import re
from enum import Enum
from pathlib import Path
from typing import (
    Any,
    ClassVar,
    DefaultDict,
    Dict,
    List,
    NamedTuple,
    Optional,
    Set,
)

from pydantic import Field
from typing_extensions import Literal

from datamodel_code_generator.model.base import UNDEFINED, DataModelFieldBase
from datamodel_code_generator.model.pydantic.base_model import (
    BaseModelBase,
)
from datamodel_code_generator.model.pydantic.base_model import (
    Constraints as _Constraints,
)
from datamodel_code_generator.model.pydantic.base_model import (
    DataModelField as DataModelFieldV1,
)
from datamodel_code_generator.model.pydantic_v2.imports import IMPORT_CONFIG_DICT
from datamodel_code_generator.reference import Reference
from datamodel_code_generator.util import field_validator, model_validator


class UnionMode(Enum):
    smart = 'smart'
    left_to_right = 'left_to_right'


class Constraints(_Constraints):
    # To override existing pattern alias
    regex: Optional[str] = Field(None, alias='regex')
    pattern: Optional[str] = Field(None, alias='pattern')

    @model_validator(mode='before')
    def validate_min_max_items(cls, values: Any) -> Dict[str, Any]:
        if not isinstance(values, dict):  # pragma: no cover
            return values
        min_items = values.pop('minItems', None)
        if min_items is not None:
            values['minLength'] = min_items
        max_items = values.pop('maxItems', None)
        if max_items is not None:
            values['maxLength'] = max_items
        return values


class DataModelField(DataModelFieldV1):
    _EXCLUDE_FIELD_KEYS: ClassVar[Set[str]] = {
        'alias',
        'default',
        'gt',
        'ge',
        'lt',
        'le',
        'multiple_of',
        'min_length',
        'max_length',
        'pattern',
    }
    _DEFAULT_FIELD_KEYS: ClassVar[Set[str]] = {
        'default',
        'default_factory',
        'alias',
        'alias_priority',
        'validation_alias',
        'serialization_alias',
        'title',
        'description',
        'examples',
        'exclude',
        'discriminator',
        'json_schema_extra',
        'frozen',
        'validate_default',
        'repr',
        'init_var',
        'kw_only',
        'pattern',
        'strict',
        'gt',
        'ge',
        'lt',
        'le',
        'multiple_of',
        'allow_inf_nan',
        'max_digits',
        'decimal_places',
        'min_length',
        'max_length',
        'union_mode',
    }
    constraints: Optional[Constraints] = None
    _PARSE_METHOD: ClassVar[str] = 'model_validate'
    can_have_extra_keys: ClassVar[bool] = False

    @field_validator('extras')
    def validate_extras(cls, values: Any) -> Dict[str, Any]:
        if not isinstance(values, dict):  # pragma: no cover
            return values
        if 'examples' in values:
            return values

        if 'example' in values:
            values['examples'] = [values.pop('example')]
        return values

    def process_const(self) -> None:
        if 'const' not in self.extras:
            return None
        self.const = True
        self.nullable = False
        const = self.extras['const']
        self.data_type = self.data_type.__class__(literals=[const])
        if not self.default:
            self.default = const

    def _process_data_in_str(self, data: Dict[str, Any]) -> None:
        if self.const:
            # const is removed in pydantic 2.0
            data.pop('const')

        # unique_items is not supported in pydantic 2.0
        data.pop('unique_items', None)

        if 'union_mode' in data:
            if self.data_type.is_union:
                data['union_mode'] = data.pop('union_mode').value
            else:
                data.pop('union_mode')

        # **extra is not supported in pydantic 2.0
        json_schema_extra = {
            k: v for k, v in data.items() if k not in self._DEFAULT_FIELD_KEYS
        }
        if json_schema_extra:
            data['json_schema_extra'] = json_schema_extra
            for key in json_schema_extra.keys():
                data.pop(key)

    def _process_annotated_field_arguments(
        self,
        field_arguments: List[str],
    ) -> List[str]:
        return field_arguments


class ConfigAttribute(NamedTuple):
    from_: str
    to: str
    invert: bool


class BaseModel(BaseModelBase):
    TEMPLATE_FILE_PATH: ClassVar[str] = 'pydantic_v2/BaseModel.jinja2'
    BASE_CLASS: ClassVar[str] = 'pydantic.BaseModel'
    CONFIG_ATTRIBUTES: ClassVar[List[ConfigAttribute]] = [
        ConfigAttribute('allow_population_by_field_name', 'populate_by_name', False),
        ConfigAttribute('populate_by_name', 'populate_by_name', False),
        ConfigAttribute('allow_mutation', 'frozen', True),
        ConfigAttribute('frozen', 'frozen', False),
    ]

    def __init__(
        self,
        *,
        reference: Reference,
        fields: List[DataModelFieldBase],
        decorators: Optional[List[str]] = None,
        base_classes: Optional[List[Reference]] = None,
        custom_base_class: Optional[str] = None,
        custom_template_dir: Optional[Path] = None,
        extra_template_data: Optional[DefaultDict[str, Any]] = None,
        path: Optional[Path] = None,
        description: Optional[str] = None,
        default: Any = UNDEFINED,
        nullable: bool = False,
    ) -> None:
        super().__init__(
            reference=reference,
            fields=fields,
            decorators=decorators,
            base_classes=base_classes,
            custom_base_class=custom_base_class,
            custom_template_dir=custom_template_dir,
            extra_template_data=extra_template_data,
            path=path,
            description=description,
            default=default,
            nullable=nullable,
        )
        config_parameters: Dict[str, Any] = {}

        extra = self._get_config_extra()
        if extra:
            config_parameters['extra'] = extra

        for from_, to, invert in self.CONFIG_ATTRIBUTES:
            if from_ in self.extra_template_data:
                config_parameters[to] = (
                    not self.extra_template_data[from_]
                    if invert
                    else self.extra_template_data[from_]
                )
        for data_type in self.all_data_types:
            if data_type.is_custom_type:  # pragma: no cover
                config_parameters['arbitrary_types_allowed'] = True
                break

        for field in self.fields:
            # Check if a regex pattern uses lookarounds.
            # Depending on the generation configuration, the pattern may end up in two different places.
            pattern = (
                isinstance(field.constraints, Constraints) and field.constraints.pattern
            ) or (field.data_type.kwargs or {}).get('pattern')
            if pattern and re.search(r'\(\?<?[=!]', pattern):
                config_parameters['regex_engine'] = '"python-re"'
                break

        if isinstance(self.extra_template_data.get('config'), dict):
            for key, value in self.extra_template_data['config'].items():
                config_parameters[key] = value

        if config_parameters:
            from datamodel_code_generator.model.pydantic_v2 import ConfigDict

            self.extra_template_data['config'] = ConfigDict.parse_obj(config_parameters)
            self._additional_imports.append(IMPORT_CONFIG_DICT)

    def _get_config_extra(self) -> Optional[Literal["'allow'", "'forbid'"]]:
        additionalProperties = self.extra_template_data.get('additionalProperties')
        allow_extra_fields = self.extra_template_data.get('allow_extra_fields')
        if additionalProperties is not None or allow_extra_fields:
            return (
                "'allow'" if additionalProperties or allow_extra_fields else "'forbid'"
            )
        return None