# 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 typing import List

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

import paddle.inference as paddle_infer


class TrtConvertNearestInterpV2Test(TrtLayerAutoScanTest):
    def is_program_valid(self, program_config: ProgramConfig) -> bool:
        return True

    def sample_program_configs(self):
        def generate_input():
            return np.ones([1, 3, 32, 32]).astype(np.float32)

        def generate_weight():
            return np.array([64]).astype(np.int32)

        ops_config = [
            {
                "op_type": "nearest_interp_v2",
                "op_inputs": {
                    "X": ["input_data"],
                    "SizeTensor": ["size_tensor_data0", "size_tensor_data1"],
                },
                "op_outputs": {"Out": ["interp_output_data"]},
                "op_attrs": {
                    "data_layout": "NCHW",
                    "interp_method": "nearest",
                    "align_corners": False,
                    "align_mode": 1,
                    "scale": [2.0, 2.0],
                    "out_d": 0,
                    "out_h": 0,
                    "out_w": 0,
                },
            }
        ]

        ops = self.generate_op_config(ops_config)
        program_config = ProgramConfig(
            ops=ops,
            weights={
                "size_tensor_data0": TensorConfig(data_gen=generate_weight),
                "size_tensor_data1": TensorConfig(data_gen=generate_weight),
            },
            inputs={"input_data": TensorConfig(data_gen=generate_input)},
            outputs=["interp_output_data"],
        )

        yield program_config

    def sample_predictor_configs(
        self, program_config
    ) -> (paddle_infer.Config, List[int], float):
        def generate_dynamic_shape(attrs):
            self.dynamic_shape.min_input_shape = {"input_data": [1, 3, 32, 32]}
            self.dynamic_shape.max_input_shape = {"input_data": [4, 3, 64, 64]}
            self.dynamic_shape.opt_input_shape = {"input_data": [1, 3, 64, 64]}

        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, 2

        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
        yield self.create_inference_config(), generate_trt_nodes_num(
            attrs, False
        ), 1e-5
        self.trt_param.precision = paddle_infer.PrecisionType.Half
        yield self.create_inference_config(), generate_trt_nodes_num(
            attrs, False
        ), 1e-2

        # for dynamic_shape
        generate_dynamic_shape(attrs)
        self.trt_param.precision = paddle_infer.PrecisionType.Float32
        yield self.create_inference_config(), generate_trt_nodes_num(
            attrs, True
        ), 1e-5
        self.trt_param.precision = paddle_infer.PrecisionType.Half
        yield self.create_inference_config(), generate_trt_nodes_num(
            attrs, True
        ), 1e-2

    def test(self):
        self.run_test()


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