JobModel

class DIRAC.WorkloadManagementSystem.Utilities.JobModel.BaseJobDescriptionModel(*, arguments: str = '', bannedSites: Annotated[set[str], BeforeValidator(func=default_set_validator, json_schema_input_type=PydanticUndefined)] = {}, cpuTime: int, executable: str, executionEnvironment: dict = None, gridCE: str = '', inputSandbox: Annotated[set[str], BeforeValidator(func=default_set_validator, json_schema_input_type=PydanticUndefined)] = {}, inputData: Annotated[set[str], BeforeValidator(func=default_set_validator, json_schema_input_type=PydanticUndefined)] = {}, inputDataPolicy: str = '', jobConfigArgs: str = '', jobGroup: str = '', jobType: str = 'User', jobName: str = 'Name', logLevel: str = 'INFO', maxNumberOfProcessors: int = None, minNumberOfProcessors: int = 1, outputData: Annotated[set[str], BeforeValidator(func=default_set_validator, json_schema_input_type=PydanticUndefined)] = {}, outputPath: str = '', outputSandbox: Annotated[set[str], BeforeValidator(func=default_set_validator, json_schema_input_type=PydanticUndefined)] = {}, outputSE: str = '', platform: str = '', priority: int, sites: Annotated[set[str], BeforeValidator(func=default_set_validator, json_schema_input_type=PydanticUndefined)] = {}, stderr: str = 'std.err', stdout: str = 'std.out', tags: Annotated[set[str], BeforeValidator(func=default_set_validator, json_schema_input_type=PydanticUndefined)] = {}, extraFields: dict[str, Any] = {})

Bases: BaseJobDescriptionModel

__init__(**data: Any) → None

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

classmethod addLFNPrefixIfStringStartsWithASlash(v: set[str])

addTagsDependingOnNumberOfProcessors() → Self

arguments: str

bannedSites: CoercibleSetStr

classmethod checkCPUTimeBounds(v)

classmethod checkExecutableIsNotAnEmptyString(v: str)

classmethod checkInputDataDoesntContainDoubleSlashes(v)

classmethod checkJobTypeIsAllowed(v: str)

classmethod checkLFNSandboxesAreWellFormated(v: set[str])

classmethod checkLogLevelIsValid(v: str)

classmethod checkMaxNumberOfProcessorsBounds(v)

classmethod checkMinNumberOfProcessorsBounds(v)

checkNumberOfInputDataFiles() → Self

classmethod checkPriorityBounds(v)

classmethod checkSites(v: set[str])

checkThatMaxNumberOfProcessorsIsGreaterThanMinNumberOfProcessors() → Self

checkThatSitesAndBannedSitesAreNotMutuallyExclusive() → Self

classmethod construct(_fields_set: set[str] | None = None, **values: Any) → Self

copy(*, include: AbstractSetIntStr | MappingIntStrAny | None = None, exclude: AbstractSetIntStr | MappingIntStrAny | None = None, update: Dict[str, Any] | None = None, deep: bool = False) → Self

Returns a copy of the model.

!!! warning “Deprecated”

This method is now deprecated; use model_copy instead.

If you need include or exclude, use:

`python {test="skip" lint="skip"} data = self.model_dump(include=include, exclude=exclude, round_trip=True) data = {**data, **(update or {})} copied = self.model_validate(data) `

Parameters:

• include – Optional set or mapping specifying which fields to include in the copied model.

• exclude – Optional set or mapping specifying which fields to exclude in the copied model.

• update – Optional dictionary of field-value pairs to override field values in the copied model.

• deep – If True, the values of fields that are Pydantic models will be deep-copied.

Returns:

A copy of the model with included, excluded and updated fields as specified.

cpuTime: int

dict(*, include: set[int] | set[str] | Mapping[int, set[int] | set[str] | Mapping[int, IncEx | bool] | Mapping[str, IncEx | bool] | bool] | Mapping[str, set[int] | set[str] | Mapping[int, IncEx | bool] | Mapping[str, IncEx | bool] | bool] | None = None, exclude: set[int] | set[str] | Mapping[int, set[int] | set[str] | Mapping[int, IncEx | bool] | Mapping[str, IncEx | bool] | bool] | Mapping[str, set[int] | set[str] | Mapping[int, IncEx | bool] | Mapping[str, IncEx | bool] | bool] | None = None, by_alias: bool = False, exclude_unset: bool = False, exclude_defaults: bool = False, exclude_none: bool = False) → Dict[str, Any]

executable: str

executionEnvironment: dict

extraFields: dict[str, Any]

classmethod from_orm(obj: Any) → Self

gridCE: str

classmethod injectDefaultValues(values: dict[str, Any]) → dict[str, Any]

inputData: CoercibleSetStr

inputDataPolicy: str

inputSandbox: CoercibleSetStr

jobConfigArgs: str

jobGroup: str

jobName: str

jobType: str

json(*, include: set[int] | set[str] | Mapping[int, set[int] | set[str] | Mapping[int, IncEx | bool] | Mapping[str, IncEx | bool] | bool] | Mapping[str, set[int] | set[str] | Mapping[int, IncEx | bool] | Mapping[str, IncEx | bool] | bool] | None = None, exclude: set[int] | set[str] | Mapping[int, set[int] | set[str] | Mapping[int, IncEx | bool] | Mapping[str, IncEx | bool] | bool] | Mapping[str, set[int] | set[str] | Mapping[int, IncEx | bool] | Mapping[str, IncEx | bool] | bool] | None = None, by_alias: bool = False, exclude_unset: bool = False, exclude_defaults: bool = False, exclude_none: bool = False, encoder: Callable[[Any], Any] | None = PydanticUndefined, models_as_dict: bool = PydanticUndefined, **dumps_kwargs: Any) → str

logLevel: str

maxNumberOfProcessors: int

minNumberOfProcessors: int

model_computed_fields = {}

model_config: ClassVar[ConfigDict] = {'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

classmethod model_construct(_fields_set: set[str] | None = None, **values: Any) → Self

Creates a new instance of the Model class with validated data.

Creates a new model setting __dict__ and __pydantic_fields_set__ from trusted or pre-validated data. Default values are respected, but no other validation is performed.

!!! note

model_construct() generally respects the model_config.extra setting on the provided model. That is, if model_config.extra == ‘allow’, then all extra passed values are added to the model instance’s __dict__ and __pydantic_extra__ fields. If model_config.extra == ‘ignore’ (the default), then all extra passed values are ignored. Because no validation is performed with a call to model_construct(), having model_config.extra == ‘forbid’ does not result in an error if extra values are passed, but they will be ignored.

Parameters:

• _fields_set – A set of field names that were originally explicitly set during instantiation. If provided, this is directly used for the [model_fields_set][pydantic.BaseModel.model_fields_set] attribute. Otherwise, the field names from the values argument will be used.

• values – Trusted or pre-validated data dictionary.

Returns:

A new instance of the Model class with validated data.

model_copy(*, update: Mapping[str, Any] | None = None, deep: bool = False) → Self

!!! abstract “Usage Documentation”

[model_copy](../concepts/models.md#model-copy)

Returns a copy of the model.

!!! note

The underlying instance’s [__dict__][object.__dict__] attribute is copied. This might have unexpected side effects if you store anything in it, on top of the model fields (e.g. the value of [cached properties][functools.cached_property]).

Parameters:

• update – Values to change/add in the new model. Note: the data is not validated before creating the new model. You should trust this data.

• deep – Set to True to make a deep copy of the model.

Returns:

New model instance.

model_dump(*, mode: Literal['json', 'python'] | str = 'python', include: set[int] | set[str] | Mapping[int, set[int] | set[str] | Mapping[int, IncEx | bool] | Mapping[str, IncEx | bool] | bool] | Mapping[str, set[int] | set[str] | Mapping[int, IncEx | bool] | Mapping[str, IncEx | bool] | bool] | None = None, exclude: set[int] | set[str] | Mapping[int, set[int] | set[str] | Mapping[int, IncEx | bool] | Mapping[str, IncEx | bool] | bool] | Mapping[str, set[int] | set[str] | Mapping[int, IncEx | bool] | Mapping[str, IncEx | bool] | bool] | None = None, context: Any | None = None, by_alias: bool | None = None, exclude_unset: bool = False, exclude_defaults: bool = False, exclude_none: bool = False, exclude_computed_fields: bool = False, round_trip: bool = False, warnings: bool | Literal['none', 'warn', 'error'] = True, fallback: Callable[[Any], Any] | None = None, serialize_as_any: bool = False, polymorphic_serialization: bool | None = None) → dict[str, Any]

!!! abstract “Usage Documentation”

[model_dump](../concepts/serialization.md#python-mode)

Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.

Parameters:

• mode – The mode in which to_python should run. If mode is ‘json’, the output will only contain JSON serializable types. If mode is ‘python’, the output may contain non-JSON-serializable Python objects.

• include – A set of fields to include in the output.

• exclude – A set of fields to exclude from the output.

• context – Additional context to pass to the serializer.

• by_alias – Whether to use the field’s alias in the dictionary key if defined.

• exclude_unset – Whether to exclude fields that have not been explicitly set.

• exclude_defaults – Whether to exclude fields that are set to their default value.

• exclude_none – Whether to exclude fields that have a value of None.

• exclude_computed_fields – Whether to exclude computed fields. While this can be useful for round-tripping, it is usually recommended to use the dedicated round_trip parameter instead.

• round_trip – If True, dumped values should be valid as input for non-idempotent types such as Json[T].

• warnings – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].

• fallback – A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised.

• serialize_as_any – Whether to serialize fields with duck-typing serialization behavior.

• polymorphic_serialization – Whether to use model and dataclass polymorphic serialization for this call.

Returns:

A dictionary representation of the model.

model_dump_json(*, indent: int | None = None, ensure_ascii: bool = False, include: set[int] | set[str] | Mapping[int, set[int] | set[str] | Mapping[int, IncEx | bool] | Mapping[str, IncEx | bool] | bool] | Mapping[str, set[int] | set[str] | Mapping[int, IncEx | bool] | Mapping[str, IncEx | bool] | bool] | None = None, exclude: set[int] | set[str] | Mapping[int, set[int] | set[str] | Mapping[int, IncEx | bool] | Mapping[str, IncEx | bool] | bool] | Mapping[str, set[int] | set[str] | Mapping[int, IncEx | bool] | Mapping[str, IncEx | bool] | bool] | None = None, context: Any | None = None, by_alias: bool | None = None, exclude_unset: bool = False, exclude_defaults: bool = False, exclude_none: bool = False, exclude_computed_fields: bool = False, round_trip: bool = False, warnings: bool | Literal['none', 'warn', 'error'] = True, fallback: Callable[[Any], Any] | None = None, serialize_as_any: bool = False, polymorphic_serialization: bool | None = None) → str

!!! abstract “Usage Documentation”

[model_dump_json](../concepts/serialization.md#json-mode)

Generates a JSON representation of the model using Pydantic’s to_json method.

Parameters:

• indent – Indentation to use in the JSON output. If None is passed, the output will be compact.

• ensure_ascii – If True, the output is guaranteed to have all incoming non-ASCII characters escaped. If False (the default), these characters will be output as-is.

• include – Field(s) to include in the JSON output.

• exclude – Field(s) to exclude from the JSON output.

• context – Additional context to pass to the serializer.

• by_alias – Whether to serialize using field aliases.

• exclude_unset – Whether to exclude fields that have not been explicitly set.

• exclude_defaults – Whether to exclude fields that are set to their default value.

• exclude_none – Whether to exclude fields that have a value of None.

• exclude_computed_fields – Whether to exclude computed fields. While this can be useful for round-tripping, it is usually recommended to use the dedicated round_trip parameter instead.

• round_trip – If True, dumped values should be valid as input for non-idempotent types such as Json[T].

• warnings – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].

• fallback – A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised.

• serialize_as_any – Whether to serialize fields with duck-typing serialization behavior.

• polymorphic_serialization – Whether to use model and dataclass polymorphic serialization for this call.

Returns:

A JSON string representation of the model.

property model_extra: dict[str, Any] | None

Get extra fields set during validation.

Returns:

A dictionary of extra fields, or None if config.extra is not set to “allow”.

model_fields = {'arguments': FieldInfo(annotation=str, required=False, default=''), 'bannedSites': FieldInfo(annotation=set[str], required=False, default=set(), metadata=[BeforeValidator(func=<function default_set_validator>, json_schema_input_type=PydanticUndefined)]), 'cpuTime': FieldInfo(annotation=int, required=True), 'executable': FieldInfo(annotation=str, required=True), 'executionEnvironment': FieldInfo(annotation=dict, required=False, default=None), 'extraFields': FieldInfo(annotation=dict[str, Any], required=False, default={}), 'gridCE': FieldInfo(annotation=str, required=False, default=''), 'inputData': FieldInfo(annotation=set[str], required=False, default=set(), metadata=[BeforeValidator(func=<function default_set_validator>, json_schema_input_type=PydanticUndefined)]), 'inputDataPolicy': FieldInfo(annotation=str, required=False, default=''), 'inputSandbox': FieldInfo(annotation=set[str], required=False, default=set(), metadata=[BeforeValidator(func=<function default_set_validator>, json_schema_input_type=PydanticUndefined)]), 'jobConfigArgs': FieldInfo(annotation=str, required=False, default=''), 'jobGroup': FieldInfo(annotation=str, required=False, default=''), 'jobName': FieldInfo(annotation=str, required=False, default='Name'), 'jobType': FieldInfo(annotation=str, required=False, default='User'), 'logLevel': FieldInfo(annotation=str, required=False, default='INFO'), 'maxNumberOfProcessors': FieldInfo(annotation=int, required=False, default=None), 'minNumberOfProcessors': FieldInfo(annotation=int, required=False, default=1), 'outputData': FieldInfo(annotation=set[str], required=False, default=set(), metadata=[BeforeValidator(func=<function default_set_validator>, json_schema_input_type=PydanticUndefined)]), 'outputPath': FieldInfo(annotation=str, required=False, default=''), 'outputSE': FieldInfo(annotation=str, required=False, default=''), 'outputSandbox': FieldInfo(annotation=set[str], required=False, default=set(), metadata=[BeforeValidator(func=<function default_set_validator>, json_schema_input_type=PydanticUndefined)]), 'platform': FieldInfo(annotation=str, required=False, default=''), 'priority': FieldInfo(annotation=int, required=True), 'sites': FieldInfo(annotation=set[str], required=False, default=set(), metadata=[BeforeValidator(func=<function default_set_validator>, json_schema_input_type=PydanticUndefined)]), 'stderr': FieldInfo(annotation=str, required=False, default='std.err'), 'stdout': FieldInfo(annotation=str, required=False, default='std.out'), 'tags': FieldInfo(annotation=set[str], required=False, default=set(), metadata=[BeforeValidator(func=<function default_set_validator>, json_schema_input_type=PydanticUndefined)])}

property model_fields_set: set[str]

Returns the set of fields that have been explicitly set on this model instance.

Returns:

A set of strings representing the fields that have been set,

i.e. that were not filled from defaults.

classmethod model_parametrized_name(params: tuple[type[Any], ...]) → str

Compute the class name for parametrizations of generic classes.

This method can be overridden to achieve a custom naming scheme for generic BaseModels.

Parameters:

params – Tuple of types of the class. Given a generic class Model with 2 type variables and a concrete model Model[str, int], the value (str, int) would be passed to params.

Returns:

String representing the new class where params are passed to cls as type variables.

Raises:

TypeError – Raised when trying to generate concrete names for non-generic models.

model_post_init(context: Any, /) → None

Override this method to perform additional initialization after __init__ and model_construct. This is useful if you want to do some validation that requires the entire model to be initialized.

classmethod model_rebuild(*, force: bool = False, raise_errors: bool = True, _parent_namespace_depth: int = 2, _types_namespace: MappingNamespace | None = None) → bool | None

Try to rebuild the pydantic-core schema for the model.

This may be necessary when one of the annotations is a ForwardRef which could not be resolved during the initial attempt to build the schema, and automatic rebuilding fails.

Parameters:

• force – Whether to force the rebuilding of the model schema, defaults to False.

• raise_errors – Whether to raise errors, defaults to True.

• _parent_namespace_depth – The depth level of the parent namespace, defaults to 2.

• _types_namespace – The types namespace, defaults to None.

Returns:

Returns None if the schema is already “complete” and rebuilding was not required. If rebuilding _was_ required, returns True if rebuilding was successful, otherwise False.

classmethod model_validate(obj: Any, *, strict: bool | None = None, extra: Literal['allow', 'ignore', 'forbid'] | None = None, from_attributes: bool | None = None, context: Any | None = None, by_alias: bool | None = None, by_name: bool | None = None) → Self

Validate a pydantic model instance.

Parameters:

• obj – The object to validate.

• strict – Whether to enforce types strictly.

• extra – Whether to ignore, allow, or forbid extra data during model validation. See the [extra configuration value][pydantic.ConfigDict.extra] for details.

• from_attributes – Whether to extract data from object attributes.

• context – Additional context to pass to the validator.

• by_alias – Whether to use the field’s alias when validating against the provided input data.

• by_name – Whether to use the field’s name when validating against the provided input data.

Raises:

ValidationError – If the object could not be validated.

Returns:

The validated model instance.

classmethod model_validate_json(json_data: str | bytes | bytearray, *, strict: bool | None = None, extra: Literal['allow', 'ignore', 'forbid'] | None = None, context: Any | None = None, by_alias: bool | None = None, by_name: bool | None = None) → Self

!!! abstract “Usage Documentation”

[JSON Parsing](../concepts/json.md#json-parsing)

Validate the given JSON data against the Pydantic model.

Parameters:

• json_data – The JSON data to validate.

• strict – Whether to enforce types strictly.

• extra – Whether to ignore, allow, or forbid extra data during model validation. See the [extra configuration value][pydantic.ConfigDict.extra] for details.

• context – Extra variables to pass to the validator.

• by_alias – Whether to use the field’s alias when validating against the provided input data.

• by_name – Whether to use the field’s name when validating against the provided input data.

Returns:

The validated Pydantic model.

Raises:

ValidationError – If json_data is not a JSON string or the object could not be validated.

classmethod model_validate_strings(obj: Any, *, strict: bool | None = None, extra: Literal['allow', 'ignore', 'forbid'] | None = None, context: Any | None = None, by_alias: bool | None = None, by_name: bool | None = None) → Self

Validate the given object with string data against the Pydantic model.

Parameters:

• obj – The object containing string data to validate.

• strict – Whether to enforce types strictly.

• extra – Whether to ignore, allow, or forbid extra data during model validation. See the [extra configuration value][pydantic.ConfigDict.extra] for details.

• context – Extra variables to pass to the validator.

• by_alias – Whether to use the field’s alias when validating against the provided input data.

• by_name – Whether to use the field’s name when validating against the provided input data.

Returns:

The validated Pydantic model.

outputData: CoercibleSetStr

outputPath: str

outputSE: str

outputSandbox: CoercibleSetStr

classmethod parse_file(path: str | Path, *, content_type: str | None = None, encoding: str = 'utf8', proto: DeprecatedParseProtocol | None = None, allow_pickle: bool = False) → Self

classmethod parse_obj(obj: Any) → Self

classmethod parse_raw(b: str | bytes, *, content_type: str | None = None, encoding: str = 'utf8', proto: DeprecatedParseProtocol | None = None, allow_pickle: bool = False) → Self

platform: str

priority: int

classmethod schema(by_alias: bool = True, ref_template: str = '#/$defs/{model}') → Dict[str, Any]

classmethod schema_json(*, by_alias: bool = True, ref_template: str = '#/$defs/{model}', **dumps_kwargs: Any) → str

sites: CoercibleSetStr

stderr: str

stdout: str

tags: CoercibleSetStr

classmethod update_forward_refs(**localns: Any) → None

classmethod validate(value: Any) → Self

class DIRAC.WorkloadManagementSystem.Utilities.JobModel.JobDescriptionModel(*, arguments: str = '', bannedSites: Annotated[set[str], BeforeValidator(func=default_set_validator, json_schema_input_type=PydanticUndefined)] = {}, cpuTime: int, executable: str, executionEnvironment: dict = None, gridCE: str = '', inputSandbox: Annotated[set[str], BeforeValidator(func=default_set_validator, json_schema_input_type=PydanticUndefined)] = {}, inputData: Annotated[set[str], BeforeValidator(func=default_set_validator, json_schema_input_type=PydanticUndefined)] = {}, inputDataPolicy: str = '', jobConfigArgs: str = '', jobGroup: str = '', jobType: str = 'User', jobName: str = 'Name', logLevel: str = 'INFO', maxNumberOfProcessors: int = None, minNumberOfProcessors: int = 1, outputData: Annotated[set[str], BeforeValidator(func=default_set_validator, json_schema_input_type=PydanticUndefined)] = {}, outputPath: str = '', outputSandbox: Annotated[set[str], BeforeValidator(func=default_set_validator, json_schema_input_type=PydanticUndefined)] = {}, outputSE: str = '', platform: str = '', priority: int, sites: Annotated[set[str], BeforeValidator(func=default_set_validator, json_schema_input_type=PydanticUndefined)] = {}, stderr: str = 'std.err', stdout: str = 'std.out', tags: Annotated[set[str], BeforeValidator(func=default_set_validator, json_schema_input_type=PydanticUndefined)] = {}, extraFields: dict[str, Any] = {}, owner: str, ownerGroup: str, vo: str)

Bases: JobDescriptionModel

__init__(**data: Any) → None

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

classmethod addLFNPrefixIfStringStartsWithASlash(v: set[str])

addTagsDependingOnNumberOfProcessors() → Self

arguments: str

bannedSites: CoercibleSetStr

classmethod checkCPUTimeBounds(v)

classmethod checkExecutableIsNotAnEmptyString(v: str)

classmethod checkInputDataDoesntContainDoubleSlashes(v)

classmethod checkJobTypeIsAllowed(v: str)

checkLFNMatchesREGEX() → Self

classmethod checkLFNSandboxesAreWellFormated(v: set[str])

classmethod checkLogLevelIsValid(v: str)

classmethod checkMaxNumberOfProcessorsBounds(v)

classmethod checkMinNumberOfProcessorsBounds(v)

checkNumberOfInputDataFiles() → Self

classmethod checkPriorityBounds(v)

classmethod checkSites(v: set[str])

checkThatMaxNumberOfProcessorsIsGreaterThanMinNumberOfProcessors() → Self

checkThatSitesAndBannedSitesAreNotMutuallyExclusive() → Self

classmethod construct(_fields_set: set[str] | None = None, **values: Any) → Self

copy(*, include: AbstractSetIntStr | MappingIntStrAny | None = None, exclude: AbstractSetIntStr | MappingIntStrAny | None = None, update: Dict[str, Any] | None = None, deep: bool = False) → Self

Returns a copy of the model.

!!! warning “Deprecated”

This method is now deprecated; use model_copy instead.

If you need include or exclude, use:

`python {test="skip" lint="skip"} data = self.model_dump(include=include, exclude=exclude, round_trip=True) data = {**data, **(update or {})} copied = self.model_validate(data) `

Parameters:

• include – Optional set or mapping specifying which fields to include in the copied model.

• exclude – Optional set or mapping specifying which fields to exclude in the copied model.

• update – Optional dictionary of field-value pairs to override field values in the copied model.

• deep – If True, the values of fields that are Pydantic models will be deep-copied.

Returns:

A copy of the model with included, excluded and updated fields as specified.

cpuTime: int

dict(*, include: set[int] | set[str] | Mapping[int, set[int] | set[str] | Mapping[int, IncEx | bool] | Mapping[str, IncEx | bool] | bool] | Mapping[str, set[int] | set[str] | Mapping[int, IncEx | bool] | Mapping[str, IncEx | bool] | bool] | None = None, exclude: set[int] | set[str] | Mapping[int, set[int] | set[str] | Mapping[int, IncEx | bool] | Mapping[str, IncEx | bool] | bool] | Mapping[str, set[int] | set[str] | Mapping[int, IncEx | bool] | Mapping[str, IncEx | bool] | bool] | None = None, by_alias: bool = False, exclude_unset: bool = False, exclude_defaults: bool = False, exclude_none: bool = False) → Dict[str, Any]

executable: str

executionEnvironment: dict

extraFields: dict[str, Any]

classmethod from_orm(obj: Any) → Self

gridCE: str

classmethod injectDefaultValues(values: dict[str, Any]) → dict[str, Any]

inputData: CoercibleSetStr

inputDataPolicy: str

inputSandbox: CoercibleSetStr

jobConfigArgs: str

jobGroup: str

jobName: str

jobType: str

json(*, include: set[int] | set[str] | Mapping[int, set[int] | set[str] | Mapping[int, IncEx | bool] | Mapping[str, IncEx | bool] | bool] | Mapping[str, set[int] | set[str] | Mapping[int, IncEx | bool] | Mapping[str, IncEx | bool] | bool] | None = None, exclude: set[int] | set[str] | Mapping[int, set[int] | set[str] | Mapping[int, IncEx | bool] | Mapping[str, IncEx | bool] | bool] | Mapping[str, set[int] | set[str] | Mapping[int, IncEx | bool] | Mapping[str, IncEx | bool] | bool] | None = None, by_alias: bool = False, exclude_unset: bool = False, exclude_defaults: bool = False, exclude_none: bool = False, encoder: Callable[[Any], Any] | None = PydanticUndefined, models_as_dict: bool = PydanticUndefined, **dumps_kwargs: Any) → str

logLevel: str

maxNumberOfProcessors: int

minNumberOfProcessors: int

model_computed_fields = {}

model_config: ClassVar[ConfigDict] = {'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

classmethod model_construct(_fields_set: set[str] | None = None, **values: Any) → Self

Creates a new instance of the Model class with validated data.

Creates a new model setting __dict__ and __pydantic_fields_set__ from trusted or pre-validated data. Default values are respected, but no other validation is performed.

!!! note

model_construct() generally respects the model_config.extra setting on the provided model. That is, if model_config.extra == ‘allow’, then all extra passed values are added to the model instance’s __dict__ and __pydantic_extra__ fields. If model_config.extra == ‘ignore’ (the default), then all extra passed values are ignored. Because no validation is performed with a call to model_construct(), having model_config.extra == ‘forbid’ does not result in an error if extra values are passed, but they will be ignored.

Parameters:

• _fields_set – A set of field names that were originally explicitly set during instantiation. If provided, this is directly used for the [model_fields_set][pydantic.BaseModel.model_fields_set] attribute. Otherwise, the field names from the values argument will be used.

• values – Trusted or pre-validated data dictionary.

Returns:

A new instance of the Model class with validated data.

model_copy(*, update: Mapping[str, Any] | None = None, deep: bool = False) → Self

!!! abstract “Usage Documentation”

[model_copy](../concepts/models.md#model-copy)

Returns a copy of the model.

!!! note

The underlying instance’s [__dict__][object.__dict__] attribute is copied. This might have unexpected side effects if you store anything in it, on top of the model fields (e.g. the value of [cached properties][functools.cached_property]).

Parameters:

• update – Values to change/add in the new model. Note: the data is not validated before creating the new model. You should trust this data.

• deep – Set to True to make a deep copy of the model.

Returns:

New model instance.

model_dump(*, mode: Literal['json', 'python'] | str = 'python', include: set[int] | set[str] | Mapping[int, set[int] | set[str] | Mapping[int, IncEx | bool] | Mapping[str, IncEx | bool] | bool] | Mapping[str, set[int] | set[str] | Mapping[int, IncEx | bool] | Mapping[str, IncEx | bool] | bool] | None = None, exclude: set[int] | set[str] | Mapping[int, set[int] | set[str] | Mapping[int, IncEx | bool] | Mapping[str, IncEx | bool] | bool] | Mapping[str, set[int] | set[str] | Mapping[int, IncEx | bool] | Mapping[str, IncEx | bool] | bool] | None = None, context: Any | None = None, by_alias: bool | None = None, exclude_unset: bool = False, exclude_defaults: bool = False, exclude_none: bool = False, exclude_computed_fields: bool = False, round_trip: bool = False, warnings: bool | Literal['none', 'warn', 'error'] = True, fallback: Callable[[Any], Any] | None = None, serialize_as_any: bool = False, polymorphic_serialization: bool | None = None) → dict[str, Any]

!!! abstract “Usage Documentation”

[model_dump](../concepts/serialization.md#python-mode)

Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.

Parameters:

• mode – The mode in which to_python should run. If mode is ‘json’, the output will only contain JSON serializable types. If mode is ‘python’, the output may contain non-JSON-serializable Python objects.

• include – A set of fields to include in the output.

• exclude – A set of fields to exclude from the output.

• context – Additional context to pass to the serializer.

• by_alias – Whether to use the field’s alias in the dictionary key if defined.

• exclude_unset – Whether to exclude fields that have not been explicitly set.

• exclude_defaults – Whether to exclude fields that are set to their default value.

• exclude_none – Whether to exclude fields that have a value of None.

• exclude_computed_fields – Whether to exclude computed fields. While this can be useful for round-tripping, it is usually recommended to use the dedicated round_trip parameter instead.

• round_trip – If True, dumped values should be valid as input for non-idempotent types such as Json[T].

• warnings – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].

• fallback – A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised.

• serialize_as_any – Whether to serialize fields with duck-typing serialization behavior.

• polymorphic_serialization – Whether to use model and dataclass polymorphic serialization for this call.

Returns:

A dictionary representation of the model.

model_dump_json(*, indent: int | None = None, ensure_ascii: bool = False, include: set[int] | set[str] | Mapping[int, set[int] | set[str] | Mapping[int, IncEx | bool] | Mapping[str, IncEx | bool] | bool] | Mapping[str, set[int] | set[str] | Mapping[int, IncEx | bool] | Mapping[str, IncEx | bool] | bool] | None = None, exclude: set[int] | set[str] | Mapping[int, set[int] | set[str] | Mapping[int, IncEx | bool] | Mapping[str, IncEx | bool] | bool] | Mapping[str, set[int] | set[str] | Mapping[int, IncEx | bool] | Mapping[str, IncEx | bool] | bool] | None = None, context: Any | None = None, by_alias: bool | None = None, exclude_unset: bool = False, exclude_defaults: bool = False, exclude_none: bool = False, exclude_computed_fields: bool = False, round_trip: bool = False, warnings: bool | Literal['none', 'warn', 'error'] = True, fallback: Callable[[Any], Any] | None = None, serialize_as_any: bool = False, polymorphic_serialization: bool | None = None) → str

!!! abstract “Usage Documentation”

[model_dump_json](../concepts/serialization.md#json-mode)

Generates a JSON representation of the model using Pydantic’s to_json method.

Parameters:

• indent – Indentation to use in the JSON output. If None is passed, the output will be compact.

• ensure_ascii – If True, the output is guaranteed to have all incoming non-ASCII characters escaped. If False (the default), these characters will be output as-is.

• include – Field(s) to include in the JSON output.

• exclude – Field(s) to exclude from the JSON output.

• context – Additional context to pass to the serializer.

• by_alias – Whether to serialize using field aliases.

• exclude_unset – Whether to exclude fields that have not been explicitly set.

• exclude_defaults – Whether to exclude fields that are set to their default value.

• exclude_none – Whether to exclude fields that have a value of None.

• exclude_computed_fields – Whether to exclude computed fields. While this can be useful for round-tripping, it is usually recommended to use the dedicated round_trip parameter instead.

• round_trip – If True, dumped values should be valid as input for non-idempotent types such as Json[T].

• warnings – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].

• fallback – A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised.

• serialize_as_any – Whether to serialize fields with duck-typing serialization behavior.

• polymorphic_serialization – Whether to use model and dataclass polymorphic serialization for this call.

Returns:

A JSON string representation of the model.

property model_extra: dict[str, Any] | None

Get extra fields set during validation.

Returns:

A dictionary of extra fields, or None if config.extra is not set to “allow”.

model_fields = {'arguments': FieldInfo(annotation=str, required=False, default=''), 'bannedSites': FieldInfo(annotation=set[str], required=False, default=set(), metadata=[BeforeValidator(func=<function default_set_validator>, json_schema_input_type=PydanticUndefined)]), 'cpuTime': FieldInfo(annotation=int, required=True), 'executable': FieldInfo(annotation=str, required=True), 'executionEnvironment': FieldInfo(annotation=dict, required=False, default=None), 'extraFields': FieldInfo(annotation=dict[str, Any], required=False, default={}), 'gridCE': FieldInfo(annotation=str, required=False, default=''), 'inputData': FieldInfo(annotation=set[str], required=False, default=set(), metadata=[BeforeValidator(func=<function default_set_validator>, json_schema_input_type=PydanticUndefined)]), 'inputDataPolicy': FieldInfo(annotation=str, required=False, default=''), 'inputSandbox': FieldInfo(annotation=set[str], required=False, default=set(), metadata=[BeforeValidator(func=<function default_set_validator>, json_schema_input_type=PydanticUndefined)]), 'jobConfigArgs': FieldInfo(annotation=str, required=False, default=''), 'jobGroup': FieldInfo(annotation=str, required=False, default=''), 'jobName': FieldInfo(annotation=str, required=False, default='Name'), 'jobType': FieldInfo(annotation=str, required=False, default='User'), 'logLevel': FieldInfo(annotation=str, required=False, default='INFO'), 'maxNumberOfProcessors': FieldInfo(annotation=int, required=False, default=None), 'minNumberOfProcessors': FieldInfo(annotation=int, required=False, default=1), 'outputData': FieldInfo(annotation=set[str], required=False, default=set(), metadata=[BeforeValidator(func=<function default_set_validator>, json_schema_input_type=PydanticUndefined)]), 'outputPath': FieldInfo(annotation=str, required=False, default=''), 'outputSE': FieldInfo(annotation=str, required=False, default=''), 'outputSandbox': FieldInfo(annotation=set[str], required=False, default=set(), metadata=[BeforeValidator(func=<function default_set_validator>, json_schema_input_type=PydanticUndefined)]), 'owner': FieldInfo(annotation=str, required=True), 'ownerGroup': FieldInfo(annotation=str, required=True), 'platform': FieldInfo(annotation=str, required=False, default=''), 'priority': FieldInfo(annotation=int, required=True), 'sites': FieldInfo(annotation=set[str], required=False, default=set(), metadata=[BeforeValidator(func=<function default_set_validator>, json_schema_input_type=PydanticUndefined)]), 'stderr': FieldInfo(annotation=str, required=False, default='std.err'), 'stdout': FieldInfo(annotation=str, required=False, default='std.out'), 'tags': FieldInfo(annotation=set[str], required=False, default=set(), metadata=[BeforeValidator(func=<function default_set_validator>, json_schema_input_type=PydanticUndefined)]), 'vo': FieldInfo(annotation=str, required=True)}

property model_fields_set: set[str]

Returns the set of fields that have been explicitly set on this model instance.

Returns:

A set of strings representing the fields that have been set,

i.e. that were not filled from defaults.

classmethod model_parametrized_name(params: tuple[type[Any], ...]) → str

Compute the class name for parametrizations of generic classes.

This method can be overridden to achieve a custom naming scheme for generic BaseModels.

Parameters:

params – Tuple of types of the class. Given a generic class Model with 2 type variables and a concrete model Model[str, int], the value (str, int) would be passed to params.

Returns:

String representing the new class where params are passed to cls as type variables.

Raises:

TypeError – Raised when trying to generate concrete names for non-generic models.

model_post_init(context: Any, /) → None

Override this method to perform additional initialization after __init__ and model_construct. This is useful if you want to do some validation that requires the entire model to be initialized.

classmethod model_rebuild(*, force: bool = False, raise_errors: bool = True, _parent_namespace_depth: int = 2, _types_namespace: MappingNamespace | None = None) → bool | None

Try to rebuild the pydantic-core schema for the model.

This may be necessary when one of the annotations is a ForwardRef which could not be resolved during the initial attempt to build the schema, and automatic rebuilding fails.

Parameters:

• force – Whether to force the rebuilding of the model schema, defaults to False.

• raise_errors – Whether to raise errors, defaults to True.

• _parent_namespace_depth – The depth level of the parent namespace, defaults to 2.

• _types_namespace – The types namespace, defaults to None.

Returns:

Returns None if the schema is already “complete” and rebuilding was not required. If rebuilding _was_ required, returns True if rebuilding was successful, otherwise False.

classmethod model_validate(obj: Any, *, strict: bool | None = None, extra: Literal['allow', 'ignore', 'forbid'] | None = None, from_attributes: bool | None = None, context: Any | None = None, by_alias: bool | None = None, by_name: bool | None = None) → Self

Validate a pydantic model instance.

Parameters:

• obj – The object to validate.

• strict – Whether to enforce types strictly.

• extra – Whether to ignore, allow, or forbid extra data during model validation. See the [extra configuration value][pydantic.ConfigDict.extra] for details.

• from_attributes – Whether to extract data from object attributes.

• context – Additional context to pass to the validator.

• by_alias – Whether to use the field’s alias when validating against the provided input data.

• by_name – Whether to use the field’s name when validating against the provided input data.

Raises:

ValidationError – If the object could not be validated.

Returns:

The validated model instance.

classmethod model_validate_json(json_data: str | bytes | bytearray, *, strict: bool | None = None, extra: Literal['allow', 'ignore', 'forbid'] | None = None, context: Any | None = None, by_alias: bool | None = None, by_name: bool | None = None) → Self

!!! abstract “Usage Documentation”

[JSON Parsing](../concepts/json.md#json-parsing)

Validate the given JSON data against the Pydantic model.

Parameters:

• json_data – The JSON data to validate.

• strict – Whether to enforce types strictly.

• extra – Whether to ignore, allow, or forbid extra data during model validation. See the [extra configuration value][pydantic.ConfigDict.extra] for details.

• context – Extra variables to pass to the validator.

• by_alias – Whether to use the field’s alias when validating against the provided input data.

• by_name – Whether to use the field’s name when validating against the provided input data.

Returns:

The validated Pydantic model.

Raises:

ValidationError – If json_data is not a JSON string or the object could not be validated.

classmethod model_validate_strings(obj: Any, *, strict: bool | None = None, extra: Literal['allow', 'ignore', 'forbid'] | None = None, context: Any | None = None, by_alias: bool | None = None, by_name: bool | None = None) → Self

Validate the given object with string data against the Pydantic model.

Parameters:

• obj – The object containing string data to validate.

• strict – Whether to enforce types strictly.

• extra – Whether to ignore, allow, or forbid extra data during model validation. See the [extra configuration value][pydantic.ConfigDict.extra] for details.

• context – Extra variables to pass to the validator.

• by_alias – Whether to use the field’s alias when validating against the provided input data.

• by_name – Whether to use the field’s name when validating against the provided input data.

Returns:

The validated Pydantic model.

outputData: CoercibleSetStr

outputPath: str

outputSE: str

outputSandbox: CoercibleSetStr

owner: str

ownerGroup: str

classmethod parse_file(path: str | Path, *, content_type: str | None = None, encoding: str = 'utf8', proto: DeprecatedParseProtocol | None = None, allow_pickle: bool = False) → Self

classmethod parse_obj(obj: Any) → Self

classmethod parse_raw(b: str | bytes, *, content_type: str | None = None, encoding: str = 'utf8', proto: DeprecatedParseProtocol | None = None, allow_pickle: bool = False) → Self

platform: str

priority: int

classmethod schema(by_alias: bool = True, ref_template: str = '#/$defs/{model}') → Dict[str, Any]

classmethod schema_json(*, by_alias: bool = True, ref_template: str = '#/$defs/{model}', **dumps_kwargs: Any) → str

sites: CoercibleSetStr

stderr: str

stdout: str

tags: CoercibleSetStr

classmethod update_forward_refs(**localns: Any) → None

classmethod validate(value: Any) → Self

vo: str