text: """Bokeh Bayesian p-value Posterior predictive plot."""
  import numpy as np
  from bokeh.models import BoxAnnotation
  from bokeh.models.annotations import Title
  from scipy import stats
  
  from ....stats.density_utils import kde
  from ....stats.stats_utils import smooth_data
  from ...kdeplot import plot_kde
  from ...plot_utils import (
      _scale_fig_size,
      is_valid_quantile,
      sample_reference_distribution,
      vectorized_to_hex,
  )
  from .. import show_layout
  from . import backend_kwarg_defaults, create_axes_grid
  
  
  def plot_bpv(
      ax,
      length_plotters,
      rows,
      cols,
      obs_plotters,
      pp_plotters,
      total_pp_samples,
      kind,
      t_stat,
      bpv,
      plot_mean,
      reference,
      mse,
      n_ref,
      hdi_prob,
      color,
      figsize,
      textsize,
      plot_ref_kwargs,
      backend_kwargs,
      show,
  ):
      """Bokeh bpv plot."""
      if backend_kwargs is None:
          backend_kwargs = {}
  
      backend_kwargs = {
          **backend_kwarg_defaults(),
          **backend_kwargs,
      }
  
      color = vectorized_to_hex(color)
  
      if plot_ref_kwargs is None:
          plot_ref_kwargs = {}
      if kind == "p_value" and reference == "analytical":
          plot_ref_kwargs.setdefault("line_color", "black")
          plot_ref_kwargs.setdefault("line_dash", "dashed")
      else:
          plot_ref_kwargs.setdefault("alpha", 0.1)
          plot_ref_kwargs.setdefault("line_color", color)
  
      (figsize, ax_labelsize, _, _, linewidth, markersize) = _scale_fig_size(
          figsize, textsize, rows, cols
      )
  
      if ax is None:
          axes = create_axes_grid(
              length_plotters,
              rows,
              cols,
              figsize=figsize,
              backend_kwargs=backend_kwargs,
          )
      else:
          axes = np.atleast_2d(ax)
  
          if len([item for item in axes.ravel() if not None]) != length_plotters:
              raise ValueError(
                  "Found {} variables to plot but {} axes instances. They must be equal.".format(
                      length_plotters, len(axes)
                  )
              )
  
      for i, ax_i in enumerate((item for item in axes.flatten() if item is not None)):
          var_name, _, obs_vals = obs_plotters[i]
          pp_var_name, _, pp_vals = pp_plotters[i]
  
          obs_vals = obs_vals.flatten()
          pp_vals = pp_vals.reshape(total_pp_samples, -1)
  
          if obs_vals.dtype.kind == "i" or pp_vals.dtype.kind == "i":
              obs_vals, pp_vals = smooth_data(obs_vals, pp_vals)
  
          if kind == "p_value":
              tstat_pit = np.mean(pp_vals <= obs_vals, axis=-1)
              x_s, tstat_pit_dens = kde(tstat_pit)
              ax_i.line(x_s, tstat_pit_dens, line_width=linewidth, line_color=color)
              if reference is not None:
                  dist = stats.beta(obs_vals.size / 2, obs_vals.size / 2)
                  if reference == "analytical":
                      lwb = dist.ppf((1 - 0.9999) / 2)
                      upb = 1 - lwb
                      x = np.linspace(lwb, upb, 500)
                      dens_ref = dist.pdf(x)
                      ax_i.line(x, dens_ref, **plot_ref_kwargs)
                  elif reference == "samples":
                      x_ss, u_dens = sample_reference_distribution(
                          dist,
                          (
                              tstat_pit_dens.size,
                              n_ref,
                          ),
                      )
                      ax_i.multi_line(
                          list(x_ss.T), list(u_dens.T), line_width=linewidth, **plot_ref_kwargs
                      )
  
          elif kind == "u_value":
              tstat_pit = np.mean(pp_vals <= obs_vals, axis=0)
              x_s, tstat_pit_dens = kde(tstat_pit)
              ax_i.line(x_s, tstat_pit_dens, color=color)
              if reference is not None:
                  if reference == "analytical":
                      n_obs = obs_vals.size
                      hdi_ = stats.beta(n_obs / 2, n_obs / 2).ppf((1 - hdi_prob) / 2)
                      hdi_odds = (hdi_ / (1 - hdi_), (1 - hdi_) / hdi_)
                      ax_i.add_layout(
                          BoxAnnotation(
                              bottom=hdi_odds[1],
                              top=hdi_odds[0],
                              fill_alpha=plot_ref_kwargs.pop("alpha"),
                              fill_color=plot_ref_kwargs.pop("line_color"),
                              **plot_ref_kwargs,
                          )
                      )
                      ax_i.line([0, 1], [1, 1], line_color="white")
                  elif reference == "samples":
                      dist = stats.uniform(0, 1)
                      x_ss, u_dens = sample_reference_distribution(dist, (tstat_pit_dens.size, n_ref))
                      for x_ss_i, u_dens_i in zip(x_ss.T, u_dens.T):
                          ax_i.line(x_ss_i, u_dens_i, line_width=linewidth, **plot_ref_kwargs)
              if mse:
                  ax_i.line(0, 0, legend_label=f"mse={np.mean((1 - tstat_pit_dens)**2) * 100:.2f}")
  
              ax_i.line(0, 0)
          else:
              if t_stat in ["mean", "median", "std"]:
                  if t_stat == "mean":
                      tfunc = np.mean
                  elif t_stat == "median":
                      tfunc = np.median
                  elif t_stat == "std":
                      tfunc = np.std
                  obs_vals = tfunc(obs_vals)
                  pp_vals = tfunc(pp_vals, axis=1)
              elif hasattr(t_stat, "__call__"):
                  obs_vals = t_stat(obs_vals.flatten())
                  pp_vals = t_stat(pp_vals)
              elif is_valid_quantile(t_stat):
                  t_stat = float(t_stat)
                  obs_vals = np.quantile(obs_vals, q=t_stat)
                  pp_vals = np.quantile(pp_vals, q=t_stat, axis=1)
              else:
                  raise ValueError(f"T statistics {t_stat} not implemented")
  
              plot_kde(pp_vals, ax=ax_i, plot_kwargs={"color": color}, backend="bokeh", show=False)
              # ax_i.set_yticks([])
              if bpv:
                  p_value = np.mean(pp_vals <= obs_vals)
                  ax_i.line(0, 0, legend_label=f"bpv={p_value:.2f}", alpha=0)
  
              if plot_mean:
                  ax_i.circle(
                      obs_vals.mean(), 0, fill_color=color, line_color="black", size=markersize
                  )
  
          if var_name != pp_var_name:
              xlabel = "{} / {}".format(var_name, pp_var_name)
          else:
              xlabel = var_name
          _title = Title()
          _title.text = xlabel
          ax_i.title = _title
          size = str(int(ax_labelsize))
          ax_i.title.text_font_size = f"{size}pt"
  
      show_layout(axes, show)
  
      return axes
  