Merge remote-tracking branch 'upstream/master'

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diff --git a/lib/sqlalchemy/dialects/postgresql/array.py b/lib/sqlalchemy/dialects/postgresql/array.py
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+++ b/lib/sqlalchemy/dialects/postgresql/array.py
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+# postgresql/array.py
+# Copyright (C) 2005-2015 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
+
+from .base import ischema_names
+from ...sql import expression, operators
+from ...sql.base import SchemaEventTarget
+from ... import types as sqltypes
+
+try:
+    from uuid import UUID as _python_UUID
+except ImportError:
+    _python_UUID = None
+
+
+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-compatiblity.
+
+    .. 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-compatiblity.
+
+    .. seealso::
+
+        :func:`.expression.all_`
+
+    """
+
+    return arrexpr.all(other, operator)
+
+
+class array(expression.Tuple):
+
+    """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:`.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)
+
+    .. versionadded:: 0.8 Added the :class:`~.postgresql.array` literal type.
+
+    See also:
+
+    :class:`.postgresql.ARRAY`
+
+    """
+    __visit_name__ = 'array'
+
+    def __init__(self, clauses, **kw):
+        super(array, self).__init__(*clauses, **kw)
+        self.type = ARRAY(self.type)
+
+    def _bind_param(self, operator, obj):
+        return array([
+            expression.BindParameter(None, o, _compared_to_operator=operator,
+                                     _compared_to_type=self.type, unique=True)
+            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(SchemaEventTarget, sqltypes.Array):
+
+    """Postgresql ARRAY type.
+
+    .. versionchanged:: 1.1 The :class:`.postgresql.ARRAY` type is now
+       a subclass of the core :class:`.Array` type.
+
+    The :class:`.postgresql.ARRAY` type is constructed in the same way
+    as the core :class:`.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:`.Array` type, including support for "dimensions", indexed
+    access, and simple matching such as :meth:`.Array.Comparator.any`
+    and :meth:`.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:`.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 _set_parent(self, column):
+        """Support SchemaEentTarget"""
+
+        if isinstance(self.item_type, SchemaEventTarget):
+            self.item_type._set_parent(column)
+
+    def _set_parent_with_dispatch(self, parent):
+        """Support SchemaEentTarget"""
+
+        if isinstance(self.item_type, SchemaEventTarget):
+            self.item_type._set_parent_with_dispatch(parent)
+
+    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
+            )
+
+    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)
+        return process
+
+ischema_names['_array'] = ARRAY