# 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 transformers import BertForSequenceClassification, BertConfig, BertTokenizer
from transformers import CLIPProcessor, CLIPModel

@register(output_schema=['vec'])
class Taiyi(NNOperator): 
    """
    Taiyi multi-modal embedding operator
    """
    def __init__(self, model_name: str, modality: str):
        self.modality = modality
        self.device = "cuda" if torch.cuda.is_available() else "cpu"
        self.text_tokenizer = BertTokenizer.from_pretrained("IDEA-CCNL/Taiyi-CLIP-Roberta-large-326M-Chinese")
        self.text_encoder = BertForSequenceClassification.from_pretrained("IDEA-CCNL/Taiyi-CLIP-Roberta-large-326M-Chinese").eval()
        self.clip_model = CLIPModel.from_pretrained("openai/clip-vit-large-patch14")
        self.processor = CLIPProcessor.from_pretrained("openai/clip-vit-large-patch14")

    def inference_single_data(self, data):
        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 __call__(self, data):
        if not isinstance(data, list):
            data = [data]
        else:
            data = data
        results = []
        for single_data in data:
            result = self.inference_single_data(single_data)
            results.append(result)
        if len(data) == 1:        
            return results[0]
        else:
            return results

    def _inference_from_text(self, text):
        self.text = self.text_tokenizer(text, return_tensors='pt', padding=True)['input_ids'].to(self.device)
        text_features = text_encoder(text).logits
        return text_features

    @arg(1, to_image_color('RGB'))
    def _inference_from_image(self, img):
        image = to_pil(image)
        image = self.processor(images=image.raw), return_tensors="pt")
        image_features = clip_model.get_image_features(**image)
        return image_features