text: import sys
  import logging
  import traceback
  from io import StringIO
  from typing import Any, Dict, Union
  
  import numpy as np
  from qcelemental.models import OptimizationInput, OptimizationResult
  from qcelemental.util import which_import
  
  import qcengine
  
  from ..config import TaskConfig
  from .model import ProcedureHarness
  
  
  class BernyProcedure(ProcedureHarness):
      _defaults = {"name": "Berny", "procedure": "optimization"}
  
      def found(self, raise_error: bool = False) -> bool:
          return which_import(
              "berny", return_bool=True, raise_error=raise_error, raise_msg="Please install via `pip install pyberny`.",
          )
  
      def build_input_model(self, data: Union[Dict[str, Any], "OptimizationInput"]) -> "OptimizationInput":
          return self._build_model(data, OptimizationInput)
  
      def compute(self, input_data: "OptimizationInput", config: "TaskConfig") -> "OptimizationResult":
          try:
              import berny
          except ModuleNotFoundError:
              raise ModuleNotFoundError("Could not find Berny in the Python path.")
  
          # Get berny version from the installed package, use setuptools'
          # pkg_resources for python < 3.8
          if sys.version_info >= (3, 8):
              from importlib.metadata import distribution
          else:
              from pkg_resources import get_distribution as distribution
          berny_version = distribution("pyberny").version
  
          # Berny uses the stdlib logging module and by default uses per-module
          # loggers. For QCEngine, we create one logger per BernyProcedure
          # instance, by using the instance's id(), and send all logging messages
          # to a string stream
          log_stream = StringIO()
          log = logging.getLogger(f"{__name__}.{id(self)}")
          log.addHandler(logging.StreamHandler(log_stream))
          log.setLevel("INFO")
  
          input_data = input_data.dict()
          geom_qcng = input_data["initial_molecule"]
          comput = {**input_data["input_specification"], "molecule": geom_qcng}
          program = input_data["keywords"].pop("program")
          trajectory = []
          output_data = input_data.copy()
          try:
              # Pyberny uses angstroms for the Cartesian geometry, but atomic
              # units for everything else, including the gradients (hartree/bohr).
              geom_berny = berny.Geometry(geom_qcng["symbols"], geom_qcng["geometry"] / berny.angstrom)
              opt = berny.Berny(geom_berny, logger=log, **input_data["keywords"])
              for geom_berny in opt:
                  geom_qcng["geometry"] = np.stack(geom_berny.coords * berny.angstrom)
                  ret = qcengine.compute(comput, program)
                  trajectory.append(ret.dict())
                  opt.send((ret.properties.return_energy, ret.return_result))
          except Exception:
              output_data["success"] = False
              output_data["error"] = {"error_type": "unknown", "error_message": f"Berny error:\n{traceback.format_exc()}"}
          else:
              output_data["success"] = True
          output_data.update(
              {
                  "schema_name": "qcschema_optimization_output",
                  "final_molecule": trajectory[-1]["molecule"],
                  "energies": [r["properties"]["return_energy"] for r in trajectory],
                  "trajectory": trajectory,
                  "provenance": {"creator": "Berny", "routine": "berny.Berny", "version": berny_version},
                  "stdout": log_stream.getvalue(),  # collect logged messages
              }
          )
          if output_data["success"]:
              output_data = OptimizationResult(**output_data)
          return output_data
  