# 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 typing import NamedTuple
from pathlib import Path
from PIL import Image
import torch
import numpy
import os

from towhee.operator import Operator
from timm.data import resolve_data_config
from timm.data.transforms_factory import create_transform
from towhee.utils.pil_utils import to_pil

import warnings
warnings.filterwarnings("ignore")

class VitImageEmbedding(Operator):
    """
    Embedding extractor using ViT.
    Args:
        model_name (`string`):
            Model name.
        weights_path (`string`):
            Path to local weights.
    """

    def __init__(self, model_name: str = 'vit_large_patch16_224',
                 framework: str = 'pytorch', weights_path: str = None) -> None:
        super().__init__()
        if framework == 'pytorch':
            import importlib.util
            path = os.path.join(str(Path(__file__).parent), 'pytorch', 'model.py')
            opname = os.path.basename(str(Path(__file__))).split('.')[0]
            spec = importlib.util.spec_from_file_location(opname, path)
            module = importlib.util.module_from_spec(spec)
            spec.loader.exec_module(module)
        self.model = module.Model(model_name, weights_path)
        config = resolve_data_config({}, model=self.model._model)
        self.tfms = create_transform(**config)

    def __call__(self, image: 'towhee.types.Image') -> NamedTuple('Outputs', [('feature_vector', numpy.ndarray)]):
        img = self.tfms(to_pil(image)).unsqueeze(0)
        Outputs = NamedTuple('Outputs', [('feature_vector', numpy.ndarray)])
        features = self.model(img)
        return Outputs(features.flatten().detach().numpy())