import torch
import clip
from PIL import Image
import glob
import os
from random import choice


device = "cuda" if torch.cuda.is_available() else "cpu"
model, preprocess = clip.load("ViT-L/14@336px", device=device)

COCO = glob.glob(os.path.join(os.getcwd(), "images", "*"))
available_models = ['RN50', 'RN101', 'RN50x4', 'RN50x16', 'RN50x64', 'ViT-B/32', 'ViT-B/16', 'ViT-L/14', 'ViT-L/14@336px']


def load_random_image():
    image_path = choice(COCO)
    image = Image.open(image_path)
    return image


def next_image():
    global image_org, image
    image_org = load_random_image()
    image = preprocess(Image.fromarray(image_org)).unsqueeze(0).to(device)


# def calculate_logits(image, text):
#     return model(image, text)[0]


def calculate_logits(image_features, text_features):
    image_features = image_features / image_features.norm(dim=1, keepdim=True)
    text_features = text_features / text_features.norm(dim=1, keepdim=True)

    logit_scale = model.logit_scale.exp()
    return logit_scale * image_features @ text_features.t()


last = -1
best = -1

goal = 23

image_org = load_random_image()
image = preprocess(image_org).unsqueeze(0).to(device)
with torch.no_grad():
    image_features = model.encode_image(image)


def answer(message):
    global last, best

    text = clip.tokenize([message]).to(device)

    with torch.no_grad():
        text_features = model.encode_text(text)
        # logits_per_image, _ = model(image, text)
        logits = calculate_logits(image_features, text_features).cpu().numpy().flatten()[0]
        # logits = calculate_logits(image, text)

    if last == -1:
        is_better = -1
    elif last > logits:
        is_better = 0
    elif last < logits:
        is_better = 1
    elif logits > goal:
        is_better = 2
    else:
        is_better = -1

    last = logits
    if logits > best:
        best = logits
        is_better = 3

    return logits, is_better


def reset_everything():
    global last, best, goal, image, image_org
    last = -1
    best = -1
    goal = 23
    image_org = load_random_image()
    image = preprocess(image_org).unsqueeze(0).to(device)