# 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 torch

from typing import List, Union

from torch import nn
from towhee.types.arg import arg, to_image_color

from timm.data import resolve_data_config, create_transform
from towhee.models import mpvit
from towhee.operator.base import NNOperator
from towhee import register
import towhee
from PIL import Image as PILImage


@register(output_schema=['vec'])
class MPViT(NNOperator):
    """
    MPViT embedding operator
    """

    def __init__(self, model_name,
                 num_classes: int = 1000,
                 weights_path: str = None,
                 device: str = None,
                 skip_preprocess: bool = False):
        super().__init__()

        if device is None:
            device = 'cuda' if torch.cuda.is_available() else 'cpu'

        self.model = mpvit.create_model(model_name=model_name,
                                        num_classes=num_classes,
                                        pretrained=True,
                                        weights_path=weights_path,
                                        device=device)
        self.model.eval()
        self.config = resolve_data_config({}, model=self.model)
        self.tfms = create_transform(**self.config)
        self.skip_tfms = skip_preprocess
        self.device = device

    def __call__(self, data: Union[List['towhee.types.Image'], 'towhee.types.Image']):
        if not isinstance(data, list):
            imgs = [data]
        else:
            imgs = data
        img_list = []
        for img in imgs:
            img = self.convert_img(img)
            img = img if self.skip_tfms else self.tfms(img)
            img_list.append(img)
        inputs = torch.stack(img_list)
        inputs = inputs.to(self.device)
        features = self.model.forward_features(inputs)
        global_pool = nn.AdaptiveAvgPool2d(1)
        features = global_pool(features)
        features = features.to('cpu').flatten(1)
        if isinstance(data, list):
            vecs = list(features.detach().numpy())
        else:
            vecs = features.squeeze(0).detach().numpy()
        return vecs

    @arg(1, to_image_color('RGB'))
    def convert_img(self, img: 'towhee.types.Image'):
        img = PILImage.fromarray(img.astype('uint8'), 'RGB')
        return img