# 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 os
import unittest
from functools import partial
from typing import Any, Dict, List

import numpy as np
from program_config import ProgramConfig, TensorConfig
from trt_layer_auto_scan_test import SkipReasons, TrtLayerAutoScanTest

import paddle.inference as paddle_infer


class TrtConvertYoloBoxTest(TrtLayerAutoScanTest):
    def is_program_valid(self, program_config: ProgramConfig) -> bool:
        return True

    def sample_program_configs(self):
        def generate_input1(attrs: List[Dict[str, Any]], batch, channel):
            if attrs[0]['iou_aware']:
                return np.ones([batch, 3 * (channel + 6), 13, 13]).astype(
                    np.float32
                )
            else:
                return np.ones([batch, 3 * (channel + 5), 13, 13]).astype(
                    np.float32
                )

        def generate_input2(attrs: List[Dict[str, Any]], batch):
            return np.random.random([batch, 2]).astype(np.int32)

        for batch in [1, 4]:
            for class_num in [80, 30]:
                for anchors in [[10, 13, 16, 30, 33, 23]]:
                    for downsample_ratio in [32, 16]:
                        for conf_thresh in [0.01, 0.02]:
                            for clip_bbox in [True, False]:
                                for scale_x_y in [1.0, 0.9]:
                                    for iou_aware in [False, True]:
                                        for iou_aware_factor in [0.5]:
                                            dics = [
                                                {
                                                    "class_num": class_num,
                                                    "anchors": anchors,
                                                    "downsample_ratio": downsample_ratio,
                                                    "conf_thresh": conf_thresh,
                                                    "clip_bbox": clip_bbox,
                                                    "scale_x_y": scale_x_y,
                                                    "iou_aware": iou_aware,
                                                    "iou_aware_factor": iou_aware_factor,
                                                },
                                                {},
                                            ]
                                            ops_config = [
                                                {
                                                    "op_type": "yolo_box",
                                                    "op_inputs": {
                                                        "X": ["yolo_box_input"],
                                                        "ImgSize": ["imgsize"],
                                                    },
                                                    "op_outputs": {
                                                        "Boxes": ["boxes"],
                                                        "Scores": ["scores"],
                                                    },
                                                    "op_attrs": dics[0],
                                                }
                                            ]
                                            ops = self.generate_op_config(
                                                ops_config
                                            )
                                            program_config = ProgramConfig(
                                                ops=ops,
                                                weights={},
                                                inputs={
                                                    "yolo_box_input": TensorConfig(
                                                        data_gen=partial(
                                                            generate_input1,
                                                            dics,
                                                            batch,
                                                            class_num,
                                                        )
                                                    ),
                                                    "imgsize": TensorConfig(
                                                        data_gen=partial(
                                                            generate_input2,
                                                            dics,
                                                            batch,
                                                        )
                                                    ),
                                                },
                                                outputs=["boxes", "scores"],
                                            )

                                            yield program_config

    def sample_predictor_configs(
        self, program_config
    ) -> (paddle_infer.Config, List[int], float):
        def generate_dynamic_shape(attrs):
            if attrs[0]['iou_aware']:
                channel = 3 * (attrs[0]['class_num'] + 6)
                self.dynamic_shape.min_input_shape = {
                    "yolo_box_input": [1, channel, 12, 12],
                    "imgsize": [1, 2],
                }
                self.dynamic_shape.max_input_shape = {
                    "yolo_box_input": [4, channel, 24, 24],
                    "imgsize": [4, 2],
                }
                self.dynamic_shape.opt_input_shape = {
                    "yolo_box_input": [1, channel, 24, 24],
                    "imgsize": [1, 2],
                }
            else:
                channel = 3 * (attrs[0]['class_num'] + 5)
                self.dynamic_shape.min_input_shape = {
                    "yolo_box_input": [1, channel, 12, 12],
                    "imgsize": [1, 2],
                }
                self.dynamic_shape.max_input_shape = {
                    "yolo_box_input": [4, channel, 24, 24],
                    "imgsize": [4, 2],
                }
                self.dynamic_shape.opt_input_shape = {
                    "yolo_box_input": [1, channel, 24, 24],
                    "imgsize": [1, 2],
                }

        def clear_dynamic_shape():
            self.dynamic_shape.min_input_shape = {}
            self.dynamic_shape.max_input_shape = {}
            self.dynamic_shape.opt_input_shape = {}

        def generate_trt_nodes_num(attrs, dynamic_shape):
            return 1, 4

        attrs = [
            program_config.ops[i].attrs for i in range(len(program_config.ops))
        ]
        # for static_shape
        clear_dynamic_shape()
        self.trt_param.precision = paddle_infer.PrecisionType.Float32
        program_config.set_input_type(np.float32)
        yield self.create_inference_config(), generate_trt_nodes_num(
            attrs, False
        ), 1e-5
        self.trt_param.precision = paddle_infer.PrecisionType.Half
        program_config.set_input_type(np.float16)
        yield self.create_inference_config(), generate_trt_nodes_num(
            attrs, False
        ), 1e-3

        # for dynamic_shape
        generate_dynamic_shape(attrs)
        self.trt_param.precision = paddle_infer.PrecisionType.Float32
        program_config.set_input_type(np.float32)
        yield self.create_inference_config(), generate_trt_nodes_num(
            attrs, True
        ), 1e-5
        self.trt_param.precision = paddle_infer.PrecisionType.Half
        program_config.set_input_type(np.float16)
        yield self.create_inference_config(), generate_trt_nodes_num(
            attrs, True
        ), 1e-3

    def add_skip_trt_case(self):
        def teller2(program_config, predictor_config):
            if len(self.dynamic_shape.min_input_shape) != 0 and os.name == 'nt':
                return True
            return False

        self.add_skip_case(
            teller2,
            SkipReasons.TRT_NOT_SUPPORT,
            "The output has diff between gpu and trt in Windows.",
        )

    def test(self):
        self.add_skip_trt_case()
        self.run_test()


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