# 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 trt_layer_auto_scan_test import TrtLayerAutoScanTest, SkipReasons
from program_config import TensorConfig, ProgramConfig
import numpy as np
import paddle.inference as paddle_infer
from functools import partial
from typing import Optional, List, Callable, Dict, Any, Set
import unittest


class TrtConvertGatherNdTest_dim_4_1(TrtLayerAutoScanTest):
    def is_program_valid(self, program_config: ProgramConfig) -> bool:
        return True

    def sample_program_configs(self):
        def generate_input1():
            return np.random.random([2, 32, 64, 64]).astype(np.float32)

        def generate_input2():
            return np.ones([1]).astype(np.int32)

        ops_config = [{
            "op_type": "gather_nd",
            "op_inputs": {
                "X": ["input_data"],
                "Index": ["index_data"]
            },
            "op_outputs": {
                "Out": ["output_data"]
            },
            "op_attrs": {}
        }]
        ops = self.generate_op_config(ops_config)

        program_config = ProgramConfig(
            ops=ops,
            weights={},
            inputs={
                "input_data": TensorConfig(data_gen=partial(generate_input1)),
                "index_data": TensorConfig(data_gen=partial(generate_input2)),
            },
            outputs=["output_data"])

        yield program_config

    def sample_predictor_configs(
            self, program_config) -> (paddle_infer.Config, List[int], float):
        def generate_dynamic_shape(attrs):
            self.dynamic_shape.min_input_shape = {
                "input_data": [1, 8, 8, 8],
                "index_data": [1]
            }
            self.dynamic_shape.max_input_shape = {
                "input_data": [4, 32, 64, 64],
                "index_data": [1]
            }
            self.dynamic_shape.opt_input_shape = {
                "input_data": [2, 4, 64, 64],
                "index_data": [1]
            }

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

        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
        yield self.create_inference_config(), (0, 4), 1e-5
        self.trt_param.precision = paddle_infer.PrecisionType.Half
        yield self.create_inference_config(), (0, 4), 1e-5

        # for dynamic_shape
        generate_dynamic_shape(attrs)
        self.trt_param.precision = paddle_infer.PrecisionType.Float32
        yield self.create_inference_config(), (0, 4), 1e-5
        self.trt_param.precision = paddle_infer.PrecisionType.Half
        yield self.create_inference_config(), (0, 4), 1e-5

    def test(self):
        self.run_test()


class TrtConvertGatherNdTest_dim_4_1_2(TrtLayerAutoScanTest):
    def is_program_valid(self, program_config: ProgramConfig) -> bool:
        return True

    def sample_program_configs(self):
        def generate_input1():
            return np.random.random([2, 32, 64, 64]).astype(np.float32)

        def generate_input2():
            return np.array([1, 2]).astype(np.int32)

        ops_config = [{
            "op_type": "gather_nd",
            "op_inputs": {
                "X": ["input_data"],
                "Index": ["index_data"]
            },
            "op_outputs": {
                "Out": ["output_data"]
            },
            "op_attrs": {}
        }]
        ops = self.generate_op_config(ops_config)

        program_config = ProgramConfig(
            ops=ops,
            weights={},
            inputs={
                "input_data": TensorConfig(data_gen=partial(generate_input1)),
                "index_data": TensorConfig(data_gen=partial(generate_input2)),
            },
            outputs=["output_data"])

        yield program_config

    def sample_predictor_configs(
            self, program_config) -> (paddle_infer.Config, List[int], float):
        def generate_dynamic_shape(attrs):
            self.dynamic_shape.min_input_shape = {
                "input_data": [1, 8, 8, 8],
                "index_data": [1]
            }
            self.dynamic_shape.max_input_shape = {
                "input_data": [4, 32, 64, 64],
                "index_data": [4]
            }
            self.dynamic_shape.opt_input_shape = {
                "input_data": [2, 4, 64, 64],
                "index_data": [2]
            }

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

        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
        yield self.create_inference_config(), (0, 4), 1e-5
        self.trt_param.precision = paddle_infer.PrecisionType.Half
        yield self.create_inference_config(), (0, 4), 1e-5

        # for dynamic_shape
        generate_dynamic_shape(attrs)
        self.trt_param.precision = paddle_infer.PrecisionType.Float32
        yield self.create_inference_config(), (0, 4), 1e-5
        self.trt_param.precision = paddle_infer.PrecisionType.Half
        yield self.create_inference_config(), (0, 4), 1e-5

    def test(self):
        self.run_test()


class TrtConvertGatherNdTest_dim_4_2(TrtLayerAutoScanTest):
    def is_program_valid(self, program_config: ProgramConfig) -> bool:
        return True

    def sample_program_configs(self):
        def generate_input1():
            return np.random.random([2, 32, 64, 64]).astype(np.float32)

        def generate_input2():
            return np.ones([2, 2]).astype(np.int32)

        ops_config = [{
            "op_type": "gather_nd",
            "op_inputs": {
                "X": ["input_data"],
                "Index": ["index_data"]
            },
            "op_outputs": {
                "Out": ["output_data"]
            },
            "op_attrs": {}
        }]
        ops = self.generate_op_config(ops_config)

        program_config = ProgramConfig(
            ops=ops,
            weights={},
            inputs={
                "input_data": TensorConfig(data_gen=partial(generate_input1)),
                "index_data": TensorConfig(data_gen=partial(generate_input2)),
            },
            outputs=["output_data"])

        yield program_config

    def sample_predictor_configs(
            self, program_config) -> (paddle_infer.Config, List[int], float):
        def generate_dynamic_shape(attrs):
            self.dynamic_shape.min_input_shape = {
                "input_data": [1, 8, 8, 8],
                "index_data": [1, 2]
            }
            self.dynamic_shape.max_input_shape = {
                "input_data": [4, 32, 64, 64],
                "index_data": [4, 4]
            }
            self.dynamic_shape.opt_input_shape = {
                "input_data": [2, 4, 64, 64],
                "index_data": [2, 2]
            }

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

        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
        yield self.create_inference_config(), (0, 4), 1e-5
        self.trt_param.precision = paddle_infer.PrecisionType.Half
        yield self.create_inference_config(), (0, 4), 1e-5

        # for dynamic_shape
        generate_dynamic_shape(attrs)
        self.trt_param.precision = paddle_infer.PrecisionType.Float32
        yield self.create_inference_config(), (0, 4), 1e-5
        self.trt_param.precision = paddle_infer.PrecisionType.Half
        yield self.create_inference_config(), (0, 4), 1e-5

    def test(self):
        self.run_test()


class TrtConvertGatherNdTest_dim_4_3(TrtLayerAutoScanTest):
    def is_program_valid(self, program_config: ProgramConfig) -> bool:
        return True

    def sample_program_configs(self):
        def generate_input1():
            return np.random.random([2, 32, 64, 64]).astype(np.float32)

        def generate_input2():
            return np.ones([2, 2, 4]).astype(np.int32)

        ops_config = [{
            "op_type": "gather_nd",
            "op_inputs": {
                "X": ["input_data"],
                "Index": ["index_data"]
            },
            "op_outputs": {
                "Out": ["output_data"]
            },
            "op_attrs": {}
        }]
        ops = self.generate_op_config(ops_config)

        program_config = ProgramConfig(
            ops=ops,
            weights={},
            inputs={
                "input_data": TensorConfig(data_gen=partial(generate_input1)),
                "index_data": TensorConfig(data_gen=partial(generate_input2)),
            },
            outputs=["output_data"])

        yield program_config

    def sample_predictor_configs(
            self, program_config) -> (paddle_infer.Config, List[int], float):
        def generate_dynamic_shape(attrs):
            self.dynamic_shape.min_input_shape = {
                "input_data": [1, 8, 8, 8],
                "index_data": [1, 2, 2]
            }
            self.dynamic_shape.max_input_shape = {
                "input_data": [4, 32, 64, 64],
                "index_data": [4, 4, 4]
            }
            self.dynamic_shape.opt_input_shape = {
                "input_data": [2, 4, 64, 64],
                "index_data": [2, 2, 2]
            }

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

        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
        yield self.create_inference_config(), (0, 4), 1e-5
        self.trt_param.precision = paddle_infer.PrecisionType.Half
        yield self.create_inference_config(), (0, 4), 1e-5

        # for dynamic_shape
        generate_dynamic_shape(attrs)
        self.trt_param.precision = paddle_infer.PrecisionType.Float32
        yield self.create_inference_config(), (0, 4), 1e-5
        self.trt_param.precision = paddle_infer.PrecisionType.Half
        yield self.create_inference_config(), (0, 4), 1e-5

    def test(self):
        self.run_test()


class TrtConvertGatherNdTest_dim_2_2(TrtLayerAutoScanTest):
    def is_program_valid(self, program_config: ProgramConfig) -> bool:
        return True

    def sample_program_configs(self):
        def generate_input1():
            return np.random.random([2, 32]).astype(np.float32)

        def generate_input2():
            return np.ones([2, 2]).astype(np.int32)

        ops_config = [{
            "op_type": "gather_nd",
            "op_inputs": {
                "X": ["input_data"],
                "Index": ["index_data"]
            },
            "op_outputs": {
                "Out": ["output_data"]
            },
            "op_attrs": {}
        }]
        ops = self.generate_op_config(ops_config)

        program_config = ProgramConfig(
            ops=ops,
            weights={},
            inputs={
                "input_data": TensorConfig(data_gen=partial(generate_input1)),
                "index_data": TensorConfig(data_gen=partial(generate_input2)),
            },
            outputs=["output_data"])

        yield program_config

    def sample_predictor_configs(
            self, program_config) -> (paddle_infer.Config, List[int], float):
        def generate_dynamic_shape(attrs):
            self.dynamic_shape.min_input_shape = {
                "input_data": [1, 4],
                "index_data": [1, 1]
            }
            self.dynamic_shape.max_input_shape = {
                "input_data": [4, 64],
                "index_data": [4, 2]
            }
            self.dynamic_shape.opt_input_shape = {
                "input_data": [2, 8],
                "index_data": [2, 2]
            }

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

        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
        yield self.create_inference_config(), (0, 4), 1e-5
        self.trt_param.precision = paddle_infer.PrecisionType.Half
        yield self.create_inference_config(), (0, 4), 1e-5

        # for dynamic_shape
        generate_dynamic_shape(attrs)
        self.trt_param.precision = paddle_infer.PrecisionType.Float32
        yield self.create_inference_config(), (1, 3), 1e-5
        self.trt_param.precision = paddle_infer.PrecisionType.Half
        yield self.create_inference_config(), (1, 3), 1e-5

    def add_skip_trt_case(self):
        def teller(program_config, predictor_config):
            if len(self.dynamic_shape.min_input_shape) != 0:
                return True
            return False

        self.add_skip_case(
            teller, SkipReasons.TRT_NOT_SUPPORT,
            "Need to repair the case: the output of trt and GPU has diff when inputs' dim is 1 and 2."
        )

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


class TrtConvertGatherNdTest_dim_3_3(TrtLayerAutoScanTest):
    def is_program_valid(self, program_config: ProgramConfig) -> bool:
        return True

    def sample_program_configs(self):
        def generate_input1():
            return np.random.random([2, 32, 256]).astype(np.float32)

        def generate_input2():
            return np.ones([2, 2, 2]).astype(np.int32)

        ops_config = [{
            "op_type": "gather_nd",
            "op_inputs": {
                "X": ["input_data"],
                "Index": ["index_data"]
            },
            "op_outputs": {
                "Out": ["output_data"]
            },
            "op_attrs": {}
        }]
        ops = self.generate_op_config(ops_config)

        program_config = ProgramConfig(
            ops=ops,
            weights={},
            inputs={
                "input_data": TensorConfig(data_gen=partial(generate_input1)),
                "index_data": TensorConfig(data_gen=partial(generate_input2)),
            },
            outputs=["output_data"])

        yield program_config

    def sample_predictor_configs(
            self, program_config) -> (paddle_infer.Config, List[int], float):
        def generate_dynamic_shape(attrs):
            self.dynamic_shape.min_input_shape = {
                "input_data": [1, 4, 4],
                "index_data": [1, 1, 1]
            }
            self.dynamic_shape.max_input_shape = {
                "input_data": [4, 64, 512],
                "index_data": [4, 2, 4]
            }
            self.dynamic_shape.opt_input_shape = {
                "input_data": [2, 8, 64],
                "index_data": [2, 2, 2]
            }

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

        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
        yield self.create_inference_config(), (0, 4), 1e-5
        self.trt_param.precision = paddle_infer.PrecisionType.Half
        yield self.create_inference_config(), (0, 4), 1e-5

        # for dynamic_shape
        generate_dynamic_shape(attrs)
        self.trt_param.precision = paddle_infer.PrecisionType.Float32
        yield self.create_inference_config(), (1, 3), 1e-5
        self.trt_param.precision = paddle_infer.PrecisionType.Half
        yield self.create_inference_config(), (1, 3), 1e-5

    def test(self):
        self.run_test()


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