# 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
from typing import Any, Dict, 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 TrtConvertFillConstantTest(TrtLayerAutoScanTest):
    def is_program_valid(self, program_config: ProgramConfig) -> bool:
        return True

    def sample_program_configs(self):
        def generate_value_data(attrs: List[Dict[str, Any]]):
            return np.array([1]).astype(np.int32)

        def generate_shape_data(attrs: List[Dict[str, Any]]):
            return np.array([4, 23]).astype(np.int32)

        def generate_shapelist_data(attrs: List[Dict[str, Any]]):
            return np.array([4]).astype(np.int32)

        for shape in [[2, 3, 4]]:
            for num_input in [0, 1, 2]:
                for dtype in [5, 2, 3]:
                    for str_value in ["2", "23", "-1"]:
                        self.num_input = num_input
                        value = float(str_value)
                        if np.random.choice([False, True]):
                            str_value = str_value
                        else:
                            str_value = ""
                        dics = [
                            {
                                "str_value": str_value,
                                "value": value,
                                "shape": shape,
                                "dtype": dtype,
                            },
                            {"axis": -1},
                        ]
                        dics_intput = [
                            {"ValueTensor": ["value_data"]},
                            {
                                "ShapeTensor": ["shape_data"],
                            },
                            {
                                "ShapeTensorList": [
                                    "shapeT1_data",
                                    "shapeT2_data",
                                ],
                            },
                            {},
                        ]
                        ops_config = [
                            {
                                "op_type": "fill_constant",
                                "op_inputs": dics_intput[num_input],
                                "op_outputs": {
                                    "Out": ["out_data"],
                                },
                                "op_attrs": dics[0],
                            },
                        ]

                        def generate_input():
                            return np.random.random([1, 1]).astype(np.float32)

                        ops = self.generate_op_config(ops_config)
                        program_config = ProgramConfig(
                            ops=ops,
                            weights={},
                            inputs={
                                "value_data": TensorConfig(
                                    data_gen=partial(generate_value_data, dics)
                                ),
                                "shape_data": TensorConfig(
                                    data_gen=partial(generate_shape_data, dics)
                                ),
                                "shapeT1_data": TensorConfig(
                                    data_gen=partial(
                                        generate_shapelist_data, dics
                                    )
                                ),
                                "shapeT2_data": TensorConfig(
                                    data_gen=partial(
                                        generate_shapelist_data, dics
                                    )
                                ),
                            },
                            outputs=["out_data"],
                        )

                        yield program_config

    def sample_predictor_configs(
        self, program_config
    ) -> (paddle_infer.Config, List[int], float):
        def generate_dynamic_shape(attrs):
            self.input_shape = [1, 1]
            max_shape = list(self.input_shape)
            min_shape = list(self.input_shape)
            opt_shape = list(self.input_shape)
            for i in range(len(self.input_shape)):
                max_shape[i] = max_shape[i] + 1
            self.dynamic_shape.min_input_shape = {"Y_data": min_shape}
            self.dynamic_shape.max_input_shape = {"Y_data": max_shape}
            self.dynamic_shape.opt_input_shape = {"Y_data": opt_shape}

        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):
            if self.num_input < 3:
                return 0, 6
            return 1, 5

        attrs = [
            program_config.ops[i].attrs for i in range(len(program_config.ops))
        ]
        # Don't test static shape

        # 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-3

    def add_skip_trt_case(self):
        pass

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


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