mirror of
https://github.com/revng/revng
synced 2026-06-21 14:07:57 +00:00
Giacomo Vercesi
ddac707952
Rework how errors are propagated from the daemon code to the HTTP client, creating an exception hierarchy to handle the most common cases.
83 lines
2.6 KiB
Python
83 lines
2.6 KiB
Python
#
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# This file is distributed under the MIT License. See LICENSE.md for details.
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#
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from typing import Any, Optional
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from revng.pypeline.model import ReadOnlyModel
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from revng.pypeline.object import Kind, ObjectID, ObjectSet
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from revng.pypeline.pipeline import Pipeline
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from revng.pypeline.storage.storage_provider import StorageProvider
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from revng.pypeline.utils import bytes_to_string
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from revng.pypeline.utils.registry import get_singleton
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from .exceptions import MalformedRequestError
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Epoch = int
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def compute_objects(
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model: ReadOnlyModel,
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kind: Kind,
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objects: Optional[list[str]],
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) -> ObjectSet:
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"""
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If objects is not None, deserialize all objects, otherwise return all the objects producible of
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the given Kind.
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"""
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# If the user did not provide any objects for this container,
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# we will use all objects of the given kind from the model.
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if objects is None or len(objects) == 0:
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return model.all_objects(kind)
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# Otherwise we have to parse the objects provided by the user.
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# Parse the objects into ObjectSet
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obj_type = get_singleton(ObjectID) # type: ignore [type-abstract]
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objset = set()
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for obj in objects:
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if not isinstance(obj, str):
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raise MalformedRequestError(f'Object "{obj}" must be a string, got "{type(obj)}"')
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# Deserialize the object ID
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try:
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obj_id = obj_type.deserialize(obj)
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except ValueError as e:
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raise MalformedRequestError(f'Invalid object ID "{obj}": {e}')
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objset.add(obj_id)
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return ObjectSet(kind, objset)
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def compute_artifact(
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storage_provider: StorageProvider,
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pipeline: Pipeline,
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model: ReadOnlyModel,
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artifact_name: str,
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artifact_data: dict,
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) -> dict[str, Any]:
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"""
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Compute the requested artifact and return it in a HTTP compatible format.
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"""
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artifact_type = pipeline.artifacts[artifact_name]
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artifact = pipeline.artifacts[artifact_name]
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container = pipeline.get_artifact(
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model=model,
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artifact=artifact,
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requests=compute_objects(
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model=model,
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kind=artifact_type.container.container_type.kind,
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objects=artifact_data.get("objects"),
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),
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pipeline_configuration=artifact_data.get("configuration", {}),
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storage_provider=storage_provider,
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)
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return {
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"is_text": container.is_text(),
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"cacheable": artifact.is_cacheable(),
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"objects": {
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key: bytes_to_string(bytes(value), container.is_text())
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for key, value in container.to_dict().items()
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},
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}
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