"""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 pydantic import PydanticDeprecatedSince211
from pydantic.errors import PydanticUserError

from . import _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

if TYPE_CHECKING:
    from annotated_types import BaseMetadata

    from ..fields import FieldInfo
    from ..main import BaseModel
    from ._dataclasses import 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 _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 collect_model_fields(  # noqa: C901
    cls: type[BaseModel],
    bases: tuple[type[Any], ...],
    config_wrapper: ConfigWrapper,
    ns_resolver: NsResolver | None,
    *,
    typevars_map: Mapping[TypeVar, Any] | None = None,
) -> tuple[dict[str, FieldInfo], set[str]]:
    """Collect the fields of a nascent pydantic model.

    Also collect the names of any ClassVars present in the type hints.

    The returned value is a tuple of two items: the fields dict, and the set of ClassVar names.

    Args:
        cls: BaseModel or dataclass.
        bases: Parents of the class, generally `cls.__bases__`.
        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 tuple contains fields and class variables.

    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.
    """
    BaseModel = import_cached_base_model()
    FieldInfo_ = import_cached_field_info()

    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 = cls.__dict__.get('__annotations__', {})
    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

        for protected_namespace in config_wrapper.protected_namespaces:
            ns_violation: bool = 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 NameError(
                                f'Field "{ann_name}" conflicts with member {getattr(b, ann_name)}'
                                f' of protected namespace "{protected_namespace}".'
                            )
                else:
                    valid_namespaces = ()
                    for pn in config_wrapper.protected_namespaces:
                        if isinstance(pn, Pattern):
                            if not pn.match(ann_name):
                                valid_namespaces += (f're.compile({pn.pattern})',)
                        else:
                            if not ann_name.startswith(pn):
                                valid_namespaces += (pn,)

                    warnings.warn(
                        f'Field "{ann_name}" in {cls.__name__} has conflict with protected namespace "{protected_namespace}".'
                        '\n\nYou may be able to resolve this warning by setting'
                        f" `model_config['protected_namespaces'] = {valid_namespaces}`.",
                        UserWarning,
                    )
        if _typing_extra.is_classvar_annotation(ann_type):
            class_vars.add(ann_name)
            continue

        assigned_value = getattr(cls, ann_name, PydanticUndefined)

        if _is_finalvar_with_default_val(ann_type, assigned_value):
            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
        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,
                )

        if assigned_value is PydanticUndefined:
            if ann_name in annotations:
                field_info = FieldInfo_.from_annotation(ann_type)
                field_info.evaluated = evaluated
            else:
                # if field has no default value and is not in __annotations__ this means that it is
                # defined in a base class and we can take it from there
                if ann_name in parent_fields_lookup:
                    # 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:
                    # The field was not found on any base classes; this seems to be caused by fields not getting
                    # generated thanks to models not being fully defined while initializing recursive models.
                    # Nothing stops us from just creating a new FieldInfo for this type hint, so we do this.
                    field_info = FieldInfo_.from_annotation(ann_type)
                    field_info.evaluated = evaluated
        else:
            _warn_on_nested_alias_in_annotation(ann_type, ann_name)
            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__')`).
                assigned_value.default = assigned_value.default.__get__(None, cls)

            field_info = FieldInfo_.from_annotated_attribute(ann_type, assigned_value)
            field_info.evaluated = evaluated
            # 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 ValueError("you can't override a field with a computed field")
        fields[ann_name] = field_info

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

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


def _warn_on_nested_alias_in_annotation(ann_type: type[Any], ann_name: str) -> None:
    FieldInfo = import_cached_field_info()

    args = getattr(ann_type, '__args__', None)
    if args:
        for anno_arg in args:
            if _typing_extra.is_annotated(anno_arg):
                for anno_type_arg in _typing_extra.get_args(anno_arg):
                    if isinstance(anno_type_arg, FieldInfo) and anno_type_arg.alias is not None:
                        warnings.warn(
                            f'`alias` specification on field "{ann_name}" must be set on outermost annotation to take effect.',
                            UserWarning,
                        )
                        return


def _is_finalvar_with_default_val(ann_type: type[Any], assigned_value: Any) -> bool:
    if assigned_value is PydanticUndefined:
        return False

    FieldInfo = import_cached_field_info()

    if isinstance(assigned_value, FieldInfo) and assigned_value.is_required():
        return False
    elif not _typing_extra.is_finalvar(ann_type):
        return False
    else:
        return True


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

    Args:
        cls: dataclass.
        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.
        config_wrapper: The config wrapper instance.

    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():
                if ann_name not in base.__dict__.get('__annotations__', {}):
                    # `__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, _ = _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)
                else:
                    field_info = FieldInfo_.from_annotated_attribute(ann_type, dataclass_field)

                fields[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 is not None and 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 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
