# 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 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 TrtConvertFlattenContiguousRangeTest(TrtLayerAutoScanTest):
    def is_program_valid(self, program_config: ProgramConfig) -> bool:
        return True

    def sample_program_configs(self):
        def generate_input(batch):
            if self.dims == 0:
                return np.random.random([]).astype(np.float32)
            elif self.dims == 1:
                return np.random.random([2]).astype(np.float32)
            else:
                return np.random.random([2, batch, 4, 8, 3]).astype(np.float32)

        for dims in [0, 1, 5]:
            self.dims = dims
            if dims == 0:
                test_dims = 1
            else:
                test_dims = dims
            for batch in [1, 2, 4]:
                for start_axis in range(0, test_dims):
                    test_start = start_axis
                    if dims == 0:
                        test_start = -1
                    for stop_axis in range(test_start, dims):
                        type = "flatten_contiguous_range"
                        op_outputs = {
                            "Out": ["output_data"],
                            "XShape": ["xshape_data"],
                        }
                        ops_config = [
                            {
                                "op_type": type,
                                "op_inputs": {"X": ["input_data"]},
                                "op_outputs": op_outputs,
                                "op_attrs": {
                                    "start_axis": start_axis,
                                    "stop_axis": stop_axis,
                                },
                            }
                        ]
                        ops = self.generate_op_config(ops_config)

                        program_config = ProgramConfig(
                            ops=ops,
                            weights={},
                            inputs={
                                "input_data": TensorConfig(
                                    data_gen=partial(generate_input, batch)
                                )
                            },
                            outputs=["output_data"],
                        )
                        yield program_config

    def sample_predictor_configs(
        self, program_config
    ) -> (paddle_infer.Config, List[int], float):
        def generate_dynamic_shape(attrs):
            if self.dims == 0:
                self.dynamic_shape.min_input_shape = {"input_data": []}
                self.dynamic_shape.max_input_shape = {"input_data": []}
                self.dynamic_shape.opt_input_shape = {"input_data": []}
            elif self.dims == 1:
                self.dynamic_shape.min_input_shape = {"input_data": [2]}
                self.dynamic_shape.max_input_shape = {"input_data": [2]}
                self.dynamic_shape.opt_input_shape = {"input_data": [2]}
            else:
                self.dynamic_shape.min_input_shape = {
                    "input_data": [2, 1, 4, 8, 3]
                }
                self.dynamic_shape.max_input_shape = {
                    "input_data": [2, 4, 4, 8, 3]
                }
                self.dynamic_shape.opt_input_shape = {
                    "input_data": [2, 2, 4, 8, 3]
                }

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

        def generate_trt_nodes_num(attrs, dynamic_shape):
            ver = paddle_infer.get_trt_compile_version()
            if ver[0] * 1000 + ver[1] * 100 + ver[0] * 10 >= 7000:
                if dynamic_shape:
                    return 1, 2
                else:
                    if (
                        attrs[0]['start_axis'] == 0
                        or self.dims == 0
                        or self.dims == 1
                    ):
                        return 0, 3
                    else:
                        return 1, 2
            else:
                return 0, 3

        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, 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, 1e-3)

    def test(self):
        self.run_test()


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