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Diffstat (limited to 'venv/Lib/site-packages/astroid/brain/brain_namedtuple_enum.py')
| -rw-r--r-- | venv/Lib/site-packages/astroid/brain/brain_namedtuple_enum.py | 449 |
1 files changed, 449 insertions, 0 deletions
diff --git a/venv/Lib/site-packages/astroid/brain/brain_namedtuple_enum.py b/venv/Lib/site-packages/astroid/brain/brain_namedtuple_enum.py new file mode 100644 index 0000000..de24067 --- /dev/null +++ b/venv/Lib/site-packages/astroid/brain/brain_namedtuple_enum.py @@ -0,0 +1,449 @@ +# -*- coding: utf-8 -*- +# Copyright (c) 2012-2015 LOGILAB S.A. (Paris, FRANCE) <contact@logilab.fr> +# Copyright (c) 2013-2014 Google, Inc. +# Copyright (c) 2014-2018 Claudiu Popa <pcmanticore@gmail.com> +# Copyright (c) 2014 Eevee (Alex Munroe) <amunroe@yelp.com> +# Copyright (c) 2015-2016 Ceridwen <ceridwenv@gmail.com> +# Copyright (c) 2015 Dmitry Pribysh <dmand@yandex.ru> +# Copyright (c) 2015 David Shea <dshea@redhat.com> +# Copyright (c) 2015 Philip Lorenz <philip@bithub.de> +# Copyright (c) 2016 Jakub Wilk <jwilk@jwilk.net> +# Copyright (c) 2016 Mateusz Bysiek <mb@mbdev.pl> +# Copyright (c) 2017 Hugo <hugovk@users.noreply.github.com> +# Copyright (c) 2017 Łukasz Rogalski <rogalski.91@gmail.com> + +# Licensed under the LGPL: https://www.gnu.org/licenses/old-licenses/lgpl-2.1.en.html +# For details: https://github.com/PyCQA/astroid/blob/master/COPYING.LESSER + +"""Astroid hooks for the Python standard library.""" + +import functools +import keyword +from textwrap import dedent + +from astroid import MANAGER, UseInferenceDefault, inference_tip, InferenceError +from astroid import arguments +from astroid import exceptions +from astroid import nodes +from astroid.builder import AstroidBuilder, extract_node +from astroid import util + + +TYPING_NAMEDTUPLE_BASENAMES = {"NamedTuple", "typing.NamedTuple"} +ENUM_BASE_NAMES = { + "Enum", + "IntEnum", + "enum.Enum", + "enum.IntEnum", + "IntFlag", + "enum.IntFlag", +} + + +def _infer_first(node, context): + if node is util.Uninferable: + raise UseInferenceDefault + try: + value = next(node.infer(context=context)) + if value is util.Uninferable: + raise UseInferenceDefault() + else: + return value + except StopIteration: + raise InferenceError() + + +def _find_func_form_arguments(node, context): + def _extract_namedtuple_arg_or_keyword(position, key_name=None): + + if len(args) > position: + return _infer_first(args[position], context) + if key_name and key_name in found_keywords: + return _infer_first(found_keywords[key_name], context) + + args = node.args + keywords = node.keywords + found_keywords = ( + {keyword.arg: keyword.value for keyword in keywords} if keywords else {} + ) + + name = _extract_namedtuple_arg_or_keyword(position=0, key_name="typename") + names = _extract_namedtuple_arg_or_keyword(position=1, key_name="field_names") + if name and names: + return name.value, names + + raise UseInferenceDefault() + + +def infer_func_form(node, base_type, context=None, enum=False): + """Specific inference function for namedtuple or Python 3 enum. """ + # node is a Call node, class name as first argument and generated class + # attributes as second argument + + # namedtuple or enums list of attributes can be a list of strings or a + # whitespace-separate string + try: + name, names = _find_func_form_arguments(node, context) + try: + attributes = names.value.replace(",", " ").split() + except AttributeError: + if not enum: + attributes = [ + _infer_first(const, context).value for const in names.elts + ] + else: + # Enums supports either iterator of (name, value) pairs + # or mappings. + if hasattr(names, "items") and isinstance(names.items, list): + attributes = [ + _infer_first(const[0], context).value + for const in names.items + if isinstance(const[0], nodes.Const) + ] + elif hasattr(names, "elts"): + # Enums can support either ["a", "b", "c"] + # or [("a", 1), ("b", 2), ...], but they can't + # be mixed. + if all(isinstance(const, nodes.Tuple) for const in names.elts): + attributes = [ + _infer_first(const.elts[0], context).value + for const in names.elts + if isinstance(const, nodes.Tuple) + ] + else: + attributes = [ + _infer_first(const, context).value for const in names.elts + ] + else: + raise AttributeError + if not attributes: + raise AttributeError + except (AttributeError, exceptions.InferenceError): + raise UseInferenceDefault() + + # If we can't infer the name of the class, don't crash, up to this point + # we know it is a namedtuple anyway. + name = name or "Uninferable" + # we want to return a Class node instance with proper attributes set + class_node = nodes.ClassDef(name, "docstring") + class_node.parent = node.parent + # set base class=tuple + class_node.bases.append(base_type) + # XXX add __init__(*attributes) method + for attr in attributes: + fake_node = nodes.EmptyNode() + fake_node.parent = class_node + fake_node.attrname = attr + class_node.instance_attrs[attr] = [fake_node] + return class_node, name, attributes + + +def _has_namedtuple_base(node): + """Predicate for class inference tip + + :type node: ClassDef + :rtype: bool + """ + return set(node.basenames) & TYPING_NAMEDTUPLE_BASENAMES + + +def _looks_like(node, name): + func = node.func + if isinstance(func, nodes.Attribute): + return func.attrname == name + if isinstance(func, nodes.Name): + return func.name == name + return False + + +_looks_like_namedtuple = functools.partial(_looks_like, name="namedtuple") +_looks_like_enum = functools.partial(_looks_like, name="Enum") +_looks_like_typing_namedtuple = functools.partial(_looks_like, name="NamedTuple") + + +def infer_named_tuple(node, context=None): + """Specific inference function for namedtuple Call node""" + tuple_base_name = nodes.Name(name="tuple", parent=node.root()) + class_node, name, attributes = infer_func_form( + node, tuple_base_name, context=context + ) + call_site = arguments.CallSite.from_call(node) + func = next(extract_node("import collections; collections.namedtuple").infer()) + try: + rename = next(call_site.infer_argument(func, "rename", context)).bool_value() + except InferenceError: + rename = False + + if rename: + attributes = _get_renamed_namedtuple_attributes(attributes) + + replace_args = ", ".join("{arg}=None".format(arg=arg) for arg in attributes) + field_def = ( + " {name} = property(lambda self: self[{index:d}], " + "doc='Alias for field number {index:d}')" + ) + field_defs = "\n".join( + field_def.format(name=name, index=index) + for index, name in enumerate(attributes) + ) + fake = AstroidBuilder(MANAGER).string_build( + """ +class %(name)s(tuple): + __slots__ = () + _fields = %(fields)r + def _asdict(self): + return self.__dict__ + @classmethod + def _make(cls, iterable, new=tuple.__new__, len=len): + return new(cls, iterable) + def _replace(self, %(replace_args)s): + return self + def __getnewargs__(self): + return tuple(self) +%(field_defs)s + """ + % { + "name": name, + "fields": attributes, + "field_defs": field_defs, + "replace_args": replace_args, + } + ) + class_node.locals["_asdict"] = fake.body[0].locals["_asdict"] + class_node.locals["_make"] = fake.body[0].locals["_make"] + class_node.locals["_replace"] = fake.body[0].locals["_replace"] + class_node.locals["_fields"] = fake.body[0].locals["_fields"] + for attr in attributes: + class_node.locals[attr] = fake.body[0].locals[attr] + # we use UseInferenceDefault, we can't be a generator so return an iterator + return iter([class_node]) + + +def _get_renamed_namedtuple_attributes(field_names): + names = list(field_names) + seen = set() + for i, name in enumerate(field_names): + if ( + not all(c.isalnum() or c == "_" for c in name) + or keyword.iskeyword(name) + or not name + or name[0].isdigit() + or name.startswith("_") + or name in seen + ): + names[i] = "_%d" % i + seen.add(name) + return tuple(names) + + +def infer_enum(node, context=None): + """ Specific inference function for enum Call node. """ + enum_meta = extract_node( + """ + class EnumMeta(object): + 'docstring' + def __call__(self, node): + class EnumAttribute(object): + name = '' + value = 0 + return EnumAttribute() + def __iter__(self): + class EnumAttribute(object): + name = '' + value = 0 + return [EnumAttribute()] + def __reversed__(self): + class EnumAttribute(object): + name = '' + value = 0 + return (EnumAttribute, ) + def __next__(self): + return next(iter(self)) + def __getitem__(self, attr): + class Value(object): + @property + def name(self): + return '' + @property + def value(self): + return attr + + return Value() + __members__ = [''] + """ + ) + class_node = infer_func_form(node, enum_meta, context=context, enum=True)[0] + return iter([class_node.instantiate_class()]) + + +INT_FLAG_ADDITION_METHODS = """ + def __or__(self, other): + return {name}(self.value | other.value) + def __and__(self, other): + return {name}(self.value & other.value) + def __xor__(self, other): + return {name}(self.value ^ other.value) + def __add__(self, other): + return {name}(self.value + other.value) + def __div__(self, other): + return {name}(self.value / other.value) + def __invert__(self): + return {name}(~self.value) + def __mul__(self, other): + return {name}(self.value * other.value) +""" + + +def infer_enum_class(node): + """ Specific inference for enums. """ + for basename in node.basenames: + # TODO: doesn't handle subclasses yet. This implementation + # is a hack to support enums. + if basename not in ENUM_BASE_NAMES: + continue + if node.root().name == "enum": + # Skip if the class is directly from enum module. + break + for local, values in node.locals.items(): + if any(not isinstance(value, nodes.AssignName) for value in values): + continue + + targets = [] + stmt = values[0].statement() + if isinstance(stmt, nodes.Assign): + if isinstance(stmt.targets[0], nodes.Tuple): + targets = stmt.targets[0].itered() + else: + targets = stmt.targets + elif isinstance(stmt, nodes.AnnAssign): + targets = [stmt.target] + + inferred_return_value = None + if isinstance(stmt, nodes.Assign): + if isinstance(stmt.value, nodes.Const): + if isinstance(stmt.value.value, str): + inferred_return_value = repr(stmt.value.value) + else: + inferred_return_value = stmt.value.value + else: + inferred_return_value = stmt.value.as_string() + + new_targets = [] + for target in targets: + # Replace all the assignments with our mocked class. + classdef = dedent( + """ + class {name}({types}): + @property + def value(self): + return {return_value} + @property + def name(self): + return "{name}" + """.format( + name=target.name, + types=", ".join(node.basenames), + return_value=inferred_return_value, + ) + ) + if "IntFlag" in basename: + # Alright, we need to add some additional methods. + # Unfortunately we still can't infer the resulting objects as + # Enum members, but once we'll be able to do that, the following + # should result in some nice symbolic execution + classdef += INT_FLAG_ADDITION_METHODS.format(name=target.name) + + fake = AstroidBuilder(MANAGER).string_build(classdef)[target.name] + fake.parent = target.parent + for method in node.mymethods(): + fake.locals[method.name] = [method] + new_targets.append(fake.instantiate_class()) + node.locals[local] = new_targets + break + return node + + +def infer_typing_namedtuple_class(class_node, context=None): + """Infer a subclass of typing.NamedTuple""" + # Check if it has the corresponding bases + annassigns_fields = [ + annassign.target.name + for annassign in class_node.body + if isinstance(annassign, nodes.AnnAssign) + ] + code = dedent( + """ + from collections import namedtuple + namedtuple({typename!r}, {fields!r}) + """ + ).format(typename=class_node.name, fields=",".join(annassigns_fields)) + node = extract_node(code) + generated_class_node = next(infer_named_tuple(node, context)) + for method in class_node.mymethods(): + generated_class_node.locals[method.name] = [method] + + for assign in class_node.body: + if not isinstance(assign, nodes.Assign): + continue + + for target in assign.targets: + attr = target.name + generated_class_node.locals[attr] = class_node.locals[attr] + + return iter((generated_class_node,)) + + +def infer_typing_namedtuple(node, context=None): + """Infer a typing.NamedTuple(...) call.""" + # This is essentially a namedtuple with different arguments + # so we extract the args and infer a named tuple. + try: + func = next(node.func.infer()) + except InferenceError: + raise UseInferenceDefault + + if func.qname() != "typing.NamedTuple": + raise UseInferenceDefault + + if len(node.args) != 2: + raise UseInferenceDefault + + if not isinstance(node.args[1], (nodes.List, nodes.Tuple)): + raise UseInferenceDefault + + names = [] + for elt in node.args[1].elts: + if not isinstance(elt, (nodes.List, nodes.Tuple)): + raise UseInferenceDefault + if len(elt.elts) != 2: + raise UseInferenceDefault + names.append(elt.elts[0].as_string()) + + typename = node.args[0].as_string() + if names: + field_names = "({},)".format(",".join(names)) + else: + field_names = "''" + node = extract_node( + "namedtuple({typename}, {fields})".format(typename=typename, fields=field_names) + ) + return infer_named_tuple(node, context) + + +MANAGER.register_transform( + nodes.Call, inference_tip(infer_named_tuple), _looks_like_namedtuple +) +MANAGER.register_transform(nodes.Call, inference_tip(infer_enum), _looks_like_enum) +MANAGER.register_transform( + nodes.ClassDef, + infer_enum_class, + predicate=lambda cls: any( + basename for basename in cls.basenames if basename in ENUM_BASE_NAMES + ), +) +MANAGER.register_transform( + nodes.ClassDef, inference_tip(infer_typing_namedtuple_class), _has_namedtuple_base +) +MANAGER.register_transform( + nodes.Call, inference_tip(infer_typing_namedtuple), _looks_like_typing_namedtuple +) |