import time
import os
import torch
import numpy as np
import torchvision
import torch.nn.functional as F
from torchvision.datasets import ImageFolder
import torchvision.transforms as transforms
from tqdm import tqdm
import pickle
import argparse
from PIL import Image

concat = lambda x: np.concatenate(x, axis=0)
to_np = lambda x: x.data.to("cpu").numpy()


class Wrapper(torch.nn.Module):
    def __init__(self, model):
        super(Wrapper, self).__init__()
        self.model = model
        self.avgpool_output = None
        self.query = None
        self.cossim_value = {}

        def fw_hook(module, input, output):
            self.avgpool_output = output.squeeze()

        self.model.avgpool.register_forward_hook(fw_hook)

    def forward(self, input):
        _ = self.model(input)
        return self.avgpool_output

    def __repr__(self):
        return "Wrappper"


def QueryToEmbedding(query_path):
    dataset_transform = transforms.Compose(
        [
            transforms.Resize(256),
            transforms.CenterCrop(224),
            transforms.ToTensor(),
            transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]),
        ]
    )

    model = torchvision.models.resnet50(pretrained=True)
    model.eval()
    myw = Wrapper(model)

    query_pil = Image.open(query_path)
    query_pt = dataset_transform(query_pil)

    with torch.no_grad():
        embedding = to_np(myw(query_pt.unsqueeze(0)))

    return np.asarray([embedding])