text: import inspect
  from abc import abstractmethod
  from typing import Any, Callable, Dict, Iterable, Mapping, Sequence, cast, List, Optional
  
  from argo_workflow_tools.dsl import building_mode_context as context
  from argo_workflow_tools.dsl.dag_task import (
      DAGReference,
      TaskReference,
      WorkflowTemplateReference,
  )
  from argo_workflow_tools.dsl.input_definition import InputDefinition, SourceType
  from argo_workflow_tools.dsl.node_properties.dag_node_properties import (
      DAGNodeProperties,
  )
  from argo_workflow_tools.dsl.node_properties.task_node_properties import (
      TaskNodeProperties,
  )
  from argo_workflow_tools.dsl.parameter_builders.default_parameter_builder import (
      DefaultParameterBuilder,
  )
  from argo_workflow_tools.dsl.utils.utils import sanitize_name, uuid_short
  from argo_workflow_tools.dsl.workflow_template_collector import (
      add_task,
      collect_conditions,
  )
  
  
  class Node(object):
      def __init__(self, func: Callable, **kwargs):
          """
  
          Parameters
          ----------
          func :
          kwargs :
          """
          self._func = func
          self.kwargs = kwargs
  
      @property
      def func(self) -> Callable:
          """
  
          Returns
          -------
  
          """
          return self._func
  
      @abstractmethod
      def __call__(self, *args, **kwargs) -> Any:
          """
  
          Returns
          -------
          object
          """
          pass
  
      @staticmethod
      def _filter_dag_args(kwargs: Dict) -> Dict:
          """
          Filters out parameters used internally to genrate control flows
          Parameters
          ----------
          kwargs : parameter dictionary
  
          Returns
          -------
          filtered parameter dictionary
          """
          filtered_kwargs = kwargs.copy()
          filtered_kwargs.pop("wait_for", None)
          filtered_kwargs.pop("continue_on_fail", None)
          filtered_kwargs.pop("when", None)
          filtered_kwargs.pop("exit", None)
          return filtered_kwargs
  
      @staticmethod
      def _get_wait(kwargs) -> List["str"]:
          """
          Generaete dependecy out of wait_for parameter
          Parameters
          ----------
          kwargs :
              fucntion argumetns
          Returns
          -------
              list of dependencies
  
          """
          if not kwargs.get("wait_for"):
              return []
          dependencies = cast(InputDefinition, kwargs.get("wait_for"))
          if (
              isinstance(dependencies, InputDefinition) and dependencies.is_node_output
          ):  # TODO or it's variants NODE_OUTPUT
              return [dependencies]
          if isinstance(dependencies, Iterable):
              return list(
                  filter(
                      lambda dependency: dependency.source_type == SourceType.NODE_OUTPUT,
                      dependencies,
                  )
              )
  
      @staticmethod
      def _reduce_fan_in_arguments(name: str, arg: Any) -> Any:
          if (
              isinstance(arg, Sequence)
              and len(arg) == 1
              and isinstance(arg[0], InputDefinition)
              and arg[0].is_node_output
              and arg[0].reference
          ):
              return InputDefinition(
                  SourceType.REDUCE,
                  name=sanitize_name(arg[0].name, snake_case=True),
                  source_node_id=arg[0].source_node_id,
                  references=arg[0],
              )
  
          if (
              not isinstance(arg, str)
              and isinstance(arg, Sequence)
              and any([item.is_node_output for item in arg])
          ):
              raise ValueError(
                  f"Argument '{name}' of type '{type(arg).__name__}' is invalid. it mixes both parameters and node outputs"
              )
  
          return arg
  
      def _bind_arguments(self, *args, **kwargs) -> Mapping[str, Any]:
          signature = inspect.signature(self._func)
          kwargs_filtered = self._filter_dag_args(kwargs)
          try:
              bound_args = signature.bind(*args, **kwargs_filtered)
              arguments = {**bound_args.arguments}
          except TypeError as e:
              raise TypeError(
                  f"You have invoked the task '{self._func.__name__}' with signature '{signature}' "
                  f"with : args=({str.join(',', map(lambda arg: arg.name, args))}) kwargs=({str.join(',', kwargs.keys())}). "
                  f"{e}."
              ) from e
  
          kwarg_parameters = [
              name
              for name, param in signature.parameters.items()
              if param.kind == inspect.Parameter.VAR_KEYWORD
          ]
          for kwarg_param_name in kwarg_parameters:
              arguments = {**arguments, **arguments[kwarg_param_name]}
              del arguments[kwarg_param_name]
  
          return {k: self._reduce_fan_in_arguments(k, v) for k, v in arguments.items()}
  
      @staticmethod
      def _get_arg(argument_name: str, argument_value: Any) -> InputDefinition:
          if isinstance(argument_value, InputDefinition):
              return argument_value
          return InputDefinition(
              source_type=SourceType.CONST, value=argument_value, name=argument_name
          )
  
      @staticmethod
      def _arguments(arguments: Mapping[str, Any]) -> Mapping[str, InputDefinition]:
          return {
              argument_name: Node._get_arg(argument_name, argument_value)
              for argument_name, argument_value in arguments.items()
          }
  
  
  class DAGNode(Node):
      def __init__(self, func: Callable, properties: DAGNodeProperties):
          """
          reporesents a DAG node in the workflow graph
          Parameters
          ----------
          func : function describing the DAG
          properties : argo properties for DAG
          """
          super().__init__(func)
          self.properties = properties
  
      def __call__(self, *args, **kwargs) -> Any:
          """
          call the DAG function, in case we are not in DSL compilation mode, the function will call the function.
          else the fucntion will return a reference response representing the node response
          """
          exit_handler = kwargs.get("exit")
          continue_on_fail_handler = kwargs.get("continue_on_fail")
          if not context.dag_building_mode.get():
              cleaned_kwargs = self._filter_dag_args(kwargs)
              conditions = collect_conditions()
              if all([condition.value for condition in conditions]):
                  try:
                      return self._func(*args, **cleaned_kwargs)
                  except Exception as e:
                      if not continue_on_fail_handler:
                          raise e
                      else:
                          return None
                  finally:
                      if exit_handler:
                          exit_handler()
              else:
                  return None
  
          guid = sanitize_name(self._func.__name__) + "-" + uuid_short()
  
          arguments = self._bind_arguments(*args, **kwargs)
          partitioned_arguments = list(
              filter(
                  lambda argument: isinstance(argument, InputDefinition)
                  and argument.is_partition,
                  arguments.values(),
              )
          )
          if len(partitioned_arguments) > 1:
              raise ValueError(
                  "Nested loops are not allowed in the same DAG, split your loops into nested DAG's instead"
              )
          template_name = sanitize_name(self._func.__name__)
          if len(self.properties.outputs.items()) == 0:
              outputs = {
                  "result": InputDefinition(
                      source_type=SourceType.NODE_OUTPUT,
                      source_node_id=guid,
                      source_template=template_name,
                      name=sanitize_name("result", snake_case=True),
                      references=partitioned_arguments,
                      parameter_builder=self.properties.outputs.get(
                          "result", DefaultParameterBuilder(None)
                      ),
                  )
              }
          else:
              outputs = {
                  name: InputDefinition(
                      source_type=SourceType.NODE_OUTPUT,
                      source_node_id=guid,
                      source_template=template_name,
                      name=sanitize_name(name, snake_case=True),
                      references=partitioned_arguments,
                      parameter_builder=parameter_builder,
                  )
                  for name, parameter_builder in self.properties.outputs.items()
              }
  
          conditions = collect_conditions()
  
          add_task(
              DAGReference(
                  id=guid,
                  name=sanitize_name(self._func.__name__),
                  func=self._func,
                  pre_func_hooks=None,
                  post_func_hooks=None,
                  wait_for=self._get_wait(kwargs),
                  continue_on_fail=kwargs.get("continue_on_fail"),
                  exit=exit_handler,
                  arguments=self._arguments(arguments),
                  outputs=outputs,
                  node=self,
                  properties=self.properties,
                  conditions=conditions,
              ),
          )
          if len(outputs.items()) == 1:
              return list(outputs.values())[0]
          else:
              return outputs
  
  
  class TaskNode(Node):
      def __init__(self, func: Callable, properties: TaskNodeProperties,
                   pre_hook: Optional[Callable[[], None]], post_hook: Optional[Callable[[], None]]):
          """
          reporesents a task leaf node in the workflow graph
          Parameters
          ----------
          func : function describing the task
          properties : argo properties for task
          """
          super().__init__(func)
          self.properties = properties
          self._pre_hook = pre_hook
          self._post_hook = post_hook
  
      def __call__(self, *args, **kwargs) -> Any:
          """
          call the DAG function, in case we are not in DSL compilation mode, the function will call the function.
          else the fucntion will return a reference response representing the node response
          """
          exit_handler = kwargs.get("exit")
          continue_on_fail_handler = kwargs.get("continue_on_fail")
          if not context.dag_building_mode.get():
              cleaned_kwargs = self._filter_dag_args(kwargs)
              conditions = collect_conditions()
              if all([condition.value for condition in conditions]):
                  try:
                      return self._call_task_func_with_hooks(args, cleaned_kwargs)
                  except Exception as e:
                      if not continue_on_fail_handler:
                          raise e
                      else:
                          return None
                  finally:
                      if exit_handler:
                          exit_handler()
              else:
                  return None
  
          arguments = self._bind_arguments(*args, **kwargs)
          partitioned_arguments = list(
              filter(
                  lambda argument: isinstance(argument, InputDefinition)
                  and argument.is_partition,
                  arguments.values(),
              )
          )
          if len(partitioned_arguments) > 1:
              raise ValueError(
                  "Nested loops are not allowed in the same DAG, split your loops into nested DAG's instead"
              )
          guid = sanitize_name(self._func.__name__) + "-" + uuid_short()
          conditions = collect_conditions()
          template_name = sanitize_name(self._func.__name__)
          if len(self.properties.outputs.items()) == 0:
  
              outputs = {
                  "result": InputDefinition(
                      source_type=SourceType.NODE_OUTPUT,
                      source_node_id=guid,
                      source_template=template_name,
                      name=sanitize_name("result", snake_case=True),
                      references=partitioned_arguments,
                      parameter_builder=self.properties.outputs.get(
                          "result",
                          DefaultParameterBuilder(
                              inspect.signature(self._func).return_annotation
                          ),
                      ),
                  )
              }
          else:
              outputs = {
                  name: InputDefinition(
                      source_type=SourceType.NODE_OUTPUT,
                      source_node_id=guid,
                      source_template=template_name,
                      name=sanitize_name(name, snake_case=True),
                      references=partitioned_arguments,
                      parameter_builder=parameter_builder,
                  )
                  for name, parameter_builder in self.properties.outputs.items()
              }
  
          add_task(
              TaskReference(
                  id=guid,
                  name=sanitize_name(self._func.__name__),
                  func=self._func,
                  pre_func_hooks=self._pre_hook,
                  post_func_hooks=self._post_hook,
                  wait_for=self._get_wait(kwargs),
                  continue_on_fail=kwargs.get("continue_on_fail"),
                  exit=exit_handler,
                  arguments=self._arguments(arguments),
                  outputs=outputs,
                  properties=self.properties,
                  node=self,
                  conditions=conditions,
              ),
          )
          if len(outputs.items()) == 1:
              return list(outputs.values())[0]
          else:
              return outputs
  
      def _call_task_func_with_hooks(self, args, cleaned_kwargs):
          if self._pre_hook is not None:
              self._pre_hook()
          try:
              return self._func(*args, **cleaned_kwargs)
          finally:
              if self._post_hook is not None:
                  self._post_hook()
  
  
  class WorkflowTemplateNode(DAGNode):
      def __init__(
          self,
          func: Callable,
          name: str,
          properties: DAGNodeProperties,
          namespace: str = None,
          on_exit: Callable = None,
          arguments: dict = None,
      ):
  
          """
          reporesents a WorkflowTempalte node in the workflow graph
          Parameters
          ----------
          func : function describing the DAG
          properties : argo properties for DAG
          """
          self.name = name
          self.namespace = namespace
          self.arguments = arguments
          self.on_exit = on_exit
          super().__init__(func, properties)
  
      def __call__(self, *args, **kwargs) -> Any:
          """
          call the DAG function, in case we are not in DSL compilation mode, the function will call the function.
          else the fucntion will return a reference response representing the node response
          """
          exit_handler = kwargs.get("exit")
          if not context.dag_building_mode.get():
              cleaned_kwargs = self._filter_dag_args(kwargs)
              conditions = collect_conditions()
              if all([condition.value for condition in conditions]):
                  try:
                      return self._func(*args, **cleaned_kwargs)
                  finally:
                      if exit_handler:
                          exit_handler()
              else:
                  return None
  
          guid = sanitize_name(self._func.__name__) + "-" + uuid_short()
  
          arguments = self._bind_arguments(*args, **kwargs)
          partitioned_arguments = list(
              filter(
                  lambda argument: isinstance(argument, InputDefinition)
                  and argument.is_partition,
                  arguments.values(),
              )
          )
          if len(partitioned_arguments) > 1:
              raise ValueError(
                  "Nested loops are not allowed in the same DAG, split your loops into nested DAG's instead"
              )
  
          if len(self.properties.outputs.items()) == 0:
              outputs = {
                  "result": InputDefinition(
                      source_type=SourceType.NODE_OUTPUT,
                      source_node_id=guid,
                      name=sanitize_name("result", snake_case=True),
                      references=partitioned_arguments,
                      parameter_builder=self.properties.outputs.get(
                          "result", DefaultParameterBuilder(None)
                      ),
                  )
              }
          else:
              outputs = {
                  name: InputDefinition(
                      source_type=SourceType.NODE_OUTPUT,
                      source_node_id=guid,
                      name=sanitize_name(name, snake_case=True),
                      references=partitioned_arguments,
                      parameter_builder=parameter_builder,
                  )
                  for name, parameter_builder in self.properties.outputs.items()
              }
  
          conditions = collect_conditions()
  
          add_task(
              WorkflowTemplateReference(
                  workflow_template_name=self.name,
                  id=guid,
                  name=sanitize_name(self._func.__name__),
                  func=self._func,
                  pre_func_hooks=None,
                  post_func_hooks=None,
                  wait_for=self._get_wait(kwargs),
                  arguments=self._arguments(arguments),
                  outputs=outputs,
                  exit=exit_handler,
                  node=self,
                  properties=self.properties,
                  conditions=conditions,
                  continue_on_fail=kwargs.get("continue_on_fail")
              ),
          )
          if len(outputs.items()) == 1:
              return list(outputs.values())[0]
          else:
              return outputs
  