,
            pooling_type=pooling_type,
            data_format=data_format,
            strides=strides,
            paddings=paddings,
            ceil_mode=ceil_mode,
            global_pooling=global_pooling,
            padding_algorithm=padding_algorithm,
            exclusive=exclusive,
        )
        ops = [pool_op]

        program_config = ProgramConfig(
            ops=ops,
            weights={},
            inputs={
                "input_data": TensorConfig(shape=x_shape),
            },
            outputs=["pool_output"],
        )

        return program_config

    def sample_predictor_configs(self, program_config):
        config = self.create_trt_inference_config()
        config.enable_tensorrt_engine(
            max_batch_size=4,
            workspace_size=102400,
            min_subgraph_size=0,
            precision_mode=paddle_infer.PrecisionType.Float32,
            use_static=False,
            use_calib_mode=False,
        )
        yield config, ['pool2d'], (1e-5, 1e-5)

    def test(self):
        self.run_and_statis(
            quant=False,
            max_examples=300,
            passes=["adaptive_pool2d_convert_global_pass"],
            min_success_num=40,
        )


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