# 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 numpy
import torch
import towhee

from PIL import Image as PILImage

from transformers import BeitFeatureExtractor, Data2VecVisionForImageClassification
from towhee.operator.base import NNOperator
from towhee.types.arg import arg, to_image_color

class Data2VecVision(NNOperator):
    def __init__(self, model_name='facebook/data2vec-vision-base'):
        self.model = Data2VecVisionForImageClassification.from_pretrained(model_name)
        self.feature_extractor = BeitFeatureExtractor.from_pretrained(model_name)

    @arg(1, to_image_color('RGB'))
    def __call__(self, img: 'towhee.types.Image') -> numpy.ndarray:
        img = PILImage.fromarray(img.astype('uint8'), 'RGB')
        inputs = self.feature_extractor(img, return_tensors="pt")
        with torch.no_grad():
            outputs = self.model.data2vec_vision(**inputs).pooler_output

        return outputs.detach().cpu().numpy().flatten()