"""Token-level activation heatmaps, feature dashboards, and visualization utilities.

Generates interactive Plotly visualizations for the dashboard:
- Token-level feature activation heatmaps
- Feature activation distributions
- Steered vs. unsteered comparison displays
- Layer-wise feature activity plots
"""

import numpy as np
import plotly.graph_objects as go
import plotly.express as px
from plotly.subplots import make_subplots
from typing import Optional


def create_token_heatmap(
    str_tokens: list[str],
    activations: list[float],
    feature_idx: int,
    description: str = "",
    colorscale: str = "YlOrRd",
) -> go.Figure:
    """Create a heatmap showing feature activation per token.

    Displays tokens along the x-axis with color intensity proportional
    to the feature's activation on that token.
    """
    # Clean up token strings for display
    display_tokens = [t.replace("▁", " ").replace("Ġ", " ") for t in str_tokens]

    # Reshape activations for heatmap (1 x n_tokens)
    z = np.array(activations).reshape(1, -1)

    fig = go.Figure(
        data=go.Heatmap(
            z=z,
            x=display_tokens,
            y=["Activation"],
            colorscale=colorscale,
            text=[[f"{v:.3f}" for v in activations]],
            texttemplate="%{text}",
            textfont={"size": 10},
            hovertemplate="Token: %{x}<br>Activation: %{z:.4f}<extra></extra>",
        )
    )

    title = f"Feature #{feature_idx}"
    if description:
        title += f": {description[:80]}"

    fig.update_layout(
        title=title,
        xaxis_title="Token",
        height=150,
        margin=dict(l=60, r=20, t=40, b=40),
        xaxis=dict(tickangle=45),
    )

    return fig


def create_multi_feature_heatmap(
    str_tokens: list[str],
    feature_data: list[dict],
    max_features: int = 10,
    colorscale: str = "YlOrRd",
) -> go.Figure:
    """Create a heatmap showing multiple features' activations across tokens.

    Each row is a feature, each column is a token. Color intensity shows
    activation strength.
    """
    display_tokens = [t.replace("▁", " ").replace("Ġ", " ") for t in str_tokens]

    data = feature_data[:max_features]
    n_features = len(data)

    # Build the z-matrix: [n_features x n_tokens]
    z = np.zeros((n_features, len(str_tokens)))
    y_labels = []

    for i, feat in enumerate(data):
        acts = feat["per_token_activations"]
        z[i, : len(acts)] = acts
        desc = feat["description"][:40]
        y_labels.append(f"#{feat['feature_idx']}: {desc}")

    fig = go.Figure(
        data=go.Heatmap(
            z=z,
            x=display_tokens,
            y=y_labels,
            colorscale=colorscale,
            hovertemplate="Token: %{x}<br>Feature: %{y}<br>Activation: %{z:.4f}<extra></extra>",
        )
    )

    fig.update_layout(
        title="Top Active Features by Token (<bos> token skipped)",
        xaxis_title="Token",
        yaxis_title="Feature",
        height=max(300, 60 * n_features),
        margin=dict(l=200, r=20, t=40, b=60),
        xaxis=dict(tickangle=45),
    )

    return fig


def create_activation_histogram(
    activations: list[float],
    feature_idx: int,
    description: str = "",
    n_bins: int = 50,
) -> go.Figure:
    """Create a histogram of feature activations across tokens."""
    acts = np.array(activations)
    nonzero = acts[acts > 0]

    fig = make_subplots(rows=1, cols=1)

    if len(nonzero) > 0:
        fig.add_trace(
            go.Histogram(
                x=nonzero,
                nbinsx=n_bins,
                name="Non-zero activations",
                marker_color="steelblue",
            )
        )

    title = f"Feature #{feature_idx} Activation Distribution"
    if description:
        title += f"\n{description[:80]}"

    sparsity = 1.0 - (len(nonzero) / len(acts)) if len(acts) > 0 else 1.0

    fig.update_layout(
        title=title,
        xaxis_title="Activation Value",
        yaxis_title="Count",
        height=300,
        margin=dict(l=60, r=20, t=60, b=40),
        annotations=[
            dict(
                text=f"Sparsity: {sparsity:.1%} | Active: {len(nonzero)}/{len(acts)}",
                xref="paper",
                yref="paper",
                x=0.95,
                y=0.95,
                showarrow=False,
                font=dict(size=11),
            )
        ],
    )

    return fig


def create_steering_comparison(
    prompt: str,
    unsteered: str,
    steered: str,
    interventions: list[dict],
) -> str:
    """Create an HTML comparison of steered vs. unsteered text.

    Returns formatted HTML string for display in Gradio.
    """
    import html

    prompt_safe = html.escape(prompt)
    unsteered_safe = html.escape(unsteered)
    steered_safe = html.escape(steered)

    intervention_desc = ", ".join(
        f"Feature #{i['feature_idx']} (strength={i['strength']:.1f})"
        for i in interventions
    )

    markup = f"""
    <div style="font-family: monospace; padding: 10px;">
        <h3>Prompt</h3>
        <p style="background: #f0f0f0; padding: 10px; border-radius: 5px;">{prompt_safe}</p>

        <div style="display: flex; gap: 20px;">
            <div style="flex: 1;">
                <h3 style="color: #666;">Unsteered</h3>
                <p style="background: #f8f8f8; padding: 10px; border-radius: 5px;
                          border-left: 3px solid #ccc; white-space: pre-wrap;">{unsteered_safe}</p>
            </div>
            <div style="flex: 1;">
                <h3 style="color: #2196F3;">Steered</h3>
                <p style="background: #f0f8ff; padding: 10px; border-radius: 5px;
                          border-left: 3px solid #2196F3; white-space: pre-wrap;">{steered_safe}</p>
            </div>
        </div>

        <p style="color: #888; font-size: 0.9em;">
            Interventions: {intervention_desc}
        </p>
    </div>
    """
    return markup


def create_top_predictions_comparison(
    clean_tokens: list[dict],
    steered_tokens: list[dict],
    kl_divergence: float,
) -> go.Figure:
    """Create a side-by-side bar chart comparing top predicted tokens.

    Shows how steering changes the model's next-token distribution.
    """
    fig = make_subplots(
        rows=1,
        cols=2,
        subplot_titles=["Unsteered Predictions", "Steered Predictions"],
        horizontal_spacing=0.15,
    )

    # Clean predictions
    fig.add_trace(
        go.Bar(
            x=[t["prob"] for t in clean_tokens],
            y=[t["token"] for t in clean_tokens],
            orientation="h",
            marker_color="lightgray",
            name="Unsteered",
        ),
        row=1,
        col=1,
    )

    # Steered predictions
    fig.add_trace(
        go.Bar(
            x=[t["prob"] for t in steered_tokens],
            y=[t["token"] for t in steered_tokens],
            orientation="h",
            marker_color="steelblue",
            name="Steered",
        ),
        row=1,
        col=2,
    )

    fig.update_layout(
        title=f"Next-Token Predictions (KL Divergence: {kl_divergence:.4f})",
        height=400,
        showlegend=False,
        margin=dict(l=80, r=20, t=60, b=40),
    )

    fig.update_xaxes(title_text="Probability", row=1, col=1)
    fig.update_xaxes(title_text="Probability", row=1, col=2)

    return fig


def create_layer_activity_plot(
    layer_activations: dict[int, float],
    feature_idx: int,
    description: str = "",
) -> go.Figure:
    """Plot feature activation strength across layers.

    Shows which layers a feature is most active in, giving insight
    into where in the model's computation the feature matters.
    """
    layers = sorted(layer_activations.keys())
    values = [layer_activations[l] for l in layers]

    fig = go.Figure(
        data=go.Bar(
            x=[f"Layer {l}" for l in layers],
            y=values,
            marker_color="steelblue",
        )
    )

    title = f"Feature #{feature_idx} Activity by Layer"
    if description:
        title += f"\n{description[:60]}"

    fig.update_layout(
        title=title,
        xaxis_title="Layer",
        yaxis_title="Mean Activation",
        height=350,
        margin=dict(l=60, r=20, t=60, b=60),
    )

    return fig


def create_logit_attribution_chart(
    top_positive: list[dict],
    top_negative: list[dict],
    bias: float,
    error: float,
    target_token: str,
    total_logit: float,
    descriptions: Optional[dict[int, str]] = None,
) -> go.Figure:
    """Create a horizontal bar chart of per-feature logit contributions.

    Positive contributions shown in blue (right), negative in red (left).
    Includes bias and reconstruction error as separate bars.
    """
    labels = []
    values = []
    colors = []

    # Add positive contributors (largest first)
    for feat in top_positive:
        idx = feat["feature_idx"]
        desc = ""
        if descriptions and idx in descriptions:
            desc = descriptions[idx][:40]
        labels.append(f"#{idx}: {desc}")
        values.append(feat["contribution"])
        colors.append("#2196F3")

    # Add negative contributors (most negative first)
    for feat in top_negative:
        idx = feat["feature_idx"]
        desc = ""
        if descriptions and idx in descriptions:
            desc = descriptions[idx][:40]
        labels.append(f"#{idx}: {desc}")
        values.append(feat["contribution"])
        colors.append("#F44336")

    # Add bias and error
    labels.append("SAE bias")
    values.append(bias)
    colors.append("#9E9E9E")

    labels.append("Reconstruction error")
    values.append(error)
    colors.append("#757575")

    fig = go.Figure(
        data=go.Bar(
            y=labels,
            x=values,
            orientation="h",
            marker_color=colors,
            hovertemplate="<b>%{y}</b><br>Contribution: %{x:.4f}<extra></extra>",
        )
    )

    fig.update_layout(
        title=f'Feature contributions to "{target_token}" (total logit: {total_logit:.2f})',
        xaxis_title="Logit Contribution",
        height=max(400, 30 * len(labels) + 100),
        margin=dict(l=250, r=20, t=60, b=40),
        yaxis=dict(autorange="reversed"),
    )

    return fig


def create_logit_decomposition_summary(
    sae_explained: float,
    bias: float,
    error: float,
    total: float,
) -> go.Figure:
    """Create a stacked bar chart showing SAE-explained vs bias vs error portions."""
    feature_sum = sae_explained - bias  # isolate pure feature contributions

    labels = ["Feature contributions", "SAE bias", "Reconstruction error"]
    values = [feature_sum, bias, error]
    bar_colors = ["#2196F3", "#9E9E9E", "#757575"]

    fig = go.Figure(
        data=go.Bar(
            x=labels,
            y=values,
            marker_color=bar_colors,
            text=[f"{v:.3f}" for v in values],
            textposition="auto",
        )
    )

    gap = total - (feature_sum + bias + error)

    fig.update_layout(
        title=f"Logit Decomposition (total: {total:.3f}, gap: {gap:.4f})",
        yaxis_title="Logit Value",
        height=350,
        margin=dict(l=60, r=20, t=60, b=40),
    )

    return fig