# 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 TestSeqconvEltaddReluFusePass(PassAutoScanTest):
    def is_program_valid(self, program_config: ProgramConfig) -> bool:
        return True

    def sample_program_config(self, draw):
        contextLength = draw(st.sampled_from([1, 2, 3, 4]))
        contextStart = draw(st.sampled_from([1, 2, 3]))
        contextStride = draw(st.sampled_from([1]))
        paddingTrainable = False
        axis = draw(st.sampled_from([1]))
        batch_size = draw(st.integers(min_value=1, max_value=4))

        def generate_input():
            shape = [batch_size, 128, 6, 120]
            return np.random.random(shape).astype(np.float32)

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

        im2sequence_op = OpConfig(
            type="im2sequence",
            inputs={"X": ["input_data"]},
            outputs={"Out": ["seq_out"]},
            attrs={
                "kernels": [6, 1],
                "out_stride": [1, 1],
                "paddings": [0, 0, 0, 0],
                "strides": [1, 1],
            },
        )

        sequence_conv_op = OpConfig(
            type="sequence_conv",
            inputs={"X": ["seq_out"], "Filter": ["conv_weight"]},
            outputs={"Out": ["conv_out"]},
            attrs={
                "contextLength": contextLength,
                "contextStart": contextStart,
                "contextStride": contextStride,
                "paddingTrainable": paddingTrainable,
            },
        )

        elementwise_add_op = OpConfig(
            type="elementwise_add",
            inputs={"X": ["conv_out"], "Y": ["elt_weight"]},
            outputs={"Out": ["elt_output"]},
            attrs={'axis': axis},
        )

        relu_op = OpConfig(
            type="relu",
            inputs={"X": ["elt_output"]},
            outputs={"Out": ["relu_output"]},
            attrs={},
        )

        model_net = [
            im2sequence_op,
            sequence_conv_op,
            elementwise_add_op,
            relu_op,
        ]

        program_config = ProgramConfig(
            ops=model_net,
            weights={
                "conv_weight": TensorConfig(
                    data_gen=partial(generate_weight, [768 * contextLength, 16])
                ),
                "elt_weight": TensorConfig(
                    data_gen=partial(generate_weight, [16])
                ),
            },
            inputs={
                "input_data": TensorConfig(data_gen=partial(generate_input))
            },
            outputs=["relu_output"],
        )

        return program_config

    def sample_predictor_configs(self, program_config):
        config = self.create_inference_config()
        yield config, ["im2sequence", "fusion_seqconv_eltadd_relu"], (
            1e-5,
            1e-5,
        )

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


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