# 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 typing import Dict, Union
from towhee.models.mdmmt.mmt import MMTVID, MMTTXT
from towhee.operator.base import NNOperator
from towhee import register
from pathlib import Path
from transformers.models.bert.modeling_bert import BertModel as TxtBertModel
from transformers import AutoTokenizer

import warnings
warnings.filterwarnings('ignore')


@register(output_schema=['vec'])
class MDMMT(NNOperator):
    """
    MDMMT multi-modal embedding operator
    """

    def __init__(self, modality: str, weight_path: str = None, device: str = None, mmtvid_params: Dict = None,
                 mmttxt_params: Dict = None):
        super().__init__()
        self.modality = modality
        if weight_path is None:
            weight_path = str(Path(__file__).parent / 'mdmmt_3mod.pth')
            # print('weight_path is None, use default path: {}'.format(weight_path))
        if device is None:
            self.device = "cuda" if torch.cuda.is_available() else "cpu"
        else:
            self.device = device
        self.mmtvid_model = None
        self.mmttxt_model = None
        state = torch.load(weight_path, map_location='cpu')
        if self.modality == 'video':
            if mmtvid_params is None:
                expert_dims = {
                    "VIDEO": {"dim": 2048, "idx": 1, "max_tok": 30},
                    "CLIP": {"dim": 512, "idx": 2, "max_tok": 30},
                    "tf_vggish": {"dim": 128, "idx": 3, "max_tok": 30},
                }
                vid_bert_params = {
                    "vocab_size_or_config_json_file": 10,
                    "hidden_size": 512,
                    "num_hidden_layers": 9,
                    "intermediate_size": 3072,
                    "hidden_act": "gelu",
                    "hidden_dropout_prob": 0.2,
                    "attention_probs_dropout_prob": 0.2,
                    "max_position_embeddings": 32,
                    "type_vocab_size": 19,
                    "initializer_range": 0.02,
                    "layer_norm_eps": 1e-12,
                    "num_attention_heads": 8,
                }

                class Struct:
                    def __init__(self, **entries):
                        self.__dict__.update(entries)

                config = Struct(**vid_bert_params)
                self.mmtvid_model = MMTVID(
                    expert_dims=expert_dims,
                    same_dim=512,
                    hidden_size=512,
                    vid_bert_config=config
                )
            else:
                self.mmtvid_model = MMTVID(**mmtvid_params)
            self.mmtvid_model.load_state_dict(state['vid_state_dict'])
            self.mmtvid_model.to(device)
            self.mmtvid_model.eval()
        elif self.modality == 'text':
            if mmttxt_params is None:
                txt_bert_params = {
                    'hidden_dropout_prob': 0.2,
                    'attention_probs_dropout_prob': 0.2,
                }
                self.mmttxt_model = MMTTXT(
                    txt_bert=TxtBertModel.from_pretrained('bert-base-cased', **txt_bert_params),
                    tokenizer=AutoTokenizer.from_pretrained('bert-base-cased'),
                    max_length=30,
                    modalities=["CLIP", "tf_vggish", "VIDEO"],
                    add_special_tokens=True,
                    add_dot=True,
                    same_dim=512,
                    dout_prob=0.2,
                )
            else:
                self.mmttxt_model = MMTTXT(**mmttxt_params)
            self.mmttxt_model.load_state_dict(state['txt_state_dict'])
            self.mmttxt_model.to(device)
            self.mmttxt_model.eval()

    def __call__(self, data: Union[Dict, str]):
        if self.modality == 'video':
            vec = self._inference_from_video(**data) # {"features"=..., "features_t"=..., "features_ind"=...}
        elif self.modality == 'text':
            vec = self._inference_from_text(data) # str
        else:
            raise ValueError("modality[{}] not implemented.".format(self._modality))
        return vec

    def _inference_from_text(self, text: str):
        self.mmttxt_model.eval()
        output = self.mmttxt_model([text])
        # self.assertTrue(output.shape == (batch_size, 1024))
        return output.detach().flatten().cpu().numpy()

    def _inference_from_video(self, features, features_t, features_ind):
        self.mmtvid_model.eval()
        output = self.mmtvid_model(
            features=self._preprocess_video_input(features),
            features_t=self._preprocess_video_input(features_t),
            features_ind=self._preprocess_video_input(features_ind),
            features_maxp=None,
        )
        return output.detach().flatten().cpu().numpy()

    def _preprocess_video_input(self, data: Dict):
        for k, v in data.items():
            data[k] = v.unsqueeze(0).to(self.device)
        return data