import os

from PIL import Image
import torchvision.transforms as transforms
import matplotlib.pyplot as plt
import pylab
import torch
import numpy as np
import cv2
import sys
sys.path.append('ViT_DeiT')
from samples.CLS2IDX import CLS2IDX
from baselines.ViT.ViT_LRP import vit_base_patch16_224 as vit_LRP
from baselines.ViT.ViT_explanation_generator import LRP

normalize = transforms.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5])
transform = transforms.Compose([
    transforms.Resize(256),
    transforms.CenterCrop(224),
    transforms.ToTensor(),
    normalize,
])
use_thresholding =  False
def show_cam_on_image(img, mask):
    heatmap = cv2.applyColorMap(np.uint8(255 * mask), cv2.COLORMAP_JET)
    heatmap = np.float32(heatmap) / 255
    cam = heatmap + np.float32(img)
    cam = cam / np.max(cam)
    return cam

# initialize ViT pretrained
model = vit_LRP(pretrained=True)
model.eval()
attribution_generator = LRP(model)

def generate_visualization(original_image, class_index=None):
    transformer_attribution = attribution_generator.generate_LRP(original_image.unsqueeze(0), method="transformer_attribution", index=class_index).detach()
    transformer_attribution = transformer_attribution.reshape(1, 1, 14, 14)
    transformer_attribution = torch.nn.functional.interpolate(transformer_attribution, scale_factor=16, mode='bilinear')
    transformer_attribution = transformer_attribution.reshape(224, 224).data.cpu().numpy()
    transformer_attribution = (transformer_attribution - transformer_attribution.min()) / (transformer_attribution.max() - transformer_attribution.min())

    if use_thresholding:
      transformer_attribution = transformer_attribution * 255
      transformer_attribution = transformer_attribution.astype(np.uint8)
      ret, transformer_attribution = cv2.threshold(transformer_attribution, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)
      transformer_attribution[transformer_attribution == 255] = 1

    image_transformer_attribution = original_image.permute(1, 2, 0).data.cpu().numpy()
    image_transformer_attribution = (image_transformer_attribution - image_transformer_attribution.min()) / (image_transformer_attribution.max() - image_transformer_attribution.min())
    vis = show_cam_on_image(image_transformer_attribution, transformer_attribution)
    vis =  np.uint8(255 * vis)
    vis = cv2.cvtColor(np.array(vis), cv2.COLOR_RGB2BGR)
    return vis


def print_top_classes(original_image, **kwargs):
    predictions = model(original_image.unsqueeze(0))
    # Print Top-5 predictions
    prob = torch.softmax(predictions, dim=1)
    class_indices = predictions.data.topk(5, dim=1)[1][0].tolist()
    max_str_len = 0
    class_names = []
    output = []
    for cls_idx in class_indices:
        class_names.append(CLS2IDX[cls_idx])
        if len(CLS2IDX[cls_idx]) > max_str_len:
            max_str_len = len(CLS2IDX[cls_idx])

    for cls_idx in class_indices:
        output_string = '{} : {}'.format(cls_idx, CLS2IDX[cls_idx])
        # output_string += ' ' * (max_str_len - len(CLS2IDX[cls_idx])) + '\t\t'
        output_string += '  value = {:.3f}  prob = {:.1f}%'.format(predictions[0, cls_idx], 100 * prob[0, cls_idx])
        output.append(output_string)
    return output