text: import re
  
  import numpy as np
  from arviz import from_pystan
  from bambi.priors import Prior
  
  from .base import BackEnd
  
  try:
      import pystan as ps
  except ImportError:
      ps = None
  
  
  class StanBackEnd(BackEnd):  # pylint: disable=too-many-instance-attributes
  
      """
      Stan/PyStan model-fitting back-end.
      """
  
      # distribution names and arg names should match those in priors.json,
      dists = {
          "Normal": {"name": "normal", "args": ["#mu", "#sd"]},
          "Bernoulli": {"name": "bernoulli", "args": ["#p"]},
          "Poisson": {"name": "poisson", "args": ["#mu"]},
          "Cauchy": {"name": "cauchy", "args": ["#alpha", "#beta"]},
          "HalfNormal": {"name": "normal", "args": ["0", "#sd"], "bounds": "<lower=0>"},
          "HalfCauchy": {"name": "cauchy", "args": ["0", "#beta"], "bounds": "<lower=0>"},
          # for Uniform, the bounds are the parameters. _map_dist fills these in
          "Uniform": {
              "name": "uniform",
              "args": ["#lower", "#upper"],
              "bounds": "<lower={}, upper={}>",
          },
          "Flat": {"name": None, "args": []},
          "HalfFlat": {"name": None, "args": [], "bounds": "<lower=0>"},
      }
  
      # maps from spec.family.name to 'dists' dictionary
      # and gives the Stan variable type for the response
      families = {
          "gaussian": {"name": "Normal", "type": float, "format": ["vector[N]", ""]},
          "bernoulli": {"name": "Bernoulli", "type": int, "format": ["int", "[N]"]},
          "poisson": {"name": "Poisson", "type": int, "format": ["int", "[N]"]},
      }
  
      # maps from spec.family.link to Stan inverse link function name
      links = {"identity": "", "logit": "inv_logit", "log": "exp", "inverse_squared": "inv_sqrt"}
  
      def __init__(self):
          if ps is None:
              raise ImportError("Could not import PyStan; please make sure it's " "installed.")
          self.reset()
  
          # Attributes defined elsewhere
          self.model_code = None  # build()
          self.spec = None  # build()
          self.stan_model = None  # build()
          self.fit = None  # run()
  
      def reset(self):
          """
          Reset Stan model and all tracked distributions and parameters.
          """
          self.parameters = []
          self.transformed_parameters = []
          self.expressions = []
          self.data = []
          self.transformed_data = []
          self.X = {}
          self.model = []
          self.mu_cont = []
          self.mu_cat = []
          self._original_names = {}
          # variables to suppress in output. Stan uses limited set for variable
          # names, so track variable names we may need to simplify for the model
          # code and then sub back later.
          self._suppress_vars = ["yhat", "lp__"]
  
      def build(self, spec, reset=True):  # pylint: disable=arguments-differ
          """
          Compile the Stan model from an abstract model specification.
          Args:
              spec (Model): A bambi Model instance containing the abstract
                  specification of the model to compile.
              reset (bool): if True (default), resets the StanBackEnd instance
                  before compiling.
          """
          if reset:
              self.reset()
  
          n_cases = len(spec.y.data)
          self.data.append("int<lower=1> N;")
          self.X["N"] = n_cases
  
          def _sanitize_name(name):
              """ Stan only allows alphanumeric chars and underscore, and
              variable names must begin with a letter. Additionally, Stan
              reserves a few hundred strings that can't be used as variable
              names. So to play it safe, we replace all invalid chars with '_',
              and prepend all variables with 'b_'. We substitute the original
              names back in later. """
              if name in self._original_names:
                  return name
              clean = "b_" + re.sub(r"[^a-zA-Z0-9\_]+", "_", name)
              self._original_names[clean] = name
              return clean
  
          def _map_dist(dist, **kwargs):
              """ Maps PyMC3 distribution names and attrs in the Prior object
              to the corresponding Stan names and argument order. """
              if dist not in self.dists:
                  raise ValueError("There is no distribution named '%s' " "in Stan." % dist)
  
              stan_dist = self.dists[dist]
              dist_name = stan_dist["name"]
              dist_args = stan_dist["args"]
              dist_bounds = stan_dist.get("bounds", "")
  
              # Flat/HalfFlat/undefined priors are handled separately
              if dist_name is None:
                  return None, dist_bounds
  
              lookup_args = [a[1:] for a in dist_args if a.startswith("#")]
              missing = set(lookup_args) - set(list(kwargs.keys()))
              if missing:
                  raise ValueError(
                      "The following mandatory parameters of "
                      "the %s distribution are missing: %s." % (dist, missing)
                  )
  
              # Named arguments to take from the Prior object are denoted with
              # a '#'; otherwise we take the value in the self.dists dict as-is.
              dist_args_reformatted = [kwargs[p[1:]] if p.startswith("#") else p for p in dist_args]
  
              # Sometimes we get numpy arrays at this stage, so convert to float
              dist_args_reformatted = [
                  float(p.ravel()[0]) if isinstance(p, np.ndarray) else p
                  for p in dist_args_reformatted
              ]
  
              dist_term = "%s(%s)" % (dist_name, ", ".join([str(p) for p in dist_args_reformatted]))
  
              # handle Uniform variables, for which the bounds are the parameters
              if dist_name == "uniform":
                  dist_bounds = dist_bounds.format(*dist_args_reformatted)
  
              return dist_term, dist_bounds
  
          def _add_data(name, data, term):
              """ Add all model components that directly touch or relate to data.
              """
              if data.shape[1] == 1:
                  # For random effects, index into grouping variable
                  if n_cols > 1:
                      index_name = _sanitize_name("%s_grp_ind" % name)
                      self.data.append("int %s[N];" % index_name)
                      self.X[index_name] = term.group_index + 1  # 1-based indexing
                  predictor = "vector[N] %s;"
              else:
                  predictor = ("matrix[N, %d]" % (n_cols)) + " %s;"
  
              data_name = _sanitize_name("%s_data" % name)
              var_name = _sanitize_name(name)
              self.data.append(predictor % data_name)
              self.X[data_name] = data.squeeze()
  
              if data.shape[1] == 1 and n_cols > 1:
                  code = "%s[%s[n]] * %s[n]" % (var_name, index_name, data_name)
                  self.mu_cat.append(code)
              else:
                  self.mu_cont.append("%s * %s" % (data_name, var_name))
  
          def _add_parameters(name, dist_name, n_cols, **dist_args):
              """ Add all model components related to latent parameters. We
              handle these separately from the data components, as the parameters
              can have nested specifications (in the case of random effects). """
  
              def _expand_args(key, value, name):
                  if isinstance(value, Prior):
                      name = _sanitize_name("%s_%s" % (name, key))
                      return _add_parameters(name, value.name, 1, **value.args)
                  return value
  
              kwargs = {k: _expand_args(k, v, name) for (k, v) in dist_args.items()}
  
              _dist, _bounds = _map_dist(dist_name, **kwargs)
  
              if n_cols == 1:
                  stan_par = "real"
              else:
                  stan_par = "vector[%d]" % n_cols
  
              var_name = _sanitize_name(name)
  
              # non-centered parameterization
              if spec.noncentered and "sd" in kwargs and isinstance(kwargs["sd"], str):
                  offset_name = _sanitize_name(name + "_offset")
                  offset = "vector[%d] %s;" % (n_cols, offset_name)
                  self.parameters.append(offset)
                  self.model.append("%s ~ normal(0, 1);" % offset_name)
                  self.transformed_parameters.append("%s%s %s;" % (stan_par, _bounds, var_name))
                  trans = "%s = multiply(%s, %s);" % (var_name, offset_name, kwargs["sd"])
                  self.expressions.append(trans)
  
              else:
                  self.parameters.append("%s%s %s;" % (stan_par, _bounds, var_name))
                  if _dist is not None:
                      self.model.append("%s ~ %s;" % (var_name, _dist))
  
              return name
  
          for t in spec.terms.values():
  
              data = t.data
              label = t.name
              dist_name = t.prior.name
              dist_args = t.prior.args
  
              n_cols = data.shape[1]
  
              if t.random:
                  data = t.predictor
  
              # Add to Stan model
              _add_data(label, data, t)
              _add_parameters(label, dist_name, n_cols, **dist_args)
  
          # yhat
          self.transformed_parameters.append("vector[N] yhat;")
          if self.mu_cont:
              yhat_cont = "yhat = %s;" % " + ".join(self.mu_cont)
              self.expressions.append(yhat_cont)
          else:
              self.mu_cat.insert(0, "0")
  
          if self.mu_cat:
              loops = "for (n in 1:N)\n\t\tyhat[n] = yhat[n] + %s" % " + ".join(self.mu_cat) + ";\n\t"
              self.expressions.append(loops)
  
          # Add expressions that go in transformed parameter block (they have
          # to come after variable definitions)
          self.transformed_parameters += self.expressions
  
          # add response variable (y)
          _response_format = self.families[spec.family.name]["format"]
          self.data.append("{} y{};".format(*_response_format))
  
          # add response distribution parameters other than the location
          # parameter
          for key, value in spec.family.prior.args.items():
              if key != spec.family.parent and isinstance(value, Prior):
                  _bounds = _map_dist(value.name, **value.args)[1]
                  _param = "real{} {}_{};".format(_bounds, spec.y.name, key)
                  self.parameters.append(_param)
  
          # specify the response distribution
          _response_dist = self.families[spec.family.name]["name"]
          _response_args = "{}(yhat)".format(self.links[spec.family.link])
          _response_args = {spec.family.parent: _response_args}
          for key, value in spec.family.prior.args.items():
              if key != spec.family.parent:
                  _response_args[key] = (
                      "{}_{}".format(spec.y.name, key) if isinstance(value, Prior) else str(value)
                  )
          _dist = _map_dist(_response_dist, **_response_args)[0]
          self.model.append("y ~ {};".format(_dist))
  
          # add the data
          _response_type = self.families[spec.family.name]["type"]
          self.X["y"] = spec.y.data.astype(_response_type).squeeze()
  
          # Construct the stan script
          def format_block(name):
              key = name.replace(" ", "_")
              els = "".join(["\t%s\n" % e for e in getattr(self, key)])
              return "%s {\n%s}\n" % (name, els)
  
          blocks = ["data", "transformed data", "parameters", "transformed parameters", "model"]
          self.model_code = "".join([format_block(bl) for bl in blocks])
          self.spec = spec
          self.stan_model = ps.StanModel(model_code=self.model_code)
  
      def run(self, samples=1000, chains=1, **kwargs):  # pylint: disable=arguments-differ
          """
          Run the Stan sampler.
  
          Parameters
          ----------
          samples : int
              Number of samples to obtain (in each chain).
          chains : int
              Number of chains to use.
          kwargs : dict
              Optional keyword arguments passed onto the PyStan StanModel.sampling() call.
  
          Returns:
          ----------
          An ArviZ InferenceData instance.
          """
          self.fit = self.stan_model.sampling(data=self.X, iter=samples, chains=chains, **kwargs)
          return from_pystan(self.fit)
  