Mini Shell
"""
Contains all classes and functions to deal with lists, dicts, generators and
iterators in general.
"""
from jedi.inference import compiled
from jedi.inference import analysis
from jedi.inference.lazy_value import LazyKnownValue, LazyKnownValues, \
LazyTreeValue
from jedi.inference.helpers import get_int_or_none, is_string, \
reraise_getitem_errors, SimpleGetItemNotFound
from jedi.inference.utils import safe_property, to_list
from jedi.inference.cache import inference_state_method_cache
from jedi.inference.filters import LazyAttributeOverwrite, publish_method
from jedi.inference.base_value import ValueSet, Value, NO_VALUES, \
ContextualizedNode, iterate_values, sentinel, \
LazyValueWrapper
from jedi.parser_utils import get_sync_comp_fors
from jedi.inference.context import CompForContext
from jedi.inference.value.dynamic_arrays import check_array_additions
class IterableMixin:
def py__next__(self, contextualized_node=None):
return self.py__iter__(contextualized_node)
def py__stop_iteration_returns(self):
return ValueSet([compiled.builtin_from_name(self.inference_state, 'None')])
# At the moment, safe values are simple values like "foo", 1 and not
# lists/dicts. Therefore as a small speed optimization we can just do the
# default instead of resolving the lazy wrapped values, that are just
# doing this in the end as well.
# This mostly speeds up patterns like `sys.version_info >= (3, 0)` in
# typeshed.
get_safe_value = Value.get_safe_value
class GeneratorBase(LazyAttributeOverwrite, IterableMixin):
array_type = None
def _get_wrapped_value(self):
instance, = self._get_cls().execute_annotation()
return instance
def _get_cls(self):
generator, = self.inference_state.typing_module.py__getattribute__('Generator')
return generator
def py__bool__(self):
return True
@publish_method('__iter__')
def _iter(self, arguments):
return ValueSet([self])
@publish_method('send')
@publish_method('__next__')
def _next(self, arguments):
return ValueSet.from_sets(lazy_value.infer() for lazy_value in self.py__iter__())
def py__stop_iteration_returns(self):
return ValueSet([compiled.builtin_from_name(self.inference_state, 'None')])
@property
def name(self):
return compiled.CompiledValueName(self, 'Generator')
def get_annotated_class_object(self):
from jedi.inference.gradual.generics import TupleGenericManager
gen_values = self.merge_types_of_iterate().py__class__()
gm = TupleGenericManager((gen_values, NO_VALUES, NO_VALUES))
return self._get_cls().with_generics(gm)
class Generator(GeneratorBase):
"""Handling of `yield` functions."""
def __init__(self, inference_state, func_execution_context):
super().__init__(inference_state)
self._func_execution_context = func_execution_context
def py__iter__(self, contextualized_node=None):
iterators = self._func_execution_context.infer_annotations()
if iterators:
return iterators.iterate(contextualized_node)
return self._func_execution_context.get_yield_lazy_values()
def py__stop_iteration_returns(self):
return self._func_execution_context.get_return_values()
def __repr__(self):
return "<%s of %s>" % (type(self).__name__, self._func_execution_context)
def comprehension_from_atom(inference_state, value, atom):
bracket = atom.children[0]
test_list_comp = atom.children[1]
if bracket == '{':
if atom.children[1].children[1] == ':':
sync_comp_for = test_list_comp.children[3]
if sync_comp_for.type == 'comp_for':
sync_comp_for = sync_comp_for.children[1]
return DictComprehension(
inference_state,
value,
sync_comp_for_node=sync_comp_for,
key_node=test_list_comp.children[0],
value_node=test_list_comp.children[2],
)
else:
cls = SetComprehension
elif bracket == '(':
cls = GeneratorComprehension
elif bracket == '[':
cls = ListComprehension
sync_comp_for = test_list_comp.children[1]
if sync_comp_for.type == 'comp_for':
sync_comp_for = sync_comp_for.children[1]
return cls(
inference_state,
defining_context=value,
sync_comp_for_node=sync_comp_for,
entry_node=test_list_comp.children[0],
)
class ComprehensionMixin:
@inference_state_method_cache()
def _get_comp_for_context(self, parent_context, comp_for):
return CompForContext(parent_context, comp_for)
def _nested(self, comp_fors, parent_context=None):
comp_for = comp_fors[0]
is_async = comp_for.parent.type == 'comp_for'
input_node = comp_for.children[3]
parent_context = parent_context or self._defining_context
input_types = parent_context.infer_node(input_node)
cn = ContextualizedNode(parent_context, input_node)
iterated = input_types.iterate(cn, is_async=is_async)
exprlist = comp_for.children[1]
for i, lazy_value in enumerate(iterated):
types = lazy_value.infer()
dct = unpack_tuple_to_dict(parent_context, types, exprlist)
context = self._get_comp_for_context(
parent_context,
comp_for,
)
with context.predefine_names(comp_for, dct):
try:
yield from self._nested(comp_fors[1:], context)
except IndexError:
iterated = context.infer_node(self._entry_node)
if self.array_type == 'dict':
yield iterated, context.infer_node(self._value_node)
else:
yield iterated
@inference_state_method_cache(default=[])
@to_list
def _iterate(self):
comp_fors = tuple(get_sync_comp_fors(self._sync_comp_for_node))
yield from self._nested(comp_fors)
def py__iter__(self, contextualized_node=None):
for set_ in self._iterate():
yield LazyKnownValues(set_)
def __repr__(self):
return "<%s of %s>" % (type(self).__name__, self._sync_comp_for_node)
class _DictMixin:
def _get_generics(self):
return tuple(c_set.py__class__() for c_set in self.get_mapping_item_values())
class Sequence(LazyAttributeOverwrite, IterableMixin):
api_type = 'instance'
@property
def name(self):
return compiled.CompiledValueName(self, self.array_type)
def _get_generics(self):
return (self.merge_types_of_iterate().py__class__(),)
@inference_state_method_cache(default=())
def _cached_generics(self):
return self._get_generics()
def _get_wrapped_value(self):
from jedi.inference.gradual.base import GenericClass
from jedi.inference.gradual.generics import TupleGenericManager
klass = compiled.builtin_from_name(self.inference_state, self.array_type)
c, = GenericClass(
klass,
TupleGenericManager(self._cached_generics())
).execute_annotation()
return c
def py__bool__(self):
return None # We don't know the length, because of appends.
@safe_property
def parent(self):
return self.inference_state.builtins_module
def py__getitem__(self, index_value_set, contextualized_node):
if self.array_type == 'dict':
return self._dict_values()
return iterate_values(ValueSet([self]))
class _BaseComprehension(ComprehensionMixin):
def __init__(self, inference_state, defining_context, sync_comp_for_node, entry_node):
assert sync_comp_for_node.type == 'sync_comp_for'
super().__init__(inference_state)
self._defining_context = defining_context
self._sync_comp_for_node = sync_comp_for_node
self._entry_node = entry_node
class ListComprehension(_BaseComprehension, Sequence):
array_type = 'list'
def py__simple_getitem__(self, index):
if isinstance(index, slice):
return ValueSet([self])
all_types = list(self.py__iter__())
with reraise_getitem_errors(IndexError, TypeError):
lazy_value = all_types[index]
return lazy_value.infer()
class SetComprehension(_BaseComprehension, Sequence):
array_type = 'set'
class GeneratorComprehension(_BaseComprehension, GeneratorBase):
pass
class _DictKeyMixin:
# TODO merge with _DictMixin?
def get_mapping_item_values(self):
return self._dict_keys(), self._dict_values()
def get_key_values(self):
# TODO merge with _dict_keys?
return self._dict_keys()
class DictComprehension(ComprehensionMixin, Sequence, _DictKeyMixin):
array_type = 'dict'
def __init__(self, inference_state, defining_context, sync_comp_for_node, key_node, value_node):
assert sync_comp_for_node.type == 'sync_comp_for'
super().__init__(inference_state)
self._defining_context = defining_context
self._sync_comp_for_node = sync_comp_for_node
self._entry_node = key_node
self._value_node = value_node
def py__iter__(self, contextualized_node=None):
for keys, values in self._iterate():
yield LazyKnownValues(keys)
def py__simple_getitem__(self, index):
for keys, values in self._iterate():
for k in keys:
# Be careful in the future if refactoring, index could be a
# slice object.
if k.get_safe_value(default=object()) == index:
return values
raise SimpleGetItemNotFound()
def _dict_keys(self):
return ValueSet.from_sets(keys for keys, values in self._iterate())
def _dict_values(self):
return ValueSet.from_sets(values for keys, values in self._iterate())
@publish_method('values')
def _imitate_values(self, arguments):
lazy_value = LazyKnownValues(self._dict_values())
return ValueSet([FakeList(self.inference_state, [lazy_value])])
@publish_method('items')
def _imitate_items(self, arguments):
lazy_values = [
LazyKnownValue(
FakeTuple(
self.inference_state,
[LazyKnownValues(key),
LazyKnownValues(value)]
)
)
for key, value in self._iterate()
]
return ValueSet([FakeList(self.inference_state, lazy_values)])
def exact_key_items(self):
# NOTE: A smarter thing can probably done here to achieve better
# completions, but at least like this jedi doesn't crash
return []
class SequenceLiteralValue(Sequence):
_TUPLE_LIKE = 'testlist_star_expr', 'testlist', 'subscriptlist'
mapping = {'(': 'tuple',
'[': 'list',
'{': 'set'}
def __init__(self, inference_state, defining_context, atom):
super().__init__(inference_state)
self.atom = atom
self._defining_context = defining_context
if self.atom.type in self._TUPLE_LIKE:
self.array_type = 'tuple'
else:
self.array_type = SequenceLiteralValue.mapping[atom.children[0]]
"""The builtin name of the array (list, set, tuple or dict)."""
def _get_generics(self):
if self.array_type == 'tuple':
return tuple(x.infer().py__class__() for x in self.py__iter__())
return super()._get_generics()
def py__simple_getitem__(self, index):
"""Here the index is an int/str. Raises IndexError/KeyError."""
if isinstance(index, slice):
return ValueSet([self])
else:
with reraise_getitem_errors(TypeError, KeyError, IndexError):
node = self.get_tree_entries()[index]
if node == ':' or node.type == 'subscript':
return NO_VALUES
return self._defining_context.infer_node(node)
def py__iter__(self, contextualized_node=None):
"""
While values returns the possible values for any array field, this
function returns the value for a certain index.
"""
for node in self.get_tree_entries():
if node == ':' or node.type == 'subscript':
# TODO this should probably use at least part of the code
# of infer_subscript_list.
yield LazyKnownValue(Slice(self._defining_context, None, None, None))
else:
yield LazyTreeValue(self._defining_context, node)
yield from check_array_additions(self._defining_context, self)
def py__len__(self):
# This function is not really used often. It's more of a try.
return len(self.get_tree_entries())
def get_tree_entries(self):
c = self.atom.children
if self.atom.type in self._TUPLE_LIKE:
return c[::2]
array_node = c[1]
if array_node in (']', '}', ')'):
return [] # Direct closing bracket, doesn't contain items.
if array_node.type == 'testlist_comp':
# filter out (for now) pep 448 single-star unpacking
return [value for value in array_node.children[::2]
if value.type != "star_expr"]
elif array_node.type == 'dictorsetmaker':
kv = []
iterator = iter(array_node.children)
for key in iterator:
if key == "**":
# dict with pep 448 double-star unpacking
# for now ignoring the values imported by **
next(iterator)
next(iterator, None) # Possible comma.
else:
op = next(iterator, None)
if op is None or op == ',':
if key.type == "star_expr":
# pep 448 single-star unpacking
# for now ignoring values imported by *
pass
else:
kv.append(key) # A set.
else:
assert op == ':' # A dict.
kv.append((key, next(iterator)))
next(iterator, None) # Possible comma.
return kv
else:
if array_node.type == "star_expr":
# pep 448 single-star unpacking
# for now ignoring values imported by *
return []
else:
return [array_node]
def __repr__(self):
return "<%s of %s>" % (self.__class__.__name__, self.atom)
class DictLiteralValue(_DictMixin, SequenceLiteralValue, _DictKeyMixin):
array_type = 'dict'
def __init__(self, inference_state, defining_context, atom):
# Intentionally don't call the super class. This is definitely a sign
# that the architecture is bad and we should refactor.
Sequence.__init__(self, inference_state)
self._defining_context = defining_context
self.atom = atom
def py__simple_getitem__(self, index):
"""Here the index is an int/str. Raises IndexError/KeyError."""
compiled_value_index = compiled.create_simple_object(self.inference_state, index)
for key, value in self.get_tree_entries():
for k in self._defining_context.infer_node(key):
for key_v in k.execute_operation(compiled_value_index, '=='):
if key_v.get_safe_value():
return self._defining_context.infer_node(value)
raise SimpleGetItemNotFound('No key found in dictionary %s.' % self)
def py__iter__(self, contextualized_node=None):
"""
While values returns the possible values for any array field, this
function returns the value for a certain index.
"""
# Get keys.
types = NO_VALUES
for k, _ in self.get_tree_entries():
types |= self._defining_context.infer_node(k)
# We don't know which dict index comes first, therefore always
# yield all the types.
for _ in types:
yield LazyKnownValues(types)
@publish_method('values')
def _imitate_values(self, arguments):
lazy_value = LazyKnownValues(self._dict_values())
return ValueSet([FakeList(self.inference_state, [lazy_value])])
@publish_method('items')
def _imitate_items(self, arguments):
lazy_values = [
LazyKnownValue(FakeTuple(
self.inference_state,
(LazyTreeValue(self._defining_context, key_node),
LazyTreeValue(self._defining_context, value_node))
)) for key_node, value_node in self.get_tree_entries()
]
return ValueSet([FakeList(self.inference_state, lazy_values)])
def exact_key_items(self):
"""
Returns a generator of tuples like dict.items(), where the key is
resolved (as a string) and the values are still lazy values.
"""
for key_node, value in self.get_tree_entries():
for key in self._defining_context.infer_node(key_node):
if is_string(key):
yield key.get_safe_value(), LazyTreeValue(self._defining_context, value)
def _dict_values(self):
return ValueSet.from_sets(
self._defining_context.infer_node(v)
for k, v in self.get_tree_entries()
)
def _dict_keys(self):
return ValueSet.from_sets(
self._defining_context.infer_node(k)
for k, v in self.get_tree_entries()
)
class _FakeSequence(Sequence):
def __init__(self, inference_state, lazy_value_list):
"""
type should be one of "tuple", "list"
"""
super().__init__(inference_state)
self._lazy_value_list = lazy_value_list
def py__simple_getitem__(self, index):
if isinstance(index, slice):
return ValueSet([self])
with reraise_getitem_errors(IndexError, TypeError):
lazy_value = self._lazy_value_list[index]
return lazy_value.infer()
def py__iter__(self, contextualized_node=None):
return self._lazy_value_list
def py__bool__(self):
return bool(len(self._lazy_value_list))
def __repr__(self):
return "<%s of %s>" % (type(self).__name__, self._lazy_value_list)
class FakeTuple(_FakeSequence):
array_type = 'tuple'
class FakeList(_FakeSequence):
array_type = 'tuple'
class FakeDict(_DictMixin, Sequence, _DictKeyMixin):
array_type = 'dict'
def __init__(self, inference_state, dct):
super().__init__(inference_state)
self._dct = dct
def py__iter__(self, contextualized_node=None):
for key in self._dct:
yield LazyKnownValue(compiled.create_simple_object(self.inference_state, key))
def py__simple_getitem__(self, index):
with reraise_getitem_errors(KeyError, TypeError):
lazy_value = self._dct[index]
return lazy_value.infer()
@publish_method('values')
def _values(self, arguments):
return ValueSet([FakeTuple(
self.inference_state,
[LazyKnownValues(self._dict_values())]
)])
def _dict_values(self):
return ValueSet.from_sets(lazy_value.infer() for lazy_value in self._dct.values())
def _dict_keys(self):
return ValueSet.from_sets(lazy_value.infer() for lazy_value in self.py__iter__())
def exact_key_items(self):
return self._dct.items()
def __repr__(self):
return '<%s: %s>' % (self.__class__.__name__, self._dct)
class MergedArray(Sequence):
def __init__(self, inference_state, arrays):
super().__init__(inference_state)
self.array_type = arrays[-1].array_type
self._arrays = arrays
def py__iter__(self, contextualized_node=None):
for array in self._arrays:
yield from array.py__iter__()
def py__simple_getitem__(self, index):
return ValueSet.from_sets(lazy_value.infer() for lazy_value in self.py__iter__())
def unpack_tuple_to_dict(context, types, exprlist):
"""
Unpacking tuple assignments in for statements and expr_stmts.
"""
if exprlist.type == 'name':
return {exprlist.value: types}
elif exprlist.type == 'atom' and exprlist.children[0] in ('(', '['):
return unpack_tuple_to_dict(context, types, exprlist.children[1])
elif exprlist.type in ('testlist', 'testlist_comp', 'exprlist',
'testlist_star_expr'):
dct = {}
parts = iter(exprlist.children[::2])
n = 0
for lazy_value in types.iterate(ContextualizedNode(context, exprlist)):
n += 1
try:
part = next(parts)
except StopIteration:
analysis.add(context, 'value-error-too-many-values', part,
message="ValueError: too many values to unpack (expected %s)" % n)
else:
dct.update(unpack_tuple_to_dict(context, lazy_value.infer(), part))
has_parts = next(parts, None)
if types and has_parts is not None:
analysis.add(context, 'value-error-too-few-values', has_parts,
message="ValueError: need more than %s values to unpack" % n)
return dct
elif exprlist.type == 'power' or exprlist.type == 'atom_expr':
# Something like ``arr[x], var = ...``.
# This is something that is not yet supported, would also be difficult
# to write into a dict.
return {}
elif exprlist.type == 'star_expr': # `a, *b, c = x` type unpackings
# Currently we're not supporting them.
return {}
raise NotImplementedError
class Slice(LazyValueWrapper):
def __init__(self, python_context, start, stop, step):
self.inference_state = python_context.inference_state
self._context = python_context
# All of them are either a Precedence or None.
self._start = start
self._stop = stop
self._step = step
def _get_wrapped_value(self):
value = compiled.builtin_from_name(self._context.inference_state, 'slice')
slice_value, = value.execute_with_values()
return slice_value
def get_safe_value(self, default=sentinel):
"""
Imitate CompiledValue.obj behavior and return a ``builtin.slice()``
object.
"""
def get(element):
if element is None:
return None
result = self._context.infer_node(element)
if len(result) != 1:
# For simplicity, we want slices to be clear defined with just
# one type. Otherwise we will return an empty slice object.
raise IndexError
value, = result
return get_int_or_none(value)
try:
return slice(get(self._start), get(self._stop), get(self._step))
except IndexError:
return slice(None, None, None)
Zerion Mini Shell 1.0