import difflib import inspect import json import os import pkgutil import re from abc import abstractmethod from copy import deepcopy from typing import Any, Dict, List, Optional, Tuple, Union, final from .dataclass import ( AbstractField, Dataclass, Field, InternalField, NonPositionalField, fields, ) from .logging_utils import get_logger from .parsing_utils import ( separate_inside_and_outside_square_brackets, ) from .settings_utils import get_constants, get_settings from .text_utils import camel_to_snake_case, is_camel_case from .type_utils import issubtype from .utils import artifacts_json_cache, json_dump, save_to_file logger = get_logger() settings = get_settings() constants = get_constants() def is_name_legal_for_catalog(name): return re.match(r"^[\w" + constants.catalog_hierarchy_sep + "]+$", name) def verify_legal_catalog_name(name): assert is_name_legal_for_catalog( name ), f'Artifict name ("{name}") should be alphanumeric. Use "." for nesting (e.g. myfolder.my_artifact)' class Artifactories: def __new__(cls): if not hasattr(cls, "instance"): cls.instance = super().__new__(cls) cls.instance.artifactories = [] return cls.instance def __iter__(self): self._index = 0 # Initialize/reset the index for iteration return self def __next__(self): while self._index < len(self.artifactories): artifactory = self.artifactories[self._index] self._index += 1 if ( settings.use_only_local_catalogs and not artifactory.is_local ): # Corrected typo from 'is_loacl' to 'is_local' continue return artifactory raise StopIteration def register(self, artifactory): assert isinstance( artifactory, Artifactory ), "Artifactory must be an instance of Artifactory" assert hasattr( artifactory, "__contains__" ), "Artifactory must have __contains__ method" assert hasattr( artifactory, "__getitem__" ), "Artifactory must have __getitem__ method" self.artifactories = [artifactory, *self.artifactories] def unregister(self, artifactory): assert isinstance( artifactory, Artifactory ), "Artifactory must be an instance of Artifactory" assert hasattr( artifactory, "__contains__" ), "Artifactory must have __contains__ method" assert hasattr( artifactory, "__getitem__" ), "Artifactory must have __getitem__ method" self.artifactories.remove(artifactory) def reset(self): self.artifactories = [] def map_values_in_place(object, mapper): if isinstance(object, dict): for key, value in object.items(): object[key] = mapper(value) return object if isinstance(object, list): for i in range(len(object)): object[i] = mapper(object[i]) return object return mapper(object) def get_closest_artifact_type(type): artifact_type_options = list(Artifact._class_register.keys()) matches = difflib.get_close_matches(type, artifact_type_options) if matches: return matches[0] # Return the closest match return None class UnrecognizedArtifactTypeError(ValueError): def __init__(self, type) -> None: maybe_class = "".join(word.capitalize() for word in type.split("_")) message = f"'{type}' is not a recognized artifact 'type'. Make sure a the class defined this type (Probably called '{maybe_class}' or similar) is defined and/or imported anywhere in the code executed." closest_artifact_type = get_closest_artifact_type(type) if closest_artifact_type is not None: message += "\n\n" f"Did you mean '{closest_artifact_type}'?" super().__init__(message) class MissingArtifactTypeError(ValueError): def __init__(self, dic) -> None: message = ( f"Missing 'type' parameter. Expected 'type' in artifact dict, got {dic}" ) super().__init__(message) class Artifact(Dataclass): _class_register = {} __type__: str = Field(default=None, final=True, init=False) __description__: str = NonPositionalField( default=None, required=False, also_positional=False ) __tags__: Dict[str, str] = NonPositionalField( default_factory=dict, required=False, also_positional=False ) __id__: str = InternalField(default=None, required=False, also_positional=False) data_classification_policy: List[str] = NonPositionalField( default=None, required=False, also_positional=False ) @classmethod def is_artifact_dict(cls, d): return isinstance(d, dict) and "__type__" in d @classmethod def verify_artifact_dict(cls, d): if not isinstance(d, dict): raise ValueError( f"Artifact dict <{d}> must be of type 'dict', got '{type(d)}'." ) if "__type__" not in d: raise MissingArtifactTypeError(d) if not cls.is_registered_type(d["__type__"]): raise UnrecognizedArtifactTypeError(d["__type__"]) @classmethod def get_artifact_type(cls): return camel_to_snake_case(cls.__name__) @classmethod def register_class(cls, artifact_class): assert issubclass( artifact_class, Artifact ), f"Artifact class must be a subclass of Artifact, got '{artifact_class}'" assert is_camel_case( artifact_class.__name__ ), f"Artifact class name must be legal camel case, got '{artifact_class.__name__}'" snake_case_key = camel_to_snake_case(artifact_class.__name__) if cls.is_registered_type(snake_case_key): assert ( str(cls._class_register[snake_case_key]) == str(artifact_class) ), f"Artifact class name must be unique, '{snake_case_key}' already exists for {cls._class_register[snake_case_key]}. Cannot be overridden by {artifact_class}." return snake_case_key cls._class_register[snake_case_key] = artifact_class return snake_case_key def __init_subclass__(cls, **kwargs): super().__init_subclass__(**kwargs) cls.register_class(cls) @classmethod def is_artifact_file(cls, path): if not os.path.exists(path) or not os.path.isfile(path): return False with open(path) as f: d = json.load(f) return cls.is_artifact_dict(d) @classmethod def is_registered_type(cls, type: str): return type in cls._class_register @classmethod def is_registered_class_name(cls, class_name: str): snake_case_key = camel_to_snake_case(class_name) return cls.is_registered_type(snake_case_key) @classmethod def is_registered_class(cls, clz: object): return clz in set(cls._class_register.values()) @classmethod def _recursive_load(cls, obj): if isinstance(obj, dict): new_d = {} for key, value in obj.items(): new_d[key] = cls._recursive_load(value) obj = new_d elif isinstance(obj, list): obj = [cls._recursive_load(value) for value in obj] else: pass if cls.is_artifact_dict(obj): cls.verify_artifact_dict(obj) return cls._class_register[obj.pop("__type__")](**obj) return obj @classmethod def from_dict(cls, d, overwrite_args=None): if overwrite_args is not None: d = {**d, **overwrite_args} cls.verify_artifact_dict(d) return cls._recursive_load(d) @classmethod def load(cls, path, artifact_identifier=None, overwrite_args=None): d = artifacts_json_cache(path) new_artifact = cls.from_dict(d, overwrite_args=overwrite_args) new_artifact.__id__ = artifact_identifier return new_artifact def get_pretty_print_name(self): if self.__id__ is not None: return self.__id__ return self.__class__.__name__ def prepare(self): pass def verify(self): pass @final def __pre_init__(self, **kwargs): self._init_dict = get_raw(kwargs) @final def verify_data_classification_policy(self): if self.data_classification_policy is not None: if not isinstance(self.data_classification_policy, list) or not all( isinstance(data_classification, str) for data_classification in self.data_classification_policy ): raise ValueError( f"The 'data_classification_policy' of {self.get_pretty_print_name()} " f"must be either None - in case when no policy applies - or a list of " f"strings, for example: ['public']. However, '{self.data_classification_policy}' " f"of type {type(self.data_classification_policy)} was provided instead." ) @final def __post_init__(self): self.__type__ = self.register_class(self.__class__) for field in fields(self): if issubtype( field.type, Union[Artifact, List[Artifact], Dict[str, Artifact]] ): value = getattr(self, field.name) value = map_values_in_place(value, maybe_recover_artifact) setattr(self, field.name, value) self.verify_data_classification_policy() if not settings.skip_artifacts_prepare_and_verify: self.prepare() self.verify() def _to_raw_dict(self): return {"__type__": self.__type__, **self._init_dict} def to_json(self): data = self.to_dict() return json_dump(data) def serialize(self): if self.__id__ is not None: return self.__id__ return self.to_json() def save(self, path): save_to_file(path, self.to_json()) @classmethod def deserialize(cls, artifact_rep): data = json.loads(artifact_rep) return Artifact.from_dict(data) def verify_instance( self, instance: Dict[str, Any], name: Optional[str] = None ) -> Dict[str, Any]: """Checks if data classifications of an artifact and instance are compatible. Raises an error if an artifact's data classification policy does not include that of processed data. The purpose is to ensure that any sensitive data is handled in a proper way (for example when sending it to some external services). Args: instance (Dict[str, Any]): data which should contain its allowed data classification policies under key 'data_classification_policy'. name (Optional[str]): name of artifact which should be used to retrieve data classification from env. If not specified, then either __id__ or __class__.__name__, are used instead, respectively. Returns: Dict[str, Any]: unchanged instance. Examples: instance = {"x": "some_text", "data_classification_policy": ["pii"]} # Will raise an error as "pii" is not included policy metric = Accuracy(data_classification_policy=["public"]) metric.verify_instance(instance) # Will not raise an error template = SpanLabelingTemplate(data_classification_policy=["pii", "propriety"]) template.verify_instance(instance) # Will not raise an error since the policy was specified in environment variable: UNITXT_DATA_CLASSIFICATION_POLICY = json.dumps({"metrics.accuracy": ["pii"]}) metric = fetch_artifact("metrics.accuracy") metric.verify_instance(instance) """ name = name or self.get_pretty_print_name() data_classification_policy = get_artifacts_data_classification(name) if not data_classification_policy: data_classification_policy = self.data_classification_policy if not data_classification_policy: return instance instance_data_classification = instance.get("data_classification_policy") if not instance_data_classification: get_logger().warning( f"The data does not provide information if it can be used by " f"'{name}' with the following data classification policy " f"'{data_classification_policy}'. This may lead to sending of undesired " f"data to external service. Set the 'data_classification_policy' " f"of the data to ensure a proper handling of sensitive information." ) return instance if not any( data_classification in data_classification_policy for data_classification in instance_data_classification ): raise ValueError( f"The instance '{instance} 'has the following data classification policy " f"'{instance_data_classification}', however, the artifact '{name}' " f"is only configured to support the data with classification " f"'{data_classification_policy}'. To enable this either change " f"the 'data_classification_policy' attribute of the artifact, " f"or modify the environment variable " f"'UNITXT_DATA_CLASSIFICATION_POLICY' accordingly." ) return instance def get_raw(obj): if isinstance(obj, Artifact): return obj._to_raw_dict() if isinstance(obj, tuple) and hasattr(obj, "_fields"): # named tuple return type(obj)(*[get_raw(v) for v in obj]) if isinstance(obj, (list, tuple)): return type(obj)([get_raw(v) for v in obj]) if isinstance(obj, dict): return type(obj)({get_raw(k): get_raw(v) for k, v in obj.items()}) return deepcopy(obj) class ArtifactList(list, Artifact): def prepare(self): for artifact in self: artifact.prepare() class Artifactory(Artifact): is_local: bool = AbstractField() @abstractmethod def __contains__(self, name: str) -> bool: pass @abstractmethod def __getitem__(self, name) -> Artifact: pass @abstractmethod def get_with_overwrite(self, name, overwrite_args) -> Artifact: pass class UnitxtArtifactNotFoundError(Exception): def __init__(self, name, artifactories): self.name = name self.artifactories = artifactories def __str__(self): msg = f"Artifact {self.name} does not exist, in artifactories:{self.artifactories}." if settings.use_only_local_catalogs: msg += f" Notice that unitxt.settings.use_only_local_catalogs is set to True, if you want to use remote catalogs set this settings or the environment variable {settings.use_only_local_catalogs_key}." return f"Artifact {self.name} does not exist, in artifactories:{self.artifactories}" def fetch_artifact(artifact_rep) -> Tuple[Artifact, Union[Artifactory, None]]: if isinstance(artifact_rep, Artifact): return artifact_rep, None if Artifact.is_artifact_file(artifact_rep): return Artifact.load(artifact_rep), None name, _ = separate_inside_and_outside_square_brackets(artifact_rep) if is_name_legal_for_catalog(name): artifactory, artifact_rep, args = get_artifactory_name_and_args( name=artifact_rep ) return artifactory.get_with_overwrite( artifact_rep, overwrite_args=args ), artifactory return Artifact.deserialize(artifact_rep), None def get_artifactory_name_and_args( name: str, artifactories: Optional[List[Artifactory]] = None ): name, args = separate_inside_and_outside_square_brackets(name) if artifactories is None: artifactories = list(Artifactories()) for artifactory in artifactories: if name in artifactory: return artifactory, name, args raise UnitxtArtifactNotFoundError(name, artifactories) def verbosed_fetch_artifact(identifier): artifact, artifactory = fetch_artifact(identifier) logger.debug(f"Artifact {identifier} is fetched from {artifactory}") return artifact def reset_artifacts_json_cache(): artifacts_json_cache.cache_clear() def maybe_recover_artifact(artifact): if isinstance(artifact, str): return verbosed_fetch_artifact(artifact) return artifact def register_all_artifacts(path): for loader, module_name, _is_pkg in pkgutil.walk_packages(path): logger.info(__name__) if module_name == __name__: continue logger.info(f"Loading {module_name}") # Import the module module = loader.find_module(module_name).load_module(module_name) # Iterate over every object in the module for _name, obj in inspect.getmembers(module): # Make sure the object is a class if inspect.isclass(obj): # Make sure the class is a subclass of Artifact (but not Artifact itself) if issubclass(obj, Artifact) and obj is not Artifact: logger.info(obj) def get_artifacts_data_classification(artifact: str) -> Optional[List[str]]: """Loads given artifact's data classification policy from an environment variable. Args: artifact (str): Name of the artifact which the data classification policy should be retrieved for. For example "metrics.accuracy". Returns: Optional[List[str]] - Data classification policies for the specified artifact if they were found, or None otherwise. """ data_classification = settings.data_classification_policy if data_classification is None: return None error_msg = ( f"If specified, the value of 'UNITXT_DATA_CLASSIFICATION_POLICY' " f"should be a valid json dictionary. Got '{data_classification}' " f"instead." ) try: data_classification = json.loads(data_classification) except json.decoder.JSONDecodeError as e: raise RuntimeError(error_msg) from e if not isinstance(data_classification, dict): raise RuntimeError(error_msg) for artifact_name, artifact_data_classifications in data_classification.items(): if ( not isinstance(artifact_name, str) or not isinstance(artifact_data_classifications, list) or not all( isinstance(artifact_data_classification, str) for artifact_data_classification in artifact_data_classifications ) ): raise RuntimeError( "'UNITXT_DATA_CLASSIFICATION_POLICY' should be of type " "'Dict[str, List[str]]', where a artifact's name is a key, and a " "value is a list of data classifications used by that artifact." ) if artifact not in data_classification.keys(): return None return data_classification.get(artifact)