# import the necessary packages
from tensorflow.keras import layers
import tensorflow as tf
import matplotlib.pyplot as plt
import math

class PlotAttention:
	def __init__(self, model):
		self.model = model
	
	def  __call__(self, image):
		# resize the image to a 224, 224 dim
		image = tf.image.convert_image_dtype(image, tf.float32)
		image = tf.image.resize(image, (224, 224))
		image = image[tf.newaxis, ...]

		# pass through the stem
		test_x = self.model.stem(image)
		# pass through the trunk
		test_x = self.model.trunk(test_x)
		# pass through the attention pooling block
		_, test_viz_weights = self.model.attention_pooling(test_x)
		test_viz_weights = test_viz_weights[tf.newaxis, ...]
		
		# reshape the vizualization weights
		num_patches = tf.shape(test_viz_weights)[-1]
		height = width = int(math.sqrt(num_patches))
		test_viz_weights = layers.Reshape((height, width))(test_viz_weights)
		
		index = 0
		selected_image = image[index]
		selected_weight = test_viz_weights[index]

		fig, ax = plt.subplots(nrows=1, ncols=2, figsize=(10, 5))
		ax[0].imshow(selected_image)
		ax[0].set_title(f"Original")
		ax[0].axis("off")
		
		img = ax[1].imshow(selected_image)
		ax[1].imshow(selected_weight, cmap='inferno', alpha=0.6, extent=img.get_extent())
		ax[1].set_title(f"Attended")
		ax[1].axis("off")

		plt.axis("off")
		return plt