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							# postgresql/array.py
# Copyright (C) 2005-2021 the SQLAlchemy authors and contributors
# <see AUTHORS file>
#
# This module is part of SQLAlchemy and is released under
# the MIT License: http://www.opensource.org/licenses/mit-license.php

import re

from ... import types as sqltypes
from ... import util
from ...sql import coercions
from ...sql import expression
from ...sql import operators
from ...sql import roles


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

    This method is legacy and is here for backwards-compatibility.

    .. seealso::

        :func:`_expression.any_`

    """

    return arrexpr.any(other, operator)


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

    This method is legacy and is here for backwards-compatibility.

    .. seealso::

        :func:`_expression.all_`

    """

    return arrexpr.all(other, operator)


class array(expression.ClauseList, expression.ColumnElement):

    """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"

    def __init__(self, clauses, **kw):
        clauses = [
            coercions.expect(roles.ExpressionElementRole, c) for c in clauses
        ]

        super(array, self).__init__(*clauses, **kw)

        self._type_tuple = [arg.type for arg in clauses]
        main_type = kw.pop(
            "type_",
            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


CONTAINS = operators.custom_op("@>", precedence=5)

CONTAINED_BY = operators.custom_op("<@", precedence=5)

OVERLAP = operators.custom_op("&&", precedence=5)


class ARRAY(sqltypes.ARRAY):

    """PostgreSQL ARRAY type.

    .. versionchanged:: 1.1 The :class:`_postgresql.ARRAY` type is now
       a subclass of the core :class:`_types.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`.

    .. 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.
            """
            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, as_tuple=False, dimensions=None, zero_indexes=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.

         .. versionadded:: 0.9.5


        """
        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

    def _proc_array(self, arr, itemproc, dim, collection):
        if dim is None:
            arr = list(arr)
        if (
            dim == 1
            or dim is None
            and (
                # this has to be (list, tuple), or at least
                # not hasattr('__iter__'), since Py3K strings
                # etc. have __iter__
                not arr
                or not isinstance(arr[0], (list, tuple))
            )
        ):
            if itemproc:
                return collection(itemproc(x) for x in arr)
            else:
                return collection(arr)
        else:
            return collection(
                self._proc_array(
                    x,
                    itemproc,
                    dim - 1 if dim is not None else None,
                    collection,
                )
                for x in arr
            )

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

    def bind_expression(self, bindvalue):
        return bindvalue

    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._proc_array(
                    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._proc_array(
                    value,
                    item_proc,
                    self.dimensions,
                    tuple if self.as_tuple else list,
                )

        if self._against_native_enum:
            super_rp = process

            def handle_raw_string(value):
                inner = re.match(r"^{(.*)}$", value).group(1)
                return inner.split(",") if inner else []

            def process(value):
                if value is None:
                    return value
                # isinstance(value, util.string_types) 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, util.string_types)
                    else value
                )

        return process