def test_call_pre_run(self, pipeline_config: PipelineConfig) -> None:
"""Test calling pre-run method on units."""
for unit in pipeline_config.iter_units():
flexmock(unit).should_receive("pre_run").with_args().and_return(
None).once()
pipeline_config.call_pre_run()
def test_call_post_run_report(self, pipeline_config: PipelineConfig) -> None:
"""Test calling post-run method with report."""
report = Report(count=2, pipeline=pipeline_config)
for unit in pipeline_config.iter_units():
flexmock(unit).should_receive("post_run_report").with_args(
report=report
).and_return(None).once()
pipeline_config.call_post_run_report(report)
def test_to_dict(self, pipeline_config: PipelineConfig) -> None:
"""Test serialization of pipeline configuration."""
assert pipeline_config.to_dict() == {
"boots": [{"name": "Boot1", "configuration": {"some_parameter": -0.2}}],
"sieves": [{"name": "Sieve1", "configuration": {"flying_circus": 1969}}],
"steps": [{"name": "Step1", "configuration": {"guido_retirement": 2019}}],
"strides": [
{
"name": "Stride1",
"configuration": {
"linus": {
"residence": "oregon",
"children": 3,
"parents": ["nils", "anna"],
}
},
}
],
"wraps": [
{
"name": "Wrap1",
"configuration": {
"thoth": [2018, 2019],
"cities": ["Brno", "Bonn", "Boston", "Milan"],
},
}
],
}
def test_to_dict(self, pipeline_config: PipelineConfig) -> None:
"""Test conversion to a dict."""
report = Report(count=3, pipeline=pipeline_config)
project = flexmock()
project_dict = {"aresto momentum": "avada kedavra"}
project.should_receive("to_dict").with_args(
keep_thoth_section=True).and_return(
project_dict).twice() # In test and in the report.
product = Product(
project=project,
score=0.666,
justification=[{
"gryffindor": "le gladium leviosa"
}],
advised_runtime_environment=RuntimeEnvironment.from_dict(
{"python_version": "3.6"}),
)
report.add_product(product)
assert report.product_count() == 1
assert list(report.iter_products()) == [product]
assert report.to_dict() == {
"pipeline": pipeline_config.to_dict(),
"products": [product.to_dict()],
"stack_info": [],
"resolver_iterations": 0,
"accepted_final_states_count": 0,
"discarded_final_states_count": 0,
}
def pipeline_config() -> PipelineConfig:
"""Create a fixture for a pipeline configuration with few representatives of each pipeline unit type."""
flexmock(PipelineConfig)
flexmock(Boot1)
flexmock(Pseudonym1)
flexmock(Sieve1)
flexmock(Step1)
flexmock(Stride1)
flexmock(Wrap1)
boot1 = Boot1()
pseudonym1 = Pseudonym1()
sieve1 = Sieve1()
step1 = Step1()
stride1 = Stride1()
wrap1 = Wrap1()
return PipelineConfig(
boots={boot1.configuration.get("package_name"): [boot1]},
pseudonyms={pseudonym1.configuration["package_name"]: [pseudonym1]},
sieves={sieve1.configuration.get("package_name"): [sieve1]},
steps={step1.configuration.get("package_name"): [step1]},
strides={stride1.configuration.get("package_name"): [stride1]},
wraps={wrap1.configuration.get("package_name"): [wrap1]},
)
def test_to_dict(self, pipeline_config: PipelineConfig) -> None:
"""Test serialization of pipeline configuration."""
pipeline_dict = pipeline_config.to_dict()
assert pipeline_dict == {
"boots": [{
"configuration": {
"package_name": "flask",
"some_parameter": -0.2
},
"name": "Boot1",
"unit_run": False
}],
"pseudonyms": [{
"configuration": {
"another_parameter": 0.33,
"package_name": "tensorflow"
},
"name": "Pseudonym1",
"unit_run": False,
}],
"sieves": [{
"configuration": {
"flying_circus": 1969,
"package_name": "tensorflow"
},
"name": "Sieve1",
"unit_run": False,
}],
"steps": [{
"configuration": {
"guido_retirement": 2019,
"package_name": "tensorflow",
"multi_package_resolution": False,
},
"name": "Step1",
"unit_run": False,
}],
"strides": [{
"configuration": {
"linus": {
"children": 3,
"parents": ["nils", "anna"],
"residence": "oregon"
},
"package_name": None,
},
"name": "Stride1",
"unit_run": False,
}],
"wraps": [{
"configuration": {
"cities": ["Brno", "Bonn", "Boston", "Milan"],
"thoth": [2018, 2019],
"package_name": None,
},
"name": "Wrap1",
"unit_run": False,
}],
}
def pipeline_config() -> PipelineConfig: # noqa: D401
"""A fixture for a pipeline config."""
return PipelineConfig(boots={},
pseudonyms={},
sieves={},
steps={},
strides={},
wraps={})
def test_iter_units(self, pipeline_config: PipelineConfig) -> None:
"""Test iteration over all units present in the pipeline configuration."""
visited = dict.fromkeys(
("Boot1", "Pseudonym1", "Sieve1", "Step1", "Stride1", "Wrap1"), 0)
for unit in pipeline_config.iter_units():
assert unit.__class__.__name__ in visited, f"Unknown unit {unit.__class__.__name__!r}"
visited[unit.__class__.__name__] += 1
assert len(visited) == 6
assert list(visited.values()) == [1] * 6
def _get_test_dm(
*, stack_output: str, with_devel: bool, products: List[Product], amun_context: Optional[Dict[str, Any]] = None
) -> DependencyMonkey:
"""Get instantiated dependency monkey ready to be tested."""
flexmock(Resolver)
(Resolver.should_receive("resolve_products").with_args(with_devel=with_devel).and_return(products).once())
flexmock(PipelineConfig)
(PipelineConfig.should_receive("call_post_run_report").and_return(None).once())
dependency_monkey = DependencyMonkey(
resolver=Resolver(
pipeline=PipelineConfig(),
project=None,
library_usage=None,
graph=None,
predictor=None,
),
stack_output=stack_output,
decision_type=DecisionType.ALL,
context=amun_context or {},
)
return dependency_monkey
def pipeline_config() -> PipelineConfig:
"""A fixture for a pipeline configuration with few representatives of each pipeline unit type."""
flexmock(PipelineConfig)
flexmock(Boot1)
flexmock(Sieve1)
flexmock(Step1)
flexmock(Stride1)
flexmock(Wrap1)
return PipelineConfig(
boots=[Boot1()],
sieves=[Sieve1()],
steps=[Step1()],
strides=[Stride1()],
wraps=[Wrap1()],
)
def test_to_dict_metadata(self, pipeline_config: PipelineConfig) -> None:
"""Test conversion to a dict with passed metadata."""
report = Report(count=3, pipeline=pipeline_config)
project = flexmock()
project_dict = {"aresto momentum": "avada kedavra"}
project.should_receive("to_dict").with_args(
keep_thoth_section=True).and_return(project_dict)
product = Product(
project=project,
score=0.666,
justification=[{
"gryffindor": "le gladium leviosa"
}],
advised_runtime_environment=RuntimeEnvironment.from_dict(
{"python_version": "3.6"}),
)
report.add_product(product)
stack_info = [{"type": "WARNING", "message": "Hello, metadata"}]
stack_info_metadata = {
"thoth.adviser": {
"stack_info": stack_info,
}
}
report.set_stack_info([{"foo": "bar"}])
assert "THOTH_ADVISER_METADATA" not in os.environ
os.environ["THOTH_ADVISER_METADATA"] = json.dumps(stack_info_metadata)
try:
assert report.product_count() == 1
assert list(report.iter_products()) == [product]
assert report.to_dict() == {
"pipeline": pipeline_config.to_dict(),
"products": [product.to_dict()],
"stack_info": list(chain(stack_info, report.stack_info)),
"resolver_iterations": 0,
"accepted_final_states_count": 0,
"discarded_final_states_count": 0,
}
except Exception:
os.environ.pop("THOTH_ADVISER_METADATA")
raise
def pipeline_config() -> PipelineConfig:
"""A fixture for a pipeline config."""
return PipelineConfig(boots=[], sieves=[], steps=[], strides=[], wraps=[])