# Copyright (c) 2020 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 numpy as np
from inference_pass_test import InferencePassTest

import paddle
from paddle import base
from paddle.base import core
from paddle.base.core import AnalysisConfig


class TransposeFlattenConcatFusePassTRTTest(InferencePassTest):
    def setUp(self):
        with base.program_guard(self.main_program, self.startup_program):
            data1 = paddle.static.data(
                name="data1", shape=[8, 32, 128], dtype="float32"
            )
            data2 = paddle.static.data(
                name="data2", shape=[8, 32, 128], dtype="float32"
            )

            trans1 = paddle.transpose(data1, perm=[0, 2, 1])
            trans2 = paddle.transpose(data2, perm=[0, 2, 1])
            flatt1 = paddle.flatten(trans1, 1, -1)
            flatt2 = paddle.flatten(trans2, 1, -1)

            concat_out = paddle.concat([flatt1, flatt2], axis=1)
            # There is no parameters for above structure.
            # Hence, append a batch_norm to avoid failure caused by load_combined.
            reshape_out = paddle.reshape(concat_out, [-1, 0, 1, 1])
            out = paddle.static.nn.batch_norm(reshape_out, is_test=True)

        self.feeds = {
            "data1": np.random.random([8, 32, 128]).astype("float32"),
            "data2": np.random.random([8, 32, 128]).astype("float32"),
        }
        self.enable_trt = True
        self.trt_parameters = (
            TransposeFlattenConcatFusePassTRTTest.TensorRTParam(
                1 << 20, 8, 0, AnalysisConfig.Precision.Float32, False, False
            )
        )
        self.fetch_list = [out]

    def test_check_output(self):
        # There is no cpu pass for transpose_flatten_concat_fuse
        if core.is_compiled_with_cuda():
            use_gpu = True
            self.check_output_with_option(use_gpu)


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