import ast
import itertools
from dataclasses import dataclass
from typing import Callable, Dict, List, Tuple

import gradio as gr
import numpy as np
import pandas as pd
import plotly.graph_objects as go


# ============================================================
# Rearrangement Algorithm visualizer
# ------------------------------------------------------------
# We represent a joint distribution by an n x d matrix X.
# Each column is one marginal sample. Rearrangement means:
#   - values inside a column may be permuted;
#   - the multiset in every column is unchanged;
#   - hence all empirical marginal distributions are preserved.
# The algorithm decreases E[psi(X)] = E[f(sum_j X_j)]
# by rearranging columns while preserving empirical marginals.
# ============================================================


@dataclass
class Step:
    step: int
    matrix: List[List[float]]
    objective: float
    action: str
    column: int | None = None
    before_order: List[int] | None = None
    after_order: List[int] | None = None


def parse_matrix(text: str) -> np.ndarray:
    """Parse a matrix from Python-list/JSON-ish text or CSV-like rows."""
    text = text.strip()
    if not text:
        raise ValueError("行列を入力してください。")

    try:
        if text.startswith("["):
            value = ast.literal_eval(text)
            x = np.array(value, dtype=float)
        else:
            rows = []
            for line in text.splitlines():
                line = line.strip()
                if not line:
                    continue
                rows.append([float(v.strip()) for v in line.split(",") if v.strip()])
            x = np.array(rows, dtype=float)
    except Exception as exc:
        raise ValueError("行列として解釈できませんでした。例: [[1,4,7],[2,5,8],[3,6,9]]") from exc

    if x.ndim != 2:
        raise ValueError("2次元の行列を入力してください。")
    if x.shape[0] < 2 or x.shape[1] < 2:
        raise ValueError("少なくとも 2 行 2 列が必要です。")
    if not np.isfinite(x).all():
        raise ValueError("NaN や Inf は使えません。")
    return x


def make_initial_matrix(n: int, d: int, seed: int, distribution: str) -> np.ndarray:
    rng = np.random.default_rng(seed)

    if distribution == "normal":
        cols = [
            np.sort(rng.normal(loc=0.0, scale=1.0 + 0.25 * j, size=n))
            for j in range(d)
        ]

    elif distribution == "uniform":
        cols = [
            np.sort(rng.uniform(-1.0 - j * 0.2, 1.0 + j * 0.2, size=n))
            for j in range(d)
        ]

    elif distribution == "lognormal":
        cols = [
            np.sort(rng.lognormal(mean=0.0, sigma=0.35 + 0.1 * j, size=n))
            for j in range(d)
        ]

    elif distribution == "integer":
        cols = [
            np.sort(
                rng.integers(
                    low=0,
                    high=10 + 2 * j + 1,
                    size=n,
                )
            )
            for j in range(d)
        ]

    else:
        raise ValueError("未知の分布です。")

    x = np.column_stack(cols)

    # Randomly permute each column independently to create an initial coupling.
    for j in range(d):
        x[:, j] = rng.permutation(x[:, j])

    return x


def matrix_to_text(x: np.ndarray) -> str:
    return "\n".join(", ".join(f"{v:.4g}" for v in row) for row in x)


def build_f(name: str, theta: float, custom_expr: str) -> Tuple[Callable[[np.ndarray], np.ndarray], str]:
    """
    Return f for

        psi(x_1, ..., x_d) = f(sum_j x_j)

    Input s is a length-n NumPy array of row sums.
    Output must also be length n.
    """
    if name == "square":
        return lambda s: s**2, "ψ(x₁, ..., x_d) = f(Σxᵢ),  f(s) = s²"

    if name == "absolute":
        return lambda s: np.abs(s), "ψ(x₁, ..., x_d) = f(Σxᵢ),  f(s) = |s|"

    if name == "exponential":
        return lambda s: np.exp(theta * s), f"ψ(x₁, ..., x_d) = f(Σxᵢ),  f(s) = exp({theta:g} · s)"

    if name == "positive_part":
        return lambda s: np.maximum(s, 0.0), "ψ(x₁, ..., x_d) = f(Σxᵢ),  f(s) = max(s, 0)"

    if name == "custom":
        expr = custom_expr.strip()
        if not expr:
            raise ValueError("custom を使う場合は f(s) の式を入力してください。")

        allowed = {
            "np": np,
            "abs": np.abs,
            "sqrt": np.sqrt,
            "exp": np.exp,
            "log": np.log,
            "maximum": np.maximum,
            "minimum": np.minimum,
        }

        def custom_f(s: np.ndarray) -> np.ndarray:
            y = eval(expr, {"__builtins__": {}}, {**allowed, "s": s})
            y = np.asarray(y, dtype=float)
            if y.ndim == 0:
                y = np.full(s.shape[0], float(y))
            return y

        return custom_f, f"ψ(x₁, ..., x_d) = f(Σxᵢ),  f(s) = {expr}"

    raise ValueError("未知の f です。")


def build_psi(name: str, theta: float, custom_expr: str) -> Tuple[Callable[[np.ndarray], np.ndarray], str]:
    """
    Return row-wise psi with

        psi(x_1, ..., x_d) = f(sum_j x_j).

    Input X is n x d.
    Output is length n.
    """
    f, f_label = build_f(name, theta, custom_expr)

    def psi(x: np.ndarray) -> np.ndarray:
        s = np.sum(x, axis=1)
        return f(s)

    return psi, f_label


def objective(x: np.ndarray, psi: Callable[[np.ndarray], np.ndarray]) -> float:
    values = psi(x)
    if values.shape[0] != x.shape[0]:
        raise ValueError("ψ は各行ごとに1つの値を返す必要があります。")
    if not np.isfinite(values).all():
        raise ValueError("ψ の値に NaN または Inf が出ました。")
    return float(np.mean(values))


def is_better(new_value: float, old_value: float, tol: float = 1e-12) -> bool:
    return new_value < old_value - tol


def greedy_sort_rearrangement(
    x: np.ndarray,
    psi_name: str,
    theta: float,
    custom_expr: str,
    max_iter: int,
    random_tie_break: bool,
    seed: int,
) -> Tuple[List[Step], str]:
    """
    Heuristic RA.

    For one chosen column j, keep all other columns fixed.
    We search for a permutation of column j that improves the selected objective.

    The objective is to minimize E[psi(X)] = E[f(sum_j X_j)].

    For common convex increasing-in-sum losses, the classical RA idea is to
    put a selected column in opposite order to the partial row sum of the other
    columns. For more general f, we use this as a proposal and also run a
    small pairwise local improvement pass.
    """
    psi, objective_label = build_psi(psi_name, theta, custom_expr)
    objective_fn = lambda z: objective(z, psi)
    rng = np.random.default_rng(seed)
    x = x.copy()
    n, d = x.shape

    steps = [Step(0, x.tolist(), objective_fn(x), "初期カップリング")]
    current = steps[-1].objective

    for it in range(1, max_iter + 1):
        improved_any = False
        columns = list(range(d))
        if random_tie_break:
            rng.shuffle(columns)

        for j in columns:
            old_col = x[:, j].copy()
            old_order = np.argsort(old_col, kind="mergesort").tolist()

            # Proposal 1: anti-monotone arrangement against partial sums.
            rest_sum = np.sum(x, axis=1) - x[:, j]
            row_order = np.argsort(rest_sum, kind="mergesort")
            col_sorted_desc = np.sort(old_col)[::-1]

            candidate = x.copy()
            candidate[row_order, j] = col_sorted_desc
            cand_obj = objective_fn(candidate)

            best = candidate
            best_obj = cand_obj
            best_action = f"列 {j + 1}: 他列の行和に対して反対順に rearrange"

            # Proposal 2: if anti-monotone does not help enough, try pair swaps.
            # This makes the app useful for non-smooth/custom f as well.
            pair_best = x.copy()
            pair_best_obj = current
            pair_action = None

            max_pair_checks = min(n * (n - 1) // 2, 2500)
            pairs = list(itertools.combinations(range(n), 2))

            if len(pairs) > max_pair_checks:
                pairs_idx = rng.choice(len(pairs), size=max_pair_checks, replace=False)
                pairs = [pairs[k] for k in pairs_idx]

            for a, b in pairs:
                tmp = x.copy()
                tmp[a, j], tmp[b, j] = tmp[b, j], tmp[a, j]
                tmp_obj = objective_fn(tmp)

                if is_better(tmp_obj, pair_best_obj):
                    pair_best = tmp
                    pair_best_obj = tmp_obj
                    pair_action = f"列 {j + 1}: 行 {a + 1} と行 {b + 1} を swap"

            if is_better(pair_best_obj, best_obj):
                best = pair_best
                best_obj = pair_best_obj
                best_action = pair_action or f"列 {j + 1}: pairwise swap"

            if is_better(best_obj, current):
                x = best
                current = best_obj
                improved_any = True

                new_col = x[:, j]
                new_order = np.argsort(new_col, kind="mergesort").tolist()

                steps.append(
                    Step(
                        len(steps),
                        x.tolist(),
                        current,
                        best_action,
                        column=j,
                        before_order=old_order,
                        after_order=new_order,
                    )
                )

        if not improved_any:
            steps.append(Step(len(steps), x.tolist(), current, "改善なし: 局所解として停止"))
            break

    return steps, objective_label


def make_matrix_df(step: Step):
    x = np.array(step.matrix)
    df = pd.DataFrame(x, columns=[f"X{j + 1}" for j in range(x.shape[1])])
    df.insert(0, "row", np.arange(1, x.shape[0] + 1))
    df["sum"] = x.sum(axis=1)
    df = df.round(6)

    min_sum = df["sum"].min()
    max_sum = df["sum"].max()

    def highlight_sum_extremes(row):
        styles = [""] * len(row)

        sum_col_idx = row.index.get_loc("sum")

        if row["sum"] == max_sum:
            styles[sum_col_idx] = "background-color: #fecaca; color: #7f1d1d; font-weight: bold;"

        if row["sum"] == min_sum:
            styles[sum_col_idx] = "background-color: #bfdbfe; color: #1e3a8a; font-weight: bold;"

        return styles

    return df.style.apply(highlight_sum_extremes, axis=1)


def make_marginal_check_df(initial: np.ndarray, current: np.ndarray) -> pd.DataFrame:
    rows = []
    for j in range(initial.shape[1]):
        same = np.allclose(np.sort(initial[:, j]), np.sort(current[:, j]))
        rows.append(
            {
                "列": f"X{j + 1}",
                "周辺分布 preserved?": "YES" if same else "NO",
                "初期 min": np.min(initial[:, j]),
                "現在 min": np.min(current[:, j]),
                "初期 max": np.max(initial[:, j]),
                "現在 max": np.max(current[:, j]),
                "初期 mean": np.mean(initial[:, j]),
                "現在 mean": np.mean(current[:, j]),
            }
        )
    return pd.DataFrame(rows).round(6)


def make_heatmap(step: Step) -> go.Figure:
    x = np.array(step.matrix)
    fig = go.Figure(
        data=go.Heatmap(
            z=x,
            x=[f"X{j + 1}" for j in range(x.shape[1])],
            y=[f"row {i + 1}" for i in range(x.shape[0])],
            colorbar=dict(title="value"),
        )
    )
    title = f"Step {step.step}: {step.action}<br>目的関数 = {step.objective:.6g}"
    fig.update_layout(title=title, height=450, margin=dict(l=70, r=30, t=80, b=40))
    return fig


def make_trace(steps: List[Step]) -> go.Figure:
    fig = go.Figure()
    fig.add_trace(
        go.Scatter(
            x=[s.step for s in steps],
            y=[s.objective for s in steps],
            mode="lines+markers",
            hovertemplate="step %{x}<br>目的関数=%{y:.6g}<extra></extra>",
        )
    )
    fig.update_layout(
        title="目的関数の推移",
        xaxis_title="step",
        yaxis_title="目的関数",
        height=360,
        margin=dict(l=50, r=20, t=60, b=40),
    )
    return fig




def _discrete_entropy_from_counts(counts: np.ndarray) -> float:
    """Shannon entropy from empirical counts, using natural log."""
    counts = np.asarray(counts, dtype=float)
    counts = counts[counts > 0]
    if counts.size == 0:
        return 0.0
    probs = counts / counts.sum()
    return float(-np.sum(probs * np.log(probs)))


def _rank_bin_matrix(x: np.ndarray, n_bins: int) -> np.ndarray:
    """
    Convert X to empirical-copula / rank-bin labels.

    RA preserves each column's multiset, so the marginal empirical distribution
    is fixed.  To visualize the copula part, we throw away the scale of each
    marginal and keep only rank-bin labels within each column.

    The bin labels approximate U_j = F_j(X_j) on a finite sample.
    """
    x = np.asarray(x, dtype=float)
    n, d = x.shape
    n_bins = int(max(2, min(int(n_bins), n)))
    z = np.zeros((n, d), dtype=int)

    for j in range(d):
        # Stable ordinal ranks. Ties are broken by row order; for heavily tied
        # integer examples this is still only a visualization, not an estimator
        # of a continuous copula density.
        order = np.argsort(x[:, j], kind="mergesort")
        ranks = np.empty(n, dtype=int)
        ranks[order] = np.arange(n)
        z[:, j] = np.floor(ranks * n_bins / n).astype(int)
        z[:, j] = np.clip(z[:, j], 0, n_bins - 1)

    return z


def copula_entropy_summary(x: np.ndarray, n_bins: int) -> Dict[str, float]:
    """
    Rank-binned empirical entropy summary.

    H_joint is H(B_1, ..., B_d), where B_j is the rank-bin of X_j.
    H_marginal_sum is sum_j H(B_j). Because RA preserves each marginal,
    H_marginal_sum should be constant up to ties/binning.

    For a continuous copula density c, copula entropy is often defined as
        H_c = h(c) = h(X_1,...,X_d) - sum_j h(X_j)
    and mutual information is
        I = sum_j h(X_j) - h(X_1,...,X_d) = -H_c.

    The values here are finite-sample, rank-binned approximations.
    """
    bins = _rank_bin_matrix(np.asarray(x, dtype=float), int(n_bins))

    _, joint_counts = np.unique(bins, axis=0, return_counts=True)
    h_joint = _discrete_entropy_from_counts(joint_counts)

    h_marginals = []
    for j in range(bins.shape[1]):
        _, counts = np.unique(bins[:, j], return_counts=True)
        h_marginals.append(_discrete_entropy_from_counts(counts))

    h_marginal_sum = float(np.sum(h_marginals))
    copula_entropy = h_joint - h_marginal_sum
    mutual_information = h_marginal_sum - h_joint

    return {
        "H_joint": h_joint,
        "H_marginal_sum": h_marginal_sum,
        "H_copula": copula_entropy,
        "mutual_information": mutual_information,
    }


def make_entropy_trace(steps: List[Step], n_bins: int) -> go.Figure:
    rows = []
    for s in steps:
        ent = copula_entropy_summary(np.array(s.matrix), int(n_bins))
        rows.append({"step": s.step, **ent})

    df = pd.DataFrame(rows)

    fig = go.Figure()
    fig.add_trace(
        go.Scatter(
            x=df["step"],
            y=df["H_joint"],
            mode="lines+markers",
            name="joint entropy H(X)",
            hovertemplate="step %{x}<br>H(X)=%{y:.6g}<extra></extra>",
        )
    )
    fig.add_trace(
        go.Scatter(
            x=df["step"],
            y=df["H_marginal_sum"],
            mode="lines+markers",
            name="marginal entropy sum ΣH(Fj)",
            hovertemplate="step %{x}<br>ΣH(Fj)=%{y:.6g}<extra></extra>",
        )
    )
    fig.add_trace(
        go.Scatter(
            x=df["step"],
            y=df["H_copula"],
            mode="lines+markers",
            name="copula entropy Hc=H(X)-ΣH(Fj)",
            hovertemplate="step %{x}<br>Hc=%{y:.6g}<extra></extra>",
        )
    )
    fig.update_layout(
        title=f"rank-binned copula entropy の推移（各列 {int(n_bins)} rank bins）",
        xaxis_title="step",
        yaxis_title="entropy / nats",
        height=420,
        margin=dict(l=50, r=20, t=70, b=80),
        legend=dict(orientation="h", yanchor="bottom", y=-0.35, xanchor="left", x=0),
    )
    return fig


def make_entropy_history_df(steps: List[Step], n_bins: int) -> pd.DataFrame:
    rows = []
    for s in steps:
        ent = copula_entropy_summary(np.array(s.matrix), int(n_bins))
        rows.append(
            {
                "step": s.step,
                "H(X) joint": ent["H_joint"],
                "ΣH(Fj) marginal sum": ent["H_marginal_sum"],
                "Hc = H(X)-ΣH(Fj)": ent["H_copula"],
                "I = ΣH(Fj)-H(X)": ent["mutual_information"],
            }
        )
    return pd.DataFrame(rows).round(6)

def make_sum_hist(step: Step) -> go.Figure:
    x = np.array(step.matrix)
    sums = x.sum(axis=1)
    fig = go.Figure()
    fig.add_trace(go.Histogram(x=sums, nbinsx=min(20, max(5, len(sums) // 2))))
    fig.update_layout(
        title="行和 S = X1 + ... + Xd の分布",
        xaxis_title="S",
        yaxis_title="count",
        height=320,
        margin=dict(l=50, r=20, t=60, b=40),
    )
    return fig


def state_to_steps(state: List[Dict]) -> List[Step]:
    return [Step(**s) for s in state]


def run_algorithm(
    matrix_text: str,
    psi_name: str,
    theta: float,
    custom_expr: str,
    max_iter: int,
    random_tie_break: bool,
    seed: int,
    entropy_bins: int,
):
    try:
        x0 = parse_matrix(matrix_text)
        steps, objective_label = greedy_sort_rearrangement(
            x0,
            psi_name,
            theta,
            custom_expr,
            int(max_iter),
            bool(random_tie_break),
            int(seed),
        )

        state = [s.__dict__ for s in steps]
        first = steps[0]
        last = steps[-1]

        improvement = steps[0].objective - last.objective
        objective_name = "E[ψ(X)]"

        summary = (
            f"{objective_label}\n"
            f"初期 {objective_name} = {steps[0].objective:.8g}\n"
            f"最終 {objective_name} = {last.objective:.8g}\n"
            f"改善量 = {improvement:.8g}\n"
            f"ステップ数 = {len(steps) - 1}\n"
            f"エントロピー: 各列を {int(entropy_bins)} 個のrank binに変換し、経験copula上で計算"
        )

        entropy_history = make_entropy_history_df(steps, int(entropy_bins))
        history = pd.DataFrame(
            {
                "step": [s.step for s in steps],
                "目的関数": [s.objective for s in steps],
                "操作": [s.action for s in steps],
            }
        ).merge(entropy_history, on="step", how="left")

        return (
            state,
            0,
            len(steps) - 1,
            make_matrix_df(first),
            make_heatmap(first),
            make_trace(steps),
            make_entropy_trace(steps, int(entropy_bins)),
            make_sum_hist(first),
            make_marginal_check_df(np.array(steps[0].matrix), np.array(first.matrix)),
            history,
            summary,
            "",
        )

    except Exception as exc:
        return (
            [],
            0,
            0,
            pd.DataFrame(),
            go.Figure(),
            go.Figure(),
            go.Figure(),
            go.Figure(),
            pd.DataFrame(),
            pd.DataFrame(),
            "",
            f"エラー: {exc}",
        )


def show_step(state: List[Dict], step_no: int):
    if not state:
        return pd.DataFrame(), go.Figure(), go.Figure(), pd.DataFrame(), "先に実行してください。"

    steps = state_to_steps(state)
    step_no = int(max(0, min(step_no, len(steps) - 1)))
    step = steps[step_no]

    initial = np.array(steps[0].matrix)
    current = np.array(step.matrix)

    message = (
        f"Step {step.step} / {len(steps) - 1}\n"
        f"{step.action}\n"
        f"目的関数 = {step.objective:.8g}"
    )

    return (
        make_matrix_df(step),
        make_heatmap(step),
        make_sum_hist(step),
        make_marginal_check_df(initial, current),
        message,
    )


def move_step(state: List[Dict], current_step: int, delta: int):
    if not state:
        return 0, pd.DataFrame(), go.Figure(), go.Figure(), pd.DataFrame(), "先に実行してください。"

    steps = state_to_steps(state)
    new_step = int(max(0, min(int(current_step) + delta, len(steps) - 1)))

    matrix_df, heatmap, hist, marginal_df, message = show_step(state, new_step)

    return new_step, matrix_df, heatmap, hist, marginal_df, message


def autoplay_tick(state: List[Dict], current_step: int, interval_sec: float):
    """Advance one step on each timer tick and stop automatically at the final step."""
    if not state:
        return (
            0,
            pd.DataFrame(),
            go.Figure(),
            go.Figure(),
            pd.DataFrame(),
            "先に実行してください。",
            gr.Timer(active=False),
        )

    steps = state_to_steps(state)
    current_step = int(current_step)

    if current_step >= len(steps) - 1:
        matrix_df, heatmap, hist, marginal_df, message = show_step(state, current_step)
        return (
            current_step,
            matrix_df,
            heatmap,
            hist,
            marginal_df,
            message + "再生終了。",
            gr.Timer(active=False),
        )

    new_step = current_step + 1
    matrix_df, heatmap, hist, marginal_df, message = show_step(state, new_step)

    return (
        new_step,
        matrix_df,
        heatmap,
        hist,
        marginal_df,
        message,
        gr.Timer(value=float(interval_sec), active=True),
    )


def generate_matrix(n: int, d: int, seed: int, distribution: str):
    try:
        x = make_initial_matrix(int(n), int(d), int(seed), distribution)
        return matrix_to_text(x), ""
    except Exception as exc:
        return "", f"エラー: {exc}"


CSS = """
#title { text-align: center; }
.note { color: #475569; font-size: 0.95rem; }
"""

with gr.Blocks(css=CSS, title="Rearrangement Algorithm Visualizer") as demo:
    gr.Markdown(
        """
        # Rearrangement Algorithm Visualizer

        各列を周辺分布の標本とみなし、**列内の並べ替えだけ**で結合分布を変えます。
        そのため、各周辺分布は保存されたまま、目的関数 `E[ψ(X)] = E[f(X1 + ... + Xd)]` が小さくなるようにヒューリスティックに rearrange します。

        <p class="note">
        行 = 同時実現シナリオ、列 = 周辺分布。列の値の multiset は不変です。
        </p>
        """,
        elem_id="title",
    )

    state = gr.State([])

    with gr.Row():
        with gr.Column(scale=2):
            matrix_input = gr.Textbox(
                label="X: n 行 d 列",
                value=(
                    "0.12, 1.80, -0.40\n"
                    "-1.10, 0.35, 1.25\n"
                    "0.65, -0.90, 0.10\n"
                    "1.40, 0.05, -1.30\n"
                    "-0.55, -1.20, 0.75\n"
                    "0.90, 1.10, -0.85"
                ),
                lines=8,
                placeholder="例:\n1, 4, 7\n2, 5, 8\n3, 6, 9",
            )

        with gr.Column(scale=1):
            n_input = gr.Slider(label="生成 n", minimum=4, maximum=100, step=1, value=12)
            d_input = gr.Slider(label="生成 d", minimum=2, maximum=10, step=1, value=3)
            seed_input = gr.Number(label="seed", value=1, precision=0)
            distribution_input = gr.Dropdown(
                label="生成する周辺分布",
                choices=["integer", "normal", "uniform", "lognormal"],
                value="integer",
            )
            generate_matrix_btn = gr.Button("ランダム初期値を生成")


    with gr.Row():
        psi_input = gr.Dropdown(
            label="f（ψ(x₁,...,x_d)=f(Σxᵢ)）",
            choices=["square", "absolute", "exponential", "positive_part", "custom"],
            value="square",
        )
        theta_input = gr.Number(label="exponential の θ", value=1.0)
        custom_expr_input = gr.Textbox(
            label="custom f(s) 式",
            value="s**2",
            placeholder="例: s**2, np.exp(0.5*s), maximum(s, 0)  / 変数: s",
        )

    with gr.Row():
        max_iter_input = gr.Slider(label="最大 sweep 数", minimum=1, maximum=100, step=1, value=20)
        random_tie_input = gr.Checkbox(label="列順をランダム化", value=False)
        entropy_bins_input = gr.Slider(
            label="copula entropy 用の rank bin 数",
            minimum=2,
            maximum=10,
            step=1,
            value=2,
            info="各列をrank binに変換し、周辺を固定したcopula側の経験エントロピーを近似します。少ない行数では2〜4を推奨。",
        )
        run_btn = gr.Button("RA を実行", variant="primary")

    error_box = gr.Textbox(label="メッセージ", interactive=False)
    summary_box = gr.Textbox(label="サマリー", lines=5, interactive=False)

    playback_timer = gr.Timer(value=0.8, active=False)

    with gr.Row():
        prev_btn = gr.Button("← 前へ")
        play_btn = gr.Button("▶ 再生", variant="secondary")
        stop_btn = gr.Button("⏸ 停止")
        next_btn = gr.Button("次へ →")

    with gr.Row():
        step_slider = gr.Slider(label="Step", minimum=0, maximum=100, step=1, value=0)
        interval_slider = gr.Slider(label="再生間隔 秒/step", minimum=0.1, maximum=3.0, step=0.1, value=0.8)

    step_message = gr.Textbox(label="現在の状態", lines=3, interactive=False)

    with gr.Row():
        matrix_df = gr.Dataframe(label="現在の X", interactive=False, wrap=True)
        marginal_df = gr.Dataframe(label="周辺分布チェック", interactive=False, wrap=True)

    with gr.Row():
        heatmap = gr.Plot(label="X のヒートマップ")
        with gr.Column():
            trace_plot = gr.Plot(label="目的関数の推移")
            entropy_plot = gr.Plot(label="rank-binned copula entropy の推移")

    sum_hist = gr.Plot(label="行和の分布")
    history_df = gr.Dataframe(label="履歴", interactive=False, wrap=True)

    generate_matrix_btn.click(
        generate_matrix,
        inputs=[n_input, d_input, seed_input, distribution_input],
        outputs=[matrix_input, error_box],
    )


    run_btn.click(
        run_algorithm,
        inputs=[
            matrix_input,
            psi_input,
            theta_input,
            custom_expr_input,
            max_iter_input,
            random_tie_input,
            seed_input,
            entropy_bins_input,
        ],
        outputs=[
            state,
            step_slider,
            step_slider,
            matrix_df,
            heatmap,
            trace_plot,
            entropy_plot,
            sum_hist,
            marginal_df,
            history_df,
            summary_box,
            error_box,
        ],
    )

    step_slider.change(
        show_step,
        inputs=[state, step_slider],
        outputs=[matrix_df, heatmap, sum_hist, marginal_df, step_message],
    )

    prev_btn.click(
        lambda s, c: move_step(s, c, -1),
        inputs=[state, step_slider],
        outputs=[step_slider, matrix_df, heatmap, sum_hist, marginal_df, step_message],
    )

    next_btn.click(
        lambda s, c: move_step(s, c, 1),
        inputs=[state, step_slider],
        outputs=[step_slider, matrix_df, heatmap, sum_hist, marginal_df, step_message],
    )

    play_btn.click(
        lambda interval: gr.Timer(value=float(interval), active=True),
        inputs=[interval_slider],
        outputs=[playback_timer],
    )

    stop_btn.click(
        lambda: gr.Timer(active=False),
        inputs=None,
        outputs=[playback_timer],
    )

    playback_timer.tick(
        autoplay_tick,
        inputs=[state, step_slider, interval_slider],
        outputs=[
            step_slider,
            matrix_df,
            heatmap,
            sum_hist,
            marginal_df,
            step_message,
            playback_timer,
        ],
    )

    gr.Markdown(
        """
        # Acknowledgments

        以下文献を参考にしました。
        
        [1] 小池, 南, 白石, 「[再配列アルゴリズムを用いたVaR境界の算出](https://www.jstage.jst.go.jp/article/jjssj/45/2/45_353/_pdf/-char/ja)」, 2016.

        """,
        elem_id="title",
    )


if __name__ == "__main__":
    demo.launch()