"""Private logic related to fields (the `Field()` function and `FieldInfo` class), and arguments to `Annotated`."""

from __future__ import annotations as _annotations

import dataclasses
import warnings
from collections.abc import Mapping
from copy import copy
from functools import cache
from inspect import Parameter, ismethoddescriptor, signature
from re import Pattern
from typing import TYPE_CHECKING, Any, Callable, TypeVar

from pydantic_core import PydanticUndefined
from typing_extensions import TypeIs
from typing_inspection.introspection import AnnotationSource

from pydantic import PydanticDeprecatedSince211
from pydantic.errors import PydanticUserError

from ..aliases import AliasGenerator
from . import _generics, _typing_extra
from ._config import ConfigWrapper
from ._docs_extraction import extract_docstrings_from_cls
from ._import_utils import import_cached_base_model, import_cached_field_info
from ._namespace_utils import NsResolver
from ._repr import Representation
from ._utils import can_be_positional, get_first_not_none

if TYPE_CHECKING:
    from annotated_types import BaseMetadata

    from ..fields import FieldInfo
    from ..main import BaseModel
    from ._dataclasses import PydanticDataclass, StandardDataclass
    from ._decorators import DecoratorInfos


class PydanticMetadata(Representation):
    """Base class for annotation markers like `Strict`."""

    __slots__ = ()


def pydantic_general_metadata(**metadata: Any) -> BaseMetadata:
    """Create a new `_PydanticGeneralMetadata` class with the given metadata.

    Args:
        **metadata: The metadata to add.

    Returns:
        The new `_PydanticGeneralMetadata` class.
    """
    return _general_metadata_cls()(metadata)  # type: ignore


@cache
def _general_metadata_cls() -> type[BaseMetadata]:
    """Do it this way to avoid importing `annotated_types` at import time."""
    from annotated_types import BaseMetadata

    class _PydanticGeneralMetadata(PydanticMetadata, BaseMetadata):
        """Pydantic general metadata like `max_digits`."""

        def __init__(self, metadata: Any):
            self.__dict__ = metadata

    return _PydanticGeneralMetadata  # type: ignore


def _check_protected_namespaces(
    protected_namespaces: tuple[str | Pattern[str], ...],
    ann_name: str,
    bases: tuple[type[Any], ...],
    cls_name: str,
) -> None:
    BaseModel = import_cached_base_model()

    for protected_namespace in protected_namespaces:
        ns_violation = False
        if isinstance(protected_namespace, Pattern):
            ns_violation = protected_namespace.match(ann_name) is not None
        elif isinstance(protected_namespace, str):
            ns_violation = ann_name.startswith(protected_namespace)

        if ns_violation:
            for b in bases:
                if hasattr(b, ann_name):
                    if not (issubclass(b, BaseModel) and ann_name in getattr(b, '__pydantic_fields__', {})):
                        raise ValueError(
                            f'Field {ann_name!r} conflicts with member {getattr(b, ann_name)}'
                            f' of protected namespace {protected_namespace!r}.'
                        )
            else:
                valid_namespaces: list[str] = []
                for pn in protected_namespaces:
                    if isinstance(pn, Pattern):
                        if not pn.match(ann_name):
                            valid_namespaces.append(f're.compile({pn.pattern!r})')
                    else:
                        if not ann_name.startswith(pn):
                            valid_namespaces.append(f"'{pn}'")

                valid_namespaces_str = f'({", ".join(valid_namespaces)}{",)" if len(valid_namespaces) == 1 else ")"}'

                warnings.warn(
                    f'Field {ann_name!r} in {cls_name!r} conflicts with protected namespace {protected_namespace!r}.\n\n'
                    f"You may be able to solve this by setting the 'protected_namespaces' configuration to {valid_namespaces_str}.",
                    UserWarning,
                    stacklevel=5,
                )


def _update_fields_from_docstrings(cls: type[Any], fields: dict[str, FieldInfo], use_inspect: bool = False) -> None:
    fields_docs = extract_docstrings_from_cls(cls, use_inspect=use_inspect)
    for ann_name, field_info in fields.items():
        if field_info.description is None and ann_name in fields_docs:
            field_info.description = fields_docs[ann_name]


def _apply_field_title_generator_to_field_info(
    title_generator: Callable[[str, FieldInfo], str],
    field_name: str,
    field_info: FieldInfo,
):
    if field_info.title is None:
        title = title_generator(field_name, field_info)
        if not isinstance(title, str):
            raise TypeError(f'field_title_generator {title_generator} must return str, not {title.__class__}')

        field_info.title = title


def _apply_alias_generator_to_field_info(
    alias_generator: Callable[[str], str] | AliasGenerator, field_name: str, field_info: FieldInfo
):
    """Apply an alias generator to aliases on a `FieldInfo` instance if appropriate.

    Args:
        alias_generator: A callable that takes a string and returns a string, or an `AliasGenerator` instance.
        field_name: The name of the field from which to generate the alias.
        field_info: The `FieldInfo` instance to which the alias generator is (maybe) applied.
    """
    # Apply an alias_generator if
    # 1. An alias is not specified
    # 2. An alias is specified, but the priority is <= 1
    if (
        field_info.alias_priority is None
        or field_info.alias_priority <= 1
        or field_info.alias is None
        or field_info.validation_alias is None
        or field_info.serialization_alias is None
    ):
        alias, validation_alias, serialization_alias = None, None, None

        if isinstance(alias_generator, AliasGenerator):
            alias, validation_alias, serialization_alias = alias_generator.generate_aliases(field_name)
        elif callable(alias_generator):
            alias = alias_generator(field_name)
            if not isinstance(alias, str):
                raise TypeError(f'alias_generator {alias_generator} must return str, not {alias.__class__}')

        # if priority is not set, we set to 1
        # which supports the case where the alias_generator from a child class is used
        # to generate an alias for a field in a parent class
        if field_info.alias_priority is None or field_info.alias_priority <= 1:
            field_info.alias_priority = 1

        # if the priority is 1, then we set the aliases to the generated alias
        if field_info.alias_priority == 1:
            field_info.serialization_alias = get_first_not_none(serialization_alias, alias)
            field_info.validation_alias = get_first_not_none(validation_alias, alias)
            field_info.alias = alias

        # if any of the aliases are not set, then we set them to the corresponding generated alias
        if field_info.alias is None:
            field_info.alias = alias
        if field_info.serialization_alias is None:
            field_info.serialization_alias = get_first_not_none(serialization_alias, alias)
        if field_info.validation_alias is None:
            field_info.validation_alias = get_first_not_none(validation_alias, alias)


def update_field_from_config(config_wrapper: ConfigWrapper, field_name: str, field_info: FieldInfo) -> None:
    """Update the `FieldInfo` instance from the configuration set on the model it belongs to.

    This will apply the title and alias generators from the configuration.

    Args:
        config_wrapper: The configuration from the model.
        field_name: The field name the `FieldInfo` instance is attached to.
        field_info: The `FieldInfo` instance to update.
    """
    field_title_generator = field_info.field_title_generator or config_wrapper.field_title_generator
    if field_title_generator is not None:
        _apply_field_title_generator_to_field_info(field_title_generator, field_name, field_info)
    if config_wrapper.alias_generator is not None:
        _apply_alias_generator_to_field_info(config_wrapper.alias_generator, field_name, field_info)


_deprecated_method_names = {'dict', 'json', 'copy', '_iter', '_copy_and_set_values', '_calculate_keys'}

_deprecated_classmethod_names = {
    'parse_obj',
    'parse_raw',
    'parse_file',
    'from_orm',
    'construct',
    'schema',
    'schema_json',
    'validate',
    'update_forward_refs',
    '_get_value',
}


def collect_model_fields(  # noqa: C901
    cls: type[BaseModel],
    config_wrapper: ConfigWrapper,
    ns_resolver: NsResolver | None,
    *,
    typevars_map: Mapping[TypeVar, Any] | None = None,
) -> tuple[dict[str, FieldInfo], set[str]]:
    """Collect the fields and class variables names of a nascent Pydantic model.

    The fields collection process is *lenient*, meaning it won't error if string annotations
    fail to evaluate. If this happens, the original annotation (and assigned value, if any)
    is stored on the created `FieldInfo` instance.

    The `rebuild_model_fields()` should be called at a later point (e.g. when rebuilding the model),
    and will make use of these stored attributes.

    Args:
        cls: BaseModel or dataclass.
        config_wrapper: The config wrapper instance.
        ns_resolver: Namespace resolver to use when getting model annotations.
        typevars_map: A dictionary mapping type variables to their concrete types.

    Returns:
        A two-tuple containing model fields and class variables names.

    Raises:
        NameError:
            - If there is a conflict between a field name and protected namespaces.
            - If there is a field other than `root` in `RootModel`.
            - If a field shadows an attribute in the parent model.
    """
    FieldInfo_ = import_cached_field_info()
    BaseModel_ = import_cached_base_model()

    bases = cls.__bases__
    parent_fields_lookup: dict[str, FieldInfo] = {}
    for base in reversed(bases):
        if model_fields := getattr(base, '__pydantic_fields__', None):
            parent_fields_lookup.update(model_fields)

    type_hints = _typing_extra.get_model_type_hints(cls, ns_resolver=ns_resolver)

    # https://docs.python.org/3/howto/annotations.html#accessing-the-annotations-dict-of-an-object-in-python-3-9-and-older
    # annotations is only used for finding fields in parent classes
    annotations = _typing_extra.safe_get_annotations(cls)

    fields: dict[str, FieldInfo] = {}

    class_vars: set[str] = set()
    for ann_name, (ann_type, evaluated) in type_hints.items():
        if ann_name == 'model_config':
            # We never want to treat `model_config` as a field
            # Note: we may need to change this logic if/when we introduce a `BareModel` class with no
            # protected namespaces (where `model_config` might be allowed as a field name)
            continue

        _check_protected_namespaces(
            protected_namespaces=config_wrapper.protected_namespaces,
            ann_name=ann_name,
            bases=bases,
            cls_name=cls.__name__,
        )

        if _typing_extra.is_classvar_annotation(ann_type):
            class_vars.add(ann_name)
            continue

        assigned_value = getattr(cls, ann_name, PydanticUndefined)
        if assigned_value is not PydanticUndefined and (
            # One of the deprecated instance methods was used as a field name (e.g. `dict()`):
            any(getattr(BaseModel_, depr_name, None) is assigned_value for depr_name in _deprecated_method_names)
            # One of the deprecated class methods was used as a field name (e.g. `schema()`):
            or (
                hasattr(assigned_value, '__func__')
                and any(
                    getattr(getattr(BaseModel_, depr_name, None), '__func__', None) is assigned_value.__func__  # pyright: ignore[reportAttributeAccessIssue]
                    for depr_name in _deprecated_classmethod_names
                )
            )
        ):
            # Then `assigned_value` would be the method, even though no default was specified:
            assigned_value = PydanticUndefined

        if not is_valid_field_name(ann_name):
            continue
        if cls.__pydantic_root_model__ and ann_name != 'root':
            raise NameError(
                f"Unexpected field with name {ann_name!r}; only 'root' is allowed as a field of a `RootModel`"
            )

        # when building a generic model with `MyModel[int]`, the generic_origin check makes sure we don't get
        # "... shadows an attribute" warnings
        generic_origin = getattr(cls, '__pydantic_generic_metadata__', {}).get('origin')
        for base in bases:
            dataclass_fields = {
                field.name for field in (dataclasses.fields(base) if dataclasses.is_dataclass(base) else ())
            }
            if hasattr(base, ann_name):
                if base is generic_origin:
                    # Don't warn when "shadowing" of attributes in parametrized generics
                    continue

                if ann_name in dataclass_fields:
                    # Don't warn when inheriting stdlib dataclasses whose fields are "shadowed" by defaults being set
                    # on the class instance.
                    continue

                if ann_name not in annotations:
                    # Don't warn when a field exists in a parent class but has not been defined in the current class
                    continue

                warnings.warn(
                    f'Field name "{ann_name}" in "{cls.__qualname__}" shadows an attribute in parent '
                    f'"{base.__qualname__}"',
                    UserWarning,
                    stacklevel=4,
                )

        if assigned_value is PydanticUndefined:  # no assignment, just a plain annotation
            if ann_name in annotations or ann_name not in parent_fields_lookup:
                # field is either:
                # - present in the current model's annotations (and *not* from parent classes)
                # - not found on any base classes; this seems to be caused by fields bot getting
                #   generated due to models not being fully defined while initializing recursive models.
                #   Nothing stops us from just creating a `FieldInfo` for this type hint, so we do this.
                field_info = FieldInfo_.from_annotation(ann_type, _source=AnnotationSource.CLASS)
                if not evaluated:
                    field_info._complete = False
                    # Store the original annotation that should be used to rebuild
                    # the field info later:
                    field_info._original_annotation = ann_type
            else:
                # The field was present on one of the (possibly multiple) base classes
                # copy the field to make sure typevar substitutions don't cause issues with the base classes
                field_info = copy(parent_fields_lookup[ann_name])

        else:  # An assigned value is present (either the default value, or a `Field()` function)
            if isinstance(assigned_value, FieldInfo_) and ismethoddescriptor(assigned_value.default):
                # `assigned_value` was fetched using `getattr`, which triggers a call to `__get__`
                # for descriptors, so we do the same if the `= field(default=...)` form is used.
                # Note that we only do this for method descriptors for now, we might want to
                # extend this to any descriptor in the future (by simply checking for
                # `hasattr(assigned_value.default, '__get__')`).
                default = assigned_value.default.__get__(None, cls)
                assigned_value.default = default
                assigned_value._attributes_set['default'] = default

            field_info = FieldInfo_.from_annotated_attribute(ann_type, assigned_value, _source=AnnotationSource.CLASS)
            # Store the original annotation and assignment value that should be used to rebuild the field info later.
            # Note that the assignment is always stored as the annotation might contain a type var that is later
            #  parameterized with an unknown forward reference (and we'll need it to rebuild the field info):
            field_info._original_assignment = assigned_value
            if not evaluated:
                field_info._complete = False
                field_info._original_annotation = ann_type
            elif 'final' in field_info._qualifiers and not field_info.is_required():
                warnings.warn(
                    f'Annotation {ann_name!r} is marked as final and has a default value. Pydantic treats {ann_name!r} as a '
                    'class variable, but it will be considered as a normal field in V3 to be aligned with dataclasses. If you '
                    f'still want {ann_name!r} to be considered as a class variable, annotate it as: `ClassVar[<type>] = <default>.`',
                    category=PydanticDeprecatedSince211,
                    # Incorrect when `create_model` is used, but the chance that final with a default is used is low in that case:
                    stacklevel=4,
                )
                class_vars.add(ann_name)
                continue

            # attributes which are fields are removed from the class namespace:
            # 1. To match the behaviour of annotation-only fields
            # 2. To avoid false positives in the NameError check above
            try:
                delattr(cls, ann_name)
            except AttributeError:
                pass  # indicates the attribute was on a parent class

        # Use cls.__dict__['__pydantic_decorators__'] instead of cls.__pydantic_decorators__
        # to make sure the decorators have already been built for this exact class
        decorators: DecoratorInfos = cls.__dict__['__pydantic_decorators__']
        if ann_name in decorators.computed_fields:
            raise TypeError(
                f'Field {ann_name!r} of class {cls.__name__!r} overrides symbol of same name in a parent class. '
                'This override with a computed_field is incompatible.'
            )
        fields[ann_name] = field_info

        if field_info._complete:
            # If not complete, this will be called in `rebuild_model_fields()`:
            update_field_from_config(config_wrapper, ann_name, field_info)

    if typevars_map:
        for field in fields.values():
            if field._complete:
                field.apply_typevars_map(typevars_map)

    if config_wrapper.use_attribute_docstrings:
        _update_fields_from_docstrings(cls, fields)
    return fields, class_vars


def rebuild_model_fields(
    cls: type[BaseModel],
    *,
    config_wrapper: ConfigWrapper,
    ns_resolver: NsResolver,
    typevars_map: Mapping[TypeVar, Any],
) -> dict[str, FieldInfo]:
    """Rebuild the (already present) model fields by trying to reevaluate annotations.

    This function should be called whenever a model with incomplete fields is encountered.

    Raises:
        NameError: If one of the annotations failed to evaluate.

    Note:
        This function *doesn't* mutate the model fields in place, as it can be called during
        schema generation, where you don't want to mutate other model's fields.
    """
    FieldInfo_ = import_cached_field_info()

    rebuilt_fields: dict[str, FieldInfo] = {}
    with ns_resolver.push(cls):
        for f_name, field_info in cls.__pydantic_fields__.items():
            if field_info._complete:
                rebuilt_fields[f_name] = field_info
            else:
                existing_desc = field_info.description
                ann = _typing_extra.eval_type(
                    field_info._original_annotation,
                    *ns_resolver.types_namespace,
                )
                ann = _generics.replace_types(ann, typevars_map)

                if (assign := field_info._original_assignment) is PydanticUndefined:
                    new_field = FieldInfo_.from_annotation(ann, _source=AnnotationSource.CLASS)
                else:
                    new_field = FieldInfo_.from_annotated_attribute(ann, assign, _source=AnnotationSource.CLASS)
                # The description might come from the docstring if `use_attribute_docstrings` was `True`:
                new_field.description = new_field.description if new_field.description is not None else existing_desc
                update_field_from_config(config_wrapper, f_name, new_field)
                rebuilt_fields[f_name] = new_field

    return rebuilt_fields


def collect_dataclass_fields(
    cls: type[StandardDataclass],
    *,
    config_wrapper: ConfigWrapper,
    ns_resolver: NsResolver | None = None,
    typevars_map: dict[Any, Any] | None = None,
) -> dict[str, FieldInfo]:
    """Collect the fields of a dataclass.

    Args:
        cls: dataclass.
        config_wrapper: The config wrapper instance.
        ns_resolver: Namespace resolver to use when getting dataclass annotations.
            Defaults to an empty instance.
        typevars_map: A dictionary mapping type variables to their concrete types.

    Returns:
        The dataclass fields.
    """
    FieldInfo_ = import_cached_field_info()

    fields: dict[str, FieldInfo] = {}
    ns_resolver = ns_resolver or NsResolver()
    dataclass_fields = cls.__dataclass_fields__

    # The logic here is similar to `_typing_extra.get_cls_type_hints`,
    # although we do it manually as stdlib dataclasses already have annotations
    # collected in each class:
    for base in reversed(cls.__mro__):
        if not dataclasses.is_dataclass(base):
            continue

        with ns_resolver.push(base):
            for ann_name, dataclass_field in dataclass_fields.items():
                base_anns = _typing_extra.safe_get_annotations(base)

                if ann_name not in base_anns:
                    # `__dataclass_fields__`contains every field, even the ones from base classes.
                    # Only collect the ones defined on `base`.
                    continue

                globalns, localns = ns_resolver.types_namespace
                ann_type, evaluated = _typing_extra.try_eval_type(dataclass_field.type, globalns, localns)

                if _typing_extra.is_classvar_annotation(ann_type):
                    continue

                if (
                    not dataclass_field.init
                    and dataclass_field.default is dataclasses.MISSING
                    and dataclass_field.default_factory is dataclasses.MISSING
                ):
                    # TODO: We should probably do something with this so that validate_assignment behaves properly
                    #   Issue: https://github.com/pydantic/pydantic/issues/5470
                    continue

                if isinstance(dataclass_field.default, FieldInfo_):
                    if dataclass_field.default.init_var:
                        if dataclass_field.default.init is False:
                            raise PydanticUserError(
                                f'Dataclass field {ann_name} has init=False and init_var=True, but these are mutually exclusive.',
                                code='clashing-init-and-init-var',
                            )

                        # TODO: same note as above re validate_assignment
                        continue
                    field_info = FieldInfo_.from_annotated_attribute(
                        ann_type, dataclass_field.default, _source=AnnotationSource.DATACLASS
                    )
                    field_info._original_assignment = dataclass_field.default
                else:
                    field_info = FieldInfo_.from_annotated_attribute(
                        ann_type, dataclass_field, _source=AnnotationSource.DATACLASS
                    )
                    field_info._original_assignment = dataclass_field

                if not evaluated:
                    field_info._complete = False
                    field_info._original_annotation = ann_type

                fields[ann_name] = field_info
                update_field_from_config(config_wrapper, ann_name, field_info)

                if field_info.default is not PydanticUndefined and isinstance(
                    getattr(cls, ann_name, field_info), FieldInfo_
                ):
                    # We need this to fix the default when the "default" from __dataclass_fields__ is a pydantic.FieldInfo
                    setattr(cls, ann_name, field_info.default)

    if typevars_map:
        for field in fields.values():
            # We don't pass any ns, as `field.annotation`
            # was already evaluated. TODO: is this method relevant?
            # Can't we juste use `_generics.replace_types`?
            field.apply_typevars_map(typevars_map)

    if config_wrapper.use_attribute_docstrings:
        _update_fields_from_docstrings(
            cls,
            fields,
            # We can't rely on the (more reliable) frame inspection method
            # for stdlib dataclasses:
            use_inspect=not hasattr(cls, '__is_pydantic_dataclass__'),
        )

    return fields


def rebuild_dataclass_fields(
    cls: type[PydanticDataclass],
    *,
    config_wrapper: ConfigWrapper,
    ns_resolver: NsResolver,
    typevars_map: Mapping[TypeVar, Any],
) -> dict[str, FieldInfo]:
    """Rebuild the (already present) dataclass fields by trying to reevaluate annotations.

    This function should be called whenever a dataclass with incomplete fields is encountered.

    Raises:
        NameError: If one of the annotations failed to evaluate.

    Note:
        This function *doesn't* mutate the dataclass fields in place, as it can be called during
        schema generation, where you don't want to mutate other dataclass's fields.
    """
    FieldInfo_ = import_cached_field_info()

    rebuilt_fields: dict[str, FieldInfo] = {}
    with ns_resolver.push(cls):
        for f_name, field_info in cls.__pydantic_fields__.items():
            if field_info._complete:
                rebuilt_fields[f_name] = field_info
            else:
                existing_desc = field_info.description
                ann = _typing_extra.eval_type(
                    field_info._original_annotation,
                    *ns_resolver.types_namespace,
                )
                ann = _generics.replace_types(ann, typevars_map)
                new_field = FieldInfo_.from_annotated_attribute(
                    ann,
                    field_info._original_assignment,
                    _source=AnnotationSource.DATACLASS,
                )

                # The description might come from the docstring if `use_attribute_docstrings` was `True`:
                new_field.description = new_field.description if new_field.description is not None else existing_desc
                update_field_from_config(config_wrapper, f_name, new_field)
                rebuilt_fields[f_name] = new_field

    return rebuilt_fields


def is_valid_field_name(name: str) -> bool:
    return not name.startswith('_')


def is_valid_privateattr_name(name: str) -> bool:
    return name.startswith('_') and not name.startswith('__')


def takes_validated_data_argument(
    default_factory: Callable[[], Any] | Callable[[dict[str, Any]], Any],
) -> TypeIs[Callable[[dict[str, Any]], Any]]:
    """Whether the provided default factory callable has a validated data parameter."""
    try:
        sig = signature(default_factory)
    except (ValueError, TypeError):
        # `inspect.signature` might not be able to infer a signature, e.g. with C objects.
        # In this case, we assume no data argument is present:
        return False

    parameters = list(sig.parameters.values())

    return len(parameters) == 1 and can_be_positional(parameters[0]) and parameters[0].default is Parameter.empty
