Coverage for python/lsst/pipe/base/tests/mocks/_pipeline_task.py: 22%
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1# This file is part of pipe_base.
2#
3# Developed for the LSST Data Management System.
4# This product includes software developed by the LSST Project
5# (http://www.lsst.org).
6# See the COPYRIGHT file at the top-level directory of this distribution
7# for details of code ownership.
8#
9# This software is dual licensed under the GNU General Public License and also
10# under a 3-clause BSD license. Recipients may choose which of these licenses
11# to use; please see the files gpl-3.0.txt and/or bsd_license.txt,
12# respectively. If you choose the GPL option then the following text applies
13# (but note that there is still no warranty even if you opt for BSD instead):
14#
15# This program is free software: you can redistribute it and/or modify
16# it under the terms of the GNU General Public License as published by
17# the Free Software Foundation, either version 3 of the License, or
18# (at your option) any later version.
19#
20# This program is distributed in the hope that it will be useful,
21# but WITHOUT ANY WARRANTY; without even the implied warranty of
22# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
23# GNU General Public License for more details.
24#
25# You should have received a copy of the GNU General Public License
26# along with this program. If not, see <http://www.gnu.org/licenses/>.
27from __future__ import annotations
29from lsst.pipe.base.connectionTypes import BaseInput, Output
31__all__ = (
32 "DynamicConnectionConfig",
33 "DynamicTestPipelineTask",
34 "DynamicTestPipelineTaskConfig",
35 "MockPipelineTask",
36 "MockPipelineTaskConfig",
37 "mock_task_defs",
38 "mock_pipeline_graph",
39)
41import dataclasses
42import logging
43from collections.abc import Collection, Iterable, Mapping
44from typing import TYPE_CHECKING, Any, ClassVar, TypeVar
46from lsst.daf.butler import DataCoordinate, DatasetRef, DeferredDatasetHandle, SerializedDatasetType
47from lsst.pex.config import Config, ConfigDictField, ConfigurableField, Field, ListField
48from lsst.utils.doImport import doImportType
49from lsst.utils.introspection import get_full_type_name
50from lsst.utils.iteration import ensure_iterable
52from ... import automatic_connection_constants as acc
53from ... import connectionTypes as cT
54from ...config import PipelineTaskConfig
55from ...connections import InputQuantizedConnection, OutputQuantizedConnection, PipelineTaskConnections
56from ...pipeline import TaskDef
57from ...pipeline_graph import PipelineGraph
58from ...pipelineTask import PipelineTask
59from ._data_id_match import DataIdMatch
60from ._storage_class import MockDataset, MockDatasetQuantum, MockStorageClass, get_mock_name
62_LOG = logging.getLogger(__name__)
64if TYPE_CHECKING:
65 from ..._quantumContext import QuantumContext
68_T = TypeVar("_T", bound=cT.BaseConnection)
71def mock_task_defs(
72 originals: Iterable[TaskDef],
73 unmocked_dataset_types: Iterable[str] = (),
74 force_failures: Mapping[str, tuple[str, type[Exception] | None]] | None = None,
75) -> list[TaskDef]:
76 """Create mocks for an iterable of TaskDefs.
78 Parameters
79 ----------
80 originals : `~collections.abc.Iterable` [ `TaskDef` ]
81 Original tasks and configuration to mock.
82 unmocked_dataset_types : `~collections.abc.Iterable` [ `str` ], optional
83 Names of overall-input dataset types that should not be replaced with
84 mocks.
85 force_failures : `~collections.abc.Mapping` [ `str`, `tuple` [ `str`, \
86 `type` [ `Exception` ] or `None` ] ]
87 Mapping from original task label to a 2-tuple indicating that some
88 quanta should raise an exception when executed. The first entry is a
89 data ID match using the butler expression language (i.e. a string of
90 the sort passed ass the ``where`` argument to butler query methods),
91 while the second is the type of exception to raise when the quantum
92 data ID matches the expression. An exception type of `None` uses
93 the default, `ValueError`.
95 Returns
96 -------
97 mocked : `list` [ `TaskDef` ]
98 List of `TaskDef` objects using `MockPipelineTask` configurations that
99 target the original tasks, in the same order.
100 """
101 unmocked_dataset_types = tuple(unmocked_dataset_types)
102 if force_failures is None:
103 force_failures = {}
104 results: list[TaskDef] = []
105 for original_task_def in originals:
106 config = MockPipelineTaskConfig()
107 config.original.retarget(original_task_def.taskClass)
108 config.original = original_task_def.config
109 config.unmocked_dataset_types.extend(unmocked_dataset_types)
110 if original_task_def.label in force_failures:
111 condition, exception_type = force_failures[original_task_def.label]
112 config.fail_condition = condition
113 if exception_type is not None:
114 config.fail_exception = get_full_type_name(exception_type)
115 mock_task_def = TaskDef(
116 config=config, taskClass=MockPipelineTask, label=get_mock_name(original_task_def.label)
117 )
118 results.append(mock_task_def)
119 return results
122def mock_pipeline_graph(
123 original_graph: PipelineGraph,
124 unmocked_dataset_types: Iterable[str] = (),
125 force_failures: Mapping[str, tuple[str, type[Exception] | None]] | None = None,
126) -> PipelineGraph:
127 """Create mocks for a full pipeline graph.
129 Parameters
130 ----------
131 original_graph : `~..pipeline_graph.PipelineGraph`
132 Original tasks and configuration to mock.
133 unmocked_dataset_types : `~collections.abc.Iterable` [ `str` ], optional
134 Names of overall-input dataset types that should not be replaced with
135 mocks.
136 force_failures : `~collections.abc.Mapping` [ `str`, `tuple` [ `str`, \
137 `type` [ `Exception` ] or `None` ] ]
138 Mapping from original task label to a 2-tuple indicating that some
139 quanta should raise an exception when executed. The first entry is a
140 data ID match using the butler expression language (i.e. a string of
141 the sort passed as the ``where`` argument to butler query methods),
142 while the second is the type of exception to raise when the quantum
143 data ID matches the expression. An exception type of `None` uses
144 the default, `ValueError`.
146 Returns
147 -------
148 mocked : `~..pipeline_graph.PipelineGraph`
149 Pipeline graph using `MockPipelineTask` configurations that target the
150 original tasks. Never resolved.
151 """
152 unmocked_dataset_types = tuple(unmocked_dataset_types)
153 if force_failures is None:
154 force_failures = {}
155 result = PipelineGraph(description=original_graph.description)
156 for original_task_node in original_graph.tasks.values():
157 config = MockPipelineTaskConfig()
158 config.original.retarget(original_task_node.task_class)
159 config.original = original_task_node.config
160 config.unmocked_dataset_types.extend(unmocked_dataset_types)
161 if original_task_node.label in force_failures:
162 condition, exception_type = force_failures[original_task_node.label]
163 config.fail_condition = condition
164 if exception_type is not None:
165 config.fail_exception = get_full_type_name(exception_type)
166 result.add_task(get_mock_name(original_task_node.label), MockPipelineTask, config=config)
167 return result
170class BaseTestPipelineTaskConnections(PipelineTaskConnections, dimensions=()):
171 pass
174class BaseTestPipelineTaskConfig(PipelineTaskConfig, pipelineConnections=BaseTestPipelineTaskConnections):
175 fail_condition = Field[str](
176 dtype=str,
177 default="",
178 doc=(
179 "Condition on Data ID to raise an exception. String expression which includes attributes of "
180 "quantum data ID using a syntax of daf_butler user expressions (e.g. 'visit = 123')."
181 ),
182 )
184 fail_exception = Field[str](
185 dtype=str,
186 default="builtins.ValueError",
187 doc=(
188 "Class name of the exception to raise when fail condition is triggered. Can be "
189 "'lsst.pipe.base.NoWorkFound' to specify non-failure exception."
190 ),
191 )
193 def data_id_match(self) -> DataIdMatch | None:
194 if not self.fail_condition:
195 return None
196 return DataIdMatch(self.fail_condition)
199class BaseTestPipelineTask(PipelineTask):
200 """A base class for test-utility `PipelineTask` classes that read and write
201 mock datasets `runQuantum`.
203 Notes
204 -----
205 This class overrides `runQuantum` to read inputs and write a bit of
206 provenance into all of its outputs (always `MockDataset` instances). It
207 can also be configured to raise exceptions on certain data IDs. It reads
208 `MockDataset` inputs and simulates reading inputs of other types by
209 creating `MockDataset` inputs from their DatasetRefs.
211 Subclasses are responsible for defining connections, but init-input and
212 init-output connections are not supported at runtime (they may be present
213 as long as the task is never constructed). All output connections must
214 use mock storage classes. `..Input` and `..PrerequisiteInput` connections
215 that do not use mock storage classes will be handled by constructing a
216 `MockDataset` from the `~lsst.daf.butler.DatasetRef` rather than actually
217 reading them.
218 """
220 ConfigClass: ClassVar[type[PipelineTaskConfig]] = BaseTestPipelineTaskConfig
222 def __init__(
223 self,
224 *,
225 config: BaseTestPipelineTaskConfig,
226 initInputs: Mapping[str, Any],
227 **kwargs: Any,
228 ):
229 super().__init__(config=config, **kwargs)
230 self.fail_exception: type | None = None
231 self.data_id_match = self.config.data_id_match()
232 if self.data_id_match:
233 self.fail_exception = doImportType(self.config.fail_exception)
234 # Look for, check, and record init-inputs.
235 task_connections = self.ConfigClass.ConnectionsClass(config=config)
236 mock_dataset_quantum = MockDatasetQuantum(task_label=self.getName(), data_id={}, inputs={})
237 for connection_name in task_connections.initInputs:
238 input_dataset = initInputs[connection_name]
239 if not isinstance(input_dataset, MockDataset):
240 raise TypeError(
241 f"Expected MockDataset instance for init-input {self.getName()}.{connection_name}: "
242 f"got {input_dataset!r} of type {type(input_dataset)!r}."
243 )
244 connection = task_connections.allConnections[connection_name]
245 if input_dataset.dataset_type.name != connection.name:
246 raise RuntimeError(
247 f"Incorrect dataset type name for init-input {self.getName()}.{connection_name}: "
248 f"got {input_dataset.dataset_type.name!r}, expected {connection.name!r}."
249 )
250 if input_dataset.storage_class != connection.storageClass:
251 raise RuntimeError(
252 f"Incorrect storage class for init-input {self.getName()}.{connection_name}: "
253 f"got {input_dataset.storage_class!r}, expected {connection.storageClass!r}."
254 )
255 # To avoid very deep provenance we trim inputs to a single
256 # level.
257 input_dataset.quantum = None
258 mock_dataset_quantum.inputs[connection_name] = [input_dataset]
259 # Add init-outputs as task instance attributes.
260 for connection_name in task_connections.initOutputs:
261 connection = task_connections.allConnections[connection_name]
262 output_dataset = MockDataset(
263 dataset_id=None, # the task has no way to get this
264 dataset_type=SerializedDatasetType(
265 name=connection.name,
266 storageClass=connection.storageClass,
267 dimensions=[],
268 ),
269 data_id={},
270 run=None, # task also has no way to get this
271 quantum=mock_dataset_quantum,
272 output_connection_name=connection_name,
273 )
274 setattr(self, connection_name, output_dataset)
276 config: BaseTestPipelineTaskConfig
278 def runQuantum(
279 self,
280 butlerQC: QuantumContext,
281 inputRefs: InputQuantizedConnection,
282 outputRefs: OutputQuantizedConnection,
283 ) -> None:
284 # docstring is inherited from the base class
285 quantum = butlerQC.quantum
287 _LOG.info("Mocking execution of task '%s' on quantum %s", self.getName(), quantum.dataId)
289 assert quantum.dataId is not None, "Quantum DataId cannot be None"
291 # Possibly raise an exception.
292 if self.data_id_match is not None and self.data_id_match.match(quantum.dataId):
293 _LOG.info("Simulating failure of task '%s' on quantum %s", self.getName(), quantum.dataId)
294 message = f"Simulated failure: task={self.getName()} dataId={quantum.dataId}"
295 assert self.fail_exception is not None, "Exception type must be defined"
296 raise self.fail_exception(message)
298 # Populate the bit of provenance we store in all outputs.
299 _LOG.info("Reading input data for task '%s' on quantum %s", self.getName(), quantum.dataId)
300 mock_dataset_quantum = MockDatasetQuantum(
301 task_label=self.getName(), data_id=dict(quantum.dataId.mapping), inputs={}
302 )
303 for name, refs in inputRefs:
304 inputs_list = []
305 ref: DatasetRef
306 for ref in ensure_iterable(refs):
307 if isinstance(ref.datasetType.storageClass, MockStorageClass):
308 input_dataset = butlerQC.get(ref)
309 if isinstance(input_dataset, DeferredDatasetHandle):
310 input_dataset = input_dataset.get()
311 if not isinstance(input_dataset, MockDataset):
312 raise TypeError(
313 f"Expected MockDataset instance for {ref}; "
314 f"got {input_dataset!r} of type {type(input_dataset)!r}."
315 )
316 # To avoid very deep provenance we trim inputs to a single
317 # level.
318 input_dataset.quantum = None
319 else:
320 input_dataset = MockDataset(
321 dataset_id=ref.id,
322 dataset_type=ref.datasetType.to_simple(),
323 data_id=dict(ref.dataId.mapping),
324 run=ref.run,
325 )
326 inputs_list.append(input_dataset)
327 mock_dataset_quantum.inputs[name] = inputs_list
329 # store mock outputs
330 for name, refs in outputRefs:
331 for ref in ensure_iterable(refs):
332 output = MockDataset(
333 dataset_id=ref.id,
334 dataset_type=ref.datasetType.to_simple(),
335 data_id=dict(ref.dataId.mapping),
336 run=ref.run,
337 quantum=mock_dataset_quantum,
338 output_connection_name=name,
339 )
340 butlerQC.put(output, ref)
342 _LOG.info("Finished mocking task '%s' on quantum %s", self.getName(), quantum.dataId)
345class MockPipelineDefaultTargetConnections(PipelineTaskConnections, dimensions=()):
346 pass
349class MockPipelineDefaultTargetConfig(
350 PipelineTaskConfig, pipelineConnections=MockPipelineDefaultTargetConnections
351):
352 pass
355class MockPipelineDefaultTargetTask(PipelineTask):
356 """A `~lsst.pipe.base.PipelineTask` class used as the default target for
357 ``MockPipelineTaskConfig.original``.
359 This is effectively a workaround for `lsst.pex.config.ConfigurableField`
360 not supporting ``optional=True``, but that is generally a reasonable
361 limitation for production code and it wouldn't make sense just to support
362 test utilities.
363 """
365 ConfigClass = MockPipelineDefaultTargetConfig
368class MockPipelineTaskConnections(BaseTestPipelineTaskConnections, dimensions=()):
369 """A connections class that creates mock connections from the connections
370 of a real PipelineTask.
371 """
373 def __init__(self, *, config: MockPipelineTaskConfig):
374 self.original: PipelineTaskConnections = config.original.connections.ConnectionsClass(
375 config=config.original.value
376 )
377 self.dimensions.update(self.original.dimensions)
378 self.unmocked_dataset_types = frozenset(config.unmocked_dataset_types)
379 for name, connection in self.original.allConnections.items():
380 if connection.name not in self.unmocked_dataset_types:
381 if connection.storageClass in (
382 acc.CONFIG_INIT_OUTPUT_STORAGE_CLASS,
383 acc.METADATA_OUTPUT_STORAGE_CLASS,
384 acc.LOG_OUTPUT_STORAGE_CLASS,
385 ):
386 # We don't mock the automatic output connections, so if
387 # they're used as an input in any other connection, we
388 # can't mock them there either.
389 storage_class_name = connection.storageClass
390 else:
391 # We register the mock storage class with the global
392 # singleton here, but can only put its name in the
393 # connection. That means the same global singleton (or one
394 # that also has these registrations) has to be available
395 # whenever this dataset type is used.
396 storage_class_name = MockStorageClass.get_or_register_mock(connection.storageClass).name
397 kwargs: dict[str, Any] = {}
398 if hasattr(connection, "dimensions"):
399 connection_dimensions = set(connection.dimensions)
400 # Replace the generic "skypix" placeholder with htm7, since
401 # that requires the dataset type to have already been
402 # registered.
403 if "skypix" in connection_dimensions:
404 connection_dimensions.remove("skypix")
405 connection_dimensions.add("htm7")
406 kwargs["dimensions"] = connection_dimensions
407 connection = dataclasses.replace(
408 connection,
409 name=get_mock_name(connection.name),
410 storageClass=storage_class_name,
411 **kwargs,
412 )
413 elif name in self.original.outputs:
414 raise ValueError(f"Unmocked dataset type {connection.name!r} cannot be used as an output.")
415 elif name in self.original.initInputs:
416 raise ValueError(
417 f"Unmocked dataset type {connection.name!r} cannot be used as an init-input."
418 )
419 elif name in self.original.initOutputs:
420 raise ValueError(
421 f"Unmocked dataset type {connection.name!r} cannot be used as an init-output."
422 )
423 setattr(self, name, connection)
425 def getSpatialBoundsConnections(self) -> Iterable[str]:
426 return self.original.getSpatialBoundsConnections()
428 def getTemporalBoundsConnections(self) -> Iterable[str]:
429 return self.original.getTemporalBoundsConnections()
431 def adjustQuantum(
432 self,
433 inputs: dict[str, tuple[BaseInput, Collection[DatasetRef]]],
434 outputs: dict[str, tuple[Output, Collection[DatasetRef]]],
435 label: str,
436 data_id: DataCoordinate,
437 ) -> tuple[
438 Mapping[str, tuple[BaseInput, Collection[DatasetRef]]],
439 Mapping[str, tuple[Output, Collection[DatasetRef]]],
440 ]:
441 # Convert the given mappings from the mock dataset types to the
442 # original dataset types they were produced from.
443 original_inputs = {}
444 for connection_name, (_, mock_refs) in inputs.items():
445 original_connection = getattr(self.original, connection_name)
446 if original_connection.name in self.unmocked_dataset_types:
447 refs = mock_refs
448 else:
449 refs = MockStorageClass.unmock_dataset_refs(mock_refs)
450 original_inputs[connection_name] = (original_connection, refs)
451 original_outputs = {}
452 for connection_name, (_, mock_refs) in outputs.items():
453 original_connection = getattr(self.original, connection_name)
454 if original_connection.name in self.unmocked_dataset_types:
455 refs = mock_refs
456 else:
457 refs = MockStorageClass.unmock_dataset_refs(mock_refs)
458 original_outputs[connection_name] = (original_connection, refs)
459 # Call adjustQuantum on the original connections class.
460 adjusted_original_inputs, adjusted_original_outputs = self.original.adjustQuantum(
461 original_inputs, original_outputs, label, data_id
462 )
463 # Convert the results back to the mock dataset type.s
464 adjusted_inputs = {}
465 for connection_name, (original_connection, original_refs) in adjusted_original_inputs.items():
466 if original_connection.name in self.unmocked_dataset_types:
467 refs = original_refs
468 else:
469 refs = MockStorageClass.mock_dataset_refs(original_refs)
470 adjusted_inputs[connection_name] = (getattr(self, connection_name), refs)
471 adjusted_outputs = {}
472 for connection_name, (original_connection, original_refs) in adjusted_original_outputs.items():
473 if original_connection.name in self.unmocked_dataset_types:
474 refs = original_refs
475 else:
476 refs = MockStorageClass.mock_dataset_refs(original_refs)
477 adjusted_outputs[connection_name] = (getattr(self, connection_name), refs)
478 return adjusted_inputs, adjusted_outputs
481class MockPipelineTaskConfig(BaseTestPipelineTaskConfig, pipelineConnections=MockPipelineTaskConnections):
482 """Configuration class for `MockPipelineTask`."""
484 original: ConfigurableField = ConfigurableField(
485 doc="The original task being mocked by this one.", target=MockPipelineDefaultTargetTask
486 )
488 unmocked_dataset_types = ListField[str](
489 doc=(
490 "Names of input dataset types that should be used as-is instead "
491 "of being mocked. May include dataset types not relevant for "
492 "this task, which will be ignored."
493 ),
494 default=(),
495 optional=False,
496 )
499class MockPipelineTask(BaseTestPipelineTask):
500 """A test-utility implementation of `PipelineTask` with connections
501 generated by mocking those of a real task.
503 Notes
504 -----
505 At present `MockPipelineTask` simply drops any ``initInput`` and
506 ``initOutput`` connections present on the original, since `MockDataset`
507 creation for those would have to happen in the code that executes the task,
508 not in the task itself. Because `MockPipelineTask` never instantiates the
509 mock task (just its connections class), this is a limitation on what the
510 mocks can be used to test, not anything deeper.
511 """
513 ConfigClass: ClassVar[type[PipelineTaskConfig]] = MockPipelineTaskConfig
516class DynamicConnectionConfig(Config):
517 """A config class that defines a completely dynamic connection."""
519 dataset_type_name = Field[str](doc="Name for the dataset type as seen by the butler.", dtype=str)
520 dimensions = ListField[str](doc="Dimensions for the dataset type.", dtype=str, default=[])
521 storage_class = Field[str](
522 doc="Name of the butler storage class for the dataset type.", dtype=str, default="StructuredDataDict"
523 )
524 is_calibration = Field[bool](doc="Whether this dataset type is a calibration.", dtype=bool, default=False)
525 multiple = Field[bool](
526 doc="Whether this connection gets or puts multiple datasets for each quantum.",
527 dtype=bool,
528 default=False,
529 )
530 mock_storage_class = Field[bool](
531 doc="Whether the storage class should actually be a mock of the storage class given.",
532 dtype=bool,
533 default=True,
534 )
536 def make_connection(self, cls: type[_T]) -> _T:
537 storage_class = self.storage_class
538 if self.mock_storage_class:
539 storage_class = MockStorageClass.get_or_register_mock(storage_class).name
540 if issubclass(cls, cT.DimensionedConnection):
541 return cls( # type: ignore
542 name=self.dataset_type_name,
543 storageClass=storage_class,
544 isCalibration=self.is_calibration,
545 multiple=self.multiple,
546 dimensions=frozenset(self.dimensions),
547 )
548 else:
549 return cls(
550 name=self.dataset_type_name,
551 storageClass=storage_class,
552 multiple=self.multiple,
553 )
556class DynamicTestPipelineTaskConnections(PipelineTaskConnections, dimensions=()):
557 """A connections class whose dimensions and connections are wholly
558 determined via configuration.
559 """
561 def __init__(self, *, config: DynamicTestPipelineTaskConfig):
562 self.dimensions.update(config.dimensions)
563 connection_config: DynamicConnectionConfig
564 for connection_name, connection_config in config.init_inputs.items():
565 setattr(self, connection_name, connection_config.make_connection(cT.InitInput))
566 for connection_name, connection_config in config.init_outputs.items():
567 setattr(self, connection_name, connection_config.make_connection(cT.InitOutput))
568 for connection_name, connection_config in config.prerequisite_inputs.items():
569 setattr(self, connection_name, connection_config.make_connection(cT.PrerequisiteInput))
570 for connection_name, connection_config in config.inputs.items():
571 setattr(self, connection_name, connection_config.make_connection(cT.Input))
572 for connection_name, connection_config in config.outputs.items():
573 setattr(self, connection_name, connection_config.make_connection(cT.Output))
576class DynamicTestPipelineTaskConfig(
577 PipelineTaskConfig, pipelineConnections=DynamicTestPipelineTaskConnections
578):
579 """Configuration for DynamicTestPipelineTask."""
581 dimensions = ListField[str](doc="Dimensions for the task's quanta.", dtype=str, default=[])
582 init_inputs = ConfigDictField(
583 doc=(
584 "Init-input connections, keyed by the connection name as seen by the task. "
585 "Must be empty if the task will be constructed."
586 ),
587 keytype=str,
588 itemtype=DynamicConnectionConfig,
589 default={},
590 )
591 init_outputs = ConfigDictField(
592 doc=(
593 "Init-output connections, keyed by the connection name as seen by the task. "
594 "Must be empty if the task will be constructed."
595 ),
596 keytype=str,
597 itemtype=DynamicConnectionConfig,
598 default={},
599 )
600 prerequisite_inputs = ConfigDictField(
601 doc="Prerequisite input connections, keyed by the connection name as seen by the task.",
602 keytype=str,
603 itemtype=DynamicConnectionConfig,
604 default={},
605 )
606 inputs = ConfigDictField(
607 doc="Regular input connections, keyed by the connection name as seen by the task.",
608 keytype=str,
609 itemtype=DynamicConnectionConfig,
610 default={},
611 )
612 outputs = ConfigDictField(
613 doc="Regular output connections, keyed by the connection name as seen by the task.",
614 keytype=str,
615 itemtype=DynamicConnectionConfig,
616 default={},
617 )
620class DynamicTestPipelineTask(BaseTestPipelineTask):
621 """A test-utility implementation of `PipelineTask` with dimensions and
622 connections determined wholly from configuration.
623 """
625 ConfigClass: ClassVar[type[PipelineTaskConfig]] = DynamicTestPipelineTaskConfig