# 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 sys
from pathlib import Path
from towhee import register
from towhee.operator.base import NNOperator, OperatorFlag
from towhee.types.arg import arg, to_image_color
import torch
import ipdb
from towhee.types.image_utils import from_pil, to_pil
from torchvision import transforms
from torchvision.transforms.functional import InterpolationMode

@register(output_schema=['vec'])
class Blip(NNOperator):
    """
    BLIP multi-modal embedding operator
    """
    def __init__(self, model_name: str, modality: str):
        super().__init__()
        sys.path.append(str(Path(__file__).parent))
        from models.blip import blip_feature_extractor
        image_size = 224
        model_url = 'https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_base.pth'
        self.model = blip_feature_extractor(pretrained=model_url, image_size=image_size, vit='base')

        self._modality = modality
        self.device = "cuda" if torch.cuda.is_available() else "cpu"

        self.tfms = transforms.Compose([
            transforms.Resize((image_size,image_size),interpolation=InterpolationMode.BICUBIC),
            transforms.ToTensor(),
            transforms.Normalize((0.48145466, 0.4578275, 0.40821073), (0.26862954, 0.26130258, 0.27577711))
            ])

    def __call__(self, data):
        ipdb.set_trace()
        if self._modality == 'image':
            vec = self._inference_from_image(data)
        elif self._modality == 'text':
            vec = self._inference_from_text(data)
        else:
            raise ValueError("modality[{}] not implemented.".format(self._modality))
        return vec.detach().cpu().numpy().flatten()

    def _inference_from_text(self, text):
        text_feature = self.model(None, text, mode='text', device=self.device)[0,0]
        return text_feature

    @arg(1, to_image_color('RGB'))
    def _inference_from_image(self, img):
        #img = to_pil(img)
        #image = self.tfms(img).unsqueeze(0).to(self.device)
        #image_features = self.model.encode_image(image)
        img = self._preprocess(img)
        caption = ''
        image_feature = self.model(img, caption, mode='image', device=self.device)[0,0]
        return image_feature

    def _preprocess(self, img):
        img = to_pil(img)
        processed_img = self.tfms(img).unsqueeze(0).to(self.device)
        return processed_img