spaCy/spacy/schemas.py

import inspect
import re
from collections import defaultdict
from enum import Enum
from typing import (
    TYPE_CHECKING,
    Any,
    Callable,
    Dict,
    Iterable,
    List,
    Optional,
    Tuple,
    Type,
    TypeVar,
    Union,
)

try:
    from pydantic.v1 import (
        BaseModel,
        ConstrainedStr,
        Field,
        StrictBool,
        StrictFloat,
        StrictInt,
        StrictStr,
        ValidationError,
        create_model,
        validator,
    )
    from pydantic.v1.main import ModelMetaclass
except ImportError:
    from pydantic import (  # type: ignore
        BaseModel,
        ConstrainedStr,
        Field,
        StrictBool,
        StrictFloat,
        StrictInt,
        StrictStr,
        ValidationError,
        create_model,
        validator,
    )
    from pydantic.main import ModelMetaclass  # type: ignore
from thinc.api import ConfigValidationError, Model, Optimizer
from thinc.config import Promise

from .attrs import NAMES
from .compat import Literal
from .lookups import Lookups
from .util import is_cython_func

if TYPE_CHECKING:
    # This lets us add type hints for mypy etc. without causing circular imports
    from .language import Language  # noqa: F401
    from .training import Example  # noqa: F401
    from .vocab import Vocab  # noqa: F401


# fmt: off
ItemT = TypeVar("ItemT")
Batcher = Union[Callable[[Iterable[ItemT]], Iterable[List[ItemT]]], Promise]
Reader = Union[Callable[["Language", str], Iterable["Example"]], Promise]
Logger = Union[Callable[["Language"], Tuple[Callable[[Dict[str, Any]], None], Callable]], Promise]
# fmt: on


def validate(schema: Type[BaseModel], obj: Dict[str, Any]) -> List[str]:
    """Validate data against a given pydantic schema.

    obj (Dict[str, Any]): JSON-serializable data to validate.
    schema (pydantic.BaseModel): The schema to validate against.
    RETURNS (List[str]): A list of error messages, if available.
    """
    try:
        schema(**obj)
        return []
    except ValidationError as e:
        errors = e.errors()
        data = defaultdict(list)
        for error in errors:
            err_loc = " -> ".join([str(p) for p in error.get("loc", [])])
            data[err_loc].append(error.get("msg"))
        return [f"[{loc}] {', '.join(msg)}" for loc, msg in data.items()]  # type: ignore[arg-type]


# Initialization


class ArgSchemaConfig:
    extra = "forbid"
    arbitrary_types_allowed = True


class ArgSchemaConfigExtra:
    extra = "forbid"
    arbitrary_types_allowed = True


def get_arg_model(
    func: Callable,
    *,
    exclude: Iterable[str] = tuple(),
    name: str = "ArgModel",
    strict: bool = True,
) -> ModelMetaclass:
    """Generate a pydantic model for function arguments.

    func (Callable): The function to generate the schema for.
    exclude (Iterable[str]): Parameter names to ignore.
    name (str): Name of created model class.
    strict (bool): Don't allow extra arguments if no variable keyword arguments
        are allowed on the function.
    RETURNS (ModelMetaclass): A pydantic model.
    """
    sig_args = {}
    try:
        sig = inspect.signature(func)
    except ValueError:
        # Typically happens if the method is part of a Cython module without
        # binding=True. Here we just use an empty model that allows everything.
        return create_model(name, __config__=ArgSchemaConfigExtra)  # type: ignore[arg-type, return-value]
    has_variable = False
    for param in sig.parameters.values():
        if param.name in exclude:
            continue
        if param.kind == param.VAR_KEYWORD:
            # The function allows variable keyword arguments so we shouldn't
            # include **kwargs etc. in the schema and switch to non-strict
            # mode and pass through all other values
            has_variable = True
            continue
        # If no annotation is specified assume it's anything
        annotation = param.annotation if param.annotation != param.empty else Any
        # If no default value is specified assume that it's required. Cython
        # functions/methods will have param.empty for default value None so we
        # need to treat them differently
        default_empty = None if is_cython_func(func) else ...
        default = param.default if param.default != param.empty else default_empty
        sig_args[param.name] = (annotation, default)
    is_strict = strict and not has_variable
    sig_args["__config__"] = ArgSchemaConfig if is_strict else ArgSchemaConfigExtra  # type: ignore[assignment]
    return create_model(name, **sig_args)  # type: ignore[call-overload, arg-type, return-value]


def validate_init_settings(
    func: Callable,
    settings: Dict[str, Any],
    *,
    section: Optional[str] = None,
    name: str = "",
    exclude: Iterable[str] = ("get_examples", "nlp"),
) -> Dict[str, Any]:
    """Validate initialization settings against the expected arguments in
    the method signature. Will parse values if possible (e.g. int to string)
    and return the updated settings dict. Will raise a ConfigValidationError
    if types don't match or required values are missing.

    func (Callable): The initialize method of a given component etc.
    settings (Dict[str, Any]): The settings from the respective [initialize] block.
    section (str): Initialize section, for error message.
    name (str): Name of the block in the section.
    exclude (Iterable[str]): Parameter names to exclude from schema.
    RETURNS (Dict[str, Any]): The validated settings.
    """
    schema = get_arg_model(func, exclude=exclude, name="InitArgModel")
    try:
        return schema(**settings).dict()
    except ValidationError as e:
        block = "initialize" if not section else f"initialize.{section}"
        title = f"Error validating initialization settings in [{block}]"
        raise ConfigValidationError(
            title=title, errors=e.errors(), config=settings, parent=name
        ) from None


# Matcher token patterns


def validate_token_pattern(obj: list) -> List[str]:
    # Try to convert non-string keys (e.g. {ORTH: "foo"} -> {"ORTH": "foo"})
    get_key = lambda k: NAMES[k] if isinstance(k, int) and k < len(NAMES) else k
    if isinstance(obj, list):
        converted = []
        for pattern in obj:
            if isinstance(pattern, dict):
                pattern = {get_key(k): v for k, v in pattern.items()}
            converted.append(pattern)
        obj = converted
    return validate(TokenPatternSchema, {"pattern": obj})


class TokenPatternString(BaseModel):
    REGEX: Optional[Union[StrictStr, "TokenPatternString"]] = Field(None, alias="regex")
    IN: Optional[List[StrictStr]] = Field(None, alias="in")
    NOT_IN: Optional[List[StrictStr]] = Field(None, alias="not_in")
    IS_SUBSET: Optional[List[StrictStr]] = Field(None, alias="is_subset")
    IS_SUPERSET: Optional[List[StrictStr]] = Field(None, alias="is_superset")
    INTERSECTS: Optional[List[StrictStr]] = Field(None, alias="intersects")
    FUZZY: Optional[Union[StrictStr, "TokenPatternString"]] = Field(None, alias="fuzzy")
    FUZZY1: Optional[Union[StrictStr, "TokenPatternString"]] = Field(
        None, alias="fuzzy1"
    )
    FUZZY2: Optional[Union[StrictStr, "TokenPatternString"]] = Field(
        None, alias="fuzzy2"
    )
    FUZZY3: Optional[Union[StrictStr, "TokenPatternString"]] = Field(
        None, alias="fuzzy3"
    )
    FUZZY4: Optional[Union[StrictStr, "TokenPatternString"]] = Field(
        None, alias="fuzzy4"
    )
    FUZZY5: Optional[Union[StrictStr, "TokenPatternString"]] = Field(
        None, alias="fuzzy5"
    )
    FUZZY6: Optional[Union[StrictStr, "TokenPatternString"]] = Field(
        None, alias="fuzzy6"
    )
    FUZZY7: Optional[Union[StrictStr, "TokenPatternString"]] = Field(
        None, alias="fuzzy7"
    )
    FUZZY8: Optional[Union[StrictStr, "TokenPatternString"]] = Field(
        None, alias="fuzzy8"
    )
    FUZZY9: Optional[Union[StrictStr, "TokenPatternString"]] = Field(
        None, alias="fuzzy9"
    )

    class Config:
        extra = "forbid"
        allow_population_by_field_name = True  # allow alias and field name

    @validator("*", pre=True, each_item=True, allow_reuse=True)
    def raise_for_none(cls, v):
        if v is None:
            raise ValueError("None / null is not allowed")
        return v


class TokenPatternNumber(BaseModel):
    REGEX: Optional[StrictStr] = Field(None, alias="regex")
    IN: Optional[List[StrictInt]] = Field(None, alias="in")
    NOT_IN: Optional[List[StrictInt]] = Field(None, alias="not_in")
    IS_SUBSET: Optional[List[StrictInt]] = Field(None, alias="is_subset")
    IS_SUPERSET: Optional[List[StrictInt]] = Field(None, alias="is_superset")
    INTERSECTS: Optional[List[StrictInt]] = Field(None, alias="intersects")
    EQ: Optional[Union[StrictInt, StrictFloat]] = Field(None, alias="==")
    NEQ: Optional[Union[StrictInt, StrictFloat]] = Field(None, alias="!=")
    GEQ: Optional[Union[StrictInt, StrictFloat]] = Field(None, alias=">=")
    LEQ: Optional[Union[StrictInt, StrictFloat]] = Field(None, alias="<=")
    GT: Optional[Union[StrictInt, StrictFloat]] = Field(None, alias=">")
    LT: Optional[Union[StrictInt, StrictFloat]] = Field(None, alias="<")

    class Config:
        extra = "forbid"
        allow_population_by_field_name = True  # allow alias and field name

    @validator("*", pre=True, each_item=True, allow_reuse=True)
    def raise_for_none(cls, v):
        if v is None:
            raise ValueError("None / null is not allowed")
        return v


class TokenPatternOperatorSimple(str, Enum):
    plus: StrictStr = StrictStr("+")
    star: StrictStr = StrictStr("*")
    question: StrictStr = StrictStr("?")
    exclamation: StrictStr = StrictStr("!")


class TokenPatternOperatorMinMax(ConstrainedStr):
    regex = re.compile(r"^({\d+}|{\d+,\d*}|{\d*,\d+})$")


TokenPatternOperator = Union[TokenPatternOperatorSimple, TokenPatternOperatorMinMax]
StringValue = Union[TokenPatternString, StrictStr]
NumberValue = Union[TokenPatternNumber, StrictInt, StrictFloat]
UnderscoreValue = Union[
    TokenPatternString, TokenPatternNumber, str, int, float, list, bool
]
IobValue = Literal["", "I", "O", "B", 0, 1, 2, 3]


class TokenPattern(BaseModel):
    orth: Optional[StringValue] = None
    text: Optional[StringValue] = None
    lower: Optional[StringValue] = None
    pos: Optional[StringValue] = None
    tag: Optional[StringValue] = None
    morph: Optional[StringValue] = None
    dep: Optional[StringValue] = None
    lemma: Optional[StringValue] = None
    shape: Optional[StringValue] = None
    ent_type: Optional[StringValue] = None
    ent_iob: Optional[IobValue] = None
    ent_id: Optional[StringValue] = None
    ent_kb_id: Optional[StringValue] = None
    norm: Optional[StringValue] = None
    length: Optional[NumberValue] = None
    spacy: Optional[StrictBool] = None
    is_alpha: Optional[StrictBool] = None
    is_ascii: Optional[StrictBool] = None
    is_digit: Optional[StrictBool] = None
    is_lower: Optional[StrictBool] = None
    is_upper: Optional[StrictBool] = None
    is_title: Optional[StrictBool] = None
    is_punct: Optional[StrictBool] = None
    is_space: Optional[StrictBool] = None
    is_bracket: Optional[StrictBool] = None
    is_quote: Optional[StrictBool] = None
    is_left_punct: Optional[StrictBool] = None
    is_right_punct: Optional[StrictBool] = None
    is_currency: Optional[StrictBool] = None
    is_stop: Optional[StrictBool] = None
    is_sent_start: Optional[StrictBool] = None
    sent_start: Optional[StrictBool] = None
    like_num: Optional[StrictBool] = None
    like_url: Optional[StrictBool] = None
    like_email: Optional[StrictBool] = None
    op: Optional[TokenPatternOperator] = None
    underscore: Optional[Dict[StrictStr, UnderscoreValue]] = Field(None, alias="_")

    class Config:
        extra = "forbid"
        allow_population_by_field_name = True
        alias_generator = lambda value: value.upper()

    @validator("*", pre=True, allow_reuse=True)
    def raise_for_none(cls, v):
        if v is None:
            raise ValueError("None / null is not allowed")
        return v


class TokenPatternSchema(BaseModel):
    pattern: List[TokenPattern] = Field(..., min_items=1)

    class Config:
        extra = "forbid"


# Model meta


class ModelMetaSchema(BaseModel):
    # fmt: off
    lang: StrictStr = Field(..., title="Two-letter language code, e.g. 'en'")
    name: StrictStr = Field(..., title="Model name")
    version: StrictStr = Field(..., title="Model version")
    spacy_version: StrictStr = Field("", title="Compatible spaCy version identifier")
    parent_package: StrictStr = Field("spacy", title="Name of parent spaCy package, e.g. spacy or spacy-nightly")
    requirements: List[StrictStr] = Field([], title="Additional Python package dependencies, used for the Python package setup")
    pipeline: List[StrictStr] = Field([], title="Names of pipeline components")
    description: StrictStr = Field("", title="Model description")
    license: StrictStr = Field("", title="Model license")
    author: StrictStr = Field("", title="Model author name")
    email: StrictStr = Field("", title="Model author email")
    url: StrictStr = Field("", title="Model author URL")
    sources: Optional[Union[List[StrictStr], List[Dict[str, str]]]] = Field(None, title="Training data sources")
    vectors: Dict[str, Any] = Field({}, title="Included word vectors")
    labels: Dict[str, List[str]] = Field({}, title="Component labels, keyed by component name")
    performance: Dict[str, Any] = Field({}, title="Accuracy and speed numbers")
    spacy_git_version: StrictStr = Field("", title="Commit of spaCy version used")
    # fmt: on


# Config schema
# We're not setting any defaults here (which is too messy) and are making all
# fields required, so we can raise validation errors for missing values. To
# provide a default, we include a separate .cfg file with all values and
# check that against this schema in the test suite to make sure it's always
# up to date.


class ConfigSchemaTraining(BaseModel):
    # fmt: off
    dev_corpus: StrictStr = Field(..., title="Path in the config to the dev data")
    train_corpus: StrictStr = Field(..., title="Path in the config to the training data")
    batcher: Batcher = Field(..., title="Batcher for the training data")
    dropout: StrictFloat = Field(..., title="Dropout rate")
    patience: StrictInt = Field(..., title="How many steps to continue without improvement in evaluation score")
    max_epochs: StrictInt = Field(..., title="Maximum number of epochs to train for")
    max_steps: StrictInt = Field(..., title="Maximum number of update steps to train for")
    eval_frequency: StrictInt = Field(..., title="How often to evaluate during training (steps)")
    seed: Optional[StrictInt] = Field(..., title="Random seed")
    gpu_allocator: Optional[StrictStr] = Field(..., title="Memory allocator when running on GPU")
    accumulate_gradient: StrictInt = Field(..., title="Whether to divide the batch up into substeps")
    score_weights: Dict[StrictStr, Optional[Union[StrictFloat, StrictInt]]] = Field(..., title="Scores to report and their weights for selecting final model")
    optimizer: Optimizer = Field(..., title="The optimizer to use")
    logger: Logger = Field(..., title="The logger to track training progress")
    frozen_components: List[str] = Field(..., title="Pipeline components that shouldn't be updated during training")
    annotating_components: List[str] = Field(..., title="Pipeline components that should set annotations during training")
    before_to_disk: Optional[Callable[["Language"], "Language"]] = Field(..., title="Optional callback to modify nlp object after training, before it's saved to disk")
    before_update: Optional[Callable[["Language", Dict[str, Any]], None]] = Field(..., title="Optional callback that is invoked at the start of each training step")
    # fmt: on

    class Config:
        extra = "forbid"
        arbitrary_types_allowed = True


class ConfigSchemaNlp(BaseModel):
    # fmt: off
    lang: StrictStr = Field(..., title="The base language to use")
    pipeline: List[StrictStr] = Field(..., title="The pipeline component names in order")
    disabled: List[StrictStr] = Field(..., title="Pipeline components to disable by default")
    tokenizer: Callable = Field(..., title="The tokenizer to use")
    before_creation: Optional[Callable[[Type["Language"]], Type["Language"]]] = Field(..., title="Optional callback to modify Language class before initialization")
    after_creation: Optional[Callable[["Language"], "Language"]] = Field(..., title="Optional callback to modify nlp object after creation and before the pipeline is constructed")
    after_pipeline_creation: Optional[Callable[["Language"], "Language"]] = Field(..., title="Optional callback to modify nlp object after the pipeline is constructed")
    batch_size: Optional[int] = Field(..., title="Default batch size")
    vectors: Callable = Field(..., title="Vectors implementation")
    # fmt: on

    class Config:
        extra = "forbid"
        arbitrary_types_allowed = True


class ConfigSchemaPretrainEmpty(BaseModel):
    class Config:
        extra = "forbid"


class ConfigSchemaPretrain(BaseModel):
    # fmt: off
    max_epochs: StrictInt = Field(..., title="Maximum number of epochs to train for")
    dropout: StrictFloat = Field(..., title="Dropout rate")
    n_save_every: Optional[StrictInt] = Field(..., title="Saving additional temporary model after n batches within an epoch")
    n_save_epoch: Optional[StrictInt] = Field(..., title="Saving model after every n epoch")
    optimizer: Optimizer = Field(..., title="The optimizer to use")
    corpus: StrictStr = Field(..., title="Path in the config to the training data")
    batcher: Batcher = Field(..., title="Batcher for the training data")
    component: str = Field(..., title="Component to find the layer to pretrain")
    layer: str = Field(..., title="Layer to pretrain. Whole model if empty.")
    objective: Callable[["Vocab", Model], Model] = Field(..., title="A function that creates the pretraining objective.")
    # fmt: on

    class Config:
        extra = "forbid"
        arbitrary_types_allowed = True


class ConfigSchemaInit(BaseModel):
    # fmt: off
    vocab_data: Optional[StrictStr] = Field(..., title="Path to JSON-formatted vocabulary file")
    lookups: Optional[Lookups] = Field(..., title="Vocabulary lookups, e.g. lexeme normalization")
    vectors: Optional[StrictStr] = Field(..., title="Path to vectors")
    init_tok2vec: Optional[StrictStr] = Field(..., title="Path to pretrained tok2vec weights")
    tokenizer: Dict[StrictStr, Any] = Field(..., help="Arguments to be passed into Tokenizer.initialize")
    components: Dict[StrictStr, Dict[StrictStr, Any]] = Field(..., help="Arguments for TrainablePipe.initialize methods of pipeline components, keyed by component")
    before_init: Optional[Callable[["Language"], "Language"]] = Field(..., title="Optional callback to modify nlp object before initialization")
    after_init: Optional[Callable[["Language"], "Language"]] = Field(..., title="Optional callback to modify nlp object after initialization")
    # fmt: on

    class Config:
        extra = "forbid"
        arbitrary_types_allowed = True


class ConfigSchema(BaseModel):
    training: ConfigSchemaTraining
    nlp: ConfigSchemaNlp
    pretraining: Union[ConfigSchemaPretrain, ConfigSchemaPretrainEmpty] = {}  # type: ignore[assignment]
    components: Dict[str, Dict[str, Any]]
    corpora: Dict[str, Reader]
    initialize: ConfigSchemaInit

    class Config:
        extra = "allow"
        arbitrary_types_allowed = True


CONFIG_SCHEMAS = {
    "nlp": ConfigSchemaNlp,
    "training": ConfigSchemaTraining,
    "pretraining": ConfigSchemaPretrain,
    "initialize": ConfigSchemaInit,
}

# Recommendations for init config workflows


class RecommendationTrfItem(BaseModel):
    name: str
    size_factor: int


class RecommendationTrf(BaseModel):
    efficiency: RecommendationTrfItem
    accuracy: RecommendationTrfItem


class RecommendationSchema(BaseModel):
    word_vectors: Optional[str] = None
    transformer: Optional[RecommendationTrf] = None
    has_letters: bool = True


class DocJSONSchema(BaseModel):
    """
    JSON/dict format for JSON representation of Doc objects.
    """

    cats: Optional[Dict[StrictStr, StrictFloat]] = Field(
        None, title="Categories with corresponding probabilities"
    )
    ents: Optional[List[Dict[StrictStr, Union[StrictInt, StrictStr]]]] = Field(
        None, title="Information on entities"
    )
    sents: Optional[List[Dict[StrictStr, StrictInt]]] = Field(
        None, title="Indices of sentences' start and end indices"
    )
    text: StrictStr = Field(..., title="Document text")
    spans: Optional[
        Dict[StrictStr, List[Dict[StrictStr, Union[StrictStr, StrictInt]]]]
    ] = Field(None, title="Span information - end/start indices, label, KB ID")
    tokens: List[Dict[StrictStr, Union[StrictStr, StrictInt]]] = Field(
        ..., title="Token information - ID, start, annotations"
    )
    underscore_doc: Optional[Dict[StrictStr, Any]] = Field(
        None,
        title="Any custom data stored in the document's _ attribute",
        alias="_",
    )
    underscore_token: Optional[Dict[StrictStr, List[Dict[StrictStr, Any]]]] = Field(
        None, title="Any custom data stored in the token's _ attribute"
    )
    underscore_span: Optional[Dict[StrictStr, List[Dict[StrictStr, Any]]]] = Field(
        None, title="Any custom data stored in the span's _ attribute"
    )