# Copyright (c) 2021-2022, NVIDIA CORPORATION. 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 torch

def update_argparser(parser):
    parser.add_argument(
        "--config", default="resnet50", type=str, required=True, help="Network to deploy")
    parser.add_argument(
        "--checkpoint", default=None, type=str, help="The checkpoint of the model. ")
    parser.add_argument("--classes", type=int, default=1000, help="Number of classes")
    parser.add_argument("--precision", type=str, default="fp32", 
                        choices=["fp32", "fp16"], help="Inference precision")

def get_model(**model_args):
    from image_classification import models

    model = models.resnet50(pretrained=False)

    if "checkpoint" in model_args:
        print(f"loading checkpoint {model_args['checkpoint']}")
        state_dict = torch.load(model_args["checkpoint"], map_location="cpu")
        try:
            model.load_state_dict(
                {
                    k.replace("module.", ""): v
                    for k, v in state_dict.items()
                }
            )
        except RuntimeError as RE:
            if not hasattr(model, "ngc_checkpoint_remap"):
                raise RE
            remap_old = model.ngc_checkpoint_remap(version="20.06.0")
            remap_dist = lambda k: k.replace("module.", "")
            model.load_state_dict(
                {
                    remap_old(remap_dist(k)): v
                    for k, v in state_dict.items()
                }
            )
    if model_args["precision"] == "fp16":
        model = model.half()

    model = model.cuda()
    model.eval()
    tensor_names = {"inputs": ["INPUT__0"],
                    "outputs": ["OUTPUT__0"]}

    return model, tensor_names