"""Agent Adoption Survey Comparison Chart

Grouped horizontal bar chart comparing key enterprise AI adoption metrics
across three major 2025 surveys: LangChain, McKinsey, and PwC.
"""
import matplotlib
matplotlib.use("Agg")

# Patch matplotlib Path.__deepcopy__ to break Python 3.14 recursion loop
try:
    from matplotlib.path import Path
    _original_path_deepcopy = Path.__deepcopy__
    def _safe_path_deepcopy(self, memo):
        if id(self) in memo:
            return memo[id(self)]
        memo[id(self)] = self
        return self
    Path.__deepcopy__ = _safe_path_deepcopy
except Exception:
    pass

import matplotlib.pyplot as plt
import matplotlib.colors as mcolors
import matplotlib.patches as mpatches
from src.data.agent_adoption import agent_survey_data
from src.utils.styling import (
    get_theme, EXPORT_DPI, AGENT_GROWTH, GRAY_DARK, BLACK, WHITE, GRAY_LIGHT,
)


def _shade(base_hex: str, factor: float) -> str:
    """Lighten or darken a hex color by a given factor (0–1)."""
    r, g, b = mcolors.to_rgb(base_hex)
    # Blend toward white to lighten
    r2 = r + (1.0 - r) * factor
    g2 = g + (1.0 - g) * factor
    b2 = b + (1.0 - b) * factor
    return mcolors.to_hex((r2, g2, b2))


def plot_agent_adoption() -> str:
    """Generate grouped horizontal bar chart of survey comparisons."""
    plt.rcParams.update(get_theme())
    fig, ax = plt.subplots(figsize=(14, 8))

    # ------------------------------------------------------------------
    # Data
    # ------------------------------------------------------------------
    lc = agent_survey_data["langchain_2025"]
    mc = agent_survey_data["mckinsey_2025"]
    pc = agent_survey_data["pwc_2025"]

    # Each row is a comparable category; values are [LangChain, McKinsey, PwC]
    # Where a survey has no direct comparable metric, we use None.
    categories = [
        "Production\nDeployment",
        "Overall\nAI Adoption",
        "Budget\nIncrease",
        "Scaling\nAgentic AI",
        "Productivity\nValue",
    ]

    # Values mapped to closest comparable metrics
    values = [
        # Production / Deployment
        [lc["production"],  None,           pc["ai_agents_already_adopted"]],
        # Overall AI Adoption / Maturity
        [lc["observability_implemented"], mc["overall_ai_adoption"], None],
        # Budget / Investment Intent
        [lc["multi_model_deployments"],  None,           pc["plan_increase_ai_budgets"]],
        # Scaling / Experimentation
        [None,                       mc["agentic_ai_scaling"], None],
        # Measurable Value / Productivity
        [None,                       None,           pc["measurable_productivity_value"]],
    ]

    # Survey identifiers
    surveys = [
        "LangChain\n(n=1,340)",
        "McKinsey\n(n=1,993)",
        "PwC\n(n=308)",
    ]

    # Colors: base AGENT_GROWTH with increasing lightness
    colors = [
        AGENT_GROWTH,                         # LangChain — full purple
        _shade(AGENT_GROWTH, 0.25),           # McKinsey — lighter
        _shade(AGENT_GROWTH, 0.50),           # PwC — lightest
    ]

    # ------------------------------------------------------------------
    # Plotting
    # ------------------------------------------------------------------
    n_cats = len(categories)
    bar_height = 0.22
    x_positions = [0, 1, 2]  # offset within each group

    y_positions = []
    for i in range(n_cats):
        base_y = i * 3  # three bars per category
        y_positions.append([base_y + off for off in [0.0, 0.22, 0.44]])

    # Plot bars
    for row_idx, (cat, row_vals) in enumerate(zip(categories, values)):
        for col_idx, val in enumerate(row_vals):
            if val is None:
                continue
            y = y_positions[row_idx][col_idx]
            ax.barh(y, val, height=bar_height,
                     color=colors[col_idx],
                     edgecolor=WHITE, linewidth=0.8,
                     label=surveys[col_idx] if row_idx == 0 else None)
            # Value label on bar
            ax.text(val + 1.0, y, f"{val:.1f}%",
                    va="center", fontsize=9, color=GRAY_DARK,
                    fontweight="bold")

    # Y-axis: category labels centered on each group
    group_centers = [i * 3 + 0.22 for i in range(n_cats)]
    ax.set_yticks(group_centers)
    ax.set_yticklabels(categories, fontsize=11, fontweight="bold")

    # Inset legend-like labels inside each group
    legend_y_offset = 0.55
    for col_idx in range(3):
        ax.text(-0.5, group_centers[0] + legend_y_offset - col_idx * 0.22,
                surveys[col_idx], fontsize=8, color=colors[col_idx],
                ha="left", va="center", fontweight="bold")

    # Axis config
    ax.set_xlim(0, 105)
    ax.set_xlabel("Percentage (%)", fontsize=11, color=GRAY_DARK)
    ax.set_xticks(range(0, 106, 10))
    ax.tick_params(axis="x", labelsize=9)

    # Grid
    ax.xaxis.grid(True, alpha=0.3, color=GRAY_LIGHT)
    ax.yaxis.grid(False)

    # Spine cleanup
    ax.spines["top"].set_visible(False)
    ax.spines["right"].set_visible(False)
    ax.spines["left"].set_color("#cccccc")
    ax.spines["bottom"].set_color("#cccccc")

    # Title
    ax.set_title(
        "Enterprise Agent Adoption — Survey Comparison",
        fontsize=18, fontweight="bold", pad=16, color=BLACK,
    )
    ax.text(
        0.5, -0.18,
        "LangChain (n=1,340) | McKinsey (n=1,993) | PwC (n=308)",
        transform=ax.transAxes,
        fontsize=11, color=GRAY_DARK, ha="center",
    )

    # Legend
    handles = []
    for col_idx in range(3):
        handles.append(
            mpatches.Rectangle((0, 0), 1, 1, color=colors[col_idx], alpha=1)
        )
    ax.legend(handles, surveys, loc="lower right", fontsize=9,
              frameon=True, edgecolor="#cccccc")

    # Adjust layout
    fig.subplots_adjust(left=0.28, right=0.95, top=0.85, bottom=0.10)

    # Save
    out_path = "output/charts/10_agent_adoption.png"
    fig.savefig(out_path, dpi=EXPORT_DPI,
                facecolor=fig.get_facecolor(), edgecolor="none")
    plt.close(fig)
    return out_path


def main():
    path = plot_agent_adoption()
    print(f"Chart saved: {path}")


if __name__ == "__main__":
    main()