"""
Script containing utilities for (attention) visualizations.
NOTE: If you are using macOS, Tkinter might crash your computer depending on your local Python,
Tkinter, and operating system versions. Check https://www.python.org/download/mac/tcltk/ for more
information.
"""

import tkinter as tk


def visualize_attention(window_name, tokens_and_weights):
    """
    Function to visualize the attention mechanism through token highlighting.

    @param (str) window_name: screen-name for the Tkinter window
    @param (list) tokens_and_weights: list containing tuples as elements with tokens and the
           corresponding attention weights, in the form (token, weight); ex. ('awesome', 0.67)
    """
    root = tk.Tk()
    root.title(window_name)
    text_widget = tk.Text(root)
    text = ''

    # List of indices, where each element will be a tuple in the form: (start_index, end_index)
    low_attention_indices = []
    medium_attention_indices = []
    high_attention_indices = []
    very_high_attention_indices = []

    # Iterate over tokens and weights and assign start and end indices depending on attention weight
    current_index = 0
    for token_and_weight in tokens_and_weights:
        token, weight = token_and_weight[0], token_and_weight[1]
        text += token + ' '

        if weight >= 0.80:
            very_high_attention_indices.append((current_index, current_index + len(token)))
        elif weight >= 0.60:
            high_attention_indices.append((current_index, current_index + len(token)))
        elif weight >= 0.40:
            medium_attention_indices.append((current_index, current_index + len(token)))
        elif weight >= 0.20:
            low_attention_indices.append((current_index, current_index + len(token)))

        current_index += len(token) + 1

    text_widget.insert(tk.INSERT, text)
    text_widget.pack(expand=1, fill=tk.BOTH)

    # Add Tkinter tags to the specified indices in text widget
    for indices in low_attention_indices:
        text_widget.tag_add('low_attention', '1.' + str(indices[0]), '1.' + str(indices[1]))

    for indices in medium_attention_indices:
        text_widget.tag_add('medium_attention', '1.' + str(indices[0]), '1.' + str(indices[1]))

    for indices in high_attention_indices:
        text_widget.tag_add('high_attention', '1.' + str(indices[0]), '1.' + str(indices[1]))

    for indices in very_high_attention_indices:
        text_widget.tag_add('very_high_attention', '1.' + str(indices[0]), '1.' + str(indices[1]))

    # Highlight attention in text based on defined tags and the corresponding indices
    text_widget.tag_config('low_attention', background='#FDA895')
    text_widget.tag_config('medium_attention', background='#FE7D61')
    text_widget.tag_config('high_attention', background='#FC5430')
    text_widget.tag_config('very_high_attention', background='#FF2D00')

    root.mainloop()

eef40983e41c20d44b31712d919dee53e305497c
<mask token>

<mask token>


def visualize_attention(window_name, tokens_and_weights):
    """
    Function to visualize the attention mechanism through token highlighting.

    @param (str) window_name: screen-name for the Tkinter window
    @param (list) tokens_and_weights: list containing tuples as elements with tokens and the
           corresponding attention weights, in the form (token, weight); ex. ('awesome', 0.67)
    """
    root = tk.Tk()
    root.title(window_name)
    text_widget = tk.Text(root)
    text = ''
    low_attention_indices = []
    medium_attention_indices = []
    high_attention_indices = []
    very_high_attention_indices = []
    current_index = 0
    for token_and_weight in tokens_and_weights:
        token, weight = token_and_weight[0], token_and_weight[1]
        text += token + ' '
        if weight >= 0.8:
            very_high_attention_indices.append((current_index, 
                current_index + len(token)))
        elif weight >= 0.6:
            high_attention_indices.append((current_index, current_index +
                len(token)))
        elif weight >= 0.4:
            medium_attention_indices.append((current_index, current_index +
                len(token)))
        elif weight >= 0.2:
            low_attention_indices.append((current_index, current_index +
                len(token)))
        current_index += len(token) + 1
    text_widget.insert(tk.INSERT, text)
    text_widget.pack(expand=1, fill=tk.BOTH)
    for indices in low_attention_indices:
        text_widget.tag_add('low_attention', '1.' + str(indices[0]), '1.' +
            str(indices[1]))
    for indices in medium_attention_indices:
        text_widget.tag_add('medium_attention', '1.' + str(indices[0]), 
            '1.' + str(indices[1]))
    for indices in high_attention_indices:
        text_widget.tag_add('high_attention', '1.' + str(indices[0]), '1.' +
            str(indices[1]))
    for indices in very_high_attention_indices:
        text_widget.tag_add('very_high_attention', '1.' + str(indices[0]), 
            '1.' + str(indices[1]))
    text_widget.tag_config('low_attention', background='#FDA895')
    text_widget.tag_config('medium_attention', background='#FE7D61')
    text_widget.tag_config('high_attention', background='#FC5430')
    text_widget.tag_config('very_high_attention', background='#FF2D00')
    root.mainloop()

<mask token>
import tkinter as tk


def visualize_attention(window_name, tokens_and_weights):
    """
    Function to visualize the attention mechanism through token highlighting.

    @param (str) window_name: screen-name for the Tkinter window
    @param (list) tokens_and_weights: list containing tuples as elements with tokens and the
           corresponding attention weights, in the form (token, weight); ex. ('awesome', 0.67)
    """
    root = tk.Tk()
    root.title(window_name)
    text_widget = tk.Text(root)
    text = ''
    low_attention_indices = []
    medium_attention_indices = []
    high_attention_indices = []
    very_high_attention_indices = []
    current_index = 0
    for token_and_weight in tokens_and_weights:
        token, weight = token_and_weight[0], token_and_weight[1]
        text += token + ' '
        if weight >= 0.8:
            very_high_attention_indices.append((current_index, 
                current_index + len(token)))
        elif weight >= 0.6:
            high_attention_indices.append((current_index, current_index +
                len(token)))
        elif weight >= 0.4:
            medium_attention_indices.append((current_index, current_index +
                len(token)))
        elif weight >= 0.2:
            low_attention_indices.append((current_index, current_index +
                len(token)))
        current_index += len(token) + 1
    text_widget.insert(tk.INSERT, text)
    text_widget.pack(expand=1, fill=tk.BOTH)
    for indices in low_attention_indices:
        text_widget.tag_add('low_attention', '1.' + str(indices[0]), '1.' +
            str(indices[1]))
    for indices in medium_attention_indices:
        text_widget.tag_add('medium_attention', '1.' + str(indices[0]), 
            '1.' + str(indices[1]))
    for indices in high_attention_indices:
        text_widget.tag_add('high_attention', '1.' + str(indices[0]), '1.' +
            str(indices[1]))
    for indices in very_high_attention_indices:
        text_widget.tag_add('very_high_attention', '1.' + str(indices[0]), 
            '1.' + str(indices[1]))
    text_widget.tag_config('low_attention', background='#FDA895')
    text_widget.tag_config('medium_attention', background='#FE7D61')
    text_widget.tag_config('high_attention', background='#FC5430')
    text_widget.tag_config('very_high_attention', background='#FF2D00')
    root.mainloop()

"""
Script containing utilities for (attention) visualizations.
NOTE: If you are using macOS, Tkinter might crash your computer depending on your local Python,
Tkinter, and operating system versions. Check https://www.python.org/download/mac/tcltk/ for more
information.
"""

import tkinter as tk


def visualize_attention(window_name, tokens_and_weights):
    """
    Function to visualize the attention mechanism through token highlighting.

    @param (str) window_name: screen-name for the Tkinter window
    @param (list) tokens_and_weights: list containing tuples as elements with tokens and the
           corresponding attention weights, in the form (token, weight); ex. ('awesome', 0.67)
    """
    root = tk.Tk()
    root.title(window_name)
    text_widget = tk.Text(root)
    text = ''

    # List of indices, where each element will be a tuple in the form: (start_index, end_index)
    low_attention_indices = []
    medium_attention_indices = []
    high_attention_indices = []
    very_high_attention_indices = []

    # Iterate over tokens and weights and assign start and end indices depending on attention weight
    current_index = 0
    for token_and_weight in tokens_and_weights:
        token, weight = token_and_weight[0], token_and_weight[1]
        text += token + ' '

        if weight >= 0.80:
            very_high_attention_indices.append((current_index, current_index + len(token)))
        elif weight >= 0.60:
            high_attention_indices.append((current_index, current_index + len(token)))
        elif weight >= 0.40:
            medium_attention_indices.append((current_index, current_index + len(token)))
        elif weight >= 0.20:
            low_attention_indices.append((current_index, current_index + len(token)))

        current_index += len(token) + 1

    text_widget.insert(tk.INSERT, text)
    text_widget.pack(expand=1, fill=tk.BOTH)

    # Add Tkinter tags to the specified indices in text widget
    for indices in low_attention_indices:
        text_widget.tag_add('low_attention', '1.' + str(indices[0]), '1.' + str(indices[1]))

    for indices in medium_attention_indices:
        text_widget.tag_add('medium_attention', '1.' + str(indices[0]), '1.' + str(indices[1]))

    for indices in high_attention_indices:
        text_widget.tag_add('high_attention', '1.' + str(indices[0]), '1.' + str(indices[1]))

    for indices in very_high_attention_indices:
        text_widget.tag_add('very_high_attention', '1.' + str(indices[0]), '1.' + str(indices[1]))

    # Highlight attention in text based on defined tags and the corresponding indices
    text_widget.tag_config('low_attention', background='#FDA895')
    text_widget.tag_config('medium_attention', background='#FE7D61')
    text_widget.tag_config('high_attention', background='#FC5430')
    text_widget.tag_config('very_high_attention', background='#FF2D00')

    root.mainloop()
