# Copyright 2021 Zilliz. 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 timm
import torch
import numpy
from torch import nn as nn
from typing import NamedTuple
from towhee.operator import NNOperator
from timm.data import resolve_data_config
from timm.data.transforms_factory import create_transform
from towhee.utils.pil_utils import to_pil

import warnings

warnings.filterwarnings("ignore")


class VitImageEmbedding(NNOperator):
    """
    Embedding extractor using ViT.
    Args:
        model_name (`string`):
            Model name.
        weights_path (`string`):
            Path to local weights.
    """

    def __init__(self, model_name: str = 'vit_large_patch16_224', num_classes: int = 1000,
                 framework: str = 'pytorch', weights_path: str = None) -> None:
        super().__init__(framework=framework)
        self.device = 'cuda' if torch.cuda.is_available() else 'cpu'
        if weights_path:
            self.model = timm.create_model(model_name, checkpoint_path=weights_path, num_classes=num_classes)
        else:
            self.model = timm.create_model(model_name, pretrained=True, num_classes=num_classes)
        self.model.eval()
        config = resolve_data_config({}, model=self.model)
        self.tfms = create_transform(**config)

    def __call__(self, image: 'towhee.types.Image') -> NamedTuple('Outputs', [('feature_vector', numpy.ndarray)]):
        img_tensor = self.tfms(to_pil(image)).unsqueeze(0)
        Outputs = NamedTuple('Outputs', [('feature_vector', numpy.ndarray)])
        self.model.to(self.device)
        self.model.eval()
        features = self.model.forward_features(img_tensor)
        if features.dim() == 4:  # if the shape of feature map is [N, C, H, W], where H > 1 and W > 1
            global_pool = nn.AdaptiveAvgPool2d(1)
            features = global_pool(features)
        features = features.to('cpu')
        features = features.flatten().detach().numpy()
        return Outputs(features)