import json
import logging
import os
import uuid
from typing import (
    Annotated,
    Any,
    cast,
    NamedTuple,
    Optional,
    TYPE_CHECKING,
    TypeAlias,
    Union,
)

import yaml
from gxformat2 import (
    from_galaxy_native,
    ImporterGalaxyInterface,
    ImportOptions,
    python_to_workflow,
)
from gxformat2.abstract import from_dict
from gxformat2.cytoscape import to_cytoscape
from gxformat2.yaml import ordered_dump
from pydantic import (
    BaseModel,
    SerializerFunctionWrapHandler,
    WrapSerializer,
)
from sqlalchemy import (
    and_,
    desc,
    false,
    func,
    or_,
    select,
    true,
)
from sqlalchemy.orm import (
    aliased,
    joinedload,
    subqueryload,
)

from galaxy import (
    exceptions,
    model,
    util,
)
from galaxy.files.uris import (
    stream_url_to_str,
    validate_uri_access,
)
from galaxy.job_execution.actions.post import ActionBox
from galaxy.managers import (
    deletable,
    sharable,
)
from galaxy.managers.base import (
    decode_id,
    security_check,
)
from galaxy.managers.context import ProvidesUserContext
from galaxy.managers.executables import artifact_class
from galaxy.model import (
    History,
    StoredWorkflow,
    StoredWorkflowTagAssociation,
    StoredWorkflowUserShareAssociation,
    to_json,
    User,
    Workflow,
    WorkflowInvocation,
    WorkflowInvocationStep,
    WorkflowInvocationToSubworkflowInvocationAssociation,
)
from galaxy.model.base import ensure_object_added_to_session
from galaxy.model.index_filter_util import (
    append_user_filter,
    raw_text_column_filter,
    tag_filter,
    text_column_filter,
)
from galaxy.model.item_attrs import UsesAnnotations
from galaxy.schema.invocation import InvocationCancellationUserRequest
from galaxy.schema.schema import WorkflowIndexQueryPayload
from galaxy.structured_app import MinimalManagerApp
from galaxy.tools.parameters import (
    params_to_incoming,
    visit_input_values,
)
from galaxy.tools.parameters.basic import (
    DataCollectionToolParameter,
    DataToolParameter,
)
from galaxy.tools.parameters.workflow_utils import (
    ConnectedValue,
    RuntimeValue,
    workflow_building_modes,
)
from galaxy.util.hash_util import md5_hash_str
from galaxy.util.json import (
    safe_dumps,
    safe_loads,
)
from galaxy.util.sanitize_html import sanitize_html
from galaxy.util.search import (
    FilteredTerm,
    parse_filters_structured,
    RawTextTerm,
)
from galaxy.work.context import WorkRequestContext
from galaxy.workflow.modules import (
    module_factory,
    ToolModule,
    WorkflowModule,
    WorkflowModuleInjector,
)
from galaxy.workflow.refactor.execute import WorkflowRefactorExecutor
from galaxy.workflow.refactor.schema import (
    RefactorActionExecution,
    RefactorActions,
)
from galaxy.workflow.render import (
    STANDALONE_SVG_TEMPLATE,
    WorkflowCanvas,
)
from galaxy.workflow.reports import generate_report
from galaxy.workflow.resources import get_resource_mapper_function
from galaxy.workflow.steps import (
    attach_ordered_steps,
    has_cycles,
)
from galaxy.workflow.trs_proxy import TrsProxy

if TYPE_CHECKING:
    from sqlalchemy.engine import ScalarResult

log = logging.getLogger(__name__)

MAX_SUBWORKFLOW_URL_DEPTH = 10

INDEX_SEARCH_FILTERS = {
    "name": "name",
    "tag": "tag",
    "n": "name",
    "t": "tag",
    "user": "user",
    "u": "user",
    "is": "is",
}


class WorkflowsManager(sharable.SharableModelManager[model.StoredWorkflow], deletable.DeletableManagerMixin):
    """Handle CRUD type operations related to workflows. More interesting
    stuff regarding workflow execution, step sorting, etc... can be found in
    the galaxy.workflow module.
    """

    model_class = model.StoredWorkflow
    foreign_key_name = "stored_workflow"
    user_share_model = model.StoredWorkflowUserShareAssociation

    def __init__(self, app: MinimalManagerApp):
        super().__init__(app)
        self.app = app

    def index_query(
        self, trans: ProvidesUserContext, payload: WorkflowIndexQueryPayload, include_total_count: bool = False
    ) -> tuple["ScalarResult[model.StoredWorkflow]", Optional[int]]:
        show_published = payload.show_published
        show_hidden = payload.show_hidden
        show_deleted = payload.show_deleted
        show_shared = payload.show_shared

        if show_shared is None:
            show_shared = not show_hidden and not show_deleted

        if show_shared and show_deleted:
            message = "show_shared and show_deleted cannot both be specified as true"
            raise exceptions.RequestParameterInvalidException(message)
        if show_shared and show_hidden:
            message = "show_shared and show_hidden cannot both be specified as true"
            raise exceptions.RequestParameterInvalidException(message)

        filters = [
            StoredWorkflow.user == trans.user,
        ]
        user = trans.user
        if user and show_shared:
            filters.append(StoredWorkflowUserShareAssociation.user == user)

        if show_published or user is None and show_published is None:
            filters.append(StoredWorkflow.published == true())

        stmt = select(StoredWorkflow)

        # Do not include workflows authored by deleted users
        if show_published or show_shared:
            stmt = stmt.join(StoredWorkflow.user).where(User.deleted == false())

        if show_shared:
            stmt = stmt.outerjoin(StoredWorkflow.users_shared_with)
        stmt = stmt.outerjoin(StoredWorkflow.tags)

        latest_workflow_load = joinedload(StoredWorkflow.latest_workflow)
        if not payload.skip_step_counts:
            latest_workflow_load = latest_workflow_load.undefer(Workflow.step_count)  # type: ignore[arg-type]
        latest_workflow_load = latest_workflow_load.lazyload(Workflow.steps)

        stmt = stmt.options(joinedload(StoredWorkflow.annotations))
        stmt = stmt.options(latest_workflow_load)
        stmt = stmt.where(or_(*filters))
        stmt = stmt.where(StoredWorkflow.hidden == (true() if show_hidden else false()))
        if payload.search:
            search_query = payload.search
            parsed_search = parse_filters_structured(search_query, INDEX_SEARCH_FILTERS)

            def w_tag_filter(term_text: str, quoted: bool):
                nonlocal stmt
                alias = aliased(StoredWorkflowTagAssociation)
                stmt = stmt.outerjoin(StoredWorkflow.tags.of_type(alias))
                return tag_filter(alias, term_text, quoted)

            def name_filter(term):
                return text_column_filter(StoredWorkflow.name, term)

            for term in parsed_search.terms:
                if isinstance(term, FilteredTerm):
                    key = term.filter
                    q = term.text
                    if key == "tag":
                        tf = w_tag_filter(term.text, term.quoted)
                        stmt = stmt.where(tf)
                    elif key == "name":
                        stmt = stmt.where(name_filter(term))
                    elif key == "user":
                        stmt = append_user_filter(stmt, StoredWorkflow, term)
                    elif key == "is":
                        if q == "published":
                            stmt = stmt.where(StoredWorkflow.published == true())
                        elif q == "importable":
                            stmt = stmt.where(StoredWorkflow.importable == true())
                        elif q == "deleted":
                            stmt = stmt.where(StoredWorkflow.deleted == true())
                            show_deleted = True
                        elif q == "shared_with_me":
                            if not show_shared:
                                message = "Can only use tag is:shared_with_me if show_shared parameter also true."
                                raise exceptions.RequestParameterInvalidException(message)
                            stmt = stmt.where(StoredWorkflowUserShareAssociation.user == user)
                        elif q == "bookmarked":
                            stmt = stmt.join(
                                model.StoredWorkflowMenuEntry,
                                model.StoredWorkflowMenuEntry.stored_workflow_id == StoredWorkflow.id,
                            ).where(model.StoredWorkflowMenuEntry.user_id == user.id)
                elif isinstance(term, RawTextTerm):
                    tf = w_tag_filter(term.text, False)
                    alias = aliased(User)
                    stmt = stmt.outerjoin(StoredWorkflow.user.of_type(alias))
                    stmt = stmt.where(
                        raw_text_column_filter(
                            [
                                StoredWorkflow.name,
                                tf,
                                alias.username,
                            ],
                            term,
                        )
                    )
        stmt = stmt.where(StoredWorkflow.deleted == (true() if show_deleted else false())).distinct()
        if include_total_count:
            total_matches = get_count(trans.sa_session, stmt)
        else:
            total_matches = None
        if payload.sort_by is None:
            if user:
                stmt = stmt.order_by(desc(StoredWorkflow.user == user))
            stmt = stmt.order_by(desc(StoredWorkflow.update_time))
        else:
            sort_column = getattr(StoredWorkflow, payload.sort_by)
            if payload.sort_desc:
                sort_column = sort_column.desc()
            stmt = stmt.order_by(sort_column)
        if payload.limit is not None:
            stmt = stmt.limit(payload.limit)
        if payload.offset is not None:
            stmt = stmt.offset(payload.offset)
        result = trans.sa_session.scalars(stmt).unique()
        return result, total_matches

    def get_stored_workflow(self, trans, workflow_id, by_stored_id=True) -> StoredWorkflow:
        """Use a supplied ID (UUID or encoded stored workflow ID) to find
        a workflow.
        """
        if util.is_uuid(workflow_id):
            workflow_uuid = uuid.UUID(workflow_id)
        else:
            workflow_uuid = None
            workflow_id = workflow_id if isinstance(workflow_id, int) else decode_id(self.app, workflow_id)

        stored_workflow = _get_stored_workflow(trans.sa_session, workflow_uuid, workflow_id, by_stored_id)

        if stored_workflow is None:
            if not by_stored_id:
                # May have a subworkflow without attached StoredWorkflow object, this was the default prior to 20.09 release.
                workflow = trans.sa_session.get(Workflow, workflow_id)
                stored_workflow = self.attach_stored_workflow(trans=trans, workflow=workflow)
                if stored_workflow:
                    return stored_workflow
            raise exceptions.ObjectNotFound("No such workflow found.")
        return stored_workflow

    def get_stored_accessible_workflow(self, trans, workflow_id, by_stored_id=True):
        """Get a stored workflow from a encoded stored workflow id and
        make sure it accessible to the user.
        """
        stored_workflow = self.get_stored_workflow(trans, workflow_id, by_stored_id=by_stored_id)

        # check to see if user has permissions to selected workflow
        if stored_workflow.user != trans.user and not trans.user_is_admin and not stored_workflow.importable:
            stmt = select(StoredWorkflowUserShareAssociation).filter_by(
                user=trans.user, stored_workflow=stored_workflow
            )
            if get_count(trans.sa_session, stmt) == 0:
                message = "Workflow is not owned by or shared with current user"
                raise exceptions.ItemAccessibilityException(message)

        return stored_workflow

    def attach_stored_workflow(self, trans, workflow):
        """Attach and return stored workflow if possible."""
        # Imported Subworkflows are not created with a StoredWorkflow association
        # To properly serialize them we do need a StoredWorkflow, so we create and attach one here.
        # We hide the new StoredWorkflow to avoid cluttering the default workflow view.
        if workflow and workflow.stored_workflow is None and self.check_security(trans, has_workflow=workflow):
            stored_workflow = StoredWorkflow(user=trans.user, name=workflow.name, workflow=workflow, hidden=True)
            trans.sa_session.add(stored_workflow)
            trans.sa_session.commit()
            return stored_workflow

    def get_owned_workflow(self, trans, encoded_workflow_id):
        """Get a workflow (non-stored) from a encoded workflow id and
        make sure it accessible to the user.
        """
        workflow_id = decode_id(self.app, encoded_workflow_id)
        workflow = trans.sa_session.get(Workflow, workflow_id)
        self.check_security(trans, workflow, check_ownership=True)
        return workflow

    def check_security(self, trans, has_workflow, check_ownership=True, check_accessible=True):
        """check accessibility or ownership of workflows, storedworkflows, and
        workflowinvocations. Throw an exception or returns True if user has
        needed level of access.
        """
        if not check_ownership and not check_accessible:
            return True

        # If given an invocation verify ownership of invocation
        if isinstance(has_workflow, model.WorkflowInvocation):
            # We use the owner of the history that is associated to the invocation as a proxy
            # for the owner of the invocation.
            security_check(
                trans, has_workflow.history, check_ownership=check_ownership, check_accessible=check_accessible
            )
            return True

        # stored workflow contains security stuff - follow that workflow to
        # that unless given a stored workflow.
        if isinstance(has_workflow, model.Workflow):
            stored_workflow = has_workflow.top_level_stored_workflow
        else:
            stored_workflow = has_workflow

        if stored_workflow.user != trans.user and not trans.user_is_admin:
            if check_ownership:
                raise exceptions.ItemOwnershipException()
            # else check_accessible...
            stmt = select(StoredWorkflowUserShareAssociation).filter_by(
                user=trans.user, stored_workflow=stored_workflow
            )
            if get_count(trans.sa_session, stmt) == 0:
                raise exceptions.ItemAccessibilityException()

        return True

    def get_workflow_svg_from_id(self, trans, id, version=None, for_embed=False) -> bytes:
        stored = self.get_stored_accessible_workflow(trans, id)
        workflow = stored.get_internal_version(version)
        return self.get_workflow_svg(trans, workflow, for_embed=for_embed)

    def get_workflow_svg(self, trans, workflow, for_embed=False) -> bytes:
        try:
            svg = self._workflow_to_svg_canvas(trans, workflow, for_embed=for_embed)
            s = STANDALONE_SVG_TEMPLATE % svg.tostring()
            return s.encode("utf-8")
        except Exception:
            message = (
                "Galaxy is unable to create the SVG image. Please check your workflow, there might be missing tools."
            )
            raise exceptions.MessageException(message)

    def _workflow_to_svg_canvas(self, trans, workflow, for_embed=False):
        workflow_canvas = WorkflowCanvas()
        for step in workflow.steps:
            # Load from database representation
            module = module_factory.from_workflow_step(trans, step)
            module_name = module.get_name()
            module_data_inputs = module.get_data_inputs()
            module_data_outputs = module.get_data_outputs()
            workflow_canvas.populate_data_for_step(
                step,
                module_name,
                module_data_inputs,
                module_data_outputs,
            )
        workflow_canvas.add_steps()
        return workflow_canvas.finish(for_embed=for_embed)

    def get_invocation(
        self, trans, decoded_invocation_id: int, check_ownership=True, check_accessible=True
    ) -> WorkflowInvocation:
        workflow_invocation = _get_invocation(trans.sa_session, decoded_invocation_id)
        if not workflow_invocation:
            encoded_wfi_id = trans.security.encode_id(decoded_invocation_id)
            message = f"'{encoded_wfi_id}' is not a valid workflow invocation id"
            raise exceptions.ObjectNotFound(message)
        self.check_security(
            trans, workflow_invocation, check_ownership=check_ownership, check_accessible=check_accessible
        )
        return workflow_invocation

    def get_invocation_report(self, trans, invocation_id, **kwd):
        decoded_workflow_invocation_id = (
            trans.security.decode_id(invocation_id) if isinstance(invocation_id, str) else invocation_id
        )
        workflow_invocation = self.get_invocation(
            trans, decoded_workflow_invocation_id, check_ownership=False, check_accessible=True
        )
        generator_plugin_type = kwd.get("generator_plugin_type")
        runtime_report_config_json = kwd.get("runtime_report_config_json")
        invocation_markdown = kwd.get("invocation_markdown", None)
        target_format = kwd.get("format", "json")
        if invocation_markdown:
            runtime_report_config_json = {"markdown": invocation_markdown}
        return generate_report(
            trans,
            workflow_invocation,
            runtime_report_config_json=runtime_report_config_json,
            plugin_type=generator_plugin_type,
            target_format=target_format,
        )

    def request_invocation_cancellation(self, trans, decoded_invocation_id: int):
        workflow_invocation = self.get_invocation(trans, decoded_invocation_id, check_ownership=True)
        cancelled = workflow_invocation.cancel()

        if cancelled:
            workflow_invocation.add_message(InvocationCancellationUserRequest(reason="user_request"))
            trans.sa_session.add(workflow_invocation)

        trans.sa_session.commit()

        return workflow_invocation

    def get_invocation_step(
        self, trans, decoded_workflow_invocation_step_id, check_ownership: bool = True, check_accessible: bool = True
    ) -> WorkflowInvocationStep:
        try:
            workflow_invocation_step = trans.sa_session.get(WorkflowInvocationStep, decoded_workflow_invocation_step_id)
        except Exception:
            raise exceptions.ObjectNotFound()
        self.check_security(
            trans,
            workflow_invocation_step.workflow_invocation,
            check_ownership=check_ownership,
            check_accessible=check_accessible,
        )
        return workflow_invocation_step

    def update_invocation_step(self, trans, decoded_workflow_invocation_step_id, action):
        if action is None:
            raise exceptions.RequestParameterMissingException(
                "Updating workflow invocation step requires an action parameter. "
            )

        workflow_invocation_step = self.get_invocation_step(trans, decoded_workflow_invocation_step_id)
        workflow_invocation = workflow_invocation_step.workflow_invocation
        if not workflow_invocation.active:
            raise exceptions.RequestParameterInvalidException(
                "Attempting to modify the state of a completed workflow invocation."
            )

        step = workflow_invocation_step.workflow_step
        module = module_factory.from_workflow_step(trans, step)
        performed_action = module.do_invocation_step_action(step, action)
        workflow_invocation_step.action = performed_action
        trans.sa_session.add(workflow_invocation_step)
        trans.sa_session.commit()
        return workflow_invocation_step

    def build_invocations_query(
        self,
        trans: ProvidesUserContext,
        stored_workflow_id=None,
        history_id=None,
        job_id=None,
        user_id=None,
        include_terminal=True,
        limit=None,
        offset=None,
        sort_by=None,
        sort_desc=None,
        include_nested_invocations=True,
        check_ownership=True,
    ) -> tuple[list, int]:
        """Get invocations owned by the current user."""

        stmt = select(WorkflowInvocation)
        if stored_workflow_id is not None:
            stored_workflow = trans.sa_session.get(StoredWorkflow, stored_workflow_id)
            if not stored_workflow:
                raise exceptions.ObjectNotFound()
            stmt = stmt.join(Workflow).where(Workflow.stored_workflow_id == stored_workflow_id)
        if user_id is not None:
            stmt = stmt.join(History).where(History.user_id == user_id)
        if history_id is not None:
            stmt = stmt.where(WorkflowInvocation.history_id == history_id)
        if job_id is not None:
            stmt = stmt.join(WorkflowInvocationStep).where(WorkflowInvocationStep.job_id == job_id)
        if not include_terminal:
            stmt = stmt.where(WorkflowInvocation.state.in_(WorkflowInvocation.non_terminal_states))
        if not include_nested_invocations:
            subquery = (
                select(WorkflowInvocationToSubworkflowInvocationAssociation.id)
                .where(
                    WorkflowInvocationToSubworkflowInvocationAssociation.subworkflow_invocation_id
                    == WorkflowInvocation.id
                )
                .exists()
            )
            stmt = stmt.where(~subquery)

        total_matches = get_count(trans.sa_session, stmt)

        if sort_by:
            sort_column = getattr(WorkflowInvocation, sort_by)
            if sort_desc:
                sort_column = sort_column.desc()
        else:
            sort_column = WorkflowInvocation.id.desc()
        stmt = stmt.order_by(sort_column)

        if limit is not None:
            stmt = stmt.limit(limit)
        if offset is not None:
            stmt = stmt.offset(offset)

        invocations = [
            inv
            for inv in trans.sa_session.scalars(stmt)
            if self.check_security(trans, inv, check_ownership=check_ownership, check_accessible=True)
        ]
        return invocations, total_matches


MissingToolsT = list[tuple[str, str, Optional[str], str]]


class CreatedWorkflow(NamedTuple):
    stored_workflow: StoredWorkflow
    workflow: model.Workflow
    missing_tools: MissingToolsT


class RefactorRequest(RefactorActions):
    style: str = "export"
    version: Optional[int] = None


class WorkflowSerializer(sharable.SharableModelSerializer):
    """
    Interface/service object for serializing stored workflows into dictionaries.

    These are used for simple workflow operations - much more detailed serialization operations
    that allow recovering the workflow in its entirity or rendering it in a workflow
    editor are available inside the WorkflowContentsManager.
    """

    model_manager_class = WorkflowsManager
    SINGLE_CHAR_ABBR = "w"

    def __init__(self, app: MinimalManagerApp):
        super().__init__(app)

    def add_serializers(self):
        super().add_serializers()
        self.serializers.update({})


class WorkflowContentsManager(UsesAnnotations):

    def __init__(self, app: MinimalManagerApp, trs_proxy: TrsProxy):
        self.app = app
        self.trs_proxy = trs_proxy
        self._resource_mapper_function = get_resource_mapper_function(app)

    def ensure_raw_description(self, dict_or_raw_description):
        if not isinstance(dict_or_raw_description, RawWorkflowDescription):
            dict_or_raw_description = RawWorkflowDescription(dict_or_raw_description)
        return dict_or_raw_description

    def read_workflow_from_path(self, app, user, path, allow_in_directory=None) -> model.Workflow:
        trans = WorkRequestContext(app=self.app, user=user)

        as_dict = {"src": "from_path", "path": path}
        workflow_class, as_dict, object_id = artifact_class(trans, as_dict, allow_in_directory=allow_in_directory)
        assert workflow_class == "GalaxyWorkflow"
        # Format 2 Galaxy workflow.
        galaxy_interface = Format2ConverterGalaxyInterface()
        import_options = ImportOptions()
        import_options.deduplicate_subworkflows = True
        as_dict = python_to_workflow(as_dict, galaxy_interface, workflow_directory=None, import_options=import_options)
        raw_description = RawWorkflowDescription(as_dict)
        created_workflow = self.build_workflow_from_raw_description(trans, raw_description, WorkflowCreateOptions())
        return created_workflow.workflow

    def normalize_workflow_format(self, trans, as_dict):
        """Process incoming workflow descriptions for consumption by other methods.

        Currently this mostly means converting format 2 workflows into standard Galaxy
        workflow JSON for consumption for the rest of this module. In the future we will
        want to be a lot more precise about this - preserve the original description along
        side the data model and apply updates in a way that largely preserves YAML structure
        so workflows can be extracted.
        """
        workflow_directory = None
        workflow_path = None

        if as_dict.get("src", None) == "from_path":
            if not trans.user_is_admin:
                raise exceptions.AdminRequiredException()

            workflow_path = as_dict.get("path")
            workflow_directory = os.path.normpath(os.path.dirname(workflow_path))

        workflow_class, as_dict, object_id = artifact_class(trans, as_dict)
        if workflow_class == "GalaxyWorkflow" or "yaml_content" in as_dict:
            # Format 2 Galaxy workflow.
            galaxy_interface = Format2ConverterGalaxyInterface()
            import_options = ImportOptions()
            import_options.deduplicate_subworkflows = True
            try:
                as_dict = python_to_workflow(
                    as_dict, galaxy_interface, workflow_directory=workflow_directory, import_options=import_options
                )
            except yaml.scanner.ScannerError as e:
                raise exceptions.MalformedContents(str(e))
        return RawWorkflowDescription(as_dict, workflow_path)

    def build_workflow_from_raw_description(
        self,
        trans,
        raw_workflow_description,
        workflow_create_options,
        source=None,
        add_to_menu=False,
        hidden=False,
        is_subworkflow=False,
        resolving_urls: frozenset[str] = frozenset(),
    ) -> CreatedWorkflow:
        data = raw_workflow_description.as_dict
        # Put parameters in workflow mode
        trans.workflow_building_mode = workflow_building_modes.ENABLED
        # If there's a source, put it in the workflow name.
        if "name" not in data:
            raise exceptions.RequestParameterInvalidException(f"Invalid workflow format detected [{data}]")

        workflow_input_name = data["name"]
        imported_sufix = f"(imported from {source})"
        if source and imported_sufix not in workflow_input_name:
            name = f"{workflow_input_name} {imported_sufix}"
        else:
            name = workflow_input_name
        workflow, missing_tool_tups = self._workflow_from_raw_description(
            trans,
            raw_workflow_description,
            workflow_create_options,
            name=name,
            is_subworkflow=is_subworkflow,
            resolving_urls=resolving_urls,
        )
        if "uuid" in data:
            workflow.uuid = data["uuid"]

        # Connect up
        stored = model.StoredWorkflow()
        stored.from_path = raw_workflow_description.workflow_path
        stored.name = workflow.name
        workflow.stored_workflow = stored
        stored.latest_workflow = workflow
        stored.user = trans.user
        stored.published = workflow_create_options.publish
        stored.hidden = hidden
        if data["annotation"]:
            annotation = sanitize_html(data["annotation"])
            self.add_item_annotation(trans.sa_session, stored.user, stored, annotation)
        workflow_tags = data.get("tags", [])
        trans.tag_handler.set_tags_from_list(user=trans.user, item=stored, new_tags_list=workflow_tags)

        # Persist
        trans.sa_session.add(stored)

        if add_to_menu:
            if trans.user.stored_workflow_menu_entries is None:
                trans.user.stored_workflow_menu_entries = []
            menuEntry = model.StoredWorkflowMenuEntry()
            menuEntry.stored_workflow = stored
            trans.user.stored_workflow_menu_entries.append(menuEntry)

        trans.sa_session.commit()

        return CreatedWorkflow(stored_workflow=stored, workflow=workflow, missing_tools=missing_tool_tups)

    def update_workflow_from_raw_description(
        self, trans, stored_workflow, raw_workflow_description, workflow_update_options
    ):
        raw_workflow_description = self.ensure_raw_description(raw_workflow_description)

        # Put parameters in workflow mode
        trans.workflow_building_mode = workflow_building_modes.ENABLED
        dry_run = workflow_update_options.dry_run
        workflow, missing_tool_tups = self._workflow_from_raw_description(
            trans,
            raw_workflow_description,
            workflow_update_options,
            name=stored_workflow.name,
            dry_run=dry_run,
        )

        if missing_tool_tups and not workflow_update_options.allow_missing_tools:
            errors = [
                f"Step {int(missing_tool_tup[3]) + 1}: Requires tool '{missing_tool_tup[0]}'."
                for missing_tool_tup in missing_tool_tups
            ]
            raise MissingToolsException(workflow, errors)

        # Connect up
        if not dry_run:
            workflow.stored_workflow = stored_workflow
            stored_workflow.latest_workflow = workflow
        else:
            stored_workflow = model.StoredWorkflow()  # detached
            stored_workflow.latest_workflow = workflow
            workflow.stored_workflow = stored_workflow

        data = raw_workflow_description.as_dict
        if isinstance(data, str):
            data = json.loads(data)
        if "tags" in data:
            trans.tag_handler.set_tags_from_list(
                trans.user,
                stored_workflow,
                data["tags"],
                flush=False,
            )

        if workflow_update_options.update_stored_workflow_attributes:
            update_dict = raw_workflow_description.as_dict
            if "name" in update_dict:
                sanitized_name = sanitize_html(update_dict["name"])
                if not sanitized_name:
                    raise exceptions.RequestParameterInvalidException("Workflow must have a valid name")
                workflow.name = sanitized_name
                stored_workflow.name = sanitized_name
            if update_dict.get("annotation") is not None:
                newAnnotation = sanitize_html(update_dict["annotation"])
                sa_session = None if dry_run else trans.sa_session
                self.add_item_annotation(sa_session, stored_workflow.user, stored_workflow, newAnnotation)

        # Persist
        if not dry_run:
            trans.sa_session.commit()
            if stored_workflow.from_path:
                self._sync_stored_workflow(trans, stored_workflow)
        # Return something informative
        errors = []
        if workflow.has_errors:
            errors.append("Some steps in this workflow have validation errors")
        if workflow.has_cycles:
            errors.append("This workflow contains cycles")
        return workflow, errors

    def _workflow_from_raw_description(
        self,
        trans,
        raw_workflow_description,
        workflow_state_resolution_options,
        name,
        is_subworkflow: bool = False,
        **kwds,
    ) -> tuple[model.Workflow, MissingToolsT]:
        # don't commit the workflow or attach its part to the sa session - just build a
        # a transient model to operate on or render.
        dry_run = kwds.pop("dry_run", False)
        resolving_urls: frozenset[str] = kwds.pop("resolving_urls", frozenset())

        data = raw_workflow_description.as_dict
        if isinstance(data, str):
            data = json.loads(data)

        # Create new workflow from source data
        workflow = model.Workflow()
        workflow.name = name

        if "report" in data:
            workflow.reports_config = data["report"]
        workflow.license = data.get("license")
        workflow.creator_metadata = data.get("creator")

        if "logo_url" in data:
            workflow.logo_url = data["logo_url"]

        if dois := data.get("doi", None):
            for doi in dois:
                if not util.validate_doi(doi):
                    raise exceptions.RequestParameterInvalidException(f"Invalid DOI format: {doi}")
            workflow.doi = data["doi"]
        try:
            if "help" in data:
                workflow.help = data["help"]
            if "readme" in data:
                workflow.readme = data["readme"]
        except ValueError as e:
            raise exceptions.RequestParameterInvalidException(str(e))

        if getattr(workflow_state_resolution_options, "archive_source", None):
            source_metadata = {}
            if workflow_state_resolution_options.archive_source in ("trs_tool", "trs_url"):
                source_metadata["trs_tool_id"] = workflow_state_resolution_options.trs_tool_id
                source_metadata["trs_version_id"] = workflow_state_resolution_options.trs_version_id
                source_metadata["trs_server"] = workflow_state_resolution_options.trs_server
                source_metadata["trs_url"] = workflow_state_resolution_options.trs_url
            elif not workflow_state_resolution_options.archive_source.startswith("file://"):  # URL import
                source_metadata["url"] = workflow_state_resolution_options.archive_source
            workflow_state_resolution_options.archive_source = None  # so trs_id is not set for subworkflows
            workflow.source_metadata = source_metadata

        # Assume no errors until we find a step that has some
        workflow.has_errors = False
        # Create each step
        steps: list[model.WorkflowStep] = []
        # The editor will provide ids for each step that we don't need to save,
        # but do need to use to make connections
        steps_by_external_id: dict[str, model.WorkflowStep] = {}

        # Preload dependent workflows with locally defined content_ids.
        subworkflow_id_map = None
        if subworkflows := data.get("subworkflows"):
            subworkflow_id_map = {}
            for key, subworkflow_dict in subworkflows.items():
                subworkflow = self.__build_embedded_subworkflow(
                    trans, subworkflow_dict, workflow_state_resolution_options, resolving_urls=resolving_urls
                )
                subworkflow_id_map[key] = subworkflow

        # Keep track of tools required by the workflow that are not available in
        # the local Galaxy instance.  Each tuple in the list of missing_tool_tups
        # will be (tool_id, tool_name, tool_version, step_id).
        missing_tool_tups = []
        for step_dict in self.__walk_step_dicts(data):
            if not dry_run:
                self.__load_subworkflows(
                    trans,
                    step_dict,
                    subworkflow_id_map,
                    workflow_state_resolution_options,
                    dry_run=dry_run,
                    resolving_urls=resolving_urls,
                )

        module_kwds = workflow_state_resolution_options.model_dump()
        module_kwds.update(kwds)  # TODO: maybe drop this?
        for step_dict in self.__walk_step_dicts(data):
            module, step = self.__module_from_dict(trans, steps, steps_by_external_id, step_dict, **module_kwds)
            if isinstance(module, ToolModule) and module.tool is None:
                missing_tool_tup = (module.tool_id, module.get_name(), module.tool_version, step_dict["id"])
                if missing_tool_tup not in missing_tool_tups:
                    missing_tool_tups.append(missing_tool_tup)
            if module.get_errors():
                workflow.has_errors = True

        # Second pass to deal with connections between steps
        self.__connect_workflow_steps(steps, steps_by_external_id, dry_run)

        workflow.has_cycles = True
        workflow.steps = steps
        # Safeguard: workflow was implicitly merged into this Session prior to SQLAlchemy 2.0.
        # when AT LEAST ONE step in steps belonged to a session.
        for step in steps:
            if ensure_object_added_to_session(workflow, object_in_session=step):
                break

        comments: list[model.WorkflowComment] = []
        comments_by_external_id: dict[str, model.WorkflowComment] = {}
        for comment_dict in data.get("comments", []):
            comment = model.WorkflowComment.from_dict(comment_dict)
            comments.append(comment)
            external_id = comment_dict.get("id")
            if external_id:
                comments_by_external_id[external_id] = comment

        workflow.comments = comments

        # populate parent_comment
        for comment, comment_dict in zip(comments, data.get("comments", [])):
            for step_external_id in comment_dict.get("child_steps", []):
                child_step = steps_by_external_id.get(step_external_id)
                if child_step:
                    child_step.parent_comment = comment

            for comment_external_id in comment_dict.get("child_comments", []):
                child_comment = comments_by_external_id.get(comment_external_id)
                if child_comment:
                    child_comment.parent_comment = comment

        # we can't reorder subworkflows, as step connections would become invalid
        if not is_subworkflow:
            # Order the steps if possible
            attach_ordered_steps(workflow)

        return workflow, missing_tool_tups

    def workflow_to_dict(
        self,
        trans,
        stored,
        style="export",
        version=None,
        history=None,
        instance_id=None,
        preserve_external_subworkflow_links=False,
    ):
        """Export the workflow contents to a dictionary ready for JSON-ification and to be
        sent out via API for instance. There are three styles of export allowed 'export', 'instance', and
        'editor'. The Galaxy team will do its best to preserve the backward compatibility of the
        'export' style - this is the export method meant to be portable across Galaxy instances and over
        time. The 'editor' style is subject to rapid and unannounced changes. The 'instance' export
        option describes the workflow in a context more tied to the current Galaxy instance and includes
        fields like 'url' and 'url' and actual unencoded step ids instead of 'order_index'.

        If preserve_external_subworkflow_links is True, subworkflow steps whose subworkflow has
        source_metadata (indicating it was fetched from a URL or TRS) will preserve the external
        reference instead of embedding the full subworkflow content.
        """

        def to_format_2(wf_dict, **kwds):
            return from_galaxy_native(wf_dict, None, **kwds)

        if version == "":
            version = None
        if version is not None:
            version = int(version)
        elif instance_id:
            # If the instance_id is provided, we need to extract the workflow instance via the version.
            for i, workflow in enumerate(reversed(stored.workflows)):
                if workflow.id == instance_id:
                    version = i
                    break
        workflow = stored.get_internal_version(version)
        if style == "export":
            style = self.app.config.default_workflow_export_format
        if style == "editor":
            wf_dict = self._workflow_to_dict_editor(trans, stored, workflow)
        elif style == "legacy":
            wf_dict = self._workflow_to_dict_instance(trans, stored, workflow=workflow, legacy=True)
        elif style == "instance":
            wf_dict = self._workflow_to_dict_instance(trans, stored, workflow=workflow, legacy=False)
        elif style == "run":
            wf_dict = self._workflow_to_dict_run(trans, stored, workflow=workflow, history=history or trans.history)
        elif style == "preview":
            wf_dict = self._workflow_to_dict_preview(trans, workflow=workflow)
        elif style == "format2":
            wf_dict = self._workflow_to_dict_export(
                trans,
                stored,
                workflow=workflow,
                preserve_external_subworkflow_links=preserve_external_subworkflow_links,
            )
            wf_dict = to_format_2(wf_dict)
        elif style == "format2_wrapped_yaml":
            wf_dict = self._workflow_to_dict_export(
                trans,
                stored,
                workflow=workflow,
                preserve_external_subworkflow_links=preserve_external_subworkflow_links,
            )
            wf_dict = to_format_2(wf_dict, json_wrapper=True)
        elif style == "ga":
            wf_dict = self._workflow_to_dict_export(
                trans,
                stored,
                workflow=workflow,
                preserve_external_subworkflow_links=preserve_external_subworkflow_links,
            )
        else:
            raise exceptions.RequestParameterInvalidException(f"Unknown workflow style {style}")
        if version is not None:
            wf_dict["version"] = version
            # If returning a run-form workflow for a specific version, use that version's name
            if style == "run":
                wf_dict["name"] = workflow.name
        else:
            wf_dict["version"] = len(stored.workflows) - 1
        return wf_dict

    def _sync_stored_workflow(self, trans, stored_workflow):
        if trans.user_is_admin:
            workflow_path = stored_workflow.from_path
            self.store_workflow_to_path(workflow_path, stored_workflow, stored_workflow.latest_workflow, trans=trans)

    def store_workflow_artifacts(self, directory, filename_base, workflow, **kwd):
        modern_workflow_path = os.path.join(directory, f"{filename_base}.gxwf.yml")
        legacy_workflow_path = os.path.join(directory, f"{filename_base}.ga")
        abstract_cwl_workflow_path = os.path.join(directory, f"{filename_base}.abstract.cwl")
        for path in [legacy_workflow_path, modern_workflow_path, abstract_cwl_workflow_path]:
            self.app.workflow_contents_manager.store_workflow_to_path(path, workflow.stored_workflow, workflow, **kwd)
        try:
            cytoscape_path = os.path.join(directory, f"{filename_base}.html")
            to_cytoscape(modern_workflow_path, cytoscape_path)
        except Exception:
            # completely optional and currently broken so ignore...
            pass

    def store_workflow_to_path(self, workflow_path, stored_workflow, workflow, **kwd):
        trans = kwd.get("trans")
        if trans is None:
            trans = WorkRequestContext(app=self.app, user=kwd.get("user"), history=kwd.get("history"))

        workflow = stored_workflow.latest_workflow
        with open(workflow_path, "w") as f:
            if workflow_path.endswith(".ga"):
                wf_dict = self._workflow_to_dict_export(trans, stored_workflow, workflow=workflow)
                json.dump(wf_dict, f, indent=4)
            elif workflow_path.endswith(".abstract.cwl"):
                wf_dict = self._workflow_to_dict_export(trans, stored_workflow, workflow=workflow)
                abstract_dict = from_dict(wf_dict)
                ordered_dump(abstract_dict, f)
            else:
                wf_dict = self._workflow_to_dict_export(trans, stored_workflow, workflow=workflow)
                wf_dict = from_galaxy_native(wf_dict, None, json_wrapper=True)
                f.write(wf_dict["yaml_content"])

    def _workflow_to_dict_run(self, trans: ProvidesUserContext, stored, workflow, history=None):
        """
        Builds workflow dictionary used by run workflow form
        """
        if len(workflow.steps) == 0:
            raise exceptions.MessageException("Workflow cannot be run because it does not have any steps.")
        if has_cycles(workflow):
            raise exceptions.MessageException("Workflow cannot be run because it contains cycles.")
        trans.workflow_building_mode = workflow_building_modes.USE_HISTORY
        module_injector = WorkflowModuleInjector(trans)
        has_upgrade_messages = False
        step_version_changes = []
        missing_tools = []
        errors = {}
        module_injector.inject_all(workflow, exact_tools=False, ignore_tool_missing_exception=True)
        for step in workflow.steps:
            try:
                module_injector.compute_runtime_state(step)
            except exceptions.ToolMissingException as e:
                # FIXME: if a subworkflow lacks multiple tools we report only the first missing tool
                if e.tool_id not in missing_tools:
                    missing_tools.append(e.tool_id)
                continue
            if step.upgrade_messages:
                has_upgrade_messages = True
            if step.type in ("tool", "subworkflow", None):
                if step.module.version_changes:
                    step_version_changes.extend(step.module.version_changes)
                step_errors = step.module.get_errors()
                if step_errors:
                    errors[step.id] = step_errors
        if missing_tools:
            workflow.annotation = self.get_item_annotation_str(trans.sa_session, trans.user, workflow)
            raise exceptions.MessageException(f"Following tools missing: {', '.join(missing_tools)}")
        workflow.annotation = self.get_item_annotation_str(trans.sa_session, trans.user, workflow)
        step_order_indices = {}
        for step in workflow.steps:
            step_order_indices[step.id] = step.order_index
        step_models = []
        for step in workflow.steps:
            step_model = None
            if step.type == "tool":
                incoming: dict[str, Any] = {}
                tool = trans.app.toolbox.get_tool(
                    step.tool_id, tool_version=step.tool_version, tool_uuid=step.tool_uuid, user=trans.user
                )
                if not tool:
                    raise exceptions.MessageException(
                        f"Following tool missing or inaccessible: '{step.tool_id}/{step.tool_uuid}'"
                    )
                params_to_incoming(incoming, tool.inputs, step.state.inputs, trans.app)
                step_model = tool.to_json(
                    trans, incoming, workflow_building_mode=workflow_building_modes.USE_HISTORY, history=history
                )
                step_model["post_job_actions"] = [
                    {
                        "short_str": ActionBox.get_short_str(pja),
                        "action_type": pja.action_type,
                        "output_name": pja.output_name,
                        "action_arguments": pja.action_arguments,
                    }
                    for pja in step.post_job_actions
                ]
            else:
                inputs = step.module.get_runtime_inputs(step, connections=step.output_connections)
                step_model = {"inputs": [input.to_dict(trans) for input in inputs.values()]}
            step_model["when"] = step.when_expression
            step_model["replacement_parameters"] = step.module.get_informal_replacement_parameters(step)
            step_model["step_type"] = step.type
            step_model["step_label"] = step.label
            step_model["step_name"] = step.module.get_name()
            step_model["step_version"] = step.module.get_version()
            step_model["step_index"] = step.order_index
            step_model["output_connections"] = [
                {
                    "input_step_index": step_order_indices.get(oc.input_step_id),
                    "output_step_index": step_order_indices.get(oc.output_step_id),
                    "input_name": oc.input_name,
                    "output_name": oc.output_name,
                }
                for oc in step.output_connections
            ]
            if step.annotations:
                step_model["annotation"] = step.annotations[0].annotation
            if step.upgrade_messages:
                step_model["messages"] = step.upgrade_messages
            step_models.append(step_model)
        return {
            "id": trans.app.security.encode_id(stored.id),
            "workflow_id": trans.app.security.encode_id(workflow.id),
            "help": workflow.help,
            "history_id": trans.app.security.encode_id(history.id) if history else None,
            "name": stored.name,
            "owner": stored.user.username,
            "steps": step_models,
            "step_version_changes": step_version_changes,
            "has_upgrade_messages": has_upgrade_messages,
            "workflow_resource_parameters": self._workflow_resource_parameters(trans, stored, workflow),
        }

    def _workflow_to_dict_preview(self, trans, workflow):
        """
        Builds workflow dictionary containing input labels and values.
        Used to create embedded workflow previews.
        """
        if len(workflow.steps) == 0:
            raise exceptions.MessageException("Workflow cannot be run because it does not have any steps.")
        if has_cycles(workflow):
            raise exceptions.MessageException("Workflow cannot be run because it contains cycles.")

        def row_for_param(input_dict, param, raw_value, other_values, prefix, step):
            input_dict["label"] = param.get_label()
            value = None
            if isinstance(param, DataToolParameter) or isinstance(param, DataCollectionToolParameter):
                if (prefix + param.name) in step.input_connections_by_name:
                    conns = step.input_connections_by_name[prefix + param.name]
                    if not isinstance(conns, list):
                        conns = [conns]
                    value_list = [
                        f"Output '{conn.output_name}' from Step {int(conn.output_step.order_index) + 1}."
                        for conn in conns
                    ]
                    value = ",".join(value_list)
                else:
                    value = "Select at Runtime."
            else:
                value = param.value_to_display_text(raw_value) or "Unavailable."
            input_dict["value"] = value
            if hasattr(step, "upgrade_messages") and step.upgrade_messages and param.name in step.upgrade_messages:
                input_dict["upgrade_messages"] = step.upgrade_messages[param.name]

        def do_inputs(inputs, values, prefix, step, other_values=None):
            input_dicts = []
            for input in inputs.values():
                input_dict = {}
                input_dict["type"] = input.type
                if input.type == "repeat":
                    repeat_values = values[input.name]
                    if len(repeat_values) > 0:
                        input_dict["title"] = input.title_plural
                        nested_input_dicts = []
                        for i in range(len(repeat_values)):
                            nested_input_dict = {}
                            index = repeat_values[i]["__index__"]
                            nested_input_dict["title"] = f"{i + 1}. {input.title}"
                            try:
                                nested_input_dict["inputs"] = do_inputs(
                                    input.inputs,
                                    repeat_values[i],
                                    f"{prefix + input.name}_{str(index)}|",
                                    step,
                                    other_values,
                                )
                            except KeyError:
                                continue
                            nested_input_dicts.append(nested_input_dict)
                        input_dict["inputs"] = nested_input_dicts
                elif input.type == "conditional":
                    group_values = values[input.name]
                    current_case = group_values["__current_case__"]
                    new_prefix = f"{prefix + input.name}|"
                    row_for_param(
                        input_dict, input.test_param, group_values[input.test_param.name], other_values, prefix, step
                    )
                    try:
                        input_dict["inputs"] = do_inputs(
                            input.cases[current_case].inputs, group_values, new_prefix, step, other_values
                        )
                    except KeyError:
                        continue
                elif input.type == "section":
                    new_prefix = f"{prefix + input.name}|"
                    group_values = values[input.name]
                    input_dict["title"] = input.title
                    try:
                        input_dict["inputs"] = do_inputs(input.inputs, group_values, new_prefix, step, other_values)
                    except KeyError:
                        continue
                else:
                    row_for_param(
                        input_dict,
                        input,
                        # Use values.get so that unspecified param values don't blow up the display
                        values.get(input.name),
                        other_values,
                        prefix,
                        step,
                    )
                input_dicts.append(input_dict)
            return input_dicts

        module_injector = WorkflowModuleInjector(trans)
        module_injector.inject_all(workflow, ignore_tool_missing_exception=True, exact_tools=False)
        step_dicts = []
        for step in workflow.steps:
            step_dict = {}
            step_dict["order_index"] = step.order_index
            step_dict["type"] = step.type
            if step.annotations:
                step_dict["annotation"] = step.annotations[0].annotation
            try:
                module_injector.compute_runtime_state(step)
            except exceptions.ToolMissingException as e:
                step_dict["label"] = f"Unknown Tool with id '{e.tool_id}'"
                step_dicts.append(step_dict)
                continue
            if step.type == "tool":
                tool = trans.app.toolbox.get_tool(step.tool_id, step.tool_version)
                step_dict["tool_id"] = step.tool_id
                step_dict["tool_version"] = step.tool_version
                step_dict["label"] = step.label or tool.name
                step_dict["inputs"] = do_inputs(tool.inputs, step.state.inputs, "", step)
            elif step.type == "subworkflow":
                step_dict["label"] = step.label or (step.subworkflow.name if step.subworkflow else "Missing workflow.")
                errors = step.module.get_errors()
                if errors:
                    step_dict["errors"] = errors
                subworkflow_dict = self._workflow_to_dict_preview(trans, step.subworkflow)
                step_dict["inputs"] = subworkflow_dict["steps"]
            else:
                module = step.module
                step_dict["label"] = module.name
                step_dict["inputs"] = do_inputs(module.get_runtime_inputs(step), step.state.inputs, "", step)
            step_dicts.append(step_dict)
        return {
            "name": workflow.name,
            "steps": step_dicts,
        }

    def _workflow_resource_parameters(self, trans, stored, workflow):
        """Get workflow scheduling resource parameters for this user and workflow or None if not configured."""
        return self._resource_mapper_function(trans=trans, stored_workflow=stored, workflow=workflow)

    def _workflow_to_dict_editor(
        self,
        trans,
        stored: Optional[StoredWorkflow],
        workflow: Workflow,
        tooltip: bool = True,
        is_subworkflow: bool = False,
    ):
        # Pack workflow data into a dictionary and return
        data: dict[str, Any] = {}
        data["name"] = workflow.name
        data["steps"] = {}
        data["upgrade_messages"] = {}
        data["report"] = workflow.reports_config or {}
        data["license"] = workflow.license
        data["creator"] = workflow.creator_metadata
        data["readme"] = workflow.readme
        data["help"] = workflow.help
        data["logo_url"] = workflow.logo_url
        data["doi"] = workflow.doi
        data["source_metadata"] = workflow.source_metadata
        data["annotation"] = self.get_item_annotation_str(trans.sa_session, trans.user, stored) or ""
        data["comments"] = [comment.to_dict() for comment in workflow.comments]
        if stored:
            # subworkflow may not have StoredWorkflow
            data["tags"] = stored.make_tag_string_list()

        output_label_index = set()
        input_step_types = set(workflow.input_step_types)
        # For each step, rebuild the form and encode the state
        for step in workflow.steps:
            # Load from database representation
            module = module_factory.from_workflow_step(trans, step, exact_tools=False)
            if not module:
                raise exceptions.MessageException(f"Unrecognized step type: {step.type}")
            # Load label from state of data input modules, necessary for backward compatibility
            self.__set_default_label(step, module, step.tool_inputs)
            # Fix any missing parameters
            upgrade_message_dict = module.check_and_update_state() or {}
            version_changes = getattr(module, "version_changes", None)
            if version_changes:
                upgrade_message_dict[module.get_name()] = "\n".join(version_changes)
            # Get user annotation.
            config_form = module.get_config_form(step=step)
            annotation_str = self.get_item_annotation_str(trans.sa_session, trans.user, step) or ""
            # Pack attributes into plain dictionary
            step_dict = {
                "id": step.order_index,
                "type": module.type,
                "label": module.label,
                "content_id": module.get_content_id(),
                "name": module.get_name(),
                "tool_state": module.get_tool_state(),
                "errors": module.get_errors(),
                "inputs": module.get_all_inputs(connectable_only=True),
                "outputs": module.get_all_outputs(),
                "config_form": config_form,
                "annotation": annotation_str,
                "post_job_actions": module.get_post_job_actions({}),
                "uuid": str(step.uuid) if step.uuid else None,
                "when": step.when_expression,
                "workflow_outputs": [],
            }
            if tooltip:
                step_dict["tooltip"] = module.get_tooltip(static_path="/static")
            # Connections
            input_connections = step.input_connections
            input_connections_type = {}
            multiple_input = {}  # Boolean value indicating if this can be multiple
            if isinstance(module, ToolModule) and module.tool:
                # Serialize tool version
                step_dict["tool_version"] = module.tool.version
                step_dict["tool_uuid"] = str(step.tool_uuid) if step.tool_uuid else None
                # Determine full (prefixed) names of valid input datasets
                data_input_names = {}

                def callback(input, prefixed_name, **kwargs):
                    if isinstance(input, DataToolParameter) or isinstance(input, DataCollectionToolParameter):
                        data_input_names[prefixed_name] = True  # noqa: B023
                        multiple_input[prefixed_name] = input.multiple  # noqa: B023
                        if isinstance(input, DataToolParameter):
                            input_connections_type[input.name] = "dataset"  # noqa: B023
                        if isinstance(input, DataCollectionToolParameter):
                            input_connections_type[input.name] = "dataset_collection"  # noqa: B023

                visit_input_values(module.tool.inputs, module.state.inputs, callback)
                # post_job_actions
                pja_dict = {}
                for pja in step.post_job_actions:
                    pja_dict[pja.action_type + pja.output_name] = dict(
                        action_type=pja.action_type, output_name=pja.output_name, action_arguments=pja.action_arguments
                    )
                step_dict["post_job_actions"] = pja_dict

            # workflow outputs
            outputs = []
            output_label_duplicate = set()
            for output in step.unique_workflow_outputs:
                if output.workflow_step.type not in input_step_types:
                    output_label = output.label
                    output_name = output.output_name
                    output_uuid = str(output.uuid) if output.uuid else None
                    outputs.append({"output_name": output_name, "uuid": output_uuid, "label": output_label})
                    if output_label is not None:
                        if output_label in output_label_index:
                            if output_label not in output_label_duplicate:
                                output_label_duplicate.add(output_label)
                        else:
                            output_label_index.add(output_label)
            step_dict["workflow_outputs"] = outputs
            if len(output_label_duplicate) > 0:
                output_label_duplicate_string = ", ".join(output_label_duplicate)
                upgrade_message_dict["output_label_duplicate"] = (
                    f"Ignoring duplicate labels: {output_label_duplicate_string}."
                )
            if upgrade_message_dict:
                data["upgrade_messages"][step.order_index] = upgrade_message_dict

            # Encode input connections as dictionary
            input_conn_dict: model.InputConnDictType = {}
            for conn in input_connections:
                input_type = "dataset"
                if conn.input_name in input_connections_type:
                    input_type = input_connections_type[conn.input_name]
                conn_dict = dict(id=conn.output_step.order_index, output_name=conn.output_name, input_type=input_type)
                if conn.input_name in multiple_input:
                    if conn.input_name in input_conn_dict:
                        cast(list, input_conn_dict[conn.input_name]).append(conn_dict)
                    else:
                        input_conn_dict[conn.input_name] = [conn_dict]
                else:
                    input_conn_dict[conn.input_name] = conn_dict
            step_dict["input_connections"] = input_conn_dict

            # Position
            step_dict["position"] = step.position
            # Add to return value
            data["steps"][step.order_index] = step_dict
        if is_subworkflow:
            data["steps"] = self._resolve_collection_type(data["steps"])
        return data

    @staticmethod
    def get_step_map_over(current_step, steps):
        """
        Given a tool step and its input steps guess that maximum level of mapping over.
        All data outputs of a step need to be mapped over to this level.
        """
        max_map_over = ""
        for input_name, input_connections in current_step["input_connections"].items():
            if isinstance(input_connections, dict):
                # if input does not accept multiple inputs
                input_connections = [input_connections]
            for input_value in input_connections:
                current_data_input = None
                for current_input in current_step["inputs"]:
                    if current_input["name"] == input_name:
                        current_data_input = current_input
                        # we've got one of the tools' input data definitions
                        break
                if current_data_input is None:
                    log.info(f"failed to find input {input_name} for get_step_map_over")
                    continue
                input_step = steps[input_value["id"]]
                for input_step_data_output in input_step["outputs"]:
                    if input_step_data_output["name"] == input_value["output_name"]:
                        collection_type = input_step_data_output.get("collection_type")
                        # This is the defined incoming collection type, in reality there may be additional
                        # mapping over of the workflows' data input, but this should be taken care of by the workflow editor /
                        # outer workflow.
                        if collection_type:
                            if current_data_input.get("input_type") == "dataset" and current_data_input.get("multiple"):
                                # We reduce the innermost collection
                                if ":" in collection_type:
                                    # more than one layer of nesting and multiple="true" input,
                                    # we consume the innermost collection
                                    collection_type = ":".join(collection_type.rsplit(":")[:-1])
                                else:
                                    # We've reduced a list or a pair
                                    collection_type = None
                            elif current_data_input.get("input_type") == "dataset_collection":
                                current_collection_types = current_data_input["collection_types"]
                                if not current_collection_types:
                                    # Accepts any input dataset collection, no mapping
                                    collection_type = None
                                elif collection_type in current_collection_types:
                                    # incoming collection type is an exact match, no mapping over
                                    collection_type = None
                                else:
                                    outer_map_over = collection_type
                                    for accepted_collection_type in current_data_input["collection_types"]:
                                        # need to find the lowest level of mapping over,
                                        # for collection_type = 'list:list:list' and accepted_collection_type = ['list:list', 'list']
                                        # it'd be outer_map_over == 'list'
                                        if collection_type.endswith(accepted_collection_type):
                                            _outer_map_over = collection_type[: -(len(accepted_collection_type) + 1)]
                                            if len(_outer_map_over.split(":")) < len(outer_map_over.split(":")):
                                                outer_map_over = _outer_map_over
                                    collection_type = outer_map_over
                            # If there is mapping over, we're going to assume it is linked, everything else is (probably)
                            # too hard to display in the workflow editor. With this assumption we should be able to
                            # set the maximum mapping over level to the most deeply nested map_over
                            if collection_type and len(collection_type.split(":")) >= len(max_map_over.split(":")):
                                max_map_over = collection_type
        if max_map_over:
            return max_map_over
        return None

    def _resolve_collection_type(self, steps):
        """
        Fill in collection type for step outputs.
        This can either be via collection_type_source and / or "inherited" from the step's input.

        This information is only needed in the workflow editor.
        """
        for order_index in sorted(steps):
            step = steps[order_index]
            if step["type"] == "tool" and not step.get("errors"):
                map_over = self.get_step_map_over(step, steps)
                for step_data_output in step["outputs"]:
                    if step_data_output.get("collection_type_source") and step_data_output["collection_type"] is None:
                        collection_type_source = step_data_output["collection_type_source"]
                        for input_connection in step["input_connections"].get(collection_type_source, []):
                            input_step = steps[input_connection["id"]]
                            for input_step_data_output in input_step["outputs"]:
                                if input_step_data_output["name"] == input_connection["output_name"]:
                                    step_data_output["collection_type"] = input_step_data_output.get("collection_type")
                    if map_over:
                        collection_type = map_over
                        step_data_output["collection"] = True
                        if step_data_output.get("collection_type"):
                            collection_type = f"{map_over}:{step_data_output['collection_type']}"
                        step_data_output["collection_type"] = collection_type
        return steps

    def _workflow_to_dict_export(
        self,
        trans,
        stored=None,
        workflow=None,
        internal=False,
        allow_upgrade=False,
        preserve_external_subworkflow_links=False,
    ):
        """Export the workflow contents to a dictionary ready for JSON-ification and export.

        If internal, use content_ids instead subworkflow definitions.
        If `allow_upgrade`, the workflow and sub-workflows might use updated tool versions when refactoring.
        If `preserve_external_subworkflow_links`, subworkflow steps with source_metadata
        will emit content_source/content_id instead of embedding the full subworkflow.
        """
        annotation_str = ""
        tags_list = []
        annotation_owner = None
        if stored is not None:
            if stored.id:
                # if the active user doesn't have an annotation on the workflow, default to the owner's annotation.
                annotation_owner = stored.user
                annotation_str = (
                    self.get_item_annotation_str(trans.sa_session, trans.user, stored)
                    or self.get_item_annotation_str(trans.sa_session, annotation_owner, stored)
                    or ""
                )
                tags_list = stored.make_tag_string_list()
            else:
                # dry run with flushed workflow objects, just use the annotation
                annotations = stored.annotations
                if annotations and len(annotations) > 0:
                    annotation_str = util.unicodify(annotations[0].annotation)

        # Pack workflow data into a dictionary and return
        data: dict[str, Any] = {}
        data["a_galaxy_workflow"] = "true"  # Placeholder for identifying galaxy workflow
        data["format-version"] = "0.1"
        data["name"] = workflow.name
        data["annotation"] = annotation_str
        data["tags"] = tags_list
        if workflow.uuid is not None:
            data["uuid"] = str(workflow.uuid)
        steps: dict[int, dict[str, Any]] = {}
        data["steps"] = steps
        data["comments"] = [comment.to_dict() for comment in workflow.comments]
        if workflow.reports_config:
            data["report"] = workflow.reports_config
        if workflow.creator_metadata:
            data["creator"] = workflow.creator_metadata
        if workflow.license:
            data["license"] = workflow.license
        if workflow.source_metadata:
            data["source_metadata"] = workflow.source_metadata
        if workflow.readme is not None:
            data["readme"] = workflow.readme
        if workflow.help is not None:
            data["help"] = workflow.help
        if workflow.logo_url is not None:
            data["logo_url"] = workflow.logo_url
        if workflow.doi is not None:
            data["doi"] = workflow.doi

        # For each step, rebuild the form and encode the state
        for step in workflow.steps:
            # Load from database representation
            module = module_factory.from_workflow_step(trans, step)
            if not module:
                raise exceptions.MessageException(f"Unrecognized step type: {step.type}")
            # Get user annotation if it exists, otherwise get owner annotation.
            annotation_str = self.get_item_annotation_str(trans.sa_session, trans.user, step) or ""
            if not annotation_str and annotation_owner:
                annotation_str = self.get_item_annotation_str(trans.sa_session, annotation_owner, step) or ""
            content_id = module.get_content_id() if allow_upgrade else step.content_id
            try:
                tool_state = module.get_export_state()
            except ValueError:
                # Fix state if necessary
                module.check_and_update_state()
                tool_state = module.get_export_state()

            # Step info
            step_dict = {
                "id": step.order_index,
                "type": module.type,
                "content_id": content_id,
                "tool_id": None,
                "tool_version": module.get_version() if allow_upgrade else step.tool_version,
                "name": module.get_name(),
                "tool_state": json.dumps(tool_state),
                "errors": module.get_errors(),
                "uuid": str(step.uuid),
                "label": step.label or None,
                "annotation": annotation_str,
                "when": step.when_expression,
            }
            if step.type == "tool":
                step_dict["tool_id"] = content_id if allow_upgrade else step.tool_id
                step_dict["tool_uuid"] = str(step.tool_uuid) if step.tool_uuid else None
            # Add tool shed repository information and post-job actions to step dict.
            if isinstance(module, ToolModule):
                if module.tool and module.tool.tool_shed:
                    step_dict["tool_shed_repository"] = {
                        "name": module.tool.repository_name,
                        "owner": module.tool.repository_owner,
                        "changeset_revision": module.tool.changeset_revision,
                        "tool_shed": module.tool.tool_shed,
                    }

                tool_representation = None
                dynamic_tool = step.dynamic_tool
                if dynamic_tool:
                    tool_representation = dynamic_tool.value
                    step_dict["tool_representation"] = tool_representation
                    if util.is_uuid(step_dict["content_id"]):
                        step_dict["content_id"] = None
                        step_dict["tool_id"] = None
                        step_dict["tool_uuid"] = None

                pja_dict = {}
                for pja in step.post_job_actions:
                    pja_dict[pja.action_type + pja.output_name] = dict(
                        action_type=pja.action_type, output_name=pja.output_name, action_arguments=pja.action_arguments
                    )
                step_dict["post_job_actions"] = pja_dict

            if module.type == "subworkflow" and not internal:
                del step_dict["errors"]
                del step_dict["tool_version"]
                del step_dict["tool_state"]
                subworkflow = step.subworkflow
                if preserve_external_subworkflow_links and subworkflow.source_metadata:
                    del step_dict["content_id"]
                    source_metadata = subworkflow.source_metadata
                    if "url" in source_metadata:
                        step_dict["content_source"] = "url"
                        step_dict["content_id"] = source_metadata["url"]
                    elif "trs_url" in source_metadata:
                        step_dict["content_source"] = "trs_url"
                        step_dict["content_id"] = source_metadata["trs_url"]
                else:
                    del step_dict["content_id"]
                    subworkflow_as_dict = self._workflow_to_dict_export(
                        trans,
                        stored=None,
                        workflow=subworkflow,
                        preserve_external_subworkflow_links=preserve_external_subworkflow_links,
                    )
                    step_dict["subworkflow"] = subworkflow_as_dict

            # Data inputs, legacy section not used anywhere within core
            input_dicts = []
            step_state = module.state.inputs or {}
            if module.type != "tool":
                name = step_state.get("name") or module.label
                if name:
                    input_dicts.append({"name": name, "description": annotation_str})
            for name, val in step_state.items():
                if isinstance(val, RuntimeValue) and not isinstance(val, ConnectedValue):
                    input_dicts.append({"name": name, "description": f"runtime parameter for tool {module.get_name()}"})
                elif isinstance(val, dict):
                    # Input type is described by a dict, e.g. indexed parameters.
                    for partval in val.values():
                        if isinstance(partval, RuntimeValue) and not isinstance(val, ConnectedValue):
                            input_dicts.append(
                                {"name": name, "description": f"runtime parameter for tool {module.get_name()}"}
                            )
            step_dict["inputs"] = input_dicts

            # User outputs
            workflow_outputs_dicts = []
            for workflow_output in step.unique_workflow_outputs:
                workflow_output_dict = dict(
                    output_name=workflow_output.output_name,
                    label=workflow_output.label,
                    uuid=str(workflow_output.uuid) if workflow_output.uuid is not None else None,
                )
                workflow_outputs_dicts.append(workflow_output_dict)
            step_dict["workflow_outputs"] = workflow_outputs_dicts

            # All step outputs
            step_dict["outputs"] = []
            if type(module) is ToolModule:
                for output in module.get_data_outputs():
                    step_dict["outputs"].append({"name": output["name"], "type": output["extensions"][0]})

            step_in = {}
            for step_input in step.inputs:
                if step_input.default_value_set:
                    step_in[step_input.name] = {"default": step_input.default_value}

            if step_in:
                step_dict["in"] = step_in

            # Connections
            input_connections = step.input_connections
            if isinstance(module, ToolModule):
                # Determine full (prefixed) names of valid input datasets
                data_input_names = {}

                def callback(input, prefixed_name, **kwargs):
                    if isinstance(input, DataToolParameter) or isinstance(input, DataCollectionToolParameter):
                        data_input_names[prefixed_name] = True  # noqa: B023

                # FIXME: this updates modules silently right now; messages from updates should be provided.
                module.check_and_update_state()
                if module.tool:
                    # If the tool is installed we attempt to verify input values
                    # and connections, otherwise the last known state will be dumped without modifications.
                    visit_input_values(module.tool.inputs, module.state.inputs, callback)

            # Encode input connections as dictionary
            input_conn_dict: dict[str, list[dict[str, Any]]] = {}
            unique_input_names = {conn.input_name for conn in input_connections}
            for input_name in unique_input_names:
                input_conn_dicts = []
                for conn in input_connections:
                    if conn.input_name != input_name:
                        continue
                    input_conn = dict(id=conn.output_step.order_index, output_name=conn.output_name)
                    if conn.input_subworkflow_step is not None:
                        subworkflow_step_id = conn.input_subworkflow_step.order_index
                        input_conn["input_subworkflow_step_id"] = subworkflow_step_id

                    input_conn_dicts.append(input_conn)
                input_conn_dict[input_name] = input_conn_dicts

            # Preserve backward compatibility. Previously Galaxy
            # assumed input connections would be dictionaries not
            # lists of dictionaries, so replace any singleton list
            # with just the dictionary so that workflows exported from
            # newer Galaxy instances can be used with older Galaxy
            # instances if they do no include multiple input
            # tools. This should be removed at some point. Mirrored
            # hack in _workflow_from_raw_description should never be removed so
            # existing workflow exports continue to function.
            back_compat_input_conn_dict: model.InputConnDictType = {}
            for input_name, input_conn_list in dict(input_conn_dict).items():
                if len(input_conn_list) == 1:
                    back_compat_input_conn_dict[input_name] = input_conn_list[0]
                else:
                    back_compat_input_conn_dict[input_name] = input_conn_list
            step_dict["input_connections"] = back_compat_input_conn_dict
            # Position
            step_dict["position"] = step.position
            # Add to return value
            steps[step.order_index] = step_dict
        return data

    def _workflow_to_dict_instance(self, trans, stored, workflow, legacy=True):
        encode = self.app.security.encode_id
        sa_session = self.app.model.context
        item = stored.to_dict(view="element")
        item["name"] = workflow.name
        item["url"] = trans.url_builder("workflow", id=encode(stored.id))
        item["owner"] = stored.user.username
        item["email_hash"] = md5_hash_str(stored.user.email)
        item["slug"] = stored.slug
        inputs = {}
        for step in workflow.input_steps:
            step_type = step.type
            step_label = step.label or step.tool_inputs and step.tool_inputs.get("name")
            if step_label:
                label = step_label
            elif step_type == "data_input":
                label = "Input Dataset"
            elif step_type == "data_collection_input":
                label = "Input Dataset Collection"
            elif step_type == "parameter_input":
                label = "Input Parameter"
            else:
                raise ValueError(f"Invalid step_type {step_type}")
            if legacy:
                index = step.id
            else:
                index = step.order_index
            step_uuid = str(step.uuid) if step.uuid else None
            inputs[index] = {"label": label, "value": "", "uuid": step_uuid}
        item["inputs"] = inputs
        item["annotation"] = self.get_item_annotation_str(sa_session, stored.user, stored)
        item["license"] = workflow.license
        item["creator"] = workflow.creator_metadata
        item["source_metadata"] = workflow.source_metadata
        item["readme"] = workflow.readme
        item["help"] = workflow.help
        item["logo_url"] = workflow.logo_url
        item["doi"] = workflow.doi

        steps = {}
        steps_to_order_index = {}
        for step in workflow.steps:
            steps_to_order_index[step.id] = step.order_index
        for step in workflow.steps:
            step_id = step.id if legacy else step.order_index
            step_type = step.type
            step_dict = {
                "id": step_id,
                "type": step_type,
                "tool_id": step.tool_id,
                "tool_uuid": str(step.tool_uuid) if step.tool_uuid else None,
                "tool_version": step.tool_version,
                "annotation": self.get_item_annotation_str(sa_session, stored.user, step),
                "tool_inputs": step.tool_inputs,
                "input_steps": {},
                "when": step.when_expression,
            }

            if step_type == "subworkflow":
                del step_dict["tool_id"]
                del step_dict["tool_version"]
                del step_dict["tool_inputs"]
                step_dict["workflow_id"] = step.subworkflow.id

            for conn in step.input_connections:
                step_id = step.id if legacy else step.order_index
                source_id = conn.output_step_id
                source_step = source_id if legacy else steps_to_order_index[source_id]
                step_dict["input_steps"][conn.input_name] = {
                    "source_step": source_step,
                    "step_output": conn.output_name,
                }

            steps[step_id] = step_dict

        item["steps"] = steps
        return item

    def __walk_step_dicts(self, data):
        """Walk over the supplied step dictionaries and return them in a way
        designed to preserve step order when possible.
        """
        supplied_steps = data["steps"]
        # Try to iterate through imported workflow in such a way as to
        # preserve step order.
        step_indices = list(supplied_steps.keys())
        try:
            step_indices = sorted(step_indices, key=int)
        except ValueError:
            # to defensive, were these ever or will they ever not be integers?
            pass

        discovered_labels = set()
        discovered_uuids = set()

        discovered_output_labels = set()
        discovered_output_uuids = set()

        # First pass to build step objects and populate basic values
        for step_index in step_indices:
            step_dict = supplied_steps[step_index]
            uuid = step_dict.get("uuid", None)
            if uuid and uuid != "None":
                if not util.is_valid_uuid_v4(uuid):
                    raise exceptions.ObjectAttributeInvalidException(f"Invalid step UUID4 '{uuid}' in request.")
                if uuid in discovered_uuids:
                    raise exceptions.DuplicatedIdentifierException(f"Duplicate step UUID '{uuid}' in request.")
                discovered_uuids.add(uuid)
            label = step_dict.get("label", None)
            if label:
                if label in discovered_labels:
                    raise exceptions.DuplicatedIdentifierException(f"Duplicated step label '{label}' in request.")
                discovered_labels.add(label)

            if "workflow_outputs" in step_dict:
                outputs = step_dict["workflow_outputs"]
                # outputs may be list of name (deprecated legacy behavior)
                # or dictionary of names to {uuid: <uuid>, label: <label>}
                if isinstance(outputs, dict):
                    for output_name in outputs:
                        output_dict = outputs[output_name]
                        output_label = output_dict.get("label", None)
                        if output_label:
                            if label in discovered_output_labels:
                                raise exceptions.DuplicatedIdentifierException(
                                    f"Duplicated workflow output label '{label}' in request."
                                )
                            discovered_output_labels.add(label)

                        output_uuid = step_dict.get("output_uuid", None)
                        if output_uuid:
                            if output_uuid in discovered_output_uuids:
                                raise exceptions.DuplicatedIdentifierException(
                                    f"Duplicate workflow output UUID '{output_uuid}' in request."
                                )
                            discovered_output_uuids.add(uuid)

            yield step_dict

    def __load_subworkflows(
        self,
        trans,
        step_dict,
        subworkflow_id_map,
        workflow_state_resolution_options,
        dry_run=False,
        resolving_urls: frozenset[str] = frozenset(),
    ):
        step_type = step_dict.get("type", None)
        if step_type == "subworkflow":
            subworkflow = self.__load_subworkflow_from_step_dict(
                trans,
                step_dict,
                subworkflow_id_map,
                workflow_state_resolution_options,
                dry_run=dry_run,
                resolving_urls=resolving_urls,
            )
            step_dict["subworkflow"] = subworkflow

    def __module_from_dict(
        self,
        trans,
        steps: list[model.WorkflowStep],
        steps_by_external_id: dict[str, model.WorkflowStep],
        step_dict,
        **kwds,
    ) -> tuple[WorkflowModule, model.WorkflowStep]:
        """Create a WorkflowStep model object and corresponding module
        representing type-specific functionality from the incoming dictionary.
        """
        dry_run = kwds.get("dry_run", False)
        step = model.WorkflowStep()
        step.position = step_dict.get("position", model.WorkflowStep.DEFAULT_POSITION)
        if step_dict.get("uuid", None) and step_dict["uuid"] != "None":
            step.uuid = step_dict["uuid"]
        if "label" in step_dict:
            step.label = step_dict["label"]

        module = module_factory.from_dict(trans, step_dict, detached=dry_run, **kwds)
        self.__set_default_label(step, module, step_dict.get("tool_state"))
        module.save_to_step(step, detached=dry_run)

        if annotation := step_dict.get("annotation"):
            annotation = sanitize_html(annotation)
            sa_session = None if dry_run else trans.sa_session
            self.add_item_annotation(sa_session, trans.get_user(), step, annotation)

        # Stick this in the step temporarily
        DictConnection: TypeAlias = dict[str, Union[int, str]]
        temp_input_connections: dict[str, Union[list[DictConnection], DictConnection]] = step_dict.get(
            "input_connections", {}
        )
        step.temp_input_connections = temp_input_connections  # type: ignore[assignment]

        # Create the model class for the step
        steps.append(step)
        external_id = step_dict["id"]
        steps_by_external_id[external_id] = step
        step.order_index = external_id
        if "workflow_outputs" in step_dict:
            workflow_outputs = step_dict["workflow_outputs"]
            found_output_names = set()
            for workflow_output in workflow_outputs:
                # Allow workflow outputs as list of output_names for backward compatibility.
                if not isinstance(workflow_output, dict):
                    workflow_output = {"output_name": workflow_output}
                output_name = workflow_output["output_name"]
                if output_name in found_output_names:
                    raise exceptions.ObjectAttributeInvalidException(
                        f"Duplicate workflow outputs with name [{output_name}] found."
                    )
                if not output_name:
                    raise exceptions.ObjectAttributeInvalidException("Workflow output with empty name encountered.")
                found_output_names.add(output_name)
                uuid = workflow_output.get("uuid", None)
                label = workflow_output.get("label", None)
                m = step.create_or_update_workflow_output(
                    output_name=output_name,
                    uuid=uuid,
                    label=label,
                )
                if not dry_run:
                    trans.sa_session.add(m)

        if "in" in step_dict:
            for input_name, input_dict in step_dict["in"].items():
                # This is just a bug in gxformat? I think the input
                # defaults should be called input to match the input modules's
                # input parameter name.
                if input_name == "default":
                    input_name = "input"
                step_input = step.get_or_add_input(input_name)
                NO_DEFAULT_DEFINED = object()
                default = input_dict.get("default", NO_DEFAULT_DEFINED)
                if default is not NO_DEFAULT_DEFINED:
                    step_input.default_value = default
                    step_input.default_value_set = True

        if "when" in step_dict:
            step.when_expression = step_dict["when"]
        if dry_run and step in trans.sa_session:
            trans.sa_session.expunge(step)

        return module, step

    def __load_subworkflow_from_step_dict(
        self,
        trans,
        step_dict,
        subworkflow_id_map,
        workflow_state_resolution_options,
        dry_run=False,
        resolving_urls: frozenset[str] = frozenset(),
    ):
        embedded_subworkflow = step_dict.get("subworkflow", None)
        subworkflow_id = step_dict.get("content_id", None)
        content_source = step_dict.get("content_source", None)
        has_trs_id = step_dict.get("trs_tool_id") and step_dict.get("trs_version_id")

        if embedded_subworkflow and (subworkflow_id or content_source or has_trs_id):
            raise exceptions.RequestParameterInvalidException(
                "Subworkflow step defines both an embedded subworkflow and an external reference, only one may be specified."
            )

        if not embedded_subworkflow and not subworkflow_id and not has_trs_id:
            raise exceptions.RequestParameterInvalidException(
                "Subworkflow step must define either subworkflow, content_id, or trs_tool_id + trs_version_id."
            )

        if embedded_subworkflow:
            assert not dry_run
            subworkflow = self.__build_embedded_subworkflow(
                trans, embedded_subworkflow, workflow_state_resolution_options, resolving_urls=resolving_urls
            )
        elif content_source == "url":
            assert not dry_run
            assert subworkflow_id, "content_id is required when content_source is 'url'"
            subworkflow = self.__build_subworkflow_from_url(trans, subworkflow_id, resolving_urls)
        elif content_source == "trs_url":
            assert not dry_run
            assert subworkflow_id, "content_id is required when content_source is 'trs_url'"
            subworkflow = self.__build_subworkflow_from_trs_url(trans, subworkflow_id, resolving_urls)
        elif content_source == "trs_id" or (not content_source and has_trs_id):
            assert not dry_run
            subworkflow = self.__build_subworkflow_from_trs_id(trans, step_dict, resolving_urls)
        elif subworkflow_id and subworkflow_id_map is not None and subworkflow_id.startswith(("#", "$")):
            assert not dry_run
            # Interpret content_id as a workflow local thing.
            subworkflow = subworkflow_id_map[subworkflow_id[1:]]
        elif subworkflow_id and not content_source and self._is_url(subworkflow_id):
            # Fallback URL detection for gxformat2 run: "url" (no content_source set)
            assert not dry_run
            if self.trs_proxy._match_url(subworkflow_id):
                subworkflow = self.__build_subworkflow_from_trs_url(trans, subworkflow_id, resolving_urls)
            else:
                subworkflow = self.__build_subworkflow_from_url(trans, subworkflow_id, resolving_urls)
        elif subworkflow_id:
            subworkflow = self.app.workflow_manager.get_owned_workflow(trans, subworkflow_id)
        else:
            raise exceptions.RequestParameterInvalidException(
                "Subworkflow step must define either subworkflow, content_id, or trs_tool_id + trs_version_id."
            )

        return subworkflow

    @staticmethod
    def _is_url(value: str) -> bool:
        return value.startswith(("http://", "https://", "base64://"))

    def __build_subworkflow_from_url(
        self,
        trans,
        url: str,
        resolving_urls: frozenset[str],
    ) -> model.Workflow:
        if url in resolving_urls:
            raise exceptions.RequestParameterInvalidException(f"Circular subworkflow reference detected for URL: {url}")
        if len(resolving_urls) >= MAX_SUBWORKFLOW_URL_DEPTH:
            raise exceptions.RequestParameterInvalidException(
                f"Maximum subworkflow URL resolution depth ({MAX_SUBWORKFLOW_URL_DEPTH}) exceeded"
            )
        validate_uri_access(url, trans.user_is_admin, trans.app.config.fetch_url_allowlist_ips)
        workflow_content = stream_url_to_str(url, file_sources=trans.app.file_sources)
        as_dict = yaml.safe_load(workflow_content)
        raw_workflow_description = self.normalize_workflow_format(trans, as_dict)
        created_workflow = self.build_workflow_from_raw_description(
            trans,
            raw_workflow_description,
            WorkflowCreateOptions(archive_source=url),
            hidden=True,
            is_subworkflow=True,
            resolving_urls=resolving_urls | {url},
        )
        return created_workflow.workflow

    def __build_subworkflow_from_trs_url(
        self,
        trans,
        trs_url: str,
        resolving_urls: frozenset[str],
    ) -> model.Workflow:
        if trs_url in resolving_urls:
            raise exceptions.RequestParameterInvalidException(
                f"Circular subworkflow reference detected for TRS URL: {trs_url}"
            )
        if len(resolving_urls) >= MAX_SUBWORKFLOW_URL_DEPTH:
            raise exceptions.RequestParameterInvalidException(
                f"Maximum subworkflow URL resolution depth ({MAX_SUBWORKFLOW_URL_DEPTH}) exceeded"
            )
        _, trs_tool_id, trs_version_id = self.trs_proxy.get_trs_id_and_version_from_trs_url(trs_url=trs_url)
        data = self.trs_proxy.get_version_from_trs_url(trs_url)
        as_dict = yaml.safe_load(data)
        raw_workflow_description = self.normalize_workflow_format(trans, as_dict)
        created_workflow = self.build_workflow_from_raw_description(
            trans,
            raw_workflow_description,
            WorkflowCreateOptions(
                archive_source="trs_url",
                trs_tool_id=trs_tool_id,
                trs_version_id=trs_version_id,
                trs_url=trs_url,
            ),
            hidden=True,
            is_subworkflow=True,
            resolving_urls=resolving_urls | {trs_url},
        )
        return created_workflow.workflow

    def __build_subworkflow_from_trs_id(
        self,
        trans,
        step_dict: dict,
        resolving_urls: frozenset[str],
    ) -> model.Workflow:
        trs_server = step_dict.get("trs_server")
        trs_tool_id = step_dict.get("trs_tool_id")
        trs_version_id = step_dict.get("trs_version_id")
        if not trs_tool_id or not trs_version_id:
            raise exceptions.RequestParameterInvalidException(
                "trs_tool_id and trs_version_id are required for TRS ID subworkflow references."
            )
        if trs_server:
            server = self.trs_proxy.get_server(trs_server)
            trs_url = server.get_trs_url(trs_tool_id, trs_version_id)
        else:
            raise exceptions.RequestParameterInvalidException(
                "trs_server is required for TRS ID subworkflow references."
            )
        return self.__build_subworkflow_from_trs_url(trans, trs_url, resolving_urls)

    def __build_embedded_subworkflow(
        self, trans, data, workflow_state_resolution_options, resolving_urls: frozenset[str] = frozenset()
    ):
        raw_workflow_description = self.ensure_raw_description(data)
        subworkflow = self.build_workflow_from_raw_description(
            trans,
            raw_workflow_description,
            workflow_state_resolution_options,
            hidden=True,
            is_subworkflow=True,
            resolving_urls=resolving_urls,
        ).workflow
        return subworkflow

    def __connect_workflow_steps(self, steps: list[model.WorkflowStep], steps_by_external_id, dry_run: bool) -> None:
        """Second pass to deal with connections between steps.

        Create workflow connection objects using externally specified ids
        using during creation or update.
        """
        for step in steps:
            # Input connections
            if step.temp_input_connections:  # populated by __module_from_dict
                for input_name, conn_list in step.temp_input_connections.items():  # type: ignore[unreachable]
                    if not conn_list:
                        continue
                    if not isinstance(conn_list, list):  # Older style singleton connection
                        conn_list = [conn_list]

                    for conn_dict in conn_list:
                        if "output_name" not in conn_dict or "id" not in conn_dict:
                            template = "Invalid connection [%s] - must be dict with output_name and id fields."
                            message = template % conn_dict
                            raise exceptions.MalformedContents(message)
                        external_id = conn_dict["id"]
                        if external_id not in steps_by_external_id:
                            raise exceptions.MalformedContents(
                                f"Invalid connection. Failed to find input step id {external_id}"
                            )
                        output_step = steps_by_external_id[external_id]

                        output_name = conn_dict["output_name"]
                        input_subworkflow_step_index = conn_dict.get("input_subworkflow_step_id", None)

                        if dry_run:
                            input_subworkflow_step_index = None
                        step.add_connection(input_name, output_name, output_step, input_subworkflow_step_index)

            step.temp_input_connections = None

    def __set_default_label(self, step, module, state):
        """Previously data input modules had a `name` attribute to rename individual steps. Here, this value is transferred
        to the actual `label` attribute which is available for all module types, unique, and mapped to its own database column.
        """
        if not module.label and module.type in ["data_input", "data_collection_input"]:
            new_state = safe_loads(state) or {}
            default_label = new_state.get("name")
            if default_label and util.unicodify(default_label).lower() not in [
                "input dataset",
                "input dataset collection",
            ]:
                step.label = module.label = default_label

    def do_refactor(
        self, trans: ProvidesUserContext, stored_workflow: StoredWorkflow, refactor_request: RefactorRequest
    ):
        """Apply supplied actions to either the latest version of the workflow or a specific version to build a new version."""
        # Get the workflow version to refactor (latest or specific version)
        workflow = stored_workflow.get_internal_version(refactor_request.version)

        as_dict = self._workflow_to_dict_export(
            trans, stored_workflow, workflow=workflow, internal=True, allow_upgrade=True
        )
        raw_workflow_description = self.normalize_workflow_format(trans, as_dict)
        workflow_update_options = WorkflowUpdateOptions(
            fill_defaults=False,
            allow_missing_tools=True,
            dry_run=refactor_request.dry_run,
        )

        module_injector = WorkflowModuleInjector(trans, allow_tool_state_corrections=True)
        refactor_executor = WorkflowRefactorExecutor(raw_workflow_description, workflow, module_injector)
        action_executions = refactor_executor.refactor(refactor_request)
        refactored_workflow, errors = self.update_workflow_from_raw_description(
            trans,
            stored_workflow,
            raw_workflow_description,
            workflow_update_options,
        )
        # errors could be three things:
        # - we allow missing tools so it won't be that.
        # - might have cycles or might have state validation issues, but it still saved...
        #   so this is really more of a warning - we disregard it the other two places
        #   it is used also. These same messages will appear in the dictified version we
        #   we send back anyway
        return refactored_workflow, action_executions

    def refactor(self, trans: ProvidesUserContext, stored_workflow: StoredWorkflow, refactor_request: RefactorRequest):
        refactored_workflow, action_executions = self.do_refactor(trans, stored_workflow, refactor_request)
        return RefactorResponse(
            action_executions=action_executions,
            workflow=self.workflow_to_dict(trans, refactored_workflow.stored_workflow, style=refactor_request.style),
            dry_run=refactor_request.dry_run,
        )

    def get_all_tools(self, workflow):
        tools = []
        for step in workflow.steps:
            if step.type == "tool":
                if step.tool_id:
                    if {
                        "tool_id": step.tool_id,
                        "tool_version": step.tool_version,
                        "tool_uuid": str(step.tool_uuid) if step.tool_uuid else None,
                    } not in tools:
                        tools.append(
                            {
                                "tool_id": step.tool_id,
                                "tool_version": step.tool_version,
                                "tool_uuid": str(step.tool_uuid) if step.tool_uuid else None,
                            }
                        )
            elif step.type == "subworkflow":
                tools.extend(self.get_all_tools(step.subworkflow))
        return tools

    def get_or_create_workflow_from_trs(
        self,
        trans: ProvidesUserContext,
        trs_url: Optional[str],
        trs_id: Optional[str] = None,
        trs_version: Optional[str] = None,
        trs_server: Optional[str] = None,
    ):
        user_id = trans.user and trans.user.id
        assert user_id, "Cannot create workflow for anonymous user"
        if not trs_url:
            assert trs_server and trs_id and trs_version, "trs_url or trs_server, trs_version and trs_id must be passed"
            server = self.trs_proxy.get_server(trs_server)
            trs_url = server.get_trs_url(trs_id, trs_version)
        else:
            _, trs_id, trs_version = self.trs_proxy.get_trs_id_and_version_from_trs_url(trs_url=trs_url)
        assert trs_id and trs_version and trs_url

        workflow = self.get_workflow_by_trs_id_and_version(trs_id=trs_id, trs_version=trs_version, user_id=user_id)
        if not workflow:
            workflow = self.create_workflow_from_trs_url(trans, trs_url, trs_server)
        return workflow

    def create_workflow_from_trs_url(
        self, trans: ProvidesUserContext, trs_url: str, trs_server: Optional[str] = None
    ) -> StoredWorkflow:
        _, trs_tool_id, trs_version_id = self.trs_proxy.get_trs_id_and_version_from_trs_url(trs_url=trs_url)
        data = self.trs_proxy.get_version_from_trs_url(trs_url)
        as_dict = yaml.safe_load(data)
        raw_workflow_description = self.normalize_workflow_format(trans, as_dict)
        created_workflow = self.build_workflow_from_raw_description(
            trans,
            raw_workflow_description,
            WorkflowCreateOptions(
                trs_tool_id=trs_tool_id,
                trs_version_id=trs_version_id,
                trs_url=trs_url,
                trs_server=trs_server,
                archive_source="trs_url",
            ),
        )
        return created_workflow.stored_workflow

    def get_or_create_workflow_from_url(self, trans: ProvidesUserContext, url: str) -> StoredWorkflow:
        """Fetch and import a workflow from an arbitrary URL.

        Supports various URL schemes including http://, https://, and base64://.
        """
        user_id = trans.user and trans.user.id
        assert user_id, "Cannot create workflow for anonymous user"

        # Fetch the workflow content from the URL
        file_sources = trans.app.file_sources
        workflow_content = stream_url_to_str(url, file_sources=file_sources)

        # Parse the workflow content
        as_dict = yaml.safe_load(workflow_content)
        raw_workflow_description = self.normalize_workflow_format(trans, as_dict)

        # Create the workflow
        created_workflow = self.build_workflow_from_raw_description(
            trans,
            raw_workflow_description,
            WorkflowCreateOptions(
                archive_source="url",
            ),
        )
        return created_workflow.stored_workflow

    def get_workflow_by_trs_id_and_version(
        self, trs_id: str, trs_version: str, user_id: Optional[int] = None
    ) -> Optional[model.StoredWorkflow]:
        sa_session = self.app.model.session

        stmnt = (
            select(model.StoredWorkflow)
            .join(model.Workflow, model.Workflow.id == model.StoredWorkflow.latest_workflow_id)
            .filter(
                and_(
                    model.StoredWorkflow.deleted == false(),
                    to_json(sa_session, model.Workflow.source_metadata, ["trs_tool_id"]) == trs_id,
                    to_json(sa_session, model.Workflow.source_metadata, ["trs_version_id"]) == trs_version,
                )
            )
        )
        if user_id:
            stmnt = stmnt.filter(
                model.StoredWorkflow.user_id == user_id,
            )
        else:
            stmnt = stmnt.filter(model.StoredWorkflow.importable == true())
        return sa_session.execute(stmnt.order_by(model.StoredWorkflow.id.desc()).limit(1)).scalar()


def safe_wraps(v: Any, nxt: SerializerFunctionWrapHandler) -> str:
    try:
        return nxt(v)
    except Exception:
        return safe_dumps(v)


class RefactorResponse(BaseModel):
    action_executions: list[RefactorActionExecution]
    workflow: Annotated[dict, WrapSerializer(safe_wraps, when_used="json")]
    dry_run: bool


class WorkflowStateResolutionOptions(BaseModel):
    # fill in default tool state when updating, may change tool_state
    fill_defaults: bool = False
    # If True, assume all tool state coming from generated form instead of potentially simpler json stored in DB/exported
    from_tool_form: bool = False
    # If False, allow running with less exact tool versions
    exact_tools: bool = True


class WorkflowUpdateOptions(WorkflowStateResolutionOptions):
    # Only used internally, don't set. If using the API assume updating the workflows
    # representation with name or annotation for instance, updates the corresponding
    # stored workflow
    update_stored_workflow_attributes: bool = True
    allow_missing_tools: bool = False
    dry_run: bool = False


# Workflow update options but with some different defaults - we allow creating
# workflows with missing tools by default but not updating.
class WorkflowCreateOptions(WorkflowStateResolutionOptions):
    import_tools: bool = False

    publish: bool = False
    # true or false, effectively defaults to ``publish`` if None/unset
    importable: Optional[bool] = None

    # following are install options, only used if import_tools is true
    install_repository_dependencies: bool = False
    install_resolver_dependencies: bool = False
    install_tool_dependencies: bool = False
    new_tool_panel_section_label: str = ""
    tool_panel_section_id: str = ""
    tool_panel_section_mapping: dict = {}
    shed_tool_conf: Optional[str] = None

    # for workflows imported by archive source
    archive_source: Optional[str] = None
    trs_tool_id: Optional[str] = None
    trs_version_id: Optional[str] = None
    trs_server: Optional[str] = None
    trs_url: Optional[str] = None

    @property
    def is_importable(self):
        # if self.importable is None, use self.publish that has a default.
        if self.importable is None:
            return self.publish
        else:
            return self.importable

    @property
    def install_options(self):
        return {
            "install_repository_dependencies": self.install_repository_dependencies,
            "install_resolver_dependencies": self.install_resolver_dependencies,
            "install_tool_dependencies": self.install_tool_dependencies,
            "new_tool_panel_section_label": self.new_tool_panel_section_label,
            "tool_panel_section_id": self.tool_panel_section_id,
            "tool_panel_section_mapping": self.tool_panel_section_mapping,
            "shed_tool_conf": self.shed_tool_conf,
        }


class MissingToolsException(exceptions.MessageException):
    def __init__(self, workflow, errors):
        self.workflow = workflow
        self.errors = errors


class RawWorkflowDescription:
    def __init__(self, as_dict, workflow_path=None):
        self.as_dict = as_dict
        self.workflow_path = workflow_path


class Format2ConverterGalaxyInterface(ImporterGalaxyInterface):
    def import_workflow(self, workflow, **kwds):
        raise NotImplementedError(
            "Direct format 2 import of nested workflows is not yet implemented, use bioblend client."
        )


def _get_stored_workflow(session, workflow_uuid, workflow_id, by_stored_id):
    stmt = select(StoredWorkflow)
    if workflow_uuid is not None:
        stmt = stmt.where(StoredWorkflow.id == Workflow.stored_workflow_id).where(Workflow.uuid == workflow_uuid)
    else:
        if by_stored_id:
            stmt = stmt.where(StoredWorkflow.id == workflow_id)
        else:
            stmt = stmt.where(StoredWorkflow.id == Workflow.stored_workflow_id).where(Workflow.id == workflow_id)
    stmt = stmt.options(
        joinedload(StoredWorkflow.annotations),
        joinedload(StoredWorkflow.tags),
        subqueryload(StoredWorkflow.latest_workflow).joinedload(Workflow.steps).joinedload("*"),
    ).limit(1)
    return session.scalars(stmt).first()


def _get_invocation(session, invocation_id):
    stmt = select(WorkflowInvocation)
    stmt = stmt.where(WorkflowInvocation.id == invocation_id).limit(1)
    return session.scalars(stmt).first()


def get_count(session, statement):
    stmt = select(func.count()).select_from(statement.subquery())
    return session.scalar(stmt)
