import plotly.graph_objects as go
import textwrap
import re
from collections import defaultdict

def apply_lcs_numbering(sentence, common_grams):
    """Apply LCS numbering based on common grams."""
    for idx, lcs in common_grams:
        sentence = re.sub(rf"\b{lcs}\b", f"({idx}){lcs}", sentence)
    return sentence

def highlight_words(sentence, color_map):
    """Highlight specified words in a sentence with corresponding colors."""
    for word, color in color_map.items():
        sentence = re.sub(f"\\b{word}\\b", f"{{{{{word}}}}}", sentence, flags=re.IGNORECASE)
    return sentence

def clean_and_wrap_nodes(nodes, highlight_info):
    """Clean nodes by removing labels and wrap text for display."""
    global_color_map = dict(highlight_info)
    cleaned_nodes = [re.sub(r'\sL[0-9]$', '', node) for node in nodes]
    highlighted_nodes = [highlight_words(node, global_color_map) for node in cleaned_nodes]
    return ['<br>'.join(textwrap.wrap(node, width=55)) for node in highlighted_nodes]

def get_levels_and_edges(nodes):
    """Determine levels and create edges dynamically."""
    levels = {}
    edges = []
    for i, node in enumerate(nodes):
        level = int(node.split()[-1][1])
        levels[i] = level

    # Create edges from level 0 to level 1 nodes
    root_node = next(i for i, level in levels.items() if level == 0)
    edges.extend((root_node, i) for i, level in levels.items() if level == 1)

    return levels, edges

def calculate_positions(levels):
    """Calculate x, y positions for each node based on levels."""
    positions = {}
    level_heights = defaultdict(int)
    y_offsets = {level: - (height - 1) / 2 for level, height in level_heights.items()}

    for node, level in levels.items():
        level_heights[level] += 1
        x_gap = 2
        l1_y_gap = 10
        positions[node] = (-level * x_gap, y_offsets[level] * l1_y_gap)
        y_offsets[level] += 1

    return positions

def color_highlighted_words(node, color_map):
    """Highlight words in a wrapped node string."""
    parts = re.split(r'(\{\{.*?\}\})', node)
    colored_parts = [
        f"<span style='color: {color_map.get(match.group(1), 'black')};'>{match.group(1)}</span>" 
        if (match := re.match(r'\{\{(.*?)\}\}', part)) 
        else part 
        for part in parts
    ]
    return ''.join(colored_parts)

def generate_subplot(paraphrased_sentence, scheme_sentences, highlight_info, common_grams, subplot_number):
    """Generate a subplot based on the input sentences and highlight info."""
    # Combine nodes into one list with appropriate labels
    nodes = [paraphrased_sentence + ' L0'] + [s + ' L1' for s in scheme_sentences]

    # Apply LCS numbering and clean/wrap nodes
    nodes = [apply_lcs_numbering(node, common_grams) for node in nodes]
    wrapped_nodes = clean_and_wrap_nodes(nodes, highlight_info)

    # Get levels and edges
    levels, edges = get_levels_and_edges(nodes)
    positions = calculate_positions(levels)

    # Create figure
    fig = go.Figure()

    # Add nodes and edges to the figure
    for i, node in enumerate(wrapped_nodes):
        colored_node = color_highlighted_words(node, dict(highlight_info))
        x, y = positions[i]
        
        fig.add_trace(go.Scatter(
            x=[-x],  # Reflect the x coordinate
            y=[y],
            mode='markers',
            marker=dict(size=10, color='blue'),
            hoverinfo='none'
        ))
        fig.add_annotation(
            x=-x,  # Reflect the x coordinate
            y=y,
            text=colored_node,
            showarrow=False,
            xshift=15,
            align="center",
            font=dict(size=12),
            bordercolor='black',
            borderwidth=1,
            borderpad=2,
            bgcolor='white',
            width=300,
            height=120
        )

    # Add edges and edge annotations
    edge_texts = [
        "Highest Entropy Masking", "Pseudo-random Masking", "Random Masking", 
        "Greedy Sampling", "Temperature Sampling", "Exponential Minimum Sampling", 
        "Inverse Transform Sampling", "Greedy Sampling", "Temperature Sampling", 
        "Exponential Minimum Sampling", "Inverse Transform Sampling", 
        "Greedy Sampling", "Temperature Sampling", "Exponential Minimum Sampling", 
        "Inverse Transform Sampling"
    ]
    
    for i, edge in enumerate(edges):
        x0, y0 = positions[edge[0]]
        x1, y1 = positions[edge[1]]
        fig.add_trace(go.Scatter(
            x=[-x0, -x1],  # Reflect the x coordinates
            y=[y0, y1],
            mode='lines',
            line=dict(color='black', width=1)
        ))

        # Add text annotation above the edge
        mid_x = (-x0 + -x1) / 2
        mid_y = (y0 + y1) / 2
        fig.add_annotation(
            x=mid_x,
            y=mid_y + 0.8,  # Adjust y position to shift text upwards
            text=edge_texts[i],  # Use the text specific to this edge
            showarrow=False,
            font=dict(size=12),
            align="center"
        )

    fig.update_layout(
        showlegend=False,
        margin=dict(t=20, b=20, l=20, r=20),
        xaxis=dict(showgrid=False, zeroline=False, showticklabels=False),
        yaxis=dict(showgrid=False, zeroline=False, showticklabels=False),
        width=1435,
        height=1000
    )

    return fig

def generate_subplot1(paraphrased_sentence, scheme_sentences, highlight_info, common_grams):
    return generate_subplot(paraphrased_sentence, scheme_sentences, highlight_info, common_grams, subplot_number=1)

def generate_subplot2(scheme_sentences, sampled_sentence, highlight_info, common_grams):
    nodes = scheme_sentences + [s + ' L1' for s in sampled_sentence]
    for i in range(len(scheme_sentences)):
        nodes[i] += ' L0'  # Reassign levels

    # Apply LCS numbering and clean/wrap nodes
    nodes = [apply_lcs_numbering(node, common_grams) for node in nodes]
    wrapped_nodes = clean_and_wrap_nodes(nodes, highlight_info)

    # Get levels and edges
    levels, edges = get_levels_and_edges(nodes)
    positions = calculate_positions(levels)

    # Create figure
    fig2 = go.Figure()

    # Add nodes and edges to the figure
    for i, node in enumerate(wrapped_nodes):
        colored_node = color_highlighted_words(node, dict(highlight_info))
        x, y = positions[i]
        
        fig2.add_trace(go.Scatter(
            x=[-x],  # Reflect the x coordinate
            y=[y],
            mode='markers',
            marker=dict(size=10, color='blue'),
            hoverinfo='none'
        ))
        fig2.add_annotation(
            x=-x,  # Reflect the x coordinate
            y=y,
            text=colored_node,
            showarrow=False,
            xshift=15,
            align="center",
            font=dict(size=12),
            bordercolor='black',
            borderwidth=1,
            borderpad=2,
            bgcolor='white',
            width=450,
            height=65
        )

    # Add edges and text above each edge
    edge_texts = [
        "Highest Entropy Masking", "Pseudo-random Masking", "Random Masking", 
        "Greedy Sampling", "Temperature Sampling", "Exponential Minimum Sampling", 
        "Inverse Transform Sampling", "Greedy Sampling", "Temperature Sampling", 
        "Exponential Minimum Sampling", "Inverse Transform Sampling", 
        "Greedy Sampling", "Temperature Sampling", "Exponential Minimum Sampling", 
        "Inverse Transform Sampling"
    ]
    
    for i, edge in enumerate(edges):
        x0, y0 = positions[edge[0]]
        x1, y1 = positions[edge[1]]
        fig2.add_trace(go.Scatter(
            x=[-x0, -x1],  # Reflect the x coordinates
            y=[y0, y1],
            mode='lines',
            line=dict(color='black', width=1)
        ))

        # Add text annotation above the edge
        mid_x = (-x0 + -x1) / 2
        mid_y = (y0 + y1) / 2
        fig2.add_annotation(
            x=mid_x,
            y=mid_y + 0.8,  # Adjust y position to shift text upwards
            text=edge_texts[i],  # Use the text specific to this edge
            showarrow=False,
            font=dict(size=12),
            align="center"
        )

    fig2.update_layout(
        showlegend=False,
        margin=dict(t=20, b=20, l=20, r=20),
        xaxis=dict(showgrid=False, zeroline=False, showticklabels=False),
        yaxis=dict(showgrid=False, zeroline=False, showticklabels=False),
        width=1435,
        height=1000
    )

    return fig2