.-.-.-.-.-

# sql/sqltypes.py
# Copyright (C) 2005-2025 the SQLAlchemy authors and contributors
# <see AUTHORS file>
#
# This module is part of SQLAlchemy and is released under
# the MIT License: https://www.opensource.org/licenses/mit-license.php
# mypy: allow-untyped-defs, allow-untyped-calls

"""SQL specific types.

"""
from __future__ import annotations

import collections.abc as collections_abc
import datetime as dt
import decimal
import enum
import json
import pickle
from typing import Any
from typing import Callable
from typing import cast
from typing import Dict
from typing import List
from typing import Optional
from typing import overload
from typing import Sequence
from typing import Tuple
from typing import Type
from typing import TYPE_CHECKING
from typing import TypeVar
from typing import Union
from uuid import UUID as _python_UUID

from . import coercions
from . import elements
from . import operators
from . import roles
from . import type_api
from .base import _NONE_NAME
from .base import NO_ARG
from .base import SchemaEventTarget
from .cache_key import HasCacheKey
from .elements import quoted_name
from .elements import Slice
from .elements import TypeCoerce as type_coerce  # noqa
from .type_api import Emulated
from .type_api import NativeForEmulated  # noqa
from .type_api import to_instance as to_instance
from .type_api import TypeDecorator as TypeDecorator
from .type_api import TypeEngine as TypeEngine
from .type_api import TypeEngineMixin
from .type_api import Variant  # noqa
from .visitors import InternalTraversal
from .. import event
from .. import exc
from .. import inspection
from .. import util
from ..engine import processors
from ..util import langhelpers
from ..util import OrderedDict
from ..util import warn_deprecated
from ..util.typing import get_args
from ..util.typing import is_literal
from ..util.typing import is_pep695
from ..util.typing import Literal

if TYPE_CHECKING:
    from ._typing import _ColumnExpressionArgument
    from ._typing import _TypeEngineArgument
    from .operators import OperatorType
    from .schema import MetaData
    from .type_api import _BindProcessorType
    from .type_api import _ComparatorFactory
    from .type_api import _LiteralProcessorType
    from .type_api import _MatchedOnType
    from .type_api import _ResultProcessorType
    from ..engine.interfaces import Dialect

_T = TypeVar("_T", bound="Any")
_CT = TypeVar("_CT", bound=Any)
_TE = TypeVar("_TE", bound="TypeEngine[Any]")

class HasExpressionLookup(TypeEngineMixin):
    """Mixin expression adaptations based on lookup tables.

These rules are currently used by the numeric, integer and date types
    which have detailed cross-expression coercion rules.

"""

@property
    def _expression_adaptations(self):
        raise NotImplementedError()

class Comparator(TypeEngine.Comparator[_CT]):
        __slots__ = ()

_blank_dict = util.EMPTY_DICT

def _adapt_expression(
            self,
            op: OperatorType,
            other_comparator: TypeEngine.Comparator[Any],
        ) -> Tuple[OperatorType, TypeEngine[Any]]:
            othertype = other_comparator.type._type_affinity
            if TYPE_CHECKING:
                assert isinstance(self.type, HasExpressionLookup)
            lookup = self.type._expression_adaptations.get(
                op, self._blank_dict
            ).get(othertype, self.type)
            if lookup is othertype:
                return (op, other_comparator.type)
            elif lookup is self.type._type_affinity:
                return (op, self.type)
            else:
                return (op, to_instance(lookup))

comparator_factory: _ComparatorFactory[Any] = Comparator

class Concatenable(TypeEngineMixin):
    """A mixin that marks a type as supporting 'concatenation',
    typically strings."""

class Comparator(TypeEngine.Comparator[_T]):
        __slots__ = ()

def _adapt_expression(
            self,
            op: OperatorType,
            other_comparator: TypeEngine.Comparator[Any],
        ) -> Tuple[OperatorType, TypeEngine[Any]]:
            if op is operators.add and isinstance(
                other_comparator,
                (Concatenable.Comparator, NullType.Comparator),
            ):
                return operators.concat_op, self.expr.type
            else:
                return super()._adapt_expression(op, other_comparator)

comparator_factory: _ComparatorFactory[Any] = Comparator

class Indexable(TypeEngineMixin):
    """A mixin that marks a type as supporting indexing operations,
    such as array or JSON structures.

"""

class Comparator(TypeEngine.Comparator[_T]):
        __slots__ = ()

def _setup_getitem(self, index):
            raise NotImplementedError()

def __getitem__(self, index):
            (
                adjusted_op,
                adjusted_right_expr,
                result_type,
            ) = self._setup_getitem(index)
            return self.operate(
                adjusted_op, adjusted_right_expr, result_type=result_type
            )

comparator_factory: _ComparatorFactory[Any] = Comparator

class String(Concatenable, TypeEngine[str]):
    """The base for all string and character types.

In SQL, corresponds to VARCHAR.

The `length` field is usually required when the `String` type is
    used within a CREATE TABLE statement, as VARCHAR requires a length
    on most databases.

"""

__visit_name__ = "string"

def __init__(
        self,
        length: Optional[int] = None,
        collation: Optional[str] = None,
    ):
        """
        Create a string-holding type.

:param length: optional, a length for the column for use in
          DDL and CAST expressions.  May be safely omitted if no ``CREATE
          TABLE`` will be issued.  Certain databases may require a
          ``length`` for use in DDL, and will raise an exception when
          the ``CREATE TABLE`` DDL is issued if a ``VARCHAR``
          with no length is included.  Whether the value is
          interpreted as bytes or characters is database specific.

:param collation: Optional, a column-level collation for
          use in DDL and CAST expressions.  Renders using the
          COLLATE keyword supported by SQLite, MySQL, and PostgreSQL.
          E.g.:

.. sourcecode:: pycon+sql

>>> from sqlalchemy import cast, select, String
            >>> print(select(cast("some string", String(collation="utf8"))))
            {printsql}SELECT CAST(:param_1 AS VARCHAR COLLATE utf8) AS anon_1

.. note::

In most cases, the :class:`.Unicode` or :class:`.UnicodeText`
            datatypes should be used for a :class:`_schema.Column` that expects
            to store non-ascii data. These datatypes will ensure that the
            correct types are used on the database.

"""

self.length = length
        self.collation = collation

def _with_collation(self, collation):
        new_type = self.copy()
        new_type.collation = collation
        return new_type

def _resolve_for_literal(self, value):
        # I was SO PROUD of my regex trick, but we dont need it.
        # re.search(r"[^\u0000-\u007F]", value)

if value.isascii():
            return _STRING
        else:
            return _UNICODE

def literal_processor(self, dialect):
        def process(value):
            value = value.replace("'", "''")

if dialect.identifier_preparer._double_percents:
                value = value.replace("%", "%%")

return "'%s'" % value

return process

def bind_processor(self, dialect):
        return None

def result_processor(self, dialect, coltype):
        return None

@property
    def python_type(self):
        return str

def get_dbapi_type(self, dbapi):
        return dbapi.STRING

class Text(String):
    """A variably sized string type.

In SQL, usually corresponds to CLOB or TEXT.  In general, TEXT objects
    do not have a length; while some databases will accept a length
    argument here, it will be rejected by others.

"""

__visit_name__ = "text"

class Unicode(String):
    """A variable length Unicode string type.

The :class:`.Unicode` type is a :class:`.String` subclass that assumes
    input and output strings that may contain non-ASCII characters, and for
    some backends implies an underlying column type that is explicitly
    supporting of non-ASCII data, such as ``NVARCHAR`` on Oracle Database and
    SQL Server.  This will impact the output of ``CREATE TABLE`` statements and
    ``CAST`` functions at the dialect level.

The character encoding used by the :class:`.Unicode` type that is used to
    transmit and receive data to the database is usually determined by the
    DBAPI itself. All modern DBAPIs accommodate non-ASCII strings but may have
    different methods of managing database encodings; if necessary, this
    encoding should be configured as detailed in the notes for the target DBAPI
    in the :ref:`dialect_toplevel` section.

In modern SQLAlchemy, use of the :class:`.Unicode` datatype does not
    imply any encoding/decoding behavior within SQLAlchemy itself.  In Python
    3, all string objects are inherently Unicode capable, and SQLAlchemy
    does not produce bytestring objects nor does it accommodate a DBAPI that
    does not return Python Unicode objects in result sets for string values.

.. warning:: Some database backends, particularly SQL Server with pyodbc,
       are known to have undesirable behaviors regarding data that is noted
       as being of ``NVARCHAR`` type as opposed to ``VARCHAR``, including
       datatype mismatch errors and non-use of indexes.  See the section
       on :meth:`.DialectEvents.do_setinputsizes` for background on working
       around unicode character issues for backends like SQL Server with
       pyodbc as well as cx_Oracle.

.. seealso::

:class:`.UnicodeText` - unlengthed textual counterpart
        to :class:`.Unicode`.

:meth:`.DialectEvents.do_setinputsizes`

"""

__visit_name__ = "unicode"

class UnicodeText(Text):
    """An unbounded-length Unicode string type.

See :class:`.Unicode` for details on the unicode
    behavior of this object.

Like :class:`.Unicode`, usage the :class:`.UnicodeText` type implies a
    unicode-capable type being used on the backend, such as
    ``NCLOB``, ``NTEXT``.

"""

__visit_name__ = "unicode_text"

class Integer(HasExpressionLookup, TypeEngine[int]):
    """A type for ``int`` integers."""

__visit_name__ = "integer"

if TYPE_CHECKING:

@util.ro_memoized_property
        def _type_affinity(self) -> Type[Integer]: ...

def get_dbapi_type(self, dbapi):
        return dbapi.NUMBER

@property
    def python_type(self):
        return int

def _resolve_for_literal(self, value):
        if value.bit_length() >= 32:
            return _BIGINTEGER
        else:
            return self

def literal_processor(self, dialect):
        def process(value):
            return str(int(value))

return process

@util.memoized_property
    def _expression_adaptations(self):
        return {
            operators.add: {
                Date: Date,
                Integer: self.__class__,
                Numeric: Numeric,
            },
            operators.mul: {
                Interval: Interval,
                Integer: self.__class__,
                Numeric: Numeric,
            },
            operators.truediv: {Integer: Numeric, Numeric: Numeric},
            operators.floordiv: {Integer: self.__class__, Numeric: Numeric},
            operators.sub: {Integer: self.__class__, Numeric: Numeric},
        }

class SmallInteger(Integer):
    """A type for smaller ``int`` integers.

Typically generates a ``SMALLINT`` in DDL, and otherwise acts like
    a normal :class:`.Integer` on the Python side.

"""

__visit_name__ = "small_integer"

class BigInteger(Integer):
    """A type for bigger ``int`` integers.

Typically generates a ``BIGINT`` in DDL, and otherwise acts like
    a normal :class:`.Integer` on the Python side.

"""

__visit_name__ = "big_integer"

_N = TypeVar("_N", bound=Union[decimal.Decimal, float])

class Numeric(HasExpressionLookup, TypeEngine[_N]):
    """Base for non-integer numeric types, such as
    ``NUMERIC``, ``FLOAT``, ``DECIMAL``, and other variants.

The :class:`.Numeric` datatype when used directly will render DDL
    corresponding to precision numerics if available, such as
    ``NUMERIC(precision, scale)``.  The :class:`.Float` subclass will
    attempt to render a floating-point datatype such as ``FLOAT(precision)``.

:class:`.Numeric` returns Python ``decimal.Decimal`` objects by default,
    based on the default value of ``True`` for the
    :paramref:`.Numeric.asdecimal` parameter.  If this parameter is set to
    False, returned values are coerced to Python ``float`` objects.

The :class:`.Float` subtype, being more specific to floating point,
    defaults the :paramref:`.Float.asdecimal` flag to False so that the
    default Python datatype is ``float``.

.. note::

When using a :class:`.Numeric` datatype against a database type that
        returns Python floating point values to the driver, the accuracy of the
        decimal conversion indicated by :paramref:`.Numeric.asdecimal` may be
        limited.   The behavior of specific numeric/floating point datatypes
        is a product of the SQL datatype in use, the Python :term:`DBAPI`
        in use, as well as strategies that may be present within
        the SQLAlchemy dialect in use.   Users requiring specific precision/
        scale are encouraged to experiment with the available datatypes
        in order to determine the best results.

"""

__visit_name__ = "numeric"

if TYPE_CHECKING:

@util.ro_memoized_property
        def _type_affinity(self) -> Type[Numeric[_N]]: ...

_default_decimal_return_scale = 10

@overload
    def __init__(
        self: Numeric[decimal.Decimal],
        precision: Optional[int] = ...,
        scale: Optional[int] = ...,
        decimal_return_scale: Optional[int] = ...,
        asdecimal: Literal[True] = ...,
    ): ...

@overload
    def __init__(
        self: Numeric[float],
        precision: Optional[int] = ...,
        scale: Optional[int] = ...,
        decimal_return_scale: Optional[int] = ...,
        asdecimal: Literal[False] = ...,
    ): ...

def __init__(
        self,
        precision: Optional[int] = None,
        scale: Optional[int] = None,
        decimal_return_scale: Optional[int] = None,
        asdecimal: bool = True,
    ):
        """
        Construct a Numeric.

:param precision: the numeric precision for use in DDL ``CREATE
          TABLE``.

:param scale: the numeric scale for use in DDL ``CREATE TABLE``.

:param asdecimal: default True.  Return whether or not
          values should be sent as Python Decimal objects, or
          as floats.   Different DBAPIs send one or the other based on
          datatypes - the Numeric type will ensure that return values
          are one or the other across DBAPIs consistently.

:param decimal_return_scale: Default scale to use when converting
         from floats to Python decimals.  Floating point values will typically
         be much longer due to decimal inaccuracy, and most floating point
         database types don't have a notion of "scale", so by default the
         float type looks for the first ten decimal places when converting.
         Specifying this value will override that length.  Types which
         do include an explicit ".scale" value, such as the base
         :class:`.Numeric` as well as the MySQL float types, will use the
         value of ".scale" as the default for decimal_return_scale, if not
         otherwise specified.

When using the ``Numeric`` type, care should be taken to ensure
        that the asdecimal setting is appropriate for the DBAPI in use -
        when Numeric applies a conversion from Decimal->float or float->
        Decimal, this conversion incurs an additional performance overhead
        for all result columns received.

DBAPIs that return Decimal natively (e.g. psycopg2) will have
        better accuracy and higher performance with a setting of ``True``,
        as the native translation to Decimal reduces the amount of floating-
        point issues at play, and the Numeric type itself doesn't need
        to apply any further conversions.  However, another DBAPI which
        returns floats natively *will* incur an additional conversion
        overhead, and is still subject to floating point data loss - in
        which case ``asdecimal=False`` will at least remove the extra
        conversion overhead.

"""
        self.precision = precision
        self.scale = scale
        self.decimal_return_scale = decimal_return_scale
        self.asdecimal = asdecimal

@property
    def _effective_decimal_return_scale(self):
        if self.decimal_return_scale is not None:
            return self.decimal_return_scale
        elif getattr(self, "scale", None) is not None:
            return self.scale
        else:
            return self._default_decimal_return_scale

def get_dbapi_type(self, dbapi):
        return dbapi.NUMBER

def literal_processor(self, dialect):
        def process(value):
            return str(value)

return process

@property
    def python_type(self):
        if self.asdecimal:
            return decimal.Decimal
        else:
            return float

def bind_processor(self, dialect):
        if dialect.supports_native_decimal:
            return None
        else:
            return processors.to_float

def result_processor(self, dialect, coltype):
        if self.asdecimal:
            if dialect.supports_native_decimal:
                # we're a "numeric", DBAPI will give us Decimal directly
                return None
            else:
                # we're a "numeric", DBAPI returns floats, convert.
                return processors.to_decimal_processor_factory(
                    decimal.Decimal,
                    (
                        self.scale
                        if self.scale is not None
                        else self._default_decimal_return_scale
                    ),
                )
        else:
            if dialect.supports_native_decimal:
                return processors.to_float
            else:
                return None

@util.memoized_property
    def _expression_adaptations(self):
        return {
            operators.mul: {
                Interval: Interval,
                Numeric: self.__class__,
                Integer: self.__class__,
            },
            operators.truediv: {
                Numeric: self.__class__,
                Integer: self.__class__,
            },
            operators.add: {Numeric: self.__class__, Integer: self.__class__},
            operators.sub: {Numeric: self.__class__, Integer: self.__class__},
        }

class Float(Numeric[_N]):
    """Type representing floating point types, such as ``FLOAT`` or ``REAL``.

This type returns Python ``float`` objects by default, unless the
    :paramref:`.Float.asdecimal` flag is set to ``True``, in which case they
    are coerced to ``decimal.Decimal`` objects.

When a :paramref:`.Float.precision` is not provided in a
    :class:`_types.Float` type some backend may compile this type as
    an 8 bytes / 64 bit float datatype. To use a 4 bytes / 32 bit float
    datatype a precision <= 24 can usually be provided or the
    :class:`_types.REAL` type can be used.
    This is known to be the case in the PostgreSQL and MSSQL dialects
    that render the type as ``FLOAT`` that's in both an alias of
    ``DOUBLE PRECISION``. Other third party dialects may have similar
    behavior.
    """

__visit_name__ = "float"

scale = None

@overload
    def __init__(
        self: Float[float],
        precision: Optional[int] = ...,
        asdecimal: Literal[False] = ...,
        decimal_return_scale: Optional[int] = ...,
    ): ...

@overload
    def __init__(
        self: Float[decimal.Decimal],
        precision: Optional[int] = ...,
        asdecimal: Literal[True] = ...,
        decimal_return_scale: Optional[int] = ...,
    ): ...

def __init__(
        self: Float[_N],
        precision: Optional[int] = None,
        asdecimal: bool = False,
        decimal_return_scale: Optional[int] = None,
    ):
        r"""
        Construct a Float.

:param precision: the numeric precision for use in DDL ``CREATE
           TABLE``. Backends **should** attempt to ensure this precision
           indicates a number of digits for the generic
           :class:`_sqltypes.Float` datatype.

.. note:: For the Oracle Database backend, the
              :paramref:`_sqltypes.Float.precision` parameter is not accepted
              when rendering DDL, as Oracle Database does not support float precision
              specified as a number of decimal places. Instead, use the
              Oracle Database-specific :class:`_oracle.FLOAT` datatype and specify the
              :paramref:`_oracle.FLOAT.binary_precision` parameter. This is new
              in version 2.0 of SQLAlchemy.

To create a database agnostic :class:`_types.Float` that
              separately specifies binary precision for Oracle Database, use
              :meth:`_types.TypeEngine.with_variant` as follows::

from sqlalchemy import Column
                    from sqlalchemy import Float
                    from sqlalchemy.dialects import oracle

Column(
                        "float_data",
                        Float(5).with_variant(oracle.FLOAT(binary_precision=16), "oracle"),
                    )

:param asdecimal: the same flag as that of :class:`.Numeric`, but
          defaults to ``False``.   Note that setting this flag to ``True``
          results in floating point conversion.

:param decimal_return_scale: Default scale to use when converting
         from floats to Python decimals.  Floating point values will typically
         be much longer due to decimal inaccuracy, and most floating point
         database types don't have a notion of "scale", so by default the
         float type looks for the first ten decimal places when converting.
         Specifying this value will override that length.  Note that the
         MySQL float types, which do include "scale", will use "scale"
         as the default for decimal_return_scale, if not otherwise specified.

"""  # noqa: E501
        self.precision = precision
        self.asdecimal = asdecimal
        self.decimal_return_scale = decimal_return_scale

def result_processor(self, dialect, coltype):
        if self.asdecimal:
            return processors.to_decimal_processor_factory(
                decimal.Decimal, self._effective_decimal_return_scale
            )
        elif dialect.supports_native_decimal:
            return processors.to_float
        else:
            return None

class Double(Float[_N]):
    """A type for double ``FLOAT`` floating point types.

Typically generates a ``DOUBLE`` or ``DOUBLE_PRECISION`` in DDL,
    and otherwise acts like a normal :class:`.Float` on the Python
    side.

.. versionadded:: 2.0

"""

__visit_name__ = "double"

class _RenderISO8601NoT:
    def _literal_processor_datetime(self, dialect):
        return self._literal_processor_portion(dialect, None)

def _literal_processor_date(self, dialect):
        return self._literal_processor_portion(dialect, 0)

def _literal_processor_time(self, dialect):
        return self._literal_processor_portion(dialect, -1)

def _literal_processor_portion(self, dialect, _portion=None):
        assert _portion in (None, 0, -1)
        if _portion is not None:

def process(value):
                return f"""'{value.isoformat().split("T")[_portion]}'"""

else:

def process(value):
                return f"""'{value.isoformat().replace("T", " ")}'"""

return process

class DateTime(
    _RenderISO8601NoT, HasExpressionLookup, TypeEngine[dt.datetime]
):
    """A type for ``datetime.datetime()`` objects.

Date and time types return objects from the Python ``datetime``
    module.  Most DBAPIs have built in support for the datetime
    module, with the noted exception of SQLite.  In the case of
    SQLite, date and time types are stored as strings which are then
    converted back to datetime objects when rows are returned.

For the time representation within the datetime type, some
    backends include additional options, such as timezone support and
    fractional seconds support.  For fractional seconds, use the
    dialect-specific datatype, such as :class:`.mysql.TIME`.  For
    timezone support, use at least the :class:`_types.TIMESTAMP` datatype,
    if not the dialect-specific datatype object.

"""

__visit_name__ = "datetime"

def __init__(self, timezone: bool = False):
        """Construct a new :class:`.DateTime`.

:param timezone: boolean.  Indicates that the datetime type should
         enable timezone support, if available on the
         **base date/time-holding type only**.   It is recommended
         to make use of the :class:`_types.TIMESTAMP` datatype directly when
         using this flag, as some databases include separate generic
         date/time-holding types distinct from the timezone-capable
         TIMESTAMP datatype, such as Oracle Database.

"""
        self.timezone = timezone

def get_dbapi_type(self, dbapi):
        return dbapi.DATETIME

def _resolve_for_literal(self, value):
        with_timezone = value.tzinfo is not None
        if with_timezone and not self.timezone:
            return DATETIME_TIMEZONE
        else:
            return self

def literal_processor(self, dialect):
        return self._literal_processor_datetime(dialect)

@property
    def python_type(self):
        return dt.datetime

@util.memoized_property
    def _expression_adaptations(self):
        # Based on
        # https://www.postgresql.org/docs/current/static/functions-datetime.html.

return {
            operators.add: {Interval: self.__class__},
            operators.sub: {Interval: self.__class__, DateTime: Interval},
        }

class Date(_RenderISO8601NoT, HasExpressionLookup, TypeEngine[dt.date]):
    """A type for ``datetime.date()`` objects."""

__visit_name__ = "date"

def get_dbapi_type(self, dbapi):
        return dbapi.DATETIME

@property
    def python_type(self):
        return dt.date

def literal_processor(self, dialect):
        return self._literal_processor_date(dialect)

@util.memoized_property
    def _expression_adaptations(self):
        # Based on
        # https://www.postgresql.org/docs/current/static/functions-datetime.html.

return {
            operators.add: {
                Integer: self.__class__,
                Interval: DateTime,
                Time: DateTime,
            },
            operators.sub: {
                # date - integer = date
                Integer: self.__class__,
                # date - date = integer.
                Date: Integer,
                Interval: DateTime,
                # date - datetime = interval,
                # this one is not in the PG docs
                # but works
                DateTime: Interval,
            },
        }

class Time(_RenderISO8601NoT, HasExpressionLookup, TypeEngine[dt.time]):
    """A type for ``datetime.time()`` objects."""

__visit_name__ = "time"

def __init__(self, timezone: bool = False):
        self.timezone = timezone

def get_dbapi_type(self, dbapi):
        return dbapi.DATETIME

@property
    def python_type(self):
        return dt.time

def _resolve_for_literal(self, value):
        with_timezone = value.tzinfo is not None
        if with_timezone and not self.timezone:
            return TIME_TIMEZONE
        else:
            return self

@util.memoized_property
    def _expression_adaptations(self):
        # Based on
        # https://www.postgresql.org/docs/current/static/functions-datetime.html.

return {
            operators.add: {Date: DateTime, Interval: self.__class__},
            operators.sub: {Time: Interval, Interval: self.__class__},
        }

def literal_processor(self, dialect):
        return self._literal_processor_time(dialect)

class _Binary(TypeEngine[bytes]):
    """Define base behavior for binary types."""

def __init__(self, length: Optional[int] = None):
        self.length = length

@util.ro_memoized_property
    def _generic_type_affinity(
        self,
    ) -> Type[TypeEngine[bytes]]:
        return LargeBinary

def literal_processor(self, dialect):
        def process(value):
            # TODO: this is useless for real world scenarios; implement
            # real binary literals
            value = value.decode(
                dialect._legacy_binary_type_literal_encoding
            ).replace("'", "''")
            return "'%s'" % value

return process

@property
    def python_type(self):
        return bytes

# Python 3 - sqlite3 doesn't need the `Binary` conversion
    # here, though pg8000 does to indicate "bytea"
    def bind_processor(self, dialect):
        if dialect.dbapi is None:
            return None

DBAPIBinary = dialect.dbapi.Binary

def process(value):
            if value is not None:
                return DBAPIBinary(value)
            else:
                return None

return process

# Python 3 has native bytes() type
    # both sqlite3 and pg8000 seem to return it,
    # psycopg2 as of 2.5 returns 'memoryview'
    def result_processor(self, dialect, coltype):
        if dialect.returns_native_bytes:
            return None

def process(value):
            if value is not None:
                value = bytes(value)
            return value

return process

def coerce_compared_value(self, op, value):
        """See :meth:`.TypeEngine.coerce_compared_value` for a description."""

if isinstance(value, str):
            return self
        else:
            return super().coerce_compared_value(op, value)

def get_dbapi_type(self, dbapi):
        return dbapi.BINARY

class LargeBinary(_Binary):
    """A type for large binary byte data.

The :class:`.LargeBinary` type corresponds to a large and/or unlengthed
    binary type for the target platform, such as BLOB on MySQL and BYTEA for
    PostgreSQL.  It also handles the necessary conversions for the DBAPI.

"""

__visit_name__ = "large_binary"

def __init__(self, length: Optional[int] = None):
        """
        Construct a LargeBinary type.

:param length: optional, a length for the column for use in
          DDL statements, for those binary types that accept a length,
          such as the MySQL BLOB type.

"""
        _Binary.__init__(self, length=length)

class SchemaType(SchemaEventTarget, TypeEngineMixin):
    """Add capabilities to a type which allow for schema-level DDL to be
    associated with a type.

Supports types that must be explicitly created/dropped (i.e. PG ENUM type)
    as well as types that are complimented by table or schema level
    constraints, triggers, and other rules.

:class:`.SchemaType` classes can also be targets for the
    :meth:`.DDLEvents.before_parent_attach` and
    :meth:`.DDLEvents.after_parent_attach` events, where the events fire off
    surrounding the association of the type object with a parent
    :class:`_schema.Column`.

.. seealso::

:class:`.Enum`

:class:`.Boolean`

"""

_use_schema_map = True

name: Optional[str]

def __init__(
        self,
        name: Optional[str] = None,
        schema: Optional[str] = None,
        metadata: Optional[MetaData] = None,
        inherit_schema: bool = False,
        quote: Optional[bool] = None,
        _create_events: bool = True,
        _adapted_from: Optional[SchemaType] = None,
    ):
        if name is not None:
            self.name = quoted_name(name, quote)
        else:
            self.name = None
        self.schema = schema
        self.metadata = metadata
        self.inherit_schema = inherit_schema
        self._create_events = _create_events

if _create_events and self.metadata:
            event.listen(
                self.metadata,
                "before_create",
                util.portable_instancemethod(self._on_metadata_create),
            )
            event.listen(
                self.metadata,
                "after_drop",
                util.portable_instancemethod(self._on_metadata_drop),
            )

if _adapted_from:
            self.dispatch = self.dispatch._join(_adapted_from.dispatch)

def _set_parent(self, parent, **kw):
        # set parent hook is when this type is associated with a column.
        # Column calls it for all SchemaEventTarget instances, either the
        # base type and/or variants in _variant_mapping.

# we want to register a second hook to trigger when that column is
        # associated with a table.  in that event, we and all of our variants
        # may want to set up some state on the table such as a CheckConstraint
        # that will conditionally render at DDL render time.

# the base SchemaType also sets up events for
        # on_table/metadata_create/drop in this method, which is used by
        # "native" types with a separate CREATE/DROP e.g. Postgresql.ENUM

parent._on_table_attach(util.portable_instancemethod(self._set_table))

def _variant_mapping_for_set_table(self, column):
        if column.type._variant_mapping:
            variant_mapping = dict(column.type._variant_mapping)
            variant_mapping["_default"] = column.type
        else:
            variant_mapping = None
        return variant_mapping

def _set_table(self, column, table):
        if self.inherit_schema:
            self.schema = table.schema
        elif self.metadata and self.schema is None and self.metadata.schema:
            self.schema = self.metadata.schema

if not self._create_events:
            return

variant_mapping = self._variant_mapping_for_set_table(column)

event.listen(
            table,
            "before_create",
            util.portable_instancemethod(
                self._on_table_create, {"variant_mapping": variant_mapping}
            ),
        )
        event.listen(
            table,
            "after_drop",
            util.portable_instancemethod(
                self._on_table_drop, {"variant_mapping": variant_mapping}
            ),
        )
        if self.metadata is None:
            # if SchemaType were created w/ a metadata argument, these
            # events would already have been associated with that metadata
            # and would preclude an association with table.metadata
            event.listen(
                table.metadata,
                "before_create",
                util.portable_instancemethod(
                    self._on_metadata_create,
                    {"variant_mapping": variant_mapping},
                ),
            )
            event.listen(
                table.metadata,
                "after_drop",
                util.portable_instancemethod(
                    self._on_metadata_drop,
                    {"variant_mapping": variant_mapping},
                ),
            )

def copy(self, **kw):
        return self.adapt(
            cast("Type[TypeEngine[Any]]", self.__class__),
            _create_events=True,
            metadata=(
                kw.get("_to_metadata", self.metadata)
                if self.metadata is not None
                else None
            ),
        )

@overload
    def adapt(self, cls: Type[_TE], **kw: Any) -> _TE: ...

@overload
    def adapt(
        self, cls: Type[TypeEngineMixin], **kw: Any
    ) -> TypeEngine[Any]: ...

def adapt(
        self, cls: Type[Union[TypeEngine[Any], TypeEngineMixin]], **kw: Any
    ) -> TypeEngine[Any]:
        kw.setdefault("_create_events", False)
        kw.setdefault("_adapted_from", self)
        return super().adapt(cls, **kw)

def create(self, bind, checkfirst=False):
        """Issue CREATE DDL for this type, if applicable."""

t = self.dialect_impl(bind.dialect)
        if isinstance(t, SchemaType) and t.__class__ is not self.__class__:
            t.create(bind, checkfirst=checkfirst)

def drop(self, bind, checkfirst=False):
        """Issue DROP DDL for this type, if applicable."""

t = self.dialect_impl(bind.dialect)
        if isinstance(t, SchemaType) and t.__class__ is not self.__class__:
            t.drop(bind, checkfirst=checkfirst)

def _on_table_create(self, target, bind, **kw):
        if not self._is_impl_for_variant(bind.dialect, kw):
            return

t = self.dialect_impl(bind.dialect)
        if isinstance(t, SchemaType) and t.__class__ is not self.__class__:
            t._on_table_create(target, bind, **kw)

def _on_table_drop(self, target, bind, **kw):
        if not self._is_impl_for_variant(bind.dialect, kw):
            return

t = self.dialect_impl(bind.dialect)
        if isinstance(t, SchemaType) and t.__class__ is not self.__class__:
            t._on_table_drop(target, bind, **kw)

def _on_metadata_create(self, target, bind, **kw):
        if not self._is_impl_for_variant(bind.dialect, kw):
            return

t = self.dialect_impl(bind.dialect)
        if isinstance(t, SchemaType) and t.__class__ is not self.__class__:
            t._on_metadata_create(target, bind, **kw)

def _on_metadata_drop(self, target, bind, **kw):
        if not self._is_impl_for_variant(bind.dialect, kw):
            return

t = self.dialect_impl(bind.dialect)
        if isinstance(t, SchemaType) and t.__class__ is not self.__class__:
            t._on_metadata_drop(target, bind, **kw)

def _is_impl_for_variant(self, dialect, kw):
        variant_mapping = kw.pop("variant_mapping", None)

if not variant_mapping:
            return True

# for types that have _variant_mapping, all the impls in the map
        # that are SchemaEventTarget subclasses get set up as event holders.
        # this is so that constructs that need
        # to be associated with the Table at dialect-agnostic time etc. like
        # CheckConstraints can be set up with that table.  they then add
        # to these constraints a DDL check_rule that among other things
        # will check this _is_impl_for_variant() method to determine when
        # the dialect is known that we are part of the table's DDL sequence.

# since PostgreSQL is the only DB that has ARRAY this can only
        # be integration tested by PG-specific tests
        def _we_are_the_impl(typ):
            return (
                typ is self
                or isinstance(typ, ARRAY)
                and typ.item_type is self  # type: ignore[comparison-overlap]
            )

if dialect.name in variant_mapping and _we_are_the_impl(
            variant_mapping[dialect.name]
        ):
            return True
        elif dialect.name not in variant_mapping:
            return _we_are_the_impl(variant_mapping["_default"])

class Enum(String, SchemaType, Emulated, TypeEngine[Union[str, enum.Enum]]):
    """Generic Enum Type.

The :class:`.Enum` type provides a set of possible string values
    which the column is constrained towards.

The :class:`.Enum` type will make use of the backend's native "ENUM"
    type if one is available; otherwise, it uses a VARCHAR datatype.
    An option also exists to automatically produce a CHECK constraint
    when the VARCHAR (so called "non-native") variant is produced;
    see the  :paramref:`.Enum.create_constraint` flag.

The :class:`.Enum` type also provides in-Python validation of string
    values during both read and write operations.  When reading a value
    from the database in a result set, the string value is always checked
    against the list of possible values and a ``LookupError`` is raised
    if no match is found.  When passing a value to the database as a
    plain string within a SQL statement, if the
    :paramref:`.Enum.validate_strings` parameter is
    set to True, a ``LookupError`` is raised for any string value that's
    not located in the given list of possible values; note that this
    impacts usage of LIKE expressions with enumerated values (an unusual
    use case).

The source of enumerated values may be a list of string values, or
    alternatively a PEP-435-compliant enumerated class.  For the purposes
    of the :class:`.Enum` datatype, this class need only provide a
    ``__members__`` method.

When using an enumerated class, the enumerated objects are used
    both for input and output, rather than strings as is the case with
    a plain-string enumerated type::

import enum
        from sqlalchemy import Enum

class MyEnum(enum.Enum):
            one = 1
            two = 2
            three = 3

t = Table("data", MetaData(), Column("value", Enum(MyEnum)))

connection.execute(t.insert(), {"value": MyEnum.two})
        assert connection.scalar(t.select()) is MyEnum.two

Above, the string names of each element, e.g. "one", "two", "three",
    are persisted to the database; the values of the Python Enum, here
    indicated as integers, are **not** used; the value of each enum can
    therefore be any kind of Python object whether or not it is persistable.

In order to persist the values and not the names, the
    :paramref:`.Enum.values_callable` parameter may be used.   The value of
    this parameter is a user-supplied callable, which  is intended to be used
    with a PEP-435-compliant enumerated class and  returns a list of string
    values to be persisted.   For a simple enumeration that uses string values,
    a callable such as  ``lambda x: [e.value for e in x]`` is sufficient.

.. seealso::

:ref:`orm_declarative_mapped_column_enums` - background on using
        the :class:`_sqltypes.Enum` datatype with the ORM's
        :ref:`ORM Annotated Declarative <orm_declarative_mapped_column>`
        feature.

:class:`_postgresql.ENUM` - PostgreSQL-specific type,
        which has additional functionality.

:class:`.mysql.ENUM` - MySQL-specific type

"""

__visit_name__ = "enum"

def __init__(self, *enums: object, **kw: Any):
        r"""Construct an enum.

Keyword arguments which don't apply to a specific backend are ignored
        by that backend.

:param \*enums: either exactly one PEP-435 compliant enumerated type
           or one or more string labels.

:param create_constraint: defaults to False.  When creating a
           non-native enumerated type, also build a CHECK constraint on the
           database against the valid values.

.. note:: it is strongly recommended that the CHECK constraint
              have an explicit name in order to support schema-management
              concerns.  This can be established either by setting the
              :paramref:`.Enum.name` parameter or by setting up an
              appropriate naming convention; see
              :ref:`constraint_naming_conventions` for background.

.. versionchanged:: 1.4 - this flag now defaults to False, meaning
              no CHECK constraint is generated for a non-native enumerated
              type.

:param metadata: Associate this type directly with a ``MetaData``
           object. For types that exist on the target database as an
           independent schema construct (PostgreSQL), this type will be
           created and dropped within ``create_all()`` and ``drop_all()``
           operations. If the type is not associated with any ``MetaData``
           object, it will associate itself with each ``Table`` in which it is
           used, and will be created when any of those individual tables are
           created, after a check is performed for its existence. The type is
           only dropped when ``drop_all()`` is called for that ``Table``
           object's metadata, however.

The value of the :paramref:`_schema.MetaData.schema` parameter of
           the :class:`_schema.MetaData` object, if set, will be used as the
           default value of the :paramref:`_types.Enum.schema` on this object
           if an explicit value is not otherwise supplied.

.. versionchanged:: 1.4.12 :class:`_types.Enum` inherits the
              :paramref:`_schema.MetaData.schema` parameter of the
              :class:`_schema.MetaData` object if present, when passed using
              the :paramref:`_types.Enum.metadata` parameter.

:param name: The name of this type. This is required for PostgreSQL
           and any future supported database which requires an explicitly
           named type, or an explicitly named constraint in order to generate
           the type and/or a table that uses it. If a PEP-435 enumerated
           class was used, its name (converted to lower case) is used by
           default.

:param native_enum: Use the database's native ENUM type when
           available. Defaults to True. When False, uses VARCHAR + check
           constraint for all backends. When False, the VARCHAR length can be
           controlled with :paramref:`.Enum.length`; currently "length" is
           ignored if native_enum=True.

:param length: Allows specifying a custom length for the VARCHAR
           when a non-native enumeration datatype is used.  By default it uses
           the length of the longest value.

.. versionchanged:: 2.0.0 The :paramref:`.Enum.length` parameter
              is used unconditionally for ``VARCHAR`` rendering regardless of
              the :paramref:`.Enum.native_enum` parameter, for those backends
              where ``VARCHAR`` is used for enumerated datatypes.

:param schema: Schema name of this type. For types that exist on the
           target database as an independent schema construct (PostgreSQL),
           this parameter specifies the named schema in which the type is
           present.

If not present, the schema name will be taken from the
           :class:`_schema.MetaData` collection if passed as
           :paramref:`_types.Enum.metadata`, for a :class:`_schema.MetaData`
           that includes the :paramref:`_schema.MetaData.schema` parameter.

Otherwise, if the :paramref:`_types.Enum.inherit_schema` flag is set
           to ``True``, the schema will be inherited from the associated
           :class:`_schema.Table` object if any; when
           :paramref:`_types.Enum.inherit_schema` is at its default of
           ``False``, the owning table's schema is **not** used.

:param quote: Set explicit quoting preferences for the type's name.

:param inherit_schema: When ``True``, the "schema" from the owning
           :class:`_schema.Table`
           will be copied to the "schema" attribute of this
           :class:`.Enum`, replacing whatever value was passed for the
           ``schema`` attribute.   This also takes effect when using the
           :meth:`_schema.Table.to_metadata` operation.

:param validate_strings: when True, string values that are being
           passed to the database in a SQL statement will be checked
           for validity against the list of enumerated values.  Unrecognized
           values will result in a ``LookupError`` being raised.

:param values_callable: A callable which will be passed the PEP-435
           compliant enumerated type, which should then return a list of string
           values to be persisted. This allows for alternate usages such as
           using the string value of an enum to be persisted to the database
           instead of its name. The callable must return the values to be
           persisted in the same order as iterating through the Enum's
           ``__member__`` attribute. For example
           ``lambda x: [i.value for i in x]``.

.. versionadded:: 1.2.3

:param sort_key_function: a Python callable which may be used as the
           "key" argument in the Python ``sorted()`` built-in.   The SQLAlchemy
           ORM requires that primary key columns which are mapped must
           be sortable in some way.  When using an unsortable enumeration
           object such as a Python 3 ``Enum`` object, this parameter may be
           used to set a default sort key function for the objects.  By
           default, the database value of the enumeration is used as the
           sorting function.

.. versionadded:: 1.3.8

:param omit_aliases: A boolean that when true will remove aliases from
           pep 435 enums. defaults to ``True``.

.. versionchanged:: 2.0 This parameter now defaults to True.

"""
        self._enum_init(enums, kw)

@property
    def _enums_argument(self):
        if self.enum_class is not None:
            return [self.enum_class]
        else:
            return self.enums

def _enum_init(self, enums, kw):
        """internal init for :class:`.Enum` and subclasses.

friendly init helper used by subclasses to remove
        all the Enum-specific keyword arguments from kw.  Allows all
        other arguments in kw to pass through.

"""
        self.native_enum = kw.pop("native_enum", True)
        self.create_constraint = kw.pop("create_constraint", False)
        self.values_callable = kw.pop("values_callable", None)
        self._sort_key_function = kw.pop("sort_key_function", NO_ARG)
        length_arg = kw.pop("length", NO_ARG)
        self._omit_aliases = kw.pop("omit_aliases", True)
        _disable_warnings = kw.pop("_disable_warnings", False)
        values, objects = self._parse_into_values(enums, kw)
        self._setup_for_values(values, objects, kw)

self.validate_strings = kw.pop("validate_strings", False)

if self.enums:
            self._default_length = length = max(len(x) for x in self.enums)
        else:
            self._default_length = length = 0

if length_arg is not NO_ARG:
            if (
                not _disable_warnings
                and length_arg is not None
                and length_arg < length
            ):
                raise ValueError(
                    "When provided, length must be larger or equal"
                    " than the length of the longest enum value. %s < %s"
                    % (length_arg, length)
                )
            length = length_arg

self._valid_lookup[None] = self._object_lookup[None] = None

super().__init__(length=length)

# assign name to the given enum class if no other name, and this
        # enum is not an "empty" enum.  if the enum is "empty" we assume
        # this is a template enum that will be used to generate
        # new Enum classes.
        if self.enum_class and values:
            kw.setdefault("name", self.enum_class.__name__.lower())
        SchemaType.__init__(
            self,
            name=kw.pop("name", None),
            schema=kw.pop("schema", None),
            metadata=kw.pop("metadata", None),
            inherit_schema=kw.pop("inherit_schema", False),
            quote=kw.pop("quote", None),
            _create_events=kw.pop("_create_events", True),
            _adapted_from=kw.pop("_adapted_from", None),
        )

def _parse_into_values(self, enums, kw):
        if not enums and "_enums" in kw:
            enums = kw.pop("_enums")

if len(enums) == 1 and hasattr(enums[0], "__members__"):
            self.enum_class = enums[0]

_members = self.enum_class.__members__

if self._omit_aliases is True:
                # remove aliases
                members = OrderedDict(
                    (n, v) for n, v in _members.items() if v.name == n
                )
            else:
                members = _members
            if self.values_callable:
                values = self.values_callable(self.enum_class)
            else:
                values = list(members)
            objects = [members[k] for k in members]
            return values, objects
        else:
            self.enum_class = None
            return enums, enums

def _resolve_for_literal(self, value: Any) -> Enum:
        tv = type(value)
        typ = self._resolve_for_python_type(tv, tv, tv)
        assert typ is not None
        return typ

def _resolve_for_python_type(
        self,
        python_type: Type[Any],
        matched_on: _MatchedOnType,
        matched_on_flattened: Type[Any],
    ) -> Optional[Enum]:
        # "generic form" indicates we were placed in a type map
        # as ``sqlalchemy.Enum(enum.Enum)`` which indicates we need to
        # get enumerated values from the datatype
        we_are_generic_form = self._enums_argument == [enum.Enum]

native_enum = None

def process_literal(pt):
            # for a literal, where we need to get its contents, parse it out.
            enum_args = get_args(pt)
            bad_args = [arg for arg in enum_args if not isinstance(arg, str)]
            if bad_args:
                raise exc.ArgumentError(
                    f"Can't create string-based Enum datatype from non-string "
                    f"values: {', '.join(repr(x) for x in bad_args)}.  Please "
                    f"provide an explicit Enum datatype for this Python type"
                )
            native_enum = False
            return enum_args, native_enum

if not we_are_generic_form and python_type is matched_on:
            # if we have enumerated values, and the incoming python
            # type is exactly the one that matched in the type map,
            # then we use these enumerated values and dont try to parse
            # what's incoming
            enum_args = self._enums_argument

elif is_literal(python_type):
            enum_args, native_enum = process_literal(python_type)
        elif is_pep695(python_type):
            value = python_type.__value__
            if is_pep695(value):
                new_value = value
                while is_pep695(new_value):
                    new_value = new_value.__value__
                if is_literal(new_value):
                    value = new_value
                    warn_deprecated(
                        f"Mapping recursive TypeAliasType '{python_type}' "
                        "that resolve to literal to generate an Enum is "
                        "deprecated. SQLAlchemy 2.1 will not support this "
                        "use case. Please avoid using recursing "
                        "TypeAliasType.",
                        "2.0",
                    )
            if not is_literal(value):
                raise exc.ArgumentError(
                    f"Can't associate TypeAliasType '{python_type}' to an "
                    "Enum since it's not a direct alias of a Literal. Only "
                    "aliases in this form `type my_alias = Literal['a', "
                    "'b']` are supported when generating Enums."
                )
            enum_args, native_enum = process_literal(value)

elif isinstance(python_type, type) and issubclass(
            python_type, enum.Enum
        ):
            # same for an enum.Enum
            enum_args = [python_type]

else:
            enum_args = self._enums_argument

# make a new Enum that looks like this one.
        # arguments or other rules
        kw = self._make_enum_kw({})

if native_enum is False:
            kw["native_enum"] = False

kw["length"] = NO_ARG if self.length == 0 else self.length
        return cast(
            Enum,
            self._generic_type_affinity(_enums=enum_args, **kw),  # type: ignore  # noqa: E501
        )

def _setup_for_values(self, values, objects, kw):
        self.enums = list(values)

self._valid_lookup = dict(zip(reversed(objects), reversed(values)))

self._object_lookup = dict(zip(values, objects))

self._valid_lookup.update(
            [
                (value, self._valid_lookup[self._object_lookup[value]])
                for value in values
            ]
        )

@property
    def sort_key_function(self):
        if self._sort_key_function is NO_ARG:
            return self._db_value_for_elem
        else:
            return self._sort_key_function

@property
    def native(self):
        return self.native_enum

def _db_value_for_elem(self, elem):
        try:
            return self._valid_lookup[elem]
        except KeyError as err:
            # for unknown string values, we return as is.  While we can
            # validate these if we wanted, that does not allow for lesser-used
            # end-user use cases, such as using a LIKE comparison with an enum,
            # or for an application that wishes to apply string tests to an
            # ENUM (see [ticket:3725]).  While we can decide to differentiate
            # here between an INSERT statement and a criteria used in a SELECT,
            # for now we're staying conservative w/ behavioral changes (perhaps
            # someone has a trigger that handles strings on INSERT)
            if not self.validate_strings and isinstance(elem, str):
                return elem
            else:
                raise LookupError(
                    "'%s' is not among the defined enum values. "
                    "Enum name: %s. Possible values: %s"
                    % (
                        elem,
                        self.name,
                        langhelpers.repr_tuple_names(self.enums),
                    )
                ) from err

class Comparator(String.Comparator[str]):
        __slots__ = ()

type: String

def _adapt_expression(
            self,
            op: OperatorType,
            other_comparator: TypeEngine.Comparator[Any],
        ) -> Tuple[OperatorType, TypeEngine[Any]]:
            op, typ = super()._adapt_expression(op, other_comparator)
            if op is operators.concat_op:
                typ = String(self.type.length)
            return op, typ

comparator_factory = Comparator

def _object_value_for_elem(self, elem):
        try:
            return self._object_lookup[elem]
        except KeyError as err:
            raise LookupError(
                "'%s' is not among the defined enum values. "
                "Enum name: %s. Possible values: %s"
                % (
                    elem,
                    self.name,
                    langhelpers.repr_tuple_names(self.enums),
                )
            ) from err

def __repr__(self):
        return util.generic_repr(
            self,
            additional_kw=[
                ("native_enum", True),
                ("create_constraint", False),
                ("length", self._default_length),
            ],
            to_inspect=[Enum, SchemaType],
        )

def as_generic(self, allow_nulltype=False):
        try:
            args = self.enums
        except AttributeError:
            raise NotImplementedError(
                "TypeEngine.as_generic() heuristic "
                "is undefined for types that inherit Enum but do not have "
                "an `enums` attribute."
            ) from None

return util.constructor_copy(
            self, self._generic_type_affinity, *args, _disable_warnings=True
        )

def _make_enum_kw(self, kw):
        kw.setdefault("validate_strings", self.validate_strings)
        if self.name:
            kw.setdefault("name", self.name)
        kw.setdefault("schema", self.schema)
        kw.setdefault("inherit_schema", self.inherit_schema)
        kw.setdefault("metadata", self.metadata)
        kw.setdefault("native_enum", self.native_enum)
        kw.setdefault("values_callable", self.values_callable)
        kw.setdefault("create_constraint", self.create_constraint)
        kw.setdefault("length", self.length)
        kw.setdefault("omit_aliases", self._omit_aliases)
        return kw

def adapt_to_emulated(self, impltype, **kw):
        self._make_enum_kw(kw)
        kw["_disable_warnings"] = True
        kw.setdefault("_create_events", False)
        assert "_enums" in kw
        return impltype(**kw)

def adapt(self, cls, **kw):
        kw["_enums"] = self._enums_argument
        kw["_disable_warnings"] = True
        return super().adapt(cls, **kw)

def _should_create_constraint(self, compiler, **kw):
        if not self._is_impl_for_variant(compiler.dialect, kw):
            return False
        return (
            not self.native_enum or not compiler.dialect.supports_native_enum
        )

@util.preload_module("sqlalchemy.sql.schema")
    def _set_table(self, column, table):
        schema = util.preloaded.sql_schema
        SchemaType._set_table(self, column, table)

if not self.create_constraint:
            return

variant_mapping = self._variant_mapping_for_set_table(column)

e = schema.CheckConstraint(
            type_coerce(column, String()).in_(self.enums),
            name=_NONE_NAME if self.name is None else self.name,
            _create_rule=util.portable_instancemethod(
                self._should_create_constraint,
                {"variant_mapping": variant_mapping},
            ),
            _type_bound=True,
        )
        assert e.table is table

def literal_processor(self, dialect):
        parent_processor = super().literal_processor(dialect)

def process(value):
            value = self._db_value_for_elem(value)
            if parent_processor:
                value = parent_processor(value)
            return value

return process

def bind_processor(self, dialect):
        parent_processor = super().bind_processor(dialect)

def process(value):
            value = self._db_value_for_elem(value)
            if parent_processor:
                value = parent_processor(value)
            return value

return process

def result_processor(self, dialect, coltype):
        parent_processor = super().result_processor(dialect, coltype)

def process(value):
            if parent_processor:
                value = parent_processor(value)

value = self._object_value_for_elem(value)
            return value

return process

def copy(self, **kw):
        return SchemaType.copy(self, **kw)

@property
    def python_type(self):
        if self.enum_class:
            return self.enum_class
        else:
            return super().python_type

class PickleType(TypeDecorator[object]):
    """Holds Python objects, which are serialized using pickle.

PickleType builds upon the Binary type to apply Python's
    ``pickle.dumps()`` to incoming objects, and ``pickle.loads()`` on
    the way out, allowing any pickleable Python object to be stored as
    a serialized binary field.

To allow ORM change events to propagate for elements associated
    with :class:`.PickleType`, see :ref:`mutable_toplevel`.

"""

impl = LargeBinary
    cache_ok = True

def __init__(
        self,
        protocol: int = pickle.HIGHEST_PROTOCOL,
        pickler: Any = None,
        comparator: Optional[Callable[[Any, Any], bool]] = None,
        impl: Optional[_TypeEngineArgument[Any]] = None,
    ):
        """
        Construct a PickleType.

:param protocol: defaults to ``pickle.HIGHEST_PROTOCOL``.

:param pickler: defaults to pickle.  May be any object with
          pickle-compatible ``dumps`` and ``loads`` methods.

:param comparator: a 2-arg callable predicate used
          to compare values of this type.  If left as ``None``,
          the Python "equals" operator is used to compare values.

:param impl: A binary-storing :class:`_types.TypeEngine` class or
          instance to use in place of the default :class:`_types.LargeBinary`.
          For example the :class: `_mysql.LONGBLOB` class may be more effective
          when using MySQL.

.. versionadded:: 1.4.20

"""
        self.protocol = protocol
        self.pickler = pickler or pickle
        self.comparator = comparator
        super().__init__()

if impl:
            # custom impl is not necessarily a LargeBinary subclass.
            # make an exception to typing for this
            self.impl = to_instance(impl)  # type: ignore

def __reduce__(self):
        return PickleType, (self.protocol, None, self.comparator)

def bind_processor(self, dialect):
        impl_processor = self.impl_instance.bind_processor(dialect)
        dumps = self.pickler.dumps
        protocol = self.protocol
        if impl_processor:
            fixed_impl_processor = impl_processor

def process(value):
                if value is not None:
                    value = dumps(value, protocol)
                return fixed_impl_processor(value)

else:

def process(value):
                if value is not None:
                    value = dumps(value, protocol)
                return value

return process

def result_processor(self, dialect, coltype):
        impl_processor = self.impl_instance.result_processor(dialect, coltype)
        loads = self.pickler.loads
        if impl_processor:
            fixed_impl_processor = impl_processor

def process(value):
                value = fixed_impl_processor(value)
                if value is None:
                    return None
                return loads(value)

else:

def process(value):
                if value is None:
                    return None
                return loads(value)

return process

def compare_values(self, x, y):
        if self.comparator:
            return self.comparator(x, y)
        else:
            return x == y

class Boolean(SchemaType, Emulated, TypeEngine[bool]):
    """A bool datatype.

:class:`.Boolean` typically uses BOOLEAN or SMALLINT on the DDL side,
    and on the Python side deals in ``True`` or ``False``.

The :class:`.Boolean` datatype currently has two levels of assertion
    that the values persisted are simple true/false values.  For all
    backends, only the Python values ``None``, ``True``, ``False``, ``1``
    or ``0`` are accepted as parameter values.   For those backends that
    don't support a "native boolean" datatype, an option exists to
    also create a CHECK constraint on the target column

.. versionchanged:: 1.2 the :class:`.Boolean` datatype now asserts that
       incoming Python values are already in pure boolean form.

"""

__visit_name__ = "boolean"
    native = True

def __init__(
        self,
        create_constraint: bool = False,
        name: Optional[str] = None,
        _create_events: bool = True,
        _adapted_from: Optional[SchemaType] = None,
    ):
        """Construct a Boolean.

:param create_constraint: defaults to False.  If the boolean
          is generated as an int/smallint, also create a CHECK constraint
          on the table that ensures 1 or 0 as a value.

.. note:: it is strongly recommended that the CHECK constraint
             have an explicit name in order to support schema-management
             concerns.  This can be established either by setting the
             :paramref:`.Boolean.name` parameter or by setting up an
             appropriate naming convention; see
             :ref:`constraint_naming_conventions` for background.

.. versionchanged:: 1.4 - this flag now defaults to False, meaning
             no CHECK constraint is generated for a non-native enumerated
             type.

:param name: if a CHECK constraint is generated, specify
          the name of the constraint.

"""
        self.create_constraint = create_constraint
        self.name = name
        self._create_events = _create_events
        if _adapted_from:
            self.dispatch = self.dispatch._join(_adapted_from.dispatch)

def copy(self, **kw):
        # override SchemaType.copy() to not include to_metadata logic
        return self.adapt(
            cast("Type[TypeEngine[Any]]", self.__class__),
            _create_events=True,
        )

def _should_create_constraint(self, compiler, **kw):
        if not self._is_impl_for_variant(compiler.dialect, kw):
            return False
        return (
            not compiler.dialect.supports_native_boolean
            and compiler.dialect.non_native_boolean_check_constraint
        )

@util.preload_module("sqlalchemy.sql.schema")
    def _set_table(self, column, table):
        schema = util.preloaded.sql_schema
        if not self.create_constraint:
            return

variant_mapping = self._variant_mapping_for_set_table(column)

e = schema.CheckConstraint(
            type_coerce(column, self).in_([0, 1]),
            name=_NONE_NAME if self.name is None else self.name,
            _create_rule=util.portable_instancemethod(
                self._should_create_constraint,
                {"variant_mapping": variant_mapping},
            ),
            _type_bound=True,
        )
        assert e.table is table

@property
    def python_type(self):
        return bool

_strict_bools = frozenset([None, True, False])

def _strict_as_bool(self, value):
        if value not in self._strict_bools:
            if not isinstance(value, int):
                raise TypeError("Not a boolean value: %r" % (value,))
            else:
                raise ValueError(
                    "Value %r is not None, True, or False" % (value,)
                )
        return value

def literal_processor(self, dialect):
        compiler = dialect.statement_compiler(dialect, None)
        true = compiler.visit_true(None)
        false = compiler.visit_false(None)

def process(value):
            return true if self._strict_as_bool(value) else false

return process

def bind_processor(self, dialect):
        _strict_as_bool = self._strict_as_bool

_coerce: Union[Type[bool], Type[int]]

if dialect.supports_native_boolean:
            _coerce = bool
        else:
            _coerce = int

def process(value):
            value = _strict_as_bool(value)
            if value is not None:
                value = _coerce(value)
            return value

return process

def result_processor(self, dialect, coltype):
        if dialect.supports_native_boolean:
            return None
        else:
            return processors.int_to_boolean

class _AbstractInterval(HasExpressionLookup, TypeEngine[dt.timedelta]):
    @util.memoized_property
    def _expression_adaptations(self):
        # Based on
        # https://www.postgresql.org/docs/current/static/functions-datetime.html.

return {
            operators.add: {
                Date: DateTime,
                Interval: self.__class__,
                DateTime: DateTime,
                Time: Time,
            },
            operators.sub: {Interval: self.__class__},
            operators.mul: {Numeric: self.__class__},
            operators.truediv: {Numeric: self.__class__},
        }

@util.ro_non_memoized_property
    def _type_affinity(self) -> Type[Interval]:
        return Interval

class Interval(Emulated, _AbstractInterval, TypeDecorator[dt.timedelta]):
    """A type for ``datetime.timedelta()`` objects.

The Interval type deals with ``datetime.timedelta`` objects.  In PostgreSQL
    and Oracle Database, the native ``INTERVAL`` type is used; for others, the
    value is stored as a date which is relative to the "epoch" (Jan. 1, 1970).

Note that the ``Interval`` type does not currently provide date arithmetic
    operations on platforms which do not support interval types natively. Such
    operations usually require transformation of both sides of the expression
    (such as, conversion of both sides into integer epoch values first) which
    currently is a manual procedure (such as via
    :attr:`~sqlalchemy.sql.expression.func`).

"""

impl = DateTime
    epoch = dt.datetime.fromtimestamp(0, dt.timezone.utc).replace(tzinfo=None)
    cache_ok = True

def __init__(
        self,
        native: bool = True,
        second_precision: Optional[int] = None,
        day_precision: Optional[int] = None,
    ):
        """Construct an Interval object.

:param native: when True, use the actual
          INTERVAL type provided by the database, if
          supported (currently PostgreSQL, Oracle Database).
          Otherwise, represent the interval data as
          an epoch value regardless.

:param second_precision: For native interval types
          which support a "fractional seconds precision" parameter,
          i.e. Oracle Database and PostgreSQL

:param day_precision: for native interval types which
          support a "day precision" parameter, i.e. Oracle Database.

"""
        super().__init__()
        self.native = native
        self.second_precision = second_precision
        self.day_precision = day_precision

class Comparator(
        TypeDecorator.Comparator[_CT],
        _AbstractInterval.Comparator[_CT],
    ):
        __slots__ = ()

comparator_factory = Comparator

@property
    def python_type(self):
        return dt.timedelta

def adapt_to_emulated(self, impltype, **kw):
        return _AbstractInterval.adapt(self, impltype, **kw)

def coerce_compared_value(self, op, value):
        return self.impl_instance.coerce_compared_value(op, value)

def bind_processor(
        self, dialect: Dialect
    ) -> _BindProcessorType[dt.timedelta]:
        if TYPE_CHECKING:
            assert isinstance(self.impl_instance, DateTime)
        impl_processor = self.impl_instance.bind_processor(dialect)
        epoch = self.epoch
        if impl_processor:
            fixed_impl_processor = impl_processor

def process(
                value: Optional[dt.timedelta],
            ) -> Any:
                if value is not None:
                    dt_value = epoch + value
                else:
                    dt_value = None
                return fixed_impl_processor(dt_value)

else:

def process(
                value: Optional[dt.timedelta],
            ) -> Any:
                if value is not None:
                    dt_value = epoch + value
                else:
                    dt_value = None
                return dt_value

return process

def result_processor(
        self, dialect: Dialect, coltype: Any
    ) -> _ResultProcessorType[dt.timedelta]:
        if TYPE_CHECKING:
            assert isinstance(self.impl_instance, DateTime)
        impl_processor = self.impl_instance.result_processor(dialect, coltype)
        epoch = self.epoch
        if impl_processor:
            fixed_impl_processor = impl_processor

def process(value: Any) -> Optional[dt.timedelta]:
                dt_value = fixed_impl_processor(value)
                if dt_value is None:
                    return None
                return dt_value - epoch

else:

def process(value: Any) -> Optional[dt.timedelta]:
                if value is None:
                    return None
                return value - epoch  # type: ignore

return process

class JSON(Indexable, TypeEngine[Any]):
    """Represent a SQL JSON type.

.. note::  :class:`_types.JSON`
       is provided as a facade for vendor-specific
       JSON types.  Since it supports JSON SQL operations, it only
       works on backends that have an actual JSON type, currently:

* PostgreSQL - see :class:`sqlalchemy.dialects.postgresql.JSON` and
         :class:`sqlalchemy.dialects.postgresql.JSONB` for backend-specific
         notes

* MySQL - see
         :class:`sqlalchemy.dialects.mysql.JSON` for backend-specific notes

* SQLite as of version 3.9 - see
         :class:`sqlalchemy.dialects.sqlite.JSON` for backend-specific notes

* Microsoft SQL Server 2016 and later - see
         :class:`sqlalchemy.dialects.mssql.JSON` for backend-specific notes

:class:`_types.JSON` is part of the Core in support of the growing
    popularity of native JSON datatypes.

The :class:`_types.JSON` type stores arbitrary JSON format data, e.g.::

data_table = Table(
            "data_table",
            metadata,
            Column("id", Integer, primary_key=True),
            Column("data", JSON),
        )

with engine.connect() as conn:
            conn.execute(
                data_table.insert(), {"data": {"key1": "value1", "key2": "value2"}}
            )

**JSON-Specific Expression Operators**

The :class:`_types.JSON`
    datatype provides these additional SQL operations:

* Keyed index operations::

data_table.c.data["some key"]

* Integer index operations::

data_table.c.data[3]

* Path index operations::

data_table.c.data[("key_1", "key_2", 5, ..., "key_n")]

* Data casters for specific JSON element types, subsequent to an index
      or path operation being invoked::

data_table.c.data["some key"].as_integer()

.. versionadded:: 1.3.11

Additional operations may be available from the dialect-specific versions
    of :class:`_types.JSON`, such as
    :class:`sqlalchemy.dialects.postgresql.JSON` and
    :class:`sqlalchemy.dialects.postgresql.JSONB` which both offer additional
    PostgreSQL-specific operations.

**Casting JSON Elements to Other Types**

Index operations, i.e. those invoked by calling upon the expression using
    the Python bracket operator as in ``some_column['some key']``, return an
    expression object whose type defaults to :class:`_types.JSON` by default,
    so that
    further JSON-oriented instructions may be called upon the result type.
    However, it is likely more common that an index operation is expected
    to return a specific scalar element, such as a string or integer.  In
    order to provide access to these elements in a backend-agnostic way,
    a series of data casters are provided:

* :meth:`.JSON.Comparator.as_string` - return the element as a string

* :meth:`.JSON.Comparator.as_boolean` - return the element as a boolean

* :meth:`.JSON.Comparator.as_float` - return the element as a float

* :meth:`.JSON.Comparator.as_integer` - return the element as an integer

These data casters are implemented by supporting dialects in order to
    assure that comparisons to the above types will work as expected, such as::

# integer comparison
        data_table.c.data["some_integer_key"].as_integer() == 5

# boolean comparison
        data_table.c.data["some_boolean"].as_boolean() == True

.. versionadded:: 1.3.11 Added type-specific casters for the basic JSON
       data element types.

.. note::

The data caster functions are new in version 1.3.11, and supersede
        the previous documented approaches of using CAST; for reference,
        this looked like::

from sqlalchemy import cast, type_coerce
           from sqlalchemy import String, JSON

cast(data_table.c.data["some_key"], String) == type_coerce(55, JSON)

The above case now works directly as::

data_table.c.data["some_key"].as_integer() == 5

For details on the previous comparison approach within the 1.3.x
        series, see the documentation for SQLAlchemy 1.2 or the included HTML
        files in the doc/ directory of the version's distribution.

**Detecting Changes in JSON columns when using the ORM**

The :class:`_types.JSON` type, when used with the SQLAlchemy ORM, does not
    detect in-place mutations to the structure.  In order to detect these, the
    :mod:`sqlalchemy.ext.mutable` extension must be used, most typically
    using the :class:`.MutableDict` class.  This extension will
    allow "in-place" changes to the datastructure to produce events which
    will be detected by the unit of work.  See the example at :class:`.HSTORE`
    for a simple example involving a dictionary.

Alternatively, assigning a JSON structure to an ORM element that
    replaces the old one will always trigger a change event.

**Support for JSON null vs. SQL NULL**

When working with NULL values, the :class:`_types.JSON` type recommends the
    use of two specific constants in order to differentiate between a column
    that evaluates to SQL NULL, e.g. no value, vs. the JSON-encoded string of
    ``"null"``. To insert or select against a value that is SQL NULL, use the
    constant :func:`.null`. This symbol may be passed as a parameter value
    specifically when using the :class:`_types.JSON` datatype, which contains
    special logic that interprets this symbol to mean that the column value
    should be SQL NULL as opposed to JSON ``"null"``::

from sqlalchemy import null

conn.execute(table.insert(), {"json_value": null()})

To insert or select against a value that is JSON ``"null"``, use the
    constant :attr:`_types.JSON.NULL`::

conn.execute(table.insert(), {"json_value": JSON.NULL})

The :class:`_types.JSON` type supports a flag
    :paramref:`_types.JSON.none_as_null` which when set to True will result
    in the Python constant ``None`` evaluating to the value of SQL
    NULL, and when set to False results in the Python constant
    ``None`` evaluating to the value of JSON ``"null"``.    The Python
    value ``None`` may be used in conjunction with either
    :attr:`_types.JSON.NULL` and :func:`.null` in order to indicate NULL
    values, but care must be taken as to the value of the
    :paramref:`_types.JSON.none_as_null` in these cases.

**Customizing the JSON Serializer**

The JSON serializer and deserializer used by :class:`_types.JSON`
    defaults to
    Python's ``json.dumps`` and ``json.loads`` functions; in the case of the
    psycopg2 dialect, psycopg2 may be using its own custom loader function.

In order to affect the serializer / deserializer, they are currently
    configurable at the :func:`_sa.create_engine` level via the
    :paramref:`_sa.create_engine.json_serializer` and
    :paramref:`_sa.create_engine.json_deserializer` parameters.  For example,
    to turn off ``ensure_ascii``::

engine = create_engine(
            "sqlite://",
            json_serializer=lambda obj: json.dumps(obj, ensure_ascii=False),
        )

.. versionchanged:: 1.3.7

SQLite dialect's ``json_serializer`` and ``json_deserializer``
        parameters renamed from ``_json_serializer`` and
        ``_json_deserializer``.

.. seealso::

:class:`sqlalchemy.dialects.postgresql.JSON`

:class:`sqlalchemy.dialects.postgresql.JSONB`

:class:`sqlalchemy.dialects.mysql.JSON`

:class:`sqlalchemy.dialects.sqlite.JSON`

"""  # noqa: E501

__visit_name__ = "JSON"

hashable = False
    NULL = util.symbol("JSON_NULL")
    """Describe the json value of NULL.

This value is used to force the JSON value of ``"null"`` to be
    used as the value.   A value of Python ``None`` will be recognized
    either as SQL NULL or JSON ``"null"``, based on the setting
    of the :paramref:`_types.JSON.none_as_null` flag; the
    :attr:`_types.JSON.NULL`
    constant can be used to always resolve to JSON ``"null"`` regardless
    of this setting.  This is in contrast to the :func:`_expression.null`
    construct,
    which always resolves to SQL NULL.  E.g.::

from sqlalchemy import null
        from sqlalchemy.dialects.postgresql import JSON

# will *always* insert SQL NULL
        obj1 = MyObject(json_value=null())

# will *always* insert JSON string "null"
        obj2 = MyObject(json_value=JSON.NULL)

session.add_all([obj1, obj2])
        session.commit()

In order to set JSON NULL as a default value for a column, the most
    transparent method is to use :func:`_expression.text`::

Table(
            "my_table", metadata, Column("json_data", JSON, default=text("'null'"))
        )

While it is possible to use :attr:`_types.JSON.NULL` in this context, the
    :attr:`_types.JSON.NULL` value will be returned as the value of the
    column,
    which in the context of the ORM or other repurposing of the default
    value, may not be desirable.  Using a SQL expression means the value
    will be re-fetched from the database within the context of retrieving
    generated defaults.

"""  # noqa: E501

def __init__(self, none_as_null: bool = False):
        """Construct a :class:`_types.JSON` type.

:param none_as_null=False: if True, persist the value ``None`` as a
         SQL NULL value, not the JSON encoding of ``null``. Note that when this
         flag is False, the :func:`.null` construct can still be used to
         persist a NULL value, which may be passed directly as a parameter
         value that is specially interpreted by the :class:`_types.JSON` type
         as SQL NULL::

from sqlalchemy import null

conn.execute(table.insert(), {"data": null()})

.. note::

:paramref:`_types.JSON.none_as_null` does **not** apply to the
              values passed to :paramref:`_schema.Column.default` and
              :paramref:`_schema.Column.server_default`; a value of ``None``
              passed for these parameters means "no default present".

Additionally, when used in SQL comparison expressions, the
              Python value ``None`` continues to refer to SQL null, and not
              JSON NULL.  The :paramref:`_types.JSON.none_as_null` flag refers
              explicitly to the **persistence** of the value within an
              INSERT or UPDATE statement.   The :attr:`_types.JSON.NULL`
              value should be used for SQL expressions that wish to compare to
              JSON null.

.. seealso::

:attr:`.types.JSON.NULL`

"""
        self.none_as_null = none_as_null

class JSONElementType(TypeEngine[Any]):
        """Common function for index / path elements in a JSON expression."""

_integer = Integer()
        _string = String()

def string_bind_processor(
            self, dialect: Dialect
        ) -> Optional[_BindProcessorType[str]]:
            return self._string._cached_bind_processor(dialect)

def string_literal_processor(
            self, dialect: Dialect
        ) -> Optional[_LiteralProcessorType[str]]:
            return self._string._cached_literal_processor(dialect)

def bind_processor(self, dialect: Dialect) -> _BindProcessorType[Any]:
            int_processor = self._integer._cached_bind_processor(dialect)
            string_processor = self.string_bind_processor(dialect)

def process(value: Optional[Any]) -> Any:
                if int_processor and isinstance(value, int):
                    value = int_processor(value)
                elif string_processor and isinstance(value, str):
                    value = string_processor(value)
                return value

return process

def literal_processor(
            self, dialect: Dialect
        ) -> _LiteralProcessorType[Any]:
            int_processor = self._integer._cached_literal_processor(dialect)
            string_processor = self.string_literal_processor(dialect)

return value

return process

class JSONIndexType(JSONElementType):
        """Placeholder for the datatype of a JSON index value.

This allows execution-time processing of JSON index values
        for special syntaxes.

"""

class JSONIntIndexType(JSONIndexType):
        """Placeholder for the datatype of a JSON index value.

This allows execution-time processing of JSON index values
        for special syntaxes.

"""

class JSONStrIndexType(JSONIndexType):
        """Placeholder for the datatype of a JSON index value.

This allows execution-time processing of JSON index values
        for special syntaxes.

"""

class JSONPathType(JSONElementType):
        """Placeholder type for JSON path operations.

This allows execution-time processing of a path-based
        index value into a specific SQL syntax.

"""

__visit_name__ = "json_path"

class Comparator(Indexable.Comparator[_T], Concatenable.Comparator[_T]):
        """Define comparison operations for :class:`_types.JSON`."""

__slots__ = ()

type: JSON

def _setup_getitem(self, index):
            if not isinstance(index, str) and isinstance(
                index, collections_abc.Sequence
            ):
                index = coercions.expect(
                    roles.BinaryElementRole,
                    index,
                    expr=self.expr,
                    operator=operators.json_path_getitem_op,
                    bindparam_type=JSON.JSONPathType,
                )

operator = operators.json_path_getitem_op
            else:
                index = coercions.expect(
                    roles.BinaryElementRole,
                    index,
                    expr=self.expr,
                    operator=operators.json_getitem_op,
                    bindparam_type=(
                        JSON.JSONIntIndexType
                        if isinstance(index, int)
                        else JSON.JSONStrIndexType
                    ),
                )
                operator = operators.json_getitem_op

return operator, index, self.type

def as_boolean(self):
            """Consider an indexed value as boolean.