import os
os.system('pip install gradio --upgrade')
os.system('pip install git+https://github.com/NielsRogge/transformers.git@add_dino --upgrade')

import gradio as gr
from transformers import ViTFeatureExtractor, ViTModel
import torch
import torch.nn as nn
import torchvision
import matplotlib.pyplot as plt

def get_attention_maps(pixel_values, attentions, nh):
  threshold = 0.6
  w_featmap = pixel_values.shape[-2] // model.config.patch_size
  h_featmap = pixel_values.shape[-1] // model.config.patch_size

  # we keep only a certain percentage of the mass
  val, idx = torch.sort(attentions)
  val /= torch.sum(val, dim=1, keepdim=True)
  cumval = torch.cumsum(val, dim=1)
  th_attn = cumval > (1 - threshold)
  idx2 = torch.argsort(idx)
  for head in range(nh):
      th_attn[head] = th_attn[head][idx2[head]]
  th_attn = th_attn.reshape(nh, w_featmap, h_featmap).float()
  # interpolate
  th_attn = nn.functional.interpolate(th_attn.unsqueeze(0), scale_factor=model.config.patch_size, mode="nearest")[0].cpu().numpy()

  attentions = attentions.reshape(nh, w_featmap, h_featmap)
  attentions = nn.functional.interpolate(attentions.unsqueeze(0), scale_factor=model.config.patch_size, mode="nearest")[0].cpu()
  attentions = attentions.detach().numpy()

  # save attentions heatmaps and return list of filenames
  output_dir = '.'
  os.makedirs(output_dir, exist_ok=True)
  attention_maps = []
  print("Number of heads:", nh)
  for j in range(nh):
      fname = os.path.join(output_dir, "attn-head" + str(j) + ".png")
      # save the attention map
      plt.imsave(fname=fname, arr=attentions[j], format='png')
      # append file name
      attention_maps.append(fname)

  return attention_maps

feature_extractor = ViTFeatureExtractor.from_pretrained("facebook/dino-vits8", do_resize=False)
model = ViTModel.from_pretrained("facebook/dino-vits8", add_pooling_layer=False)

def visualize_attention(image):
  # normalize channels
  pixel_values = feature_extractor(images=image, return_tensors="pt").pixel_values 

  # forward pass
  outputs = model(pixel_values, output_attentions=True, interpolate_pos_encoding=True)

  # get attentions of last layer
  attentions = outputs.attentions[-1] 
  nh = attentions.shape[1] # number of heads

  # we keep only the output patch attention
  attentions = attentions[0, :, 0, 1:].reshape(nh, -1)

  attention_maps = get_attention_maps(pixel_values, attentions, nh)
  
  return attention_maps

iface = gr.Interface(fn=visualize_attention, 
                     inputs=gr.inputs.Image(shape=(480, 480), type="pil"), 
                     outputs=[gr.outputs.Image(type='file', label=f'attention_head_{i}') for i in range(6)])
iface.launch()