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import re
from typing import List, Tuple, Optional
import numpy as np
def clean_label(token: str) -> str:
"""
Cleans token labels for visualization.
Handles various tokenizer-specific formatting.
"""
label = str(token)
# Handle common tokenizer prefixes
label = label.replace('Ġ', ' ') # GPT-2 style space
label = label.replace('▁', ' ') # SentencePiece style space
label = label.replace('Ċ', '\\n') # Newline
# Handle special tokens
label = label.replace('</s>', '[EOS]')
label = label.replace('<s>', '[BOS]')
label = label.replace('<unk>', '[UNK]')
label = label.replace('<pad>', '[PAD]')
label = label.replace('<|begin_of_text|>', '[BOS]')
label = label.replace('<|end_of_text|>', '[EOS]')
label = label.replace('<|endoftext|>', '[EOS]')
# Remove byte-level encoding markers
label = re.sub(r'<0x[0-9A-Fa-f]{2}>', '', label)
# Clean up whitespace
label = label.strip()
# Return cleaned label or placeholder
return label if label else "[EMPTY]"
def scale_weight_to_width(
weight: float,
min_width: float = 0.5,
max_width: float = 3.0,
scale_factor: float = 5.0
) -> float:
"""
Scale attention weight to line width for visualization.
Args:
weight: Attention weight (0-1)
min_width: Minimum line width
max_width: Maximum line width
scale_factor: Scaling factor for weight
Returns:
Scaled line width
"""
scaled = min(1.0, weight * scale_factor)
return min_width + (max_width - min_width) * scaled
def scale_weight_to_opacity(
weight: float,
min_opacity: float = 0.1,
max_opacity: float = 1.0,
threshold: float = 0.0
) -> float:
"""
Scale attention weight to opacity for visualization.
Args:
weight: Attention weight (0-1)
min_opacity: Minimum opacity
max_opacity: Maximum opacity
threshold: Threshold below which opacity is 0
Returns:
Scaled opacity
"""
if weight < threshold:
return 0.0
# Linear scaling above threshold
normalized = (weight - threshold) / (1.0 - threshold) if threshold < 1.0 else weight
return min_opacity + (max_opacity - min_opacity) * normalized
def get_node_positions(
num_input: int,
num_output: int,
spacing: str = 'linear'
) -> Tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray]:
"""
Calculate node positions for visualization.
Args:
num_input: Number of input tokens
num_output: Number of output tokens
spacing: Spacing strategy ('linear', 'equal')
Returns:
Tuple of (input_x, input_y, output_x, output_y)
"""
# Y positions (vertical)
if spacing == 'linear':
input_y = np.linspace(0.1, 0.9, num_input) if num_input > 1 else np.array([0.5])
output_y = np.linspace(0.1, 0.9, num_output) if num_output > 1 else np.array([0.5])
else: # equal spacing
total_height = 0.8
input_spacing = total_height / (num_input + 1)
output_spacing = total_height / (num_output + 1)
input_y = np.array([0.1 + (i + 1) * input_spacing for i in range(num_input)])
output_y = np.array([0.1 + (i + 1) * output_spacing for i in range(num_output)])
# X positions (horizontal)
input_x = np.full(num_input, 0.1)
output_x = np.full(num_output, 0.9)
return input_x, input_y, output_x, output_y
def create_spline_path(
start_x: float,
start_y: float,
end_x: float,
end_y: float,
control_offset: float = 0.15
) -> Tuple[List[float], List[float]]:
"""
Create a spline path for output-to-output connections.
Args:
start_x, start_y: Starting position
end_x, end_y: Ending position
control_offset: Offset for control points
Returns:
Tuple of (x_path, y_path) for spline
"""
# Create control points for smooth curve
path_x = [
start_x,
start_x + control_offset,
end_x + control_offset,
end_x
]
path_y = [
start_y,
start_y,
end_y,
end_y
]
return path_x, path_y
def format_attention_text(
from_token: str,
to_token: str,
weight: float,
connection_type: str = "attention"
) -> str:
"""
Format hover text for attention connections.
Args:
from_token: Source token
to_token: Target token
weight: Attention weight
connection_type: Type of connection
Returns:
Formatted hover text
"""
return (
f"{from_token} → {to_token}<br>"
f"{connection_type.capitalize()} Weight: {weight:.4f}"
)
def get_color_for_weight(
weight: float,
base_color: str = "blue",
use_gradient: bool = True
) -> str:
"""
Get color for attention weight visualization.
Args:
weight: Attention weight (0-1)
base_color: Base color name
use_gradient: Whether to use gradient based on weight
Returns:
Color string for plotly
"""
if not use_gradient:
if base_color == "blue":
return "rgba(0, 0, 255, 0.6)"
elif base_color == "orange":
return "rgba(255, 165, 0, 0.6)"
else:
return "rgba(128, 128, 128, 0.6)"
# Create gradient based on weight
if base_color == "blue":
# Light blue to dark blue
intensity = int(255 - weight * 155) # 255 to 100
return f"rgba(0, {intensity}, 255, {0.3 + weight * 0.4})"
elif base_color == "orange":
# Light orange to dark orange
intensity = int(255 - weight * 100) # 255 to 155
return f"rgba(255, {intensity}, 0, {0.3 + weight * 0.4})"
else:
# Gray scale
intensity = int(200 - weight * 100) # 200 to 100
return f"rgba({intensity}, {intensity}, {intensity}, {0.3 + weight * 0.4})"
def truncate_token_label(token: str, max_length: int = 15) -> str:
"""
Truncate long token labels for display.
Args:
token: Token string
max_length: Maximum length
Returns:
Truncated token with ellipsis if needed
"""
cleaned = clean_label(token)
if len(cleaned) > max_length:
return cleaned[:max_length-3] + "..."
return cleaned |