forked from VimPlug/jedi
302 lines
10 KiB
Python
302 lines
10 KiB
Python
"""
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Implementations of standard library functions, because it's not possible to
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understand them with Jedi.
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To add a new implementation, create a function and add it to the
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``_implemented`` dict at the bottom of this module.
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Note that this module exists only to implement very specific functionality in
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the standard library. The usual way to understand the standard library is the
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compiled module that returns the types for C-builtins.
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"""
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import collections
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import re
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from jedi.common import unite
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from jedi.evaluate import compiled
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from jedi.evaluate import representation as er
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from jedi.evaluate.instance import InstanceFunctionExecution, \
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AbstractInstanceContext, CompiledInstance, BoundMethod
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from jedi.evaluate import iterable
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from jedi.parser.python import parse
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from jedi import debug
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from jedi.evaluate import precedence
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from jedi.evaluate import param
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from jedi.evaluate import analysis
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from jedi.evaluate.context import LazyTreeContext, ContextualizedNode
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class NotInStdLib(LookupError):
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pass
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def execute(evaluator, obj, arguments):
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if isinstance(obj, BoundMethod):
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raise NotInStdLib()
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try:
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obj_name = obj.name.string_name
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except AttributeError:
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pass
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else:
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if obj.parent_context == evaluator.BUILTINS:
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module_name = 'builtins'
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elif isinstance(obj.parent_context, er.ModuleContext):
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module_name = obj.parent_context.name.string_name
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else:
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module_name = ''
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# for now we just support builtin functions.
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try:
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func = _implemented[module_name][obj_name]
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except KeyError:
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pass
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else:
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return func(evaluator, obj, arguments)
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raise NotInStdLib()
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def _follow_param(evaluator, arguments, index):
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try:
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key, lazy_context = list(arguments.unpack())[index]
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except IndexError:
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return set()
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else:
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return lazy_context.infer()
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def argument_clinic(string, want_obj=False, want_context=False, want_arguments=False):
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"""
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Works like Argument Clinic (PEP 436), to validate function params.
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"""
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clinic_args = []
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allow_kwargs = False
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optional = False
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while string:
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# Optional arguments have to begin with a bracket. And should always be
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# at the end of the arguments. This is therefore not a proper argument
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# clinic implementation. `range()` for exmple allows an optional start
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# value at the beginning.
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match = re.match('(?:(?:(\[),? ?|, ?|)(\w+)|, ?/)\]*', string)
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string = string[len(match.group(0)):]
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if not match.group(2): # A slash -> allow named arguments
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allow_kwargs = True
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continue
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optional = optional or bool(match.group(1))
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word = match.group(2)
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clinic_args.append((word, optional, allow_kwargs))
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def f(func):
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def wrapper(evaluator, obj, arguments):
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debug.dbg('builtin start %s' % obj, color='MAGENTA')
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try:
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lst = list(arguments.eval_argument_clinic(clinic_args))
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except ValueError:
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return set()
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else:
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kwargs = {}
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if want_context:
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kwargs['context'] = arguments.context
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if want_obj:
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kwargs['obj'] = obj
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if want_arguments:
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kwargs['arguments'] = arguments
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return func(evaluator, *lst, **kwargs)
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finally:
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debug.dbg('builtin end', color='MAGENTA')
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return wrapper
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return f
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@argument_clinic('iterator[, default], /')
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def builtins_next(evaluator, iterators, defaults):
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"""
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TODO this function is currently not used. It's a stab at implementing next
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in a different way than fake objects. This would be a bit more flexible.
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"""
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if evaluator.python_version[0] == 2:
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name = 'next'
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else:
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name = '__next__'
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types = set()
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for iterator in iterators:
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if isinstance(iterator, AbstractInstanceContext):
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for filter in iterator.get_filters(include_self_names=True):
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for n in filter.get(name):
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for context in n.infer():
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types |= context.execute_evaluated()
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if types:
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return types
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return defaults
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@argument_clinic('object, name[, default], /')
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def builtins_getattr(evaluator, objects, names, defaults=None):
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# follow the first param
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for obj in objects:
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for name in names:
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if precedence.is_string(name):
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return obj.py__getattribute__(name.obj)
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else:
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debug.warning('getattr called without str')
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continue
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return set()
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@argument_clinic('object[, bases, dict], /')
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def builtins_type(evaluator, objects, bases, dicts):
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if bases or dicts:
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# It's a type creation... maybe someday...
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return set()
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else:
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return set([o.py__class__() for o in objects])
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class SuperInstance(AbstractInstanceContext):
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"""To be used like the object ``super`` returns."""
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def __init__(self, evaluator, cls):
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su = cls.py_mro()[1]
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super().__init__(evaluator, su and su[0] or self)
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@argument_clinic('[type[, obj]], /', want_context=True)
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def builtins_super(evaluator, types, objects, context):
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# TODO make this able to detect multiple inheritance super
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if isinstance(context, InstanceFunctionExecution):
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su = context.instance.py__class__().py__bases__()
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return unite(context.execute_evaluated() for context in su[0].infer())
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return set()
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@argument_clinic('sequence, /', want_obj=True, want_arguments=True)
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def builtins_reversed(evaluator, sequences, obj, arguments):
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# While we could do without this variable (just by using sequences), we
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# want static analysis to work well. Therefore we need to generated the
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# values again.
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key, lazy_context = next(arguments.unpack())
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cn = None
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if isinstance(lazy_context, LazyTreeContext):
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# TODO access private
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cn = ContextualizedNode(lazy_context._context, lazy_context.data)
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ordered = list(iterable.py__iter__(evaluator, sequences, cn))
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rev = list(reversed(ordered))
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# Repack iterator values and then run it the normal way. This is
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# necessary, because `reversed` is a function and autocompletion
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# would fail in certain cases like `reversed(x).__iter__` if we
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# just returned the result directly.
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seq = iterable.FakeSequence(evaluator, 'list', rev)
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arguments = param.ValuesArguments([[seq]])
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return set([CompiledInstance(evaluator, evaluator.BUILTINS, obj, arguments)])
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@argument_clinic('obj, type, /', want_arguments=True)
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def builtins_isinstance(evaluator, objects, types, arguments):
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bool_results = set([])
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for o in objects:
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try:
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mro_func = o.py__class__().py__mro__
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except AttributeError:
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# This is temporary. Everything should have a class attribute in
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# Python?! Maybe we'll leave it here, because some numpy objects or
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# whatever might not.
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return set([compiled.create(True), compiled.create(False)])
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mro = mro_func()
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for cls_or_tup in types:
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if cls_or_tup.is_class():
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bool_results.add(cls_or_tup in mro)
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elif cls_or_tup.name.string_name == 'tuple' \
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and cls_or_tup.get_root_context() == evaluator.BUILTINS:
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# Check for tuples.
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classes = unite(
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lazy_context.infer()
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for lazy_context in cls_or_tup.py__iter__()
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)
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bool_results.add(any(cls in mro for cls in classes))
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else:
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_, lazy_context = list(arguments.unpack())[1]
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if isinstance(lazy_context, LazyTreeContext):
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node = lazy_context.data
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message = 'TypeError: isinstance() arg 2 must be a ' \
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'class, type, or tuple of classes and types, ' \
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'not %s.' % cls_or_tup
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analysis.add(lazy_context._context, 'type-error-isinstance', node, message)
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return set(compiled.create(evaluator, x) for x in bool_results)
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def collections_namedtuple(evaluator, obj, arguments):
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"""
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Implementation of the namedtuple function.
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This has to be done by processing the namedtuple class template and
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evaluating the result.
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.. note:: |jedi| only supports namedtuples on Python >2.6.
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"""
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# Namedtuples are not supported on Python 2.6
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if not hasattr(collections, '_class_template'):
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return set()
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# Process arguments
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# TODO here we only use one of the types, we should use all.
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name = list(_follow_param(evaluator, arguments, 0))[0].obj
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_fields = list(_follow_param(evaluator, arguments, 1))[0]
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if isinstance(_fields, compiled.CompiledObject):
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fields = _fields.obj.replace(',', ' ').split()
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elif isinstance(_fields, iterable.AbstractSequence):
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fields = [
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v.obj
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for lazy_context in _fields.py__iter__()
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for v in lazy_context.infer() if hasattr(v, 'obj')
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]
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else:
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return set()
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# Build source
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source = collections._class_template.format(
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typename=name,
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field_names=fields,
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num_fields=len(fields),
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arg_list=', '.join(fields),
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repr_fmt=', '.join(collections._repr_template.format(name=name) for name in fields),
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field_defs='\n'.join(collections._field_template.format(index=index, name=name)
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for index, name in enumerate(fields))
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)
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# Parse source
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generated_class = parse(source, grammar=evaluator.grammar).subscopes[0]
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return set([er.ClassContext(evaluator, generated_class, evaluator.BUILTINS)])
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@argument_clinic('first, /')
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def _return_first_param(evaluator, firsts):
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return firsts
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_implemented = {
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'builtins': {
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'getattr': builtins_getattr,
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'type': builtins_type,
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'super': builtins_super,
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'reversed': builtins_reversed,
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'isinstance': builtins_isinstance,
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},
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'copy': {
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'copy': _return_first_param,
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'deepcopy': _return_first_param,
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},
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'json': {
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'load': lambda *args: set(),
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'loads': lambda *args: set(),
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},
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'collections': {
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'namedtuple': collections_namedtuple,
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},
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}
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