text: from collections import namedtuple
  from typing import Any, Callable, Dict, NamedTuple, Optional, Set, Type, TypeVar, Union
  
  from dagster import check
  from dagster.core.definitions.events import AssetKey
  from dagster.core.errors import DagsterError, DagsterInvalidDefinitionError
  from dagster.core.types.dagster_type import DagsterType, resolve_dagster_type
  from dagster.utils.backcompat import experimental_arg_warning
  
  from .inference import InferredOutputProps
  from .input import NoValueSentinel
  from .utils import DEFAULT_OUTPUT, check_valid_name
  
  TOut = TypeVar("TOut", bound="OutputDefinition")
  
  
  class NoNameSentinel:
      pass
  
  
  class OutputDefinition:
      """Defines an output from a solid's compute function.
  
      Solids can have multiple outputs, in which case outputs cannot be anonymous.
  
      Many solids have only one output, in which case the user can provide a single output definition
      that will be given the default name, "result".
  
      Output definitions may be typed using the Dagster type system.
  
      Args:
          dagster_type (Optional[Union[Type, DagsterType]]]): The type of this output.
              Users should provide the Python type of the objects that they expect the solid to yield
              for this output, or a :py:class:`DagsterType` that defines a runtime check that they
              want to be run on this output. Defaults to :py:class:`Any`.
          name (Optional[str]): Name of the output. (default: "result")
          description (Optional[str]): Human-readable description of the output.
          is_required (Optional[bool]): Whether the presence of this field is required. (default: True)
          io_manager_key (Optional[str]): The resource key of the IOManager used for storing this
              output and loading it in downstream steps (default: "io_manager").
          metadata (Optional[Dict[str, Any]]): A dict of the metadata for the output.
              For example, users can provide a file path if the data object will be stored in a
              filesystem, or provide information of a database table when it is going to load the data
              into the table.
          asset_key (Optional[Union[AssetKey, OutputContext -> AssetKey]]): (Experimental) An AssetKey
              (or function that produces an AssetKey from the OutputContext) which should be associated
              with this OutputDefinition. Used for tracking lineage information through Dagster.
          asset_partitions (Optional[Union[Set[str], OutputContext -> Set[str]]]): (Experimental) A
              set of partitions of the given asset_key (or a function that produces this list of
              partitions from the OutputContext) which should be associated with this OutputDefinition.
      """
  
      def __init__(
          self,
          dagster_type=None,
          name=None,
          description=None,
          is_required=None,
          io_manager_key=None,
          metadata=None,
          asset_key=None,
          asset_partitions=None,
          # make sure new parameters are updated in combine_with_inferred below
      ):
          self._name = (
              check_valid_name(check.opt_str_param(name, "name", DEFAULT_OUTPUT))
              if name is not NoNameSentinel
              else None
          )
          self._type_not_set = dagster_type is None
          self._dagster_type = resolve_dagster_type(dagster_type)
          self._description = check.opt_str_param(description, "description")
          self._is_required = check.opt_bool_param(is_required, "is_required", default=True)
          self._manager_key = check.opt_str_param(
              io_manager_key, "io_manager_key", default="io_manager"
          )
          self._metadata = metadata
  
          if asset_key:
              experimental_arg_warning("asset_key", "OutputDefinition.__init__")
  
          if not callable(asset_key):
              check.opt_inst_param(asset_key, "asset_key", AssetKey)
  
          self._asset_key = asset_key
  
          if asset_partitions:
              experimental_arg_warning("asset_partitions", "OutputDefinition.__init__")
              check.param_invariant(
                  asset_key is not None,
                  "asset_partitions",
                  'Cannot specify "asset_partitions" argument without also specifying "asset_key"',
              )
  
          if callable(asset_partitions):
              self._asset_partitions_fn = asset_partitions
          elif asset_partitions is not None:
              asset_partitions = check.opt_set_param(asset_partitions, "asset_partitions", str)
              self._asset_partitions_fn = lambda _: asset_partitions
          else:
              self._asset_partitions_fn = None
  
      @property
      def name(self):
          return self._name
  
      @property
      def dagster_type(self):
          return self._dagster_type
  
      @property
      def description(self):
          return self._description
  
      @property
      def optional(self):
          return not self._is_required
  
      @property
      def is_required(self):
          return self._is_required
  
      @property
      def io_manager_key(self):
          return self._manager_key
  
      @property
      def metadata(self):
          return self._metadata
  
      @property
      def is_dynamic(self):
          return False
  
      @property
      def is_asset(self):
          return self._asset_key is not None
  
      @property
      def hardcoded_asset_key(self) -> Optional[AssetKey]:
          if not callable(self._asset_key):
              return self._asset_key
          else:
              return None
  
      def get_asset_key(self, context) -> Optional[AssetKey]:
          """Get the AssetKey associated with this OutputDefinition for the given
          :py:class:`OutputContext` (if any).
  
          Args:
              context (OutputContext): The OutputContext that this OutputDefinition is being evaluated
                  in
          """
          if callable(self._asset_key):
              return self._asset_key(context)
          else:
              return self.hardcoded_asset_key
  
      def get_asset_partitions(self, context) -> Optional[Set[str]]:
          """Get the set of partitions associated with this OutputDefinition for the given
          :py:class:`OutputContext` (if any).
  
          Args:
              context (OutputContext): The OutputContext that this OutputDefinition is being evaluated
              in
          """
          if self._asset_partitions_fn is None:
              return None
  
          return self._asset_partitions_fn(context)
  
      def mapping_from(self, solid_name, output_name=None):
          """Create an output mapping from an output of a child solid.
  
          In a CompositeSolidDefinition, you can use this helper function to construct
          an :py:class:`OutputMapping` from the output of a child solid.
  
          Args:
              solid_name (str): The name of the child solid from which to map this output.
              input_name (str): The name of the child solid's output from which to map this output.
  
          Examples:
  
              .. code-block:: python
  
                  output_mapping = OutputDefinition(Int).mapping_from('child_solid')
          """
          return OutputMapping(self, OutputPointer(solid_name, output_name))
  
      @staticmethod
      def create_from_inferred(inferred: InferredOutputProps) -> "OutputDefinition":
          return OutputDefinition(
              dagster_type=_checked_inferred_type(inferred.annotation),
              description=inferred.description,
          )
  
      def combine_with_inferred(self: TOut, inferred: InferredOutputProps) -> TOut:
          dagster_type = self._dagster_type
          if self._type_not_set:
              dagster_type = _checked_inferred_type(inferred.annotation)
          if self._description is None:
              description = inferred.description
          else:
              description = self.description
  
          return self.__class__(
              name=self._name,
              dagster_type=dagster_type,
              description=description,
              is_required=self._is_required,
              io_manager_key=self._manager_key,
              metadata=self._metadata,
              asset_key=self._asset_key,
              asset_partitions=self._asset_partitions_fn,
          )
  
  
  def _checked_inferred_type(inferred: Any) -> DagsterType:
      try:
          return resolve_dagster_type(inferred)
      except DagsterError as e:
          raise DagsterInvalidDefinitionError(
              f"Problem using type '{inferred}' from return type annotation, correct the issue "
              "or explicitly set the dagster_type on your OutputDefinition."
          ) from e
  
  
  class DynamicOutputDefinition(OutputDefinition):
      """
      Variant of :py:class:`OutputDefinition <dagster.OutputDefinition>` for an
      output that will dynamically alter the graph at runtime.
  
      When using in a composition function such as :py:func:`@pipeline <dagster.pipeline>`,
      dynamic outputs must be used with either
  
      * ``map`` - clone downstream solids for each separate :py:class:`DynamicOutput`
      * ``collect`` - gather across all :py:class:`DynamicOutput` in to a list
  
      Uses the same constructor as :py:class:`OutputDefinition <dagster.OutputDefinition>`
  
          .. code-block:: python
  
              @solid(
                  config_schema={
                      "path": Field(str, default_value=file_relative_path(__file__, "sample"))
                  },
                  output_defs=[DynamicOutputDefinition(str)],
              )
              def files_in_directory(context):
                  path = context.solid_config["path"]
                  dirname, _, filenames = next(os.walk(path))
                  for file in filenames:
                      yield DynamicOutput(os.path.join(dirname, file), mapping_key=_clean(file))
  
              @pipeline
              def process_directory():
                  files = files_in_directory()
  
                  # use map to invoke a solid on each dynamic output
                  file_results = files.map(process_file)
  
                  # use collect to gather the results in to a list
                  summarize_directory(file_results.collect())
      """
  
      @property
      def is_dynamic(self):
          return True
  
  
  class OutputPointer(namedtuple("_OutputPointer", "solid_name output_name")):
      def __new__(cls, solid_name, output_name=None):
          return super(OutputPointer, cls).__new__(
              cls,
              check.str_param(solid_name, "solid_name"),
              check.opt_str_param(output_name, "output_name", DEFAULT_OUTPUT),
          )
  
  
  class OutputMapping(namedtuple("_OutputMapping", "definition maps_from")):
      """Defines an output mapping for a composite solid.
  
      Args:
          definition (OutputDefinition): Defines the output of the composite solid.
          solid_name (str): The name of the child solid from which to map the output.
          output_name (str): The name of the child solid's output from which to map the output.
      """
  
      def __new__(cls, definition, maps_from):
          return super(OutputMapping, cls).__new__(
              cls,
              check.inst_param(definition, "definition", OutputDefinition),
              check.inst_param(maps_from, "maps_from", OutputPointer),
          )
  
  
  class Out(
      NamedTuple(
          "_Out",
          [
              ("dagster_type", Union[DagsterType, Type[NoValueSentinel]]),
              ("description", Optional[str]),
              ("is_required", Optional[bool]),
              ("io_manager_key", Optional[str]),
              ("metadata", Optional[Dict[str, Any]]),
              ("asset_key", Optional[Union[AssetKey, Callable[..., AssetKey]]]),
              ("asset_partitions", Optional[Union[Set[str], Callable[..., Set[str]]]]),
          ],
      )
  ):
      """
      Experimental replacement for :py:class:`OutputDefinition` intended to decrease verbosity.
  
      Args:
          dagster_type (Optional[Union[Type, DagsterType]]]):
              The type of this output. Should only be set if the correct type can not
              be inferred directly from the type signature of the decorated function.
          description (Optional[str]): Human-readable description of the output.
          is_required (Optional[bool]): Whether the presence of this field is required. (default: True)
          io_manager_key (Optional[str]): The resource key of the output manager used for this output.
              (default: "io_manager").
          metadata (Optional[Dict[str, Any]]): A dict of the metadata for the output.
              For example, users can provide a file path if the data object will be stored in a
              filesystem, or provide information of a database table when it is going to load the data
              into the table.
          asset_key (Optional[Union[AssetKey, OutputContext -> AssetKey]]): (Experimental) An AssetKey
              (or function that produces an AssetKey from the OutputContext) which should be associated
              with this OutputDefinition. Used for tracking lineage information through Dagster.
          asset_partitions (Optional[Union[Set[str], OutputContext -> Set[str]]]): (Experimental) A
              set of partitions of the given asset_key (or a function that produces this list of
              partitions from the OutputContext) which should be associated with this OutputDefinition.
      """
  
      def __new__(
          cls,
          dagster_type=NoValueSentinel,
          description=None,
          is_required=None,
          io_manager_key=None,
          metadata=None,
          asset_key=None,
          asset_partitions=None,
          # make sure new parameters are updated in combine_with_inferred below
      ):
          return super(Out, cls).__new__(
              cls,
              dagster_type=dagster_type,
              description=description,
              is_required=is_required,
              io_manager_key=io_manager_key,
              metadata=metadata,
              asset_key=asset_key,
              asset_partitions=asset_partitions,
          )
  
      def to_definition(self, annotation_type: type, name: Optional[str]) -> "OutputDefinition":
          dagster_type = (
              self.dagster_type if self.dagster_type is not NoValueSentinel else annotation_type
          )
  
          return OutputDefinition(
              dagster_type=dagster_type,
              name=name,
              description=self.description,
              is_required=self.is_required,
              io_manager_key=self.io_manager_key,
              metadata=self.metadata,
              asset_key=self.asset_key,
              asset_partitions=self.asset_partitions,
          )
  
  
  class DynamicOut(Out):
      """
      Experimental replacement for :py:class:`DynamicOutputDefinition` intended to decrease verbosity.
      Variant of :py:class:`Out` for an output that will dynamically alter the graph at runtime.
      """
  
      def to_definition(self, annotation_type: type, name: Optional[str]) -> "OutputDefinition":
          dagster_type = (
              self.dagster_type if self.dagster_type is not NoValueSentinel else annotation_type
          )
  
          return DynamicOutputDefinition(
              dagster_type=dagster_type,
              name=name,
              description=self.description,
              is_required=self.is_required,
              io_manager_key=self.io_manager_key,
              metadata=self.metadata,
              asset_key=self.asset_key,
              asset_partitions=self.asset_partitions,
          )
  
  
  class GraphOut(
      NamedTuple(
          "_GraphOut",
          [
              ("dagster_type", Union[DagsterType, Type[NoValueSentinel]]),
              ("description", Optional[str]),
          ],
      )
  ):
      """
      Experimental replacement for :py:class:`OutputDefinition` on graphs intended to decrease verbosity.
      It represents the information about the outputs that the graph maps.
  
      Args:
          dagster_type (Optional[Union[Type, DagsterType]]]):
              The type of this output. Should only be set if the correct type can not
              be inferred directly from the type signature of the decorated function.
          description (Optional[str]): Human-readable description of the output.
      """
  
      def __new__(cls, dagster_type=NoValueSentinel, description=None):
          return super(GraphOut, cls).__new__(
              cls,
              dagster_type=dagster_type,
              description=description,
          )
  
      def to_definition(self, name: Optional[str]) -> "OutputDefinition":
          dagster_type = self.dagster_type if self.dagster_type is not NoValueSentinel else None
          return OutputDefinition(
              dagster_type=dagster_type,
              name=name,
              description=self.description,
          )
  