# 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

import torch
from torchvision import transforms
from torchvision.transforms.functional import InterpolationMode

from transformers import AutoProcessor, BlipForImageTextRetrieval
from towhee import register
from towhee.operator.base import NNOperator, OperatorFlag
from towhee.types.arg import arg, to_image_color
from towhee.types.image_utils import from_pil, to_pil

#@accelerate
class BLIPModelVision(nn.Module):
    def __init__(self, model):
        super().__init__()
        self.model = model
    
    def forward(self, image):
        image_embeds = self.model.visual_encoder(image)
        image_embeds = self.model.vision_proj(image_embeds[:,0,:])
        return image_embeds
    
#@accelerate
class BLIPModelText(nn.Module):
    def __init__(self, model):
        super().__init__()
        self.model = model

    def forward(self, input_ids, attention_mask):
        text_features = self.model.text_encoder(input_ids, attention_mask = attention_mask,
                                            return_dict = False, mode = 'text')[0]
        text_features = self.model.text_proj(text_features[:,0,:])j
        return text_features    

@register(output_schema=['vec'])
class Blip(NNOperator):
    """
    BLIP multi-modal embedding operator
    """
    def __init__(self, model_name: str, modality: str, device:str = 'cpu', checkpoint_path: str = None):
        super().__init__()
        self.model_name = model_name
        self.device = device
        cfg = self._configs()[model_name]

        model_url = cfg['weights']
        image_size = cfg['image_size']
        
        model = BlipForImageTextRetrieval.from_pretrained("Salesforce/blip-itm-base-coco")
        self.processor = AutoProcessor.from_pretrained("Salesforce/blip-itm-base-coco")

        if self.modality == 'image':
            self.model = BLIPModelVision(model)
        elif self.modality == 'text':
            self.model = BLIPModelText(model)
        else:
            raise ValueError("modality[{}] not implemented.".format(self.modality))

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

        #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):
        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):
        inputs = self.processor(text=text, padding=True, return_tensors="pt")
        inputs = inputs.to(self.device)
        text_feature = self.model(input_ids = inputs. , attention_mask)[0]
        return text_feature

    @arg(1, to_image_color('RGB'))
    def _inference_from_image(self, img):
        inputs = self.processor(images=img, return_tensors="pt")
        inputs = inputs.to(self.device)
        image_feature = self.model(inputs)
        return image_feature

    def _configs(self):
        config = {}
        config['blip_base'] = {} 
        config['blip_base']['weights'] = 'https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_base.pth'
        config['blip_base']['image_size'] = 224
        return config