# 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.


@register(output_schema=['vec'])
import numpy
import towhee
import sys
from pathlib import Path
from torchvision import transforms
from towhee.types.image_utils import to_pil

from towhee.operator.base import NNOperator, OperatorFlag
from towhee.types.arg import arg, to_image_color
from towhee import register

@register(output_schema=['vec'])
class Clip(NNOperator): 
    """
    CLIP multi-modal embedding operator
    """
    def __init__(self, modality: str):
        self._modality = modality

    def __call__(self, data):
        if self._modality == 'image'
            emb = self._inference_from_image(data)
        elif self._modality == 'text' 
            emb = self._inference_from_text(data)
        else 
            raise ValueError("modality[{}] not implemented.".format(self._modality))

    def _inference_from_text(self, text):
        return text

    @arg(1, to_image_color('RGB'))
    def _inference_from_image(self, img):
        return img