# 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.

from __future__ import annotations

import unittest
from functools import partial
from itertools import product
from typing import TYPE_CHECKING, Any

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

import paddle.inference as paddle_infer

if TYPE_CHECKING:
    from collections.abc import Generator


class TrtConvertNearestInterpTest(TrtLayerAutoScanTest):
    def is_program_valid(self, program_config: ProgramConfig) -> bool:
        inputs = program_config.inputs
        weights = program_config.weights
        attrs = [
            program_config.ops[i].attrs for i in range(len(program_config.ops))
        ]

        if attrs[0]['scale'] <= 0 and (
            attrs[0]['out_h'] <= 0 or attrs[0]['out_w'] <= 0
        ):
            return False
        if (attrs[0]['out_h'] <= 0) ^ (attrs[0]['out_w'] <= 0):
            return False

        return True

    def sample_program_configs(self):
        def generate_input1(attrs: list[dict[str, Any]]):
            return np.ones([1, 3, 64, 64]).astype(np.float32)

        for (
            data_layout,
            interp_method,
            align_corners,
            scale,
            out_h,
            out_w,
        ) in product(
            ["NCHW", "NHWC"],
            ["nearest"],
            [True, False],
            [2.0, -1.0, 0.0],
            [32, 64, 128 - 32],
            [32, -32],
        ):
            dics = [
                {
                    "data_layout": data_layout,
                    "interp_method": interp_method,
                    "align_corners": align_corners,
                    "scale": scale,
                    "out_h": out_h,
                    "out_w": out_w,
                }
            ]

            ops_config = [
                {
                    "op_type": "nearest_interp",
                    "op_inputs": {"X": ["input_data"]},
                    "op_outputs": {"Out": ["nearest_interp_output_data"]},
                    "op_attrs": dics[0],
                }
            ]
            ops = self.generate_op_config(ops_config)

            program_config = ProgramConfig(
                ops=ops,
                weights={},
                inputs={
                    "input_data": TensorConfig(
                        data_gen=partial(generate_input1, dics)
                    )
                },
                outputs=["nearest_interp_output_data"],
            )

            yield program_config

    def sample_predictor_configs(
        self, program_config
    ) -> Generator[
        tuple[paddle_infer.Config, list[int], float] | None, Any, Any
    ]:
        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 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-2,
        )

    def add_skip_trt_case(self):
        def teller1(program_config, predictor_config):
            if (
                program_config.ops[0].attrs['scale'] <= 0
                and self.dynamic_shape.min_input_shape
            ):
                return True
            if program_config.ops[0].attrs['align_corners']:
                return True
            return False

        self.add_skip_case(
            teller1,
            SkipReasons.TRT_NOT_IMPLEMENTED,
            "NOT Implemented: we need to add support scale <= 0 in dynamic shape in the future",
        )

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


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