# Copyright 2021 The KubeEdge Authors.
#
# 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 sedna.algorithms.aggregation import MistNet
from sedna.algorithms.client_choose import SimpleClientChoose
from sedna.common.config import Context
from sedna.core.federated_learning import FederatedLearning

simple_chooser = SimpleClientChoose(per_round=1)

# It has been determined that mistnet is required here.
mistnet = MistNet(cut_layer=Context.get_parameters("cut_layer"),
                  epsilon=Context.get_parameters("epsilon"))

# The function `get_transmitter_from_config()` returns an object instance.
s3_transmitter = FederatedLearning.get_transmitter_from_config()


class Dataset:
    def __init__(self) -> None:
        self.parameters = {
            "datasource": "YOLO",
            "data_params": "./coco128.yaml",
            # Where the dataset is located
            "data_path": "./data/COCO",
            "train_path": "./data/COCO/coco128/images/train2017/",
            "test_path": "./data/COCO/coco128/images/train2017/",
            # number of training examples
            "num_train_examples": 128,
            # number of testing examples
            "num_test_examples": 128,
            # number of classes
            "num_classes": 80,
            # image size
            "image_size": 640,
            "classes":
                [
                    "person",
                    "bicycle",
                    "car",
                    "motorcycle",
                    "airplane",
                    "bus",
                    "train",
                    "truck",
                    "boat",
                    "traffic light",
                    "fire hydrant",
                    "stop sign",
                    "parking meter",
                    "bench",
                    "bird",
                    "cat",
                    "dog",
                    "horse",
                    "sheep",
                    "cow",
                    "elephant",
                    "bear",
                    "zebra",
                    "giraffe",
                    "backpack",
                    "umbrella",
                    "handbag",
                    "tie",
                    "suitcase",
                    "frisbee",
                    "skis",
                    "snowboard",
                    "sports ball",
                    "kite",
                    "baseball bat",
                    "baseball glove",
                    "skateboard",
                    "surfboard",
                    "tennis racket",
                    "bottle",
                    "wine glass",
                    "cup",
                    "fork",
                    "knife",
                    "spoon",
                    "bowl",
                    "banana",
                    "apple",
                    "sandwich",
                    "orange",
                    "broccoli",
                    "carrot",
                    "hot dog",
                    "pizza",
                    "donut",
                    "cake",
                    "chair",
                    "couch",
                    "potted plant",
                    "bed",
                    "dining table",
                    "toilet",
                    "tv",
                    "laptop",
                    "mouse",
                    "remote",
                    "keyboard",
                    "cell phone",
                    "microwave",
                    "oven",
                    "toaster",
                    "sink",
                    "refrigerator",
                    "book",
                    "clock",
                    "vase",
                    "scissors",
                    "teddy bear",
                    "hair drier",
                    "toothbrush",
                ],
            "partition_size": 128,
        }


class Estimator:
    def __init__(self) -> None:
        self.model = None
        self.hyperparameters = {
            "type": "yolov5",
            "rounds": 1,
            "target_accuracy": 0.99,
            "epochs": 500,
            "batch_size": 16,
            "optimizer": "SGD",
            "linear_lr": False,
            # The machine learning model
            "model_name": "yolov5",
            "model_config": "./yolov5s.yaml",
            "train_params": "./hyp.scratch.yaml"
        }