# 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, SkipReasons
from program_config import TensorConfig, ProgramConfig, OpConfig
import numpy as np
import paddle.inference as paddle_infer
from functools import partial
from typing import Optional, List, Callable, Dict, Any, Set
import unittest

import hypothesis
from hypothesis import given, settings, seed, example, assume, reproduce_failure
import hypothesis.strategies as st


class TestConvElementwiseAdd2ActPass(PassAutoScanTest):
    """
        x_var   f_var(persistable)   
            \       /
                conv2d 
                |
                conv2d_var    y_var(persistable)
                    \          /
                elementwise_add
                        |
       x1_var  elementwise_add_out_var
         \              /
         elementwise_add
                |
               act
                |
              act_var
    """

    def sample_predictor_configs(self, program_config):
        # for gpu
        config = self.create_inference_config(use_gpu=True)
        yield config, ["conv2d_fusion"], (1e-4, 1e-5)

    def is_program_valid(self, prog_config):
        paddings = prog_config.ops[0].attrs["paddings"]
        strides = prog_config.ops[0].attrs["strides"]
        groups = prog_config.ops[0].attrs["groups"]
        padding_algorithm = prog_config.ops[0].attrs["padding_algorithm"]
        dilations = prog_config.ops[0].attrs["dilations"]
        data_format = prog_config.ops[0].attrs["data_format"]
        filter_shape = prog_config.weights["filter"].shape
        input_shape = prog_config.inputs["input_x"].shape
        if data_format != "NCHW":
            return False
        if padding_algorithm == "VALID":
            if int(((input_shape[2] - (dilations[0] * (filter_shape[2] - 1) + 1)) / strides[0] + 1)) <= 0 or \
            int(((input_shape[3] - (dilations[1] * (filter_shape[3] - 1) + 1)) / strides[1] + 1)) <= 0:
                return False
        if padding_algorithm == "EXPLICIT":
            if int(((input_shape[2] + paddings[0] + paddings[1] - (dilations[0] * (filter_shape[2] - 1) + 1)) / strides[0] + 1)) <= 0 or \
                int(((input_shape[3] + paddings[2] + paddings[3] - (dilations[1] * (filter_shape[3] - 1) + 1)) / strides[1] + 1)) <= 0:
                return False
        if padding_algorithm == "SAME":
            if int((input_shape[2] + strides[0] - 1) / strides[0]) <= 0 or int(
                (input_shape[3] + strides[1] - 1) / strides[1]) <= 0:
                return False
        if data_format == "NCHW":
            if input_shape[1] != filter_shape[1] * groups:
                return False
            if filter_shape[0] % groups != 0:
                return False
        else:
            if input_shape[3] != filter_shape[1] * groups:
                return False
            if filter_shape[0] % groups != 0:
                return False

        return True

    def sample_program_config(self, draw):

        is_not_valid = True
        program_config = None
        while is_not_valid:
            # 1. Generate shape of input:X of conv2d
            x_shape = draw(
                st.lists(st.integers(min_value=1, max_value=100),
                         min_size=4,
                         max_size=4))
            x_shape[1] = draw(st.integers(min_value=1, max_value=10))

            # 2. Generate legal attr:data_format of conv2d
            data_format = draw(st.sampled_from(["NCHW", "NHWC"]))

            # 3. Generate legal shape of input:Y of conv2d
            f_shape = draw(
                st.lists(st.integers(min_value=1, max_value=7),
                         min_size=4,
                         max_size=4))
            if data_format == "NCHW":
                f_shape[1] = x_shape[1]
            else:
                f_shape[1] = x_shape[3]

            # 4. Generate legal attr:strides of conv2d
            strides = draw(
                st.lists(st.integers(min_value=1, max_value=5),
                         min_size=2,
                         max_size=2))

            # 5. Generate legal attr:padding_algorithm of conv2d
            padding_algorithm = draw(
                st.sampled_from(["EXPLICIT", "SAME", "VALID"]))

            # 6. Generate legal attr:padding of conv2d
            padding = draw(
                st.lists(st.integers(min_value=1, max_value=5),
                         min_size=4,
                         max_size=4))

            # 7. Generate legal attr:groups of conv2d
            groups = draw(st.integers(min_value=1, max_value=3))

            # 8. Generate legal attr:dilations of conv2d
            dilations = draw(
                st.lists(st.integers(min_value=1, max_value=5),
                         min_size=2,
                         max_size=2))

            # 9. Generate legal elemntwise_add: X of conv2d
            bias_2_dict = dict()
            bias_2_dict[1] = [x_shape[0], f_shape[0], \
                int(((x_shape[2] + padding[0] + padding[1] - (dilations[0] * (f_shape[2] - 1) + 1)) / strides[0] + 1)), \
                int(((x_shape[3] + padding[2] + padding[3] - (dilations[1] * (f_shape[3] - 1) + 1)) / strides[1] + 1))]

            bias_2_dict[2] = [x_shape[0], f_shape[0], \
                int((x_shape[2] + strides[0] - 1) / strides[0]), \
                int((x_shape[3] + strides[1] - 1) / strides[1])]

            bias_2_dict[3] = [x_shape[0], f_shape[0], \
                int(((x_shape[2] - (dilations[0] * (f_shape[2] - 1) + 1)) / strides[0] + 1)), \
                int(((x_shape[3] - (dilations[1] * (f_shape[3] - 1) + 1)) / strides[1] + 1))]
            bias_index = 1
            if padding_algorithm == "SAME":
                bias_index = 2
            if padding_algorithm == "VALID":
                bias_index = 3
            bias_2_shape = bias_2_dict[bias_index]

            if np.sum(np.array(bias_2_shape) <= 0) == 0:
                is_not_valid = False
            else:
                continue

            # 10. Generate legal shape of input:bias of elementwise_add
            bias_shape = [f_shape[0]]

            # 11. Generate legal attr:axis of elementwise_add_1
            axis_1 = 1

            # 12. Generate legal attr:axis of elementwise_add_2
            axis_2 = -1

            conv2d_op = OpConfig("conv2d",
                                 inputs={
                                     "Input": ["input_x"],
                                     "Filter": ["filter"]
                                 },
                                 outputs={"Output": ["conv2d_out"]},
                                 strides=strides,
                                 padding_algorithm=padding_algorithm,
                                 paddings=padding,
                                 groups=groups,
                                 dilations=dilations,
                                 data_format=data_format)
            add_1_op = OpConfig("elementwise_add",
                                inputs={
                                    "X": ["conv2d_out"],
                                    "Y": ["bias_1"]
                                },
                                outputs={"Out": ["add_1_out"]},
                                axis=axis_1)

            add_2_op = OpConfig("elementwise_add",
                                inputs={
                                    "X": ["bias_2"],
                                    "Y": ["add_1_out"]
                                },
                                outputs={"Out": ["add_out"]},
                                axis=axis_2)

            relu_op = OpConfig("relu",
                               inputs={"X": ["add_out"]},
                               outputs={"Out": ["relu_out"]})

            ops = [conv2d_op, add_1_op, add_2_op, relu_op]

            program_config = ProgramConfig(
                ops=ops,
                weights={
                    "filter": TensorConfig(shape=f_shape),
                    "bias_1": TensorConfig(shape=bias_shape),
                },
                inputs={
                    "input_x": TensorConfig(shape=x_shape),
                    "bias_2": TensorConfig(shape=bias_2_shape)
                },
                outputs=ops[-1].outputs["Out"],
            )
        return program_config

    def test(self):
        self.run_and_statis(quant=False,
                            max_examples=300,
                            passes=["conv_elementwise_add2_act_fuse_pass"])


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