def test_preprocess_co_filename(self):
        def undecorated():
            pass

        decorated = preprocess()(undecorated)

        self.assertEqual(undecorated.__code__.co_filename, decorated.__code__.co_filename)

def expect_types(*_pos, **named):
    """
    Preprocessing decorator that verifies inputs have expected types.

    Usage
    -----
    >>> @expect_types(x=int, y=str)
    ... def foo(x, y):
    ...    return x, y
    ...
    >>> foo(2, '3')
    (2, '3')
    >>> foo(2.0, '3')
    Traceback (most recent call last):
       ...
    TypeError: foo() expected an argument of type 'int' for argument 'x', but got float instead.  # noqa
    """
    if _pos:
        raise TypeError("expect_types() only takes keyword arguments.")

    for name, type_ in iteritems(named):
        if not isinstance(type_, (type, tuple)):
            raise TypeError(
                "expect_types() expected a type or tuple of types for "
                "argument '{name}', but got {type_} instead.".format(
                    name=name, type_=type_,
                )
            )

    return preprocess(**valmap(_expect_type, named))

    def test_preprocess_bad_processor_name(self):
        a_processor = preprocess(a=int)

        # Should work fine.
        @a_processor
        def func_with_arg_named_a(a):
            pass

        @a_processor
        def func_with_default_arg_named_a(a=1):
            pass

        message = "Got processors for unknown arguments: %s." % {"a"}
        with self.assertRaises(TypeError) as e:

            @a_processor
            def func_with_no_args():
                pass

        self.assertEqual(e.exception.args[0], message)

        with self.assertRaises(TypeError) as e:

            @a_processor
            def func_with_arg_named_b(b):
                pass

        self.assertEqual(e.exception.args[0], message)

def expect_dtypes(**named):
    """
    Preprocessing decorator that verifies inputs have expected numpy dtypes.

    Usage
    -----
    >>> from numpy import dtype, arange, int8, float64
    >>> @expect_dtypes(x=dtype(int8))
    ... def foo(x, y):
    ...    return x, y
    ...
    >>> foo(arange(3, dtype=int8), 'foo')
    (array([0, 1, 2], dtype=int8), 'foo')
    >>> foo(arange(3, dtype=float64), 'foo')  # doctest: +NORMALIZE_WHITESPACE
    ...                                       # doctest: +ELLIPSIS
    Traceback (most recent call last):
       ...
    TypeError: ...foo() expected a value with dtype 'int8' for argument 'x',
    but got 'float64' instead.
    """
    for name, type_ in iteritems(named):
        if not isinstance(type_, (dtype, tuple)):
            raise TypeError(
                "expect_dtypes() expected a numpy dtype or tuple of dtypes"
                " for argument {name!r}, but got {dtype} instead.".format(
                    name=name, dtype=dtype,
                )
            )

    @preprocess(dtypes=call(lambda x: x if isinstance(x, tuple) else (x,)))
    def _expect_dtype(dtypes):
        """
        Factory for dtype-checking functions that work with the @preprocess
        decorator.
        """
        def error_message(func, argname, value):
            # If the bad value has a dtype, but it's wrong, show the dtype
            # name.  Otherwise just show the value.
            try:
                value_to_show = value.dtype.name
            except AttributeError:
                value_to_show = value
            return (
                "{funcname}() expected a value with dtype {dtype_str} "
                "for argument {argname!r}, but got {value!r} instead."
            ).format(
                funcname=_qualified_name(func),
                dtype_str=' or '.join(repr(d.name) for d in dtypes),
                argname=argname,
                value=value_to_show,
            )

        def _actual_preprocessor(func, argname, argvalue):
            if getattr(argvalue, 'dtype', object()) not in dtypes:
                raise TypeError(error_message(func, argname, argvalue))
            return argvalue

        return _actual_preprocessor

    return preprocess(**valmap(_expect_dtype, named))

def expect_element(*_pos, **named):
    """
    Preprocessing decorator that verifies inputs are elements of some
    expected collection.

    Usage
    -----
    >>> @expect_element(x=('a', 'b'))
    ... def foo(x):
    ...    return x.upper()
    ...
    >>> foo('a')
    'A'
    >>> foo('b')
    'B'
    >>> foo('c')
    Traceback (most recent call last):
       ...
    ValueError: foo() expected a value in ('a', 'b') for argument 'x', but got 'c' instead.  # noqa

    Notes
    -----
    This uses the `in` operator (__contains__) to make the containment check.
    This allows us to use any custom container as long as the object supports
    the container protocol.
    """
    if _pos:
        raise TypeError("expect_element() only takes keyword arguments.")

    return preprocess(**valmap(_expect_element, named))

def expect_dtypes(*_pos, **named):
    """
    Preprocessing decorator that verifies inputs have expected numpy dtypes.

    Usage
    -----
    >>> from numpy import dtype, arange
    >>> @expect_dtypes(x=dtype(int))
    ... def foo(x, y):
    ...    return x, y
    ...
    >>> foo(arange(3), 'foo')
    (array([0, 1, 2]), 'foo')
    >>> foo(arange(3, dtype=float), 'foo')
    Traceback (most recent call last):
       ...
    TypeError: foo() expected an argument with dtype 'int64' for argument 'x', but got dtype 'float64' instead.  # noqa
    """
    if _pos:
        raise TypeError("expect_dtypes() only takes keyword arguments.")

    for name, type_ in iteritems(named):
        if not isinstance(type_, (dtype, tuple)):
            raise TypeError(
                "expect_dtypes() expected a numpy dtype or tuple of dtypes"
                " for argument {name!r}, but got {dtype} instead.".format(
                    name=name, dtype=dtype,
                )
            )
    return preprocess(**valmap(_expect_dtype, named))

def expect_kinds(**named):
    """
    Preprocessing decorator that verifies inputs have expected dtype kinds.

    Usage
    -----
    >>> from numpy import int64, int32, float32
    >>> @expect_kinds(x='i')
    ... def foo(x):
    ...    return x
    ...
    >>> foo(int64(2))
    2
    >>> foo(int32(2))
    2
    >>> foo(float32(2))
    Traceback (most recent call last):
       ...n
    TypeError: foo() expected a numpy object of kind 'i' for argument 'x', but got 'f' instead.  # noqa
    """
    for name, kind in iteritems(named):
        if not isinstance(kind, (str, tuple)):
            raise TypeError(
                "expect_dtype_kinds() expected a string or tuple of strings"
                " for argument {name!r}, but got {kind} instead.".format(
                    name=name, kind=dtype,
                )
            )

    @preprocess(kinds=call(lambda x: x if isinstance(x, tuple) else (x,)))
    def _expect_kind(kinds):
        """
        Factory for kind-checking functions that work the @preprocess
        decorator.
        """
        def error_message(func, argname, value):
            # If the bad value has a dtype, but it's wrong, show the dtype
            # kind.  Otherwise just show the value.
            try:
                value_to_show = value.dtype.kind
            except AttributeError:
                value_to_show = value
            return (
                "{funcname}() expected a numpy object of kind {kinds} "
                "for argument {argname!r}, but got {value!r} instead."
            ).format(
                funcname=_qualified_name(func),
                kinds=' or '.join(map(repr, kinds)),
                argname=argname,
                value=value_to_show,
            )

        def _actual_preprocessor(func, argname, argvalue):
            if getattrs(argvalue, ('dtype', 'kind'), object()) not in kinds:
                raise TypeError(error_message(func, argname, argvalue))
            return argvalue

        return _actual_preprocessor

    return preprocess(**valmap(_expect_kind, named))

def expect_dtypes(*_pos, **named):
    """
    Preprocessing decorator that verifies inputs have expected numpy dtypes.

    Usage
    -----
    >>> from numpy import dtype, arange
    >>> @expect_dtypes(x=dtype(int))
    ... def foo(x, y):
    ...    return x, y
    ...
    >>> foo(arange(3), 'foo')
    (array([0, 1, 2]), 'foo')
    >>> foo(arange(3, dtype=float), 'foo')
    Traceback (most recent call last):
       ...
    TypeError: foo() expected an argument with dtype 'int64' for argument 'x', but got dtype 'float64' instead.  # noqa
    """
    if _pos:
        raise TypeError("expect_dtypes() only takes keyword arguments.")

    for name, type_ in iteritems(named):
        if not isinstance(type_, (dtype, tuple)):
            raise TypeError(
                "expect_dtypes() expected a numpy dtype or tuple of dtypes"
                " for argument {name!r}, but got {dtype} instead.".format(name=name, dtype=dtype)
            )

    def _expect_dtype(_dtype_or_dtype_tuple):
        """
        Factory for dtype-checking functions that work the @preprocess
        decorator.
        """
        # Slightly different messages for dtype and tuple of dtypes.
        if isinstance(_dtype_or_dtype_tuple, tuple):
            allowed_dtypes = _dtype_or_dtype_tuple
        else:
            allowed_dtypes = (_dtype_or_dtype_tuple,)
        template = (
            "%(funcname)s() expected a value with dtype {dtype_str} "
            "for argument '%(argname)s', but got %(actual)r instead."
        ).format(dtype_str=" or ".join(repr(d.name) for d in allowed_dtypes))

        def check_dtype(value):
            return getattr(value, "dtype", None) not in allowed_dtypes

        def display_bad_value(value):
            # If the bad value has a dtype, but it's wrong, show the dtype
            # name.
            try:
                return value.dtype.name
            except AttributeError:
                return value

        return make_check(exc_type=TypeError, template=template, pred=check_dtype, actual=display_bad_value)

    return preprocess(**valmap(_expect_dtype, named))

def expect_types(__funcname=_qualified_name, **named):
    """
    Preprocessing decorator that verifies inputs have expected types.

    Examples
    --------
    >>> @expect_types(x=int, y=str)
    ... def foo(x, y):
    ...    return x, y
    ...
    >>> foo(2, '3')
    (2, '3')
    >>> foo(2.0, '3')  # doctest: +NORMALIZE_WHITESPACE +ELLIPSIS
    Traceback (most recent call last):
       ...
    TypeError: ...foo() expected a value of type int for argument 'x',
    but got float instead.

    Notes
    -----
    A special argument, __funcname, can be provided as a string to override the
    function name shown in error messages.  This is most often used on __init__
    or __new__ methods to make errors refer to the class name instead of the
    function name.
    """
    for name, type_ in iteritems(named):
        if not isinstance(type_, (type, tuple)):
            raise TypeError(
                "expect_types() expected a type or tuple of types for "
                "argument '{name}', but got {type_} instead.".format(
                    name=name, type_=type_,
                )
            )

    def _expect_type(type_):
        # Slightly different messages for type and tuple of types.
        _template = (
            "%(funcname)s() expected a value of type {type_or_types} "
            "for argument '%(argname)s', but got %(actual)s instead."
        )
        if isinstance(type_, tuple):
            template = _template.format(
                type_or_types=' or '.join(map(_qualified_name, type_))
            )
        else:
            template = _template.format(type_or_types=_qualified_name(type_))

        return make_check(
            exc_type=TypeError,
            template=template,
            pred=lambda v: not isinstance(v, type_),
            actual=compose(_qualified_name, type),
            funcname=__funcname,
        )

    return preprocess(**valmap(_expect_type, named))

    def test_preprocess_on_function(self, args, kwargs):

        decorators = [
            preprocess(a=call(str), b=call(float), c=call(lambda x: x + 1)),
        ]

        for decorator in decorators:
            @decorator
            def func(a, b, c=3):
                return a, b, c
            self.assertEqual(func(*args, **kwargs), ('1', 2.0, 4))

def expect_element(__funcname=_qualified_name, **named):
    """
    Preprocessing decorator that verifies inputs are elements of some
    expected collection.

    Examples
    --------
    >>> @expect_element(x=('a', 'b'))
    ... def foo(x):
    ...    return x.upper()
    ...
    >>> foo('a')
    'A'
    >>> foo('b')
    'B'
    >>> foo('c')  # doctest: +NORMALIZE_WHITESPACE +ELLIPSIS
    Traceback (most recent call last):
       ...
    ValueError: ...foo() expected a value in ('a', 'b') for argument 'x',
    but got 'c' instead.

    Notes
    -----
    A special argument, __funcname, can be provided as a string to override the
    function name shown in error messages.  This is most often used on __init__
    or __new__ methods to make errors refer to the class name instead of the
    function name.

    This uses the `in` operator (__contains__) to make the containment check.
    This allows us to use any custom container as long as the object supports
    the container protocol.
    """
    def _expect_element(collection):
        if isinstance(collection, (set, frozenset)):
            # Special case the error message for set and frozen set to make it
            # less verbose.
            collection_for_error_message = tuple(sorted(collection))
        else:
            collection_for_error_message = collection

        template = (
            "%(funcname)s() expected a value in {collection} "
            "for argument '%(argname)s', but got %(actual)s instead."
        ).format(collection=collection_for_error_message)
        return make_check(
            ValueError,
            template,
            complement(op.contains(collection)),
            repr,
            funcname=__funcname,
        )
    return preprocess(**valmap(_expect_element, named))

def expect_types(*_pos, **named):
    """
    Preprocessing decorator that verifies inputs have expected types.

    Usage
    -----
    >>> @expect_types(x=int, y=str)
    ... def foo(x, y):
    ...    return x, y
    ...
    >>> foo(2, '3')
    (2, '3')
    >>> foo(2.0, '3')  # doctest: +NORMALIZE_WHITESPACE +ELLIPSIS
    Traceback (most recent call last):
       ...
    TypeError: ...foo() expected a value of type int for argument 'x',
    but got float instead.
    """
    if _pos:
        raise TypeError("expect_types() only takes keyword arguments.")

    for name, type_ in iteritems(named):
        if not isinstance(type_, (type, tuple)):
            raise TypeError(
                "expect_types() expected a type or tuple of types for "
                "argument '{name}', but got {type_} instead.".format(
                    name=name, type_=type_,
                )
            )

    def _expect_type(type_):
        # Slightly different messages for type and tuple of types.
        _template = (
            "%(funcname)s() expected a value of type {type_or_types} "
            "for argument '%(argname)s', but got %(actual)s instead."
        )
        if isinstance(type_, tuple):
            template = _template.format(
                type_or_types=' or '.join(map(_qualified_name, type_))
            )
        else:
            template = _template.format(type_or_types=_qualified_name(type_))

        return make_check(
            TypeError,
            template,
            lambda v: not isinstance(v, type_),
            compose(_qualified_name, type),
        )

    return preprocess(**valmap(_expect_type, named))

def expect_element(*_pos, **named):
    """
    Preprocessing decorator that verifies inputs are elements of some
    expected collection.

    Usage
    -----
    >>> @expect_element(x=('a', 'b'))
    ... def foo(x):
    ...    return x.upper()
    ...
    >>> foo('a')
    'A'
    >>> foo('b')
    'B'
    >>> foo('c')  # doctest: +NORMALIZE_WHITESPACE +ELLIPSIS
    Traceback (most recent call last):
       ...
    ValueError: ...foo() expected a value in ('a', 'b') for argument 'x',
    but got 'c' instead.

    Notes
    -----
    This uses the `in` operator (__contains__) to make the containment check.
    This allows us to use any custom container as long as the object supports
    the container protocol.
    """
    if _pos:
        raise TypeError("expect_element() only takes keyword arguments.")

    def _expect_element(collection):
        if isinstance(collection, (set, frozenset)):
            # Special case the error message for set and frozen set to make it
            # less verbose.
            collection_for_error_message = tuple(sorted(collection))
        else:
            collection_for_error_message = collection

        template = (
            "%(funcname)s() expected a value in {collection} "
            "for argument '%(argname)s', but got %(actual)s instead."
        ).format(collection=collection_for_error_message)
        return make_check(
            ValueError,
            template,
            complement(op.contains(collection)),
            repr,
        )
    return preprocess(**valmap(_expect_element, named))

def expect_dimensions(__funcname=_qualified_name, **dimensions):
    """
    Preprocessing decorator that verifies inputs are numpy arrays with a
    specific dimensionality.

    Examples
    --------
    >>> from numpy import array
    >>> @expect_dimensions(x=1, y=2)
    ... def foo(x, y):
    ...    return x[0] + y[0, 0]
    ...
    >>> foo(array([1, 1]), array([[1, 1], [2, 2]]))
    2
    >>> foo(array([1, 1]), array([1, 1]))  # doctest: +NORMALIZE_WHITESPACE
    ...                                    # doctest: +ELLIPSIS
    Traceback (most recent call last):
       ...
    ValueError: ...foo() expected a 2-D array for argument 'y',
    but got a 1-D array instead.
    """
    if isinstance(__funcname, str):
        def get_funcname(_):
            return __funcname
    else:
        get_funcname = __funcname

    def _expect_dimension(expected_ndim):
        def _check(func, argname, argvalue):
            actual_ndim = argvalue.ndim
            if actual_ndim != expected_ndim:
                if actual_ndim == 0:
                    actual_repr = 'scalar'
                else:
                    actual_repr = "%d-D array" % actual_ndim
                raise ValueError(
                    "{func}() expected a {expected:d}-D array"
                    " for argument {argname!r}, but got a {actual}"
                    " instead.".format(
                        func=get_funcname(func),
                        expected=expected_ndim,
                        argname=argname,
                        actual=actual_repr,
                    )
                )
            return argvalue
        return _check
    return preprocess(**valmap(_expect_dimension, dimensions))

    def test_preprocess_on_method(self, args, kwargs):
        decorators = [preprocess(a=call(str), b=call(float), c=call(lambda x: x + 1))]

        for decorator in decorators:

            class Foo(object):
                @decorator
                def method(self, a, b, c=3):
                    return a, b, c

                @classmethod
                @decorator
                def clsmeth(cls, a, b, c=3):
                    return a, b, c

            self.assertEqual(Foo.clsmeth(*args, **kwargs), ("1", 2.0, 4))
            self.assertEqual(Foo().method(*args, **kwargs), ("1", 2.0, 4))

def restrict_to_dtype(dtype, message_template):
    """
    A factory for decorators that restricting Factor methods to only be
    callable on Factors with a specific dtype.

    This is conceptually similar to
    zipline.utils.input_validation.expect_dtypes, but provides more flexibility
    for providing error messages that are specifically targeting Factor
    methods.

    Parameters
    ----------
    dtype : numpy.dtype
        The dtype on which the decorated method may be called.
    message_template : str
        A template for the error message to be raised.
        `message_template.format` will be called with keyword arguments
        `method_name`, `expected_dtype`, and `received_dtype`.

    Usage
    -----
    @restrict_to_dtype(
        dtype=float64_dtype,
        message_template=(
            "{method_name}() was called on a factor of dtype {received_dtype}."
            "{method_name}() requires factors of dtype{expected_dtype}."

        ),
    )
    def some_factor_method(self, ...):
        self.stuff_that_requires_being_float64(...)
    """
    def processor(factor_method, _, factor_instance):
        factor_dtype = factor_instance.dtype
        if factor_dtype != dtype:
            raise TypeError(
                message_template.format(
                    method_name=factor_method.__name__,
                    expected_dtype=dtype.name,
                    received_dtype=factor_dtype,
                )
            )
        return factor_instance
    return preprocess(self=processor)

def _expect_bounded(make_bounded_check, __funcname, **named):
    def valid_bounds(t):
        return (
            isinstance(t, tuple)
            and len(t) == 2
            and t != (None, None)
        )

    for name, bounds in iteritems(named):
        if not valid_bounds(bounds):
            raise TypeError(
                "expect_bounded() expected a tuple of bounds for"
                " argument '{name}', but got {bounds} instead.".format(
                    name=name,
                    bounds=bounds,
                )
            )

    return preprocess(**valmap(make_bounded_check, named))

def expect_dimensions(**dimensions):
    """
    Preprocessing decorator that verifies inputs are numpy arrays with a
    specific dimensionality.

    Usage
    -----
    >>> from numpy import array
    >>> @expect_dimensions(x=1, y=2)
    ... def foo(x, y):
    ...    return x[0] + y[0, 0]
    ...
    >>> foo(array([1, 1]), array([[1, 1], [2, 2]]))
    2
    >>> foo(array([1, 1], array([1, 1])))
    Traceback (most recent call last):
       ...
    TypeError: foo() expected a 2-D array for argument 'y', but got a 1-D array instead.  # noqa
    """
    def _expect_dimension(expected_ndim):
        def _check(func, argname, argvalue):
            funcname = _qualified_name(func)
            actual_ndim = argvalue.ndim
            if actual_ndim != expected_ndim:
                if actual_ndim == 0:
                    actual_repr = 'scalar'
                else:
                    actual_repr = "%d-D array" % actual_ndim
                raise ValueError(
                    "{func}() expected a {expected:d}-D array"
                    " for argument {argname!r}, but got a {actual}"
                    " instead.".format(
                        func=funcname,
                        expected=expected_ndim,
                        argname=argname,
                        actual=actual_repr,
                    )
                )
            return argvalue
        return _check
    return preprocess(**valmap(_expect_dimension, dimensions))

    def test_preprocess_doesnt_change_TypeErrors(self, name, args, kwargs):
        """
        Verify that the validate decorator doesn't swallow typeerrors that
        would be raised when calling a function with invalid arguments
        """
        def undecorated(x, y):
            return x, y

        decorated = preprocess(x=noop, y=noop)(undecorated)

        with self.assertRaises(TypeError) as e:
            undecorated(*args, **kwargs)
        undecorated_errargs = e.exception.args

        with self.assertRaises(TypeError) as e:
            decorated(*args, **kwargs)
        decorated_errargs = e.exception.args

        self.assertEqual(len(decorated_errargs), 1)
        self.assertEqual(len(undecorated_errargs), 1)

        self.assertEqual(decorated_errargs[0], undecorated_errargs[0])

def coerce_types(**kwargs):
    """
    Preprocessing decorator that applies type coercions.

    Parameters
    ----------
    **kwargs : dict[str -> (type, callable)]
         Keyword arguments mapping function parameter names to pairs of
         (from_type, to_type).

    Usage
    -----
    >>> @coerce_types(x=(float, int), y=(int, str))
    ... def func(x, y):
    ...     return (x, y)
    ...
    >>> func(1.0, 3)
    (1, '3')
    """
    def _coerce(types):
        return coerce(*types)

    return preprocess(**valmap(_coerce, kwargs))