# 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.

from auto_scan_test import PassAutoScanTest
from program_config import TensorConfig, ProgramConfig, OpConfig
import paddle.inference as paddle_infer
import unittest

import hypothesis.strategies as st


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()
