# Copyright (c) 2023 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 __future__ import annotations

import unittest
from functools import partial

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

    def sample_program_configs(self):
        def generate_input(batch):
            if self.dims == 4:
                return np.random.random([batch, 3, 3, 24]).astype(np.bool_)
            elif self.dims == 3:
                return np.random.random([batch, 3, 24]).astype(np.bool_)
            elif self.dims == 2:
                return np.random.random([batch, 24]).astype(np.bool_)

        for dims in [2, 3, 4]:
            for batch in [3, 6, 9]:
                self.dims = dims
                ops_config = [
                    {
                        "op_type": "bitwise_and",
                        "op_inputs": {
                            "X": ["input_data1"],
                            "Y": ["input_data2"],
                        },
                        "op_outputs": {"Out": ["output_data"]},
                        "op_attrs": {},
                    },
                ]
                ops = self.generate_op_config(ops_config)

                program_config = ProgramConfig(
                    ops=ops,
                    weights={},
                    inputs={
                        "input_data1": TensorConfig(
                            data_gen=partial(generate_input, batch)
                        ),
                        "input_data2": TensorConfig(
                            data_gen=partial(generate_input, batch)
                        ),
                    },
                    outputs=["output_data"],
                )

                yield program_config

    def generate_dynamic_shape(self, attrs):
        if self.dims == 4:
            self.dynamic_shape.min_input_shape = {
                "input_data1": [1, 3 - 1, 3 - 1, 24 - 1],
                "input_data2": [1, 3 - 1, 3 - 1, 24 - 1],
            }
            self.dynamic_shape.max_input_shape = {
                "input_data1": [9, 3 + 1, 3 + 1, 24 + 1],
                "input_data2": [9, 3 + 1, 3 + 1, 24 + 1],
            }
            self.dynamic_shape.opt_input_shape = {
                "input_data1": [1, 3, 3, 24],
                "input_data2": [1, 3, 3, 24],
            }
        elif self.dims == 3:
            self.dynamic_shape.min_input_shape = {
                "input_data1": [1, 3 - 1, 24 - 1],
                "input_data2": [1, 3 - 1, 24 - 1],
            }
            self.dynamic_shape.max_input_shape = {
                "input_data1": [9, 3 + 1, 24 + 1],
                "input_data2": [9, 3 + 1, 24 + 1],
            }
            self.dynamic_shape.opt_input_shape = {
                "input_data1": [1, 3, 24],
                "input_data2": [1, 3, 24],
            }
        elif self.dims == 2:
            self.dynamic_shape.min_input_shape = {
                "input_data1": [1, 24],
                "input_data2": [1, 24],
            }
            self.dynamic_shape.max_input_shape = {
                "input_data1": [9, 24],
                "input_data2": [9, 24],
            }
            self.dynamic_shape.opt_input_shape = {
                "input_data1": [1, 24],
                "input_data2": [1, 24],
            }
        return self.dynamic_shape

    def sample_predictor_configs(
        self, program_config, run_pir=False
    ) -> tuple[paddle_infer.Config, list[int], float]:
        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):
            ver = paddle_infer.get_trt_compile_version()
            trt_version = ver[0] * 1000 + ver[1] * 100 + ver[2] * 10
            if trt_version < 8400:
                return 0, 4
            if self.dims == 4 or self.dims == 1:
                return 0, 4
            return 1, 3

        attrs = [
            program_config.ops[i].attrs for i in range(len(program_config.ops))
        ]
        self.trt_param.max_batch_size = 9
        self.trt_param.workspace_size = 1073741824

        # for dynamic_shape
        self.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 test(self):
        self.run_test(run_pir=True)


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