# Copyright (c) 2022 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, Tuple

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 TrtConvertArgMinTest(TrtLayerAutoScanTest):
    def is_program_valid(self, program_config: ProgramConfig) -> bool:
        input_shape = program_config.inputs["arg_min_input"].shape
        axis = program_config.ops[0].attrs["axis"]
        if axis < 0:
            axis += len(input_shape)
        if len(input_shape) <= axis or axis == 0:
            return False
        return True

    def sample_program_configs(self):
        def generate_input(rank, batch):
            dims = [batch]
            for i in range(rank - 1):
                dims.append((i + 1) * 8)
            size = np.prod(dims)
            return (np.arange(size) % 10 - 5).astype("float32").reshape(dims)

        for rank in [3, 4]:
            for batch in [1, 4]:
                for axis in [-1, 0, 1, 2, 3]:
                    for keepdims in [True, False]:
                        self.rank = rank
                        flatten = False
                        dtype = 2
                        ops_config = [
                            {
                                "op_type": "arg_min",
                                "op_inputs": {"X": ["arg_min_input"]},
                                "op_outputs": {"Out": ["arg_min_out"]},
                                "op_attrs": {
                                    "axis": axis,
                                    "keepdims": keepdims,
                                    "flatten": flatten,
                                    "dtype": dtype,
                                },
                                "outputs_dtype": {"arg_min_out": np.int32},
                            }
                        ]
                        ops = self.generate_op_config(ops_config)
                        program_config = ProgramConfig(
                            ops=ops,
                            weights={},
                            inputs={
                                "arg_min_input": TensorConfig(
                                    data_gen=partial(
                                        generate_input, rank, batch
                                    )
                                )
                            },
                            outputs=["arg_min_out"],
                        )
                        yield program_config

    def sample_predictor_configs(
        self, program_config
    ) -> Tuple[paddle_infer.Config, List[int], float]:
        def generate_dynamic_shape(attrs):
            if self.rank == 3:
                self.dynamic_shape.min_input_shape = {
                    "arg_min_input": [1, 8, 16]
                }
                self.dynamic_shape.max_input_shape = {
                    "arg_min_input": [4, 8, 16]
                }
                self.dynamic_shape.opt_input_shape = {
                    "arg_min_input": [3, 8, 16]
                }
            else:
                self.dynamic_shape.min_input_shape = {
                    "arg_min_input": [1, 8, 16, 24]
                }
                self.dynamic_shape.max_input_shape = {
                    "arg_min_input": [4, 8, 16, 24]
                }
                self.dynamic_shape.opt_input_shape = {
                    "arg_min_input": [1, 8, 16, 24]
                }

        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))
        ]

        self.trt_param.workspace_size = 1024000
        # 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-3

        # 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 test(self):
        self.run_test()


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