# 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 PIL import Image
import torch
import yaml
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 Albef(NNOperator):
    """
    ALBEF multi-modal embedding operator
    """
    def prepare_model(checkpoint_path, model):
        checkpoint = torch.load(checkpoint_path, map_location='cpu')
        state_dict = checkpoint['model']
        pos_embed_reshaped = interpolate_pos_embed(state_dict['visual_encoder.pos_embed'],model.visual_encoder)
        state_dict['visual_encoder.pos_embed'] = pos_embed_reshaped
        m_pos_embed_reshaped = interpolate_pos_embed(state_dict['visual_encoder_m.pos_embed'],model.visual_encoder_m)
        state_dict['visual_encoder_m.pos_embed'] = m_pos_embed_reshaped
        for key in list(state_dict.keys()):
            if 'bert' in key:
                encoder_key = key.replace('bert.','')
                state_dict[encoder_key] = state_dict[key]
                del state_dict[key]
        msg = model.load_state_dict(state_dict,strict=False)
        print('load checkpoint from ' + checkpoint_path)
        return model

    def __init__(self, model_name: str, modality: str):
        self.modality = modality
        config = self._configs()[model_name]

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

        normalize = transforms.Normalize((0.48145466, 0.4578275, 0.40821073), (0.26862954, 0.26130258, 0.27577711))
        tokenizer = BertTokenizer.from_pretrained(config)
        model = ALBEF(config=config, text_encoder=config['text_encoder'], tokenizer=tokenizer)
        cfg = yaml.load(open(config['cfg'], 'r'), Loader=yaml.Loader)
        checkpoint_path = cfg['ckpt_path'] 

        self.model = self.prepare_model(checkpoint_path, model)

        self.test_transform = transforms.Compose([
            transforms.Resize((cfg['image_res'],cfg['image_res']),interpolation=Image.BICUBIC),
            transforms.ToTensor(),
            normalize,
            ])
        

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

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

    def _configs(self):
        config = {}
        config['albef_4m'] = {}
        config['albef_4m']['tokenizer'] = 'bert-base-uncased'
        config['albef_4m']['text_encoder'] = 'bert-base-uncased'
        config['albef_4m']['cfg_path'] = './configs/Retrieval_flickr.yaml'
        config['albef_4m']['ckpt_path'] = ''