bazarr/libs/sqlalchemy/dialects/postgresql/array.py

# dialects/postgresql/array.py
# Copyright (C) 2005-2024 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: ignore-errors


from __future__ import annotations

import re
from typing import Any
from typing import Optional
from typing import TypeVar

from .operators import CONTAINED_BY
from .operators import CONTAINS
from .operators import OVERLAP
from ... import types as sqltypes
from ... import util
from ...sql import expression
from ...sql import operators
from ...sql._typing import _TypeEngineArgument


_T = TypeVar("_T", bound=Any)


def Any(other, arrexpr, operator=operators.eq):
    """A synonym for the ARRAY-level :meth:`.ARRAY.Comparator.any` method.
    See that method for details.

    """

    return arrexpr.any(other, operator)


def All(other, arrexpr, operator=operators.eq):
    """A synonym for the ARRAY-level :meth:`.ARRAY.Comparator.all` method.
    See that method for details.

    """

    return arrexpr.all(other, operator)


class array(expression.ExpressionClauseList[_T]):
    """A PostgreSQL ARRAY literal.

    This is used to produce ARRAY literals in SQL expressions, e.g.::

        from sqlalchemy.dialects.postgresql import array
        from sqlalchemy.dialects import postgresql
        from sqlalchemy import select, func

        stmt = select(array([1,2]) + array([3,4,5]))

        print(stmt.compile(dialect=postgresql.dialect()))

    Produces the SQL::

        SELECT ARRAY[%(param_1)s, %(param_2)s] ||
            ARRAY[%(param_3)s, %(param_4)s, %(param_5)s]) AS anon_1

    An instance of :class:`.array` will always have the datatype
    :class:`_types.ARRAY`.  The "inner" type of the array is inferred from
    the values present, unless the ``type_`` keyword argument is passed::

        array(['foo', 'bar'], type_=CHAR)

    Multidimensional arrays are produced by nesting :class:`.array` constructs.
    The dimensionality of the final :class:`_types.ARRAY`
    type is calculated by
    recursively adding the dimensions of the inner :class:`_types.ARRAY`
    type::

        stmt = select(
            array([
                array([1, 2]), array([3, 4]), array([column('q'), column('x')])
            ])
        )
        print(stmt.compile(dialect=postgresql.dialect()))

    Produces::

        SELECT ARRAY[ARRAY[%(param_1)s, %(param_2)s],
        ARRAY[%(param_3)s, %(param_4)s], ARRAY[q, x]] AS anon_1

    .. versionadded:: 1.3.6 added support for multidimensional array literals

    .. seealso::

        :class:`_postgresql.ARRAY`

    """

    __visit_name__ = "array"

    stringify_dialect = "postgresql"
    inherit_cache = True

    def __init__(self, clauses, **kw):
        type_arg = kw.pop("type_", None)
        super().__init__(operators.comma_op, *clauses, **kw)

        self._type_tuple = [arg.type for arg in self.clauses]

        main_type = (
            type_arg
            if type_arg is not None
            else self._type_tuple[0] if self._type_tuple else sqltypes.NULLTYPE
        )

        if isinstance(main_type, ARRAY):
            self.type = ARRAY(
                main_type.item_type,
                dimensions=(
                    main_type.dimensions + 1
                    if main_type.dimensions is not None
                    else 2
                ),
            )
        else:
            self.type = ARRAY(main_type)

    @property
    def _select_iterable(self):
        return (self,)

    def _bind_param(self, operator, obj, _assume_scalar=False, type_=None):
        if _assume_scalar or operator is operators.getitem:
            return expression.BindParameter(
                None,
                obj,
                _compared_to_operator=operator,
                type_=type_,
                _compared_to_type=self.type,
                unique=True,
            )

        else:
            return array(
                [
                    self._bind_param(
                        operator, o, _assume_scalar=True, type_=type_
                    )
                    for o in obj
                ]
            )

    def self_group(self, against=None):
        if against in (operators.any_op, operators.all_op, operators.getitem):
            return expression.Grouping(self)
        else:
            return self


class ARRAY(sqltypes.ARRAY):
    """PostgreSQL ARRAY type.

    The :class:`_postgresql.ARRAY` type is constructed in the same way
    as the core :class:`_types.ARRAY` type; a member type is required, and a
    number of dimensions is recommended if the type is to be used for more
    than one dimension::

        from sqlalchemy.dialects import postgresql

        mytable = Table("mytable", metadata,
                Column("data", postgresql.ARRAY(Integer, dimensions=2))
            )

    The :class:`_postgresql.ARRAY` type provides all operations defined on the
    core :class:`_types.ARRAY` type, including support for "dimensions",
    indexed access, and simple matching such as
    :meth:`.types.ARRAY.Comparator.any` and
    :meth:`.types.ARRAY.Comparator.all`.  :class:`_postgresql.ARRAY`
    class also
    provides PostgreSQL-specific methods for containment operations, including
    :meth:`.postgresql.ARRAY.Comparator.contains`
    :meth:`.postgresql.ARRAY.Comparator.contained_by`, and
    :meth:`.postgresql.ARRAY.Comparator.overlap`, e.g.::

        mytable.c.data.contains([1, 2])

    The :class:`_postgresql.ARRAY` type may not be supported on all
    PostgreSQL DBAPIs; it is currently known to work on psycopg2 only.

    Additionally, the :class:`_postgresql.ARRAY`
    type does not work directly in
    conjunction with the :class:`.ENUM` type.  For a workaround, see the
    special type at :ref:`postgresql_array_of_enum`.

    .. container:: topic

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

        The :class:`_postgresql.ARRAY` type, when used with the SQLAlchemy ORM,
        does not detect in-place mutations to the array. In order to detect
        these, the :mod:`sqlalchemy.ext.mutable` extension must be used, using
        the :class:`.MutableList` class::

            from sqlalchemy.dialects.postgresql import ARRAY
            from sqlalchemy.ext.mutable import MutableList

            class SomeOrmClass(Base):
                # ...

                data = Column(MutableList.as_mutable(ARRAY(Integer)))

        This extension will allow "in-place" changes such to the array
        such as ``.append()`` to produce events which will be detected by the
        unit of work.  Note that changes to elements **inside** the array,
        including subarrays that are mutated in place, are **not** detected.

        Alternatively, assigning a new array value to an ORM element that
        replaces the old one will always trigger a change event.

    .. seealso::

        :class:`_types.ARRAY` - base array type

        :class:`_postgresql.array` - produces a literal array value.

    """

    class Comparator(sqltypes.ARRAY.Comparator):
        """Define comparison operations for :class:`_types.ARRAY`.

        Note that these operations are in addition to those provided
        by the base :class:`.types.ARRAY.Comparator` class, including
        :meth:`.types.ARRAY.Comparator.any` and
        :meth:`.types.ARRAY.Comparator.all`.

        """

        def contains(self, other, **kwargs):
            """Boolean expression.  Test if elements are a superset of the
            elements of the argument array expression.

            kwargs may be ignored by this operator but are required for API
            conformance.
            """
            return self.operate(CONTAINS, other, result_type=sqltypes.Boolean)

        def contained_by(self, other):
            """Boolean expression.  Test if elements are a proper subset of the
            elements of the argument array expression.
            """
            return self.operate(
                CONTAINED_BY, other, result_type=sqltypes.Boolean
            )

        def overlap(self, other):
            """Boolean expression.  Test if array has elements in common with
            an argument array expression.
            """
            return self.operate(OVERLAP, other, result_type=sqltypes.Boolean)

    comparator_factory = Comparator

    def __init__(
        self,
        item_type: _TypeEngineArgument[Any],
        as_tuple: bool = False,
        dimensions: Optional[int] = None,
        zero_indexes: bool = False,
    ):
        """Construct an ARRAY.

        E.g.::

          Column('myarray', ARRAY(Integer))

        Arguments are:

        :param item_type: The data type of items of this array. Note that
          dimensionality is irrelevant here, so multi-dimensional arrays like
          ``INTEGER[][]``, are constructed as ``ARRAY(Integer)``, not as
          ``ARRAY(ARRAY(Integer))`` or such.

        :param as_tuple=False: Specify whether return results
          should be converted to tuples from lists. DBAPIs such
          as psycopg2 return lists by default. When tuples are
          returned, the results are hashable.

        :param dimensions: if non-None, the ARRAY will assume a fixed
         number of dimensions.  This will cause the DDL emitted for this
         ARRAY to include the exact number of bracket clauses ``[]``,
         and will also optimize the performance of the type overall.
         Note that PG arrays are always implicitly "non-dimensioned",
         meaning they can store any number of dimensions no matter how
         they were declared.

        :param zero_indexes=False: when True, index values will be converted
         between Python zero-based and PostgreSQL one-based indexes, e.g.
         a value of one will be added to all index values before passing
         to the database.

        """
        if isinstance(item_type, ARRAY):
            raise ValueError(
                "Do not nest ARRAY types; ARRAY(basetype) "
                "handles multi-dimensional arrays of basetype"
            )
        if isinstance(item_type, type):
            item_type = item_type()
        self.item_type = item_type
        self.as_tuple = as_tuple
        self.dimensions = dimensions
        self.zero_indexes = zero_indexes

    @property
    def hashable(self):
        return self.as_tuple

    @property
    def python_type(self):
        return list

    def compare_values(self, x, y):
        return x == y

    @util.memoized_property
    def _against_native_enum(self):
        return (
            isinstance(self.item_type, sqltypes.Enum)
            and self.item_type.native_enum
        )

    def literal_processor(self, dialect):
        item_proc = self.item_type.dialect_impl(dialect).literal_processor(
            dialect
        )
        if item_proc is None:
            return None

        def to_str(elements):
            return f"ARRAY[{', '.join(elements)}]"

        def process(value):
            inner = self._apply_item_processor(
                value, item_proc, self.dimensions, to_str
            )
            return inner

        return process

    def bind_processor(self, dialect):
        item_proc = self.item_type.dialect_impl(dialect).bind_processor(
            dialect
        )

        def process(value):
            if value is None:
                return value
            else:
                return self._apply_item_processor(
                    value, item_proc, self.dimensions, list
                )

        return process

    def result_processor(self, dialect, coltype):
        item_proc = self.item_type.dialect_impl(dialect).result_processor(
            dialect, coltype
        )

        def process(value):
            if value is None:
                return value
            else:
                return self._apply_item_processor(
                    value,
                    item_proc,
                    self.dimensions,
                    tuple if self.as_tuple else list,
                )

        if self._against_native_enum:
            super_rp = process
            pattern = re.compile(r"^{(.*)}$")

            def handle_raw_string(value):
                inner = pattern.match(value).group(1)
                return _split_enum_values(inner)

            def process(value):
                if value is None:
                    return value
                # isinstance(value, str) is required to handle
                # the case where a TypeDecorator for and Array of Enum is
                # used like was required in sa < 1.3.17
                return super_rp(
                    handle_raw_string(value)
                    if isinstance(value, str)
                    else value
                )

        return process


def _split_enum_values(array_string):
    if '"' not in array_string:
        # no escape char is present so it can just split on the comma
        return array_string.split(",") if array_string else []

    # handles quoted strings from:
    # r'abc,"quoted","also\\\\quoted", "quoted, comma", "esc \" quot", qpr'
    # returns
    # ['abc', 'quoted', 'also\\quoted', 'quoted, comma', 'esc " quot', 'qpr']
    text = array_string.replace(r"\"", "_$ESC_QUOTE$_")
    text = text.replace(r"\\", "\\")
    result = []
    on_quotes = re.split(r'(")', text)
    in_quotes = False
    for tok in on_quotes:
        if tok == '"':
            in_quotes = not in_quotes
        elif in_quotes:
            result.append(tok.replace("_$ESC_QUOTE$_", '"'))
        else:
            result.extend(re.findall(r"([^\s,]+),?", tok))
    return result