cr-connect-workflow/crc/services/workflow_processor.py

import re

from SpiffWorkflow.serializer.exceptions import MissingSpecError
from lxml import etree
import shlex
from datetime import datetime
from typing import List

from SpiffWorkflow import Task as SpiffTask, WorkflowException
from SpiffWorkflow.bpmn.BpmnScriptEngine import BpmnScriptEngine
from SpiffWorkflow.bpmn.parser.ValidationException import ValidationException
from SpiffWorkflow.bpmn.serializer.BpmnSerializer import BpmnSerializer
from SpiffWorkflow.bpmn.specs.EndEvent import EndEvent
from SpiffWorkflow.bpmn.workflow import BpmnWorkflow
from SpiffWorkflow.camunda.parser.CamundaParser import CamundaParser
from SpiffWorkflow.dmn.parser.BpmnDmnParser import BpmnDmnParser
from SpiffWorkflow.exceptions import WorkflowTaskExecException
from SpiffWorkflow.specs import WorkflowSpec

import crc
from crc import session, app
from crc.api.common import ApiError
from crc.models.file import FileDataModel, FileModel, FileType
from crc.models.workflow import WorkflowStatus, WorkflowModel, WorkflowSpecDependencyFile
from crc.scripts.script import Script
from crc.services.file_service import FileService
from crc import app


class CustomBpmnScriptEngine(BpmnScriptEngine):
    """This is a custom script processor that can be easily injected into Spiff Workflow.
    Rather than execute arbitrary code, this assumes the script references a fully qualified python class
    such as myapp.RandomFact. """

    def execute(self, task: SpiffTask, script, data):
        """
        Functions in two modes.
        1. If the command is proceeded by #! then this is assumed to be a python script, and will
           attempt to load that python module and execute the do_task method on that script.  Scripts
           must be located in the scripts package and they must extend the script.py class.
        2. If not proceeded by the #! this will attempt to execute the script directly and assumes it is
           valid Python.
        """
        # Shlex splits the whole string while respecting double quoted strings within
        study_id = task.workflow.data[WorkflowProcessor.STUDY_ID_KEY]
        if WorkflowProcessor.WORKFLOW_ID_KEY in task.workflow.data:
            workflow_id = task.workflow.data[WorkflowProcessor.WORKFLOW_ID_KEY]
        else:
            workflow_id = None

        try:
            augmentMethods = Script.generate_augmented_list(task,study_id,workflow_id)

            super().execute(task, script, data, externalMethods=augmentMethods)
        except SyntaxError as e:
            del(task.data['task'])
            raise ApiError('syntax_error',
                           f'Something is wrong with your python script '
                           f'please correct the following:'
                           f' {script}, {e.msg}')
        except NameError as e:
            del(task.data['task'])
            raise ApiError('name_error',
                            f'something you are referencing does not exist:'
                            f' {script}, {e.name}')

       # else:
       #     self.run_predefined_script(task, script[2:], data)  # strip off the first two characters.

    # def run_predefined_script(self, task: SpiffTask, script, data):
    #     commands = shlex.split(script)
    #     path_and_command = commands[0].rsplit(".", 1)
    #     if len(path_and_command) == 1:
    #         module_name = "crc.scripts." + self.camel_to_snake(path_and_command[0])
    #         class_name = path_and_command[0]
    #     else:
    #         module_name = "crc.scripts." + path_and_command[0] + "." + self.camel_to_snake(path_and_command[1])
    #         class_name = path_and_command[1]
    #     try:
    #         mod = __import__(module_name, fromlist=[class_name])
    #         klass = getattr(mod, class_name)
    #         study_id = task.workflow.data[WorkflowProcessor.STUDY_ID_KEY]
    #         if WorkflowProcessor.WORKFLOW_ID_KEY in task.workflow.data:
    #             workflow_id = task.workflow.data[WorkflowProcessor.WORKFLOW_ID_KEY]
    #         else:
    #             workflow_id = None
    #
    #         if not isinstance(klass(), Script):
    #             raise ApiError.from_task("invalid_script",
    #                 "This is an internal error. The script '%s:%s' you called " %
    #                 (module_name, class_name) +
    #                 "does not properly implement the CRC Script class.",
    #                 task=task)
    #         if task.workflow.data[WorkflowProcessor.VALIDATION_PROCESS_KEY]:
    #             """If this is running a validation, and not a normal process, then we want to
    #             mimic running the script, but not make any external calls or database changes."""
    #             klass().do_task_validate_only(task, study_id, workflow_id, *commands[1:])
    #         else:
    #             klass().do_task(task, study_id, workflow_id, *commands[1:])
    #     except ModuleNotFoundError:
    #         raise ApiError.from_task("invalid_script",
    #              "Unable to locate Script: '%s:%s'" % (module_name, class_name),
    #              task=task)

    def evaluate_expression(self, task, expression):
        """
        Evaluate the given expression, within the context of the given task and
        return the result.
        """
        exp, valid = self.validateExpression(expression)
        return self._eval(exp, **task.data)

    @staticmethod
    def camel_to_snake(camel):
        camel = camel.strip()
        return re.sub(r'(?<!^)(?=[A-Z])', '_', camel).lower()


class MyCustomParser(BpmnDmnParser):
    """
    A BPMN and DMN parser that can also parse Camunda forms.
    """
    OVERRIDE_PARSER_CLASSES = BpmnDmnParser.OVERRIDE_PARSER_CLASSES
    OVERRIDE_PARSER_CLASSES.update(CamundaParser.OVERRIDE_PARSER_CLASSES)


class WorkflowProcessor(object):
    _script_engine = CustomBpmnScriptEngine()
    _serializer = BpmnSerializer()

    WORKFLOW_ID_KEY = "workflow_id"
    STUDY_ID_KEY = "study_id"
    VALIDATION_PROCESS_KEY = "validate_only"

    def __init__(self, workflow_model: WorkflowModel, soft_reset=False, hard_reset=False, validate_only=False):
        """Create a Workflow Processor based on the serialized information available in the workflow model.
        If soft_reset is set to true, it will try to use the latest version of the workflow specification
            without resetting to the beginning of the workflow.  This will work for some minor changes to the spec.
        If hard_reset is set to true, it will use the latest spec, and start the workflow over from the beginning.
            which should work in casees where a soft reset fails.
        If neither flag is set, it will use the same version of the specification that was used to originally
        create the workflow model. """
        self.workflow_model = workflow_model

        if soft_reset or len(workflow_model.dependencies) == 0:  # Depenencies of 0 means the workflow was never started.
            self.spec_data_files = FileService.get_spec_data_files(
                workflow_spec_id=workflow_model.workflow_spec_id)
        else:
            self.spec_data_files = FileService.get_spec_data_files(
                workflow_spec_id=workflow_model.workflow_spec_id,
                workflow_id=workflow_model.id)

        spec = self.get_spec(self.spec_data_files, workflow_model.workflow_spec_id)
        self.workflow_spec_id = workflow_model.workflow_spec_id
        try:
            self.bpmn_workflow = self.__get_bpmn_workflow(workflow_model, spec, validate_only)
            self.bpmn_workflow.script_engine = self._script_engine

            if self.WORKFLOW_ID_KEY not in self.bpmn_workflow.data:
                if not workflow_model.id:
                    session.add(workflow_model)
                    session.commit()
                    # If the model is new, and has no id, save it, write it into the workflow model
                    # and save it again.  In this way, the workflow process is always aware of the
                    # database model to which it is associated, and scripts running within the model
                    # can then load data as needed.
                self.bpmn_workflow.data[WorkflowProcessor.WORKFLOW_ID_KEY] = workflow_model.id
                workflow_model.bpmn_workflow_json = WorkflowProcessor._serializer.serialize_workflow(self.bpmn_workflow)
                self.save()

        except MissingSpecError as ke:
            raise ApiError(code="unexpected_workflow_structure",
                           message="Failed to deserialize workflow"
                                   " '%s' version %s, due to a mis-placed or missing task '%s'" %
                                   (self.workflow_spec_id, self.get_version_string(), str(ke)) +
                                   " This is very likely due to a soft reset where there was a structural change.")
        if hard_reset:
            # Now that the spec is loaded, get the data and rebuild the bpmn with the new details
            self.hard_reset()
            workflow_model.bpmn_workflow_json = WorkflowProcessor._serializer.serialize_workflow(self.bpmn_workflow)
            self.save()
        if soft_reset:
            self.save()

        # set whether this is the latest spec file.
        if self.spec_data_files == FileService.get_spec_data_files(workflow_spec_id=workflow_model.workflow_spec_id):
            self.is_latest_spec = True
        else:
            self.is_latest_spec = False

    def __get_bpmn_workflow(self, workflow_model: WorkflowModel, spec: WorkflowSpec, validate_only=False):
        if workflow_model.bpmn_workflow_json:
            bpmn_workflow = self._serializer.deserialize_workflow(workflow_model.bpmn_workflow_json, workflow_spec=spec)
        else:
            bpmn_workflow = BpmnWorkflow(spec, script_engine=self._script_engine)
            bpmn_workflow.data[WorkflowProcessor.STUDY_ID_KEY] = workflow_model.study_id
            bpmn_workflow.data[WorkflowProcessor.VALIDATION_PROCESS_KEY] = validate_only
            #try:
            bpmn_workflow.do_engine_steps()
           # except WorkflowException as we:
           #     raise ApiError.from_task_spec("error_loading_workflow", str(we), we.sender)
        return bpmn_workflow

    def save(self):
        """Saves the current state of this processor to the database """
        self.workflow_model.bpmn_workflow_json = self.serialize()
        complete_states = [SpiffTask.CANCELLED, SpiffTask.COMPLETED]
        tasks = list(self.get_all_user_tasks())
        self.workflow_model.status = self.get_status()
        self.workflow_model.total_tasks = len(tasks)
        self.workflow_model.completed_tasks = sum(1 for t in tasks if t.state in complete_states)
        self.workflow_model.last_updated = datetime.now()
        self.update_dependencies(self.spec_data_files)
        session.add(self.workflow_model)
        session.commit()

    def get_version_string(self):
        # this could potentially become expensive to load all the data in the data models.
        # in which case we might consider using a deferred loader for the actual data, but
        # trying not to pre-optimize.
        file_data_models = FileService.get_spec_data_files(self.workflow_model.workflow_spec_id,
                                                           self.workflow_model.id)
        return WorkflowProcessor.__get_version_string_for_data_models(file_data_models)

    @staticmethod
    def get_latest_version_string_for_spec(spec_id):
        file_data_models = FileService.get_spec_data_files(spec_id)
        return WorkflowProcessor.__get_version_string_for_data_models(file_data_models)

    @staticmethod
    def __get_version_string_for_data_models(file_data_models):
        """Version is in the format v[VERSION] (FILE_ID_LIST)
         For example, a single bpmn file with only one version would be
         v1 (12)  Where 12 is the id of the file data model that is used to create the
         specification.  If multiple files exist, they are added on in
         dot notation to both the version number and the file list. So
         a Spec that includes a BPMN, DMN, an a Word file all on the first
         version would be v1.1.1 (12.45.21)"""

        major_version = 0  # The version of the primary file.
        minor_version = []  # The versions of the minor files if any.
        file_ids = []
        for file_data in file_data_models:
            file_ids.append(file_data.id)
            if file_data.file_model.primary:
                major_version = file_data.version
            else:
                minor_version.append(file_data.version)
        minor_version.insert(0, major_version)  # Add major version to beginning.
        version = ".".join(str(x) for x in minor_version)
        files = ".".join(str(x) for x in file_ids)
        full_version = "v%s (%s)" % (version, files)
        return full_version

    def update_dependencies(self, spec_data_files):
        existing_dependencies = FileService.get_spec_data_files(
            workflow_spec_id=self.workflow_model.workflow_spec_id,
            workflow_id=self.workflow_model.id)

        # Don't save the dependencies if they haven't changed.
        if existing_dependencies == spec_data_files:
            return

        # Remove all existing dependencies, and replace them.
        self.workflow_model.dependencies = []
        for file_data in spec_data_files:
            self.workflow_model.dependencies.append(WorkflowSpecDependencyFile(file_data_id=file_data.id))

    @staticmethod
    def run_master_spec(spec_model, study):
        """Executes a BPMN specification for the given study, without recording any information to the database
        Useful for running the master specification, which should not persist. """
        spec_data_files = FileService.get_spec_data_files(spec_model.id)
        spec = WorkflowProcessor.get_spec(spec_data_files, spec_model.id)
        try:
            bpmn_workflow = BpmnWorkflow(spec, script_engine=WorkflowProcessor._script_engine)
            bpmn_workflow.data[WorkflowProcessor.STUDY_ID_KEY] = study.id
            bpmn_workflow.data[WorkflowProcessor.VALIDATION_PROCESS_KEY] = False
            bpmn_workflow.do_engine_steps()
        except WorkflowException as we:
            raise ApiError.from_task_spec("error_running_master_spec", str(we), we.sender)

        if not bpmn_workflow.is_completed():
            raise ApiError("master_spec_not_automatic",
                           "The master spec should only contain fully automated tasks, it failed to complete.")

        return bpmn_workflow.last_task.data

    @staticmethod
    def get_parser():
        parser = MyCustomParser()
        return parser

    @staticmethod
    def get_spec(file_data_models: List[FileDataModel], workflow_spec_id):
        """Returns a SpiffWorkflow specification for the given workflow spec,
        using the files provided.  The Workflow_spec_id is only used to generate
        better error messages."""
        parser = WorkflowProcessor.get_parser()
        process_id = None

        for file_data in file_data_models:
            if file_data.file_model.type == FileType.bpmn:
                bpmn: etree.Element = etree.fromstring(file_data.data)
                if file_data.file_model.primary:
                    process_id = FileService.get_process_id(bpmn)
                parser.add_bpmn_xml(bpmn, filename=file_data.file_model.name)
            elif file_data.file_model.type == FileType.dmn:
                dmn: etree.Element = etree.fromstring(file_data.data)
                parser.add_dmn_xml(dmn, filename=file_data.file_model.name)
        if process_id is None:
            raise (ApiError(code="no_primary_bpmn_error",
                            message="There is no primary BPMN model defined for workflow %s" % workflow_spec_id))
        try:
            spec = parser.get_spec(process_id)
        except ValidationException as ve:
            raise ApiError(code="workflow_validation_error",
                           message="Failed to parse Workflow Specification '%s'" % workflow_spec_id +
                                   "Error is %s" % str(ve),
                           file_name=ve.filename,
                           task_id=ve.id,
                           tag=ve.tag)
        return spec

    @staticmethod
    def status_of(bpmn_workflow):
        if bpmn_workflow.is_completed():
            return WorkflowStatus.complete
        user_tasks = bpmn_workflow.get_ready_user_tasks()
        if len(user_tasks) > 0:
            return WorkflowStatus.user_input_required
        else:
            return WorkflowStatus.waiting

    def hard_reset(self):
        """Recreate this workflow. This will be useful when a workflow specification changes.
         """
        self.spec_data_files = FileService.get_spec_data_files(workflow_spec_id=self.workflow_spec_id)
        new_spec = WorkflowProcessor.get_spec(self.spec_data_files, self.workflow_spec_id)
        new_bpmn_workflow = BpmnWorkflow(new_spec, script_engine=self._script_engine)
        new_bpmn_workflow.data = self.bpmn_workflow.data
        try:
            new_bpmn_workflow.do_engine_steps()
        except WorkflowException as we:
            raise ApiError.from_task_spec("hard_reset_engine_steps_error", str(we), we.sender)
        self.bpmn_workflow = new_bpmn_workflow

    def get_status(self):
        return self.status_of(self.bpmn_workflow)

    def do_engine_steps(self):
        try:
            self.bpmn_workflow.do_engine_steps()
        except WorkflowTaskExecException as we:
            raise ApiError.from_task("task_error", str(we), we.task)

    def serialize(self):
        return self._serializer.serialize_workflow(self.bpmn_workflow)

    def next_user_tasks(self):
        return self.bpmn_workflow.get_ready_user_tasks()

    def next_task(self):
        """Returns the next task that should be completed
        even if there are parallel tasks and multiple options are
        available.
        If the workflow is complete
        it will return the final end task.
        """

        # If the whole blessed mess is done, return the end_event task in the tree
        if self.bpmn_workflow.is_completed():
            for task in SpiffTask.Iterator(self.bpmn_workflow.task_tree, SpiffTask.ANY_MASK):
                if isinstance(task.task_spec, EndEvent):
                    return task

        # If there are ready tasks to complete, return the next ready task, but return the one
        # in the active parallel path if possible.
        ready_tasks = self.bpmn_workflow.get_tasks(SpiffTask.READY)
        if len(ready_tasks) > 0:
            for task in ready_tasks:
                if task.parent == self.bpmn_workflow.last_task:
                    return task
            return ready_tasks[0]

        # If there are no ready tasks, but the thing isn't complete yet, find the first non-complete task
        # and return that
        next_task = None
        for task in SpiffTask.Iterator(self.bpmn_workflow.task_tree, SpiffTask.NOT_FINISHED_MASK):
            next_task = task
        return next_task

    def previous_task(self):
        return None

    def complete_task(self, task):
        self.bpmn_workflow.complete_task_from_id(task.id)

    def get_data(self):
        return self.bpmn_workflow.data

    def get_workflow_id(self):
        return self.workflow_model.id

    def get_study_id(self):
        return self.bpmn_workflow.data[self.STUDY_ID_KEY]

    def get_ready_user_tasks(self):
        return self.bpmn_workflow.get_ready_user_tasks()

    def get_current_user_tasks(self):
        """Return a list of all user tasks that are READY or
        COMPLETE and are parallel to the READY Task."""
        ready_tasks = self.bpmn_workflow.get_ready_user_tasks()
        additional_tasks = []
        if len(ready_tasks) > 0:
            for child in ready_tasks[0].parent.children:
                if child.state == SpiffTask.COMPLETED:
                    additional_tasks.append(child)
        return ready_tasks + additional_tasks

    def get_all_user_tasks(self):
        all_tasks = self.bpmn_workflow.get_tasks(SpiffTask.ANY_MASK)
        return [t for t in all_tasks if not self.bpmn_workflow._is_engine_task(t.task_spec)]

    def get_all_completed_tasks(self):
        all_tasks = self.bpmn_workflow.get_tasks(SpiffTask.ANY_MASK)
        return [t for t in all_tasks
                if not self.bpmn_workflow._is_engine_task(t.task_spec) and t.state in [t.COMPLETED, t.CANCELLED]]

    def get_nav_item(self, task):
        for nav_item in self.bpmn_workflow.get_nav_list():
            if nav_item['task_id'] == task.id:
                return nav_item

    def find_task_and_field_by_field_id(self, field_id):
        """Tracks down a form field by name in the workflow spec,
         only looks at ready tasks. Returns a tuple of the task, and form"""
        for spiff_task in self.bpmn_workflow.get_tasks():
            if hasattr(spiff_task.task_spec, "form"):
                for field in spiff_task.task_spec.form.fields:
                    if field.id == field_id:
                        return spiff_task, field
        raise ApiError("invalid_field",
                       "Unable to find a task in the workflow with a lookup field called: %s" % field_id)