# 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
import os
from pathlib import Path

import torch
from torchvision import transforms
from transformers import GPT2Tokenizer

from towhee.types.arg import arg, to_image_color
from towhee.types.image_utils import to_pil
from towhee.operator.base import NNOperator, OperatorFlag
from towhee import register
from towhee.models import clip

class ExpansionNetV2(NNOperator):
    """
    ExpansionNet V2 image captioning operator
    """
    def __init__(self, model_name: str):
        super().__init__()
        sys.path.append(str(Path(__file__).parent))
        from models.End_ExpansionNet_v2 import End_ExpansionNet_v2
        sys.path.pop()
        with open('demo_coco_tokens.pickle') as fw:

        self.model = End_ExpansionNet_v2(swin_img_size=img_size, swin_patch_size=4, swin_in_chans=3,
                                    swin_embed_dim=192, swin_depths=[2, 2, 18, 2], swin_num_heads=[6, 12, 24, 48],
                                    swin_window_size=12, swin_mlp_ratio=4., swin_qkv_bias=True, swin_qk_scale=None,
                                    swin_drop_rate=0.0, swin_attn_drop_rate=0.0, swin_drop_path_rate=0.0,
                                    swin_norm_layer=torch.nn.LayerNorm, swin_ape=False, swin_patch_norm=True,
                                    swin_use_checkpoint=False,
                                    final_swin_dim=1536,

                                    d_model=model_args.model_dim, N_enc=model_args.N_enc,
                                    N_dec=model_args.N_dec, num_heads=8, ff=2048,
                                    num_exp_enc_list=[32, 64, 128, 256, 512],
                                    num_exp_dec=16,
                                    output_word2idx=coco_tokens['word2idx_dict'],
                                    output_idx2word=coco_tokens['idx2word_list'],
                                    max_seq_len=args.max_seq_len, drop_args=model_args.drop_args,
                                rank='cpu')