def from_v1_component_spec(
cls,
v1_component_spec: v1_structures.ComponentSpec) -> 'ComponentSpec':
"""Converts V1 ComponentSpec to V2 ComponentSpec.
Args:
v1_component_spec: The V1 ComponentSpec.
Returns:
Component spec in the form of V2 ComponentSpec.
Raises:
ValueError: If implementation is not found.
TypeError: if any argument is neither a str nor Dict.
"""
component_dict = v1_component_spec.to_dict()
if component_dict.get('implementation') is None:
raise ValueError('Implementation field not found')
if 'container' not in component_dict.get('implementation'):
raise NotImplementedError
def _transform_arg(
arg: Union[str, Dict[str, str]]) -> ValidCommandArgs:
if isinstance(arg, str):
return arg
if 'inputValue' in arg:
return InputValuePlaceholder(
input_name=utils.sanitize_input_name(arg['inputValue']))
if 'inputPath' in arg:
return InputPathPlaceholder(
input_name=utils.sanitize_input_name(arg['inputPath']))
if 'inputUri' in arg:
return InputUriPlaceholder(
input_name=utils.sanitize_input_name(arg['inputUri']))
if 'outputPath' in arg:
return OutputPathPlaceholder(
output_name=utils.sanitize_input_name(arg['outputPath']))
if 'outputUri' in arg:
return OutputUriPlaceholder(
output_name=utils.sanitize_input_name(arg['outputUri']))
if 'if' in arg:
if_placeholder_values = arg['if']
if_placeholder_values_then = list(
if_placeholder_values['then'])
try:
if_placeholder_values_else = list(
if_placeholder_values['else'])
except KeyError:
if_placeholder_values_else = []
IfPresentPlaceholderStructure.update_forward_refs()
return IfPresentPlaceholder(
if_structure=IfPresentPlaceholderStructure(
input_name=utils.sanitize_input_name(
if_placeholder_values['cond']['isPresent']),
then=list(
_transform_arg(val)
for val in if_placeholder_values_then),
otherwise=list(
_transform_arg(val)
for val in if_placeholder_values_else)))
if 'concat' in arg:
ConcatPlaceholder.update_forward_refs()
return ConcatPlaceholder(concat=list(
_transform_arg(val) for val in arg['concat']))
raise ValueError(
f'Unexpected command/argument type: "{arg}" of type "{type(arg)}".'
)
implementation = component_dict['implementation']['container']
implementation['commands'] = [
_transform_arg(command)
for command in implementation.pop('command', [])
]
implementation['arguments'] = [
_transform_arg(command)
for command in implementation.pop('args', [])
]
implementation['env'] = {
key: _transform_arg(command)
for key, command in implementation.pop('env', {}).items()
}
container_spec = ContainerSpec(image=implementation['image'])
# Workaround for https://github.com/samuelcolvin/pydantic/issues/2079
def _copy_model(obj):
if isinstance(obj, BaseModel):
return obj.copy(deep=True)
return obj
# Must assign these after the constructor call, otherwise it won't work.
if implementation['commands']:
container_spec.commands = [
_copy_model(cmd) for cmd in implementation['commands']
]
if implementation['arguments']:
container_spec.arguments = [
_copy_model(arg) for arg in implementation['arguments']
]
if implementation['env']:
container_spec.env = {
k: _copy_model(v)
for k, v in implementation['env']
}
return ComponentSpec(
name=component_dict.get('name', 'name'),
description=component_dict.get('description'),
implementation=Implementation(container=container_spec),
inputs={
utils.sanitize_input_name(spec['name']):
InputSpec(type=spec.get('type', 'Artifact'),
default=spec.get('default', None))
for spec in component_dict.get('inputs', [])
},
outputs={
utils.sanitize_input_name(spec['name']):
OutputSpec(type=spec.get('type', 'Artifact'))
for spec in component_dict.get('outputs', [])
})
def test_to_dict(self):
component_meta = ComponentSpec(
name='foobar',
description='foobar example',
inputs=[
InputSpec(name='input1',
description='input1 desc',
type={
'GCSPath': {
'bucket_type': 'directory',
'file_type': 'csv'
}
},
default='default1'),
InputSpec(name='input2',
description='input2 desc',
type={
'TFModel': {
'input_data': 'tensor',
'version': '1.8.0'
}
},
default='default2'),
InputSpec(name='input3',
description='input3 desc',
type='Integer',
default='default3'),
],
outputs=[
OutputSpec(
name='output1',
description='output1 desc',
type={'Schema': {
'file_type': 'tsv'
}},
)
])
golden_meta = {
'name':
'foobar',
'description':
'foobar example',
'inputs': [{
'name': 'input1',
'description': 'input1 desc',
'type': {
'GCSPath': {
'bucket_type': 'directory',
'file_type': 'csv'
}
},
'default': 'default1'
}, {
'name': 'input2',
'description': 'input2 desc',
'type': {
'TFModel': {
'input_data': 'tensor',
'version': '1.8.0'
}
},
'default': 'default2'
}, {
'name': 'input3',
'description': 'input3 desc',
'type': 'Integer',
'default': 'default3'
}],
'outputs': [{
'name': 'output1',
'description': 'output1 desc',
'type': {
'Schema': {
'file_type': 'tsv'
}
},
}]
}
self.assertEqual(component_meta.to_dict(), golden_meta)