This file is indexed.

/usr/lib/python3/dist-packages/astroid/brain/brain_builtin_inference.py is in python3-astroid 1.4.4-1.

This file is owned by root:root, with mode 0o644.

The actual contents of the file can be viewed below.

  1
  2
  3
  4
  5
  6
  7
  8
  9
 10
 11
 12
 13
 14
 15
 16
 17
 18
 19
 20
 21
 22
 23
 24
 25
 26
 27
 28
 29
 30
 31
 32
 33
 34
 35
 36
 37
 38
 39
 40
 41
 42
 43
 44
 45
 46
 47
 48
 49
 50
 51
 52
 53
 54
 55
 56
 57
 58
 59
 60
 61
 62
 63
 64
 65
 66
 67
 68
 69
 70
 71
 72
 73
 74
 75
 76
 77
 78
 79
 80
 81
 82
 83
 84
 85
 86
 87
 88
 89
 90
 91
 92
 93
 94
 95
 96
 97
 98
 99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
"""Astroid hooks for various builtins."""

import sys
from functools import partial
from textwrap import dedent

import six
from astroid import (MANAGER, UseInferenceDefault,
                     inference_tip, YES, InferenceError, UnresolvableName)
from astroid import arguments
from astroid import nodes
from astroid import objects
from astroid.builder import AstroidBuilder
from astroid import util

def _extend_str(class_node, rvalue):
    """function to extend builtin str/unicode class"""
    # TODO(cpopa): this approach will make astroid to believe
    # that some arguments can be passed by keyword, but
    # unfortunately, strings and bytes don't accept keyword arguments.
    code = dedent('''
    class whatever(object):
        def join(self, iterable):
            return {rvalue}
        def replace(self, old, new, count=None):
            return {rvalue}
        def format(self, *args, **kwargs):
            return {rvalue}
        def encode(self, encoding='ascii', errors=None):
            return ''
        def decode(self, encoding='ascii', errors=None):
            return u''
        def capitalize(self):
            return {rvalue}
        def title(self):
            return {rvalue}
        def lower(self):
            return {rvalue}
        def upper(self):
            return {rvalue}
        def swapcase(self):
            return {rvalue}
        def index(self, sub, start=None, end=None):
            return 0
        def find(self, sub, start=None, end=None):
            return 0
        def count(self, sub, start=None, end=None):
            return 0
        def strip(self, chars=None):
            return {rvalue}
        def lstrip(self, chars=None):
            return {rvalue}
        def rstrip(self, chars=None):
            return {rvalue}
        def rjust(self, width, fillchar=None):
            return {rvalue}
        def center(self, width, fillchar=None):
            return {rvalue}
        def ljust(self, width, fillchar=None):
            return {rvalue}
    ''')
    code = code.format(rvalue=rvalue)
    fake = AstroidBuilder(MANAGER).string_build(code)['whatever']
    for method in fake.mymethods():
        class_node._locals[method.name] = [method]
        method.parent = class_node

def extend_builtins(class_transforms):
    from astroid.bases import BUILTINS
    builtin_ast = MANAGER.astroid_cache[BUILTINS]
    for class_name, transform in class_transforms.items():
        transform(builtin_ast[class_name])

if sys.version_info > (3, 0):
    extend_builtins({'bytes': partial(_extend_str, rvalue="b''"),
                     'str': partial(_extend_str, rvalue="''")})
else:
    extend_builtins({'str': partial(_extend_str, rvalue="''"),
                     'unicode': partial(_extend_str, rvalue="u''")})


def register_builtin_transform(transform, builtin_name):
    """Register a new transform function for the given *builtin_name*.

    The transform function must accept two parameters, a node and
    an optional context.
    """
    def _transform_wrapper(node, context=None):
        result = transform(node, context=context)
        if result:
            if not result.parent:
                # Let the transformation function determine
                # the parent for its result. Otherwise,
                # we set it to be the node we transformed from.
                result.parent = node

            result.lineno = node.lineno
            result.col_offset = node.col_offset
        return iter([result])

    MANAGER.register_transform(nodes.Call,
                               inference_tip(_transform_wrapper),
                               lambda n: (isinstance(n.func, nodes.Name) and
                                          n.func.name == builtin_name))


def _generic_inference(node, context, node_type, transform):
    args = node.args
    if not args:
        return node_type()
    if len(node.args) > 1:
        raise UseInferenceDefault()

    arg, = args
    transformed = transform(arg)
    if not transformed:
        try:
            inferred = next(arg.infer(context=context))
        except (InferenceError, StopIteration):
            raise UseInferenceDefault()
        if inferred is util.YES:
            raise UseInferenceDefault()
        transformed = transform(inferred)
    if not transformed or transformed is util.YES:
        raise UseInferenceDefault()
    return transformed


def _generic_transform(arg, klass, iterables, build_elts):
    if isinstance(arg, klass):
        return arg
    elif isinstance(arg, iterables):
        if not all(isinstance(elt, nodes.Const)
                   for elt in arg.elts):
            # TODO(cpopa): Don't support heterogenous elements.
            # Not yet, though.
            raise UseInferenceDefault()
        elts = [elt.value for elt in arg.elts]
    elif isinstance(arg, nodes.Dict):
        if not all(isinstance(elt[0], nodes.Const)
                   for elt in arg.items):
            raise UseInferenceDefault()
        elts = [item[0].value for item in arg.items]
    elif (isinstance(arg, nodes.Const) and
          isinstance(arg.value, (six.string_types, six.binary_type))):
        elts = arg.value
    else:
        return
    return klass(elts=build_elts(elts))


def _infer_builtin(node, context,
                   klass=None, iterables=None,
                   build_elts=None):
    transform_func = partial(
        _generic_transform,
        klass=klass,
        iterables=iterables,
        build_elts=build_elts)

    return _generic_inference(node, context, klass, transform_func)

# pylint: disable=invalid-name
infer_tuple = partial(
    _infer_builtin,
    klass=nodes.Tuple,
    iterables=(nodes.List, nodes.Set),
    build_elts=tuple)

infer_list = partial(
    _infer_builtin,
    klass=nodes.List,
    iterables=(nodes.Tuple, nodes.Set),
    build_elts=list)

infer_set = partial(
    _infer_builtin,
    klass=nodes.Set,
    iterables=(nodes.List, nodes.Tuple),
    build_elts=set)

infer_frozenset = partial(
    _infer_builtin,
    klass=objects.FrozenSet,
    iterables=(nodes.List, nodes.Tuple, nodes.Set),
    build_elts=frozenset)


def _get_elts(arg, context):
    is_iterable = lambda n: isinstance(n,
                                       (nodes.List, nodes.Tuple, nodes.Set))
    try:
        inferred = next(arg.infer(context))
    except (InferenceError, UnresolvableName):
        raise UseInferenceDefault()
    if isinstance(inferred, nodes.Dict):
        items = inferred.items
    elif is_iterable(inferred):
        items = []
        for elt in inferred.elts:
            # If an item is not a pair of two items,
            # then fallback to the default inference.
            # Also, take in consideration only hashable items,
            # tuples and consts. We are choosing Names as well.
            if not is_iterable(elt):
                raise UseInferenceDefault()
            if len(elt.elts) != 2:
                raise UseInferenceDefault()
            if not isinstance(elt.elts[0],
                              (nodes.Tuple, nodes.Const, nodes.Name)):
                raise UseInferenceDefault()
            items.append(tuple(elt.elts))
    else:
        raise UseInferenceDefault()
    return items

def infer_dict(node, context=None):
    """Try to infer a dict call to a Dict node.

    The function treats the following cases:

        * dict()
        * dict(mapping)
        * dict(iterable)
        * dict(iterable, **kwargs)
        * dict(mapping, **kwargs)
        * dict(**kwargs)

    If a case can't be inferred, we'll fallback to default inference.
    """
    call = arguments.CallSite.from_call(node)
    if call.has_invalid_arguments() or call.has_invalid_keywords():
        raise UseInferenceDefault

    args = call.positional_arguments
    kwargs = list(call.keyword_arguments.items())

    if not args and not kwargs:
        # dict()
        return nodes.Dict()
    elif kwargs and not args:
        # dict(a=1, b=2, c=4)
        items = [(nodes.Const(key), value) for key, value in kwargs]
    elif len(args) == 1 and kwargs:
        # dict(some_iterable, b=2, c=4)
        elts = _get_elts(args[0], context)
        keys = [(nodes.Const(key), value) for key, value in kwargs]
        items = elts + keys
    elif len(args) == 1:
        items = _get_elts(args[0], context)
    else:
        raise UseInferenceDefault()

    empty = nodes.Dict()
    empty.items = items
    return empty


def _node_class(node):
    klass = node.frame()
    while klass is not None and not isinstance(klass, nodes.ClassDef):
        if klass.parent is None:
            klass = None
        else:
            klass = klass.parent.frame()
    return klass


def infer_super(node, context=None):
    """Understand super calls.

    There are some restrictions for what can be understood:

        * unbounded super (one argument form) is not understood.

        * if the super call is not inside a function (classmethod or method),
          then the default inference will be used.

        * if the super arguments can't be infered, the default inference
          will be used.
    """
    if len(node.args) == 1:
        # Ignore unbounded super.
        raise UseInferenceDefault

    scope = node.scope()
    if not isinstance(scope, nodes.FunctionDef):
        # Ignore non-method uses of super.
        raise UseInferenceDefault
    if scope.type not in ('classmethod', 'method'):
        # Not interested in staticmethods.
        raise UseInferenceDefault

    cls = _node_class(scope)
    if not len(node.args):
        mro_pointer = cls
        # In we are in a classmethod, the interpreter will fill
        # automatically the class as the second argument, not an instance.
        if scope.type == 'classmethod':
            mro_type = cls
        else:
            mro_type = cls.instantiate_class()
    else:
        # TODO(cpopa): support flow control (multiple inference values).
        try:
            mro_pointer = next(node.args[0].infer(context=context))
        except InferenceError:
            raise UseInferenceDefault
        try:
            mro_type = next(node.args[1].infer(context=context))
        except InferenceError:
            raise UseInferenceDefault

    if mro_pointer is YES or mro_type is YES:
        # No way we could understand this.
        raise UseInferenceDefault

    super_obj = objects.Super(mro_pointer=mro_pointer,
                              mro_type=mro_type,
                              self_class=cls,
                              scope=scope)
    super_obj.parent = node
    return iter([super_obj])


# Builtins inference
MANAGER.register_transform(nodes.Call,
                           inference_tip(infer_super),
                           lambda n: (isinstance(n.func, nodes.Name) and
                                      n.func.name == 'super'))

register_builtin_transform(infer_tuple, 'tuple')
register_builtin_transform(infer_set, 'set')
register_builtin_transform(infer_list, 'list')
register_builtin_transform(infer_dict, 'dict')
register_builtin_transform(infer_frozenset, 'frozenset')