227 lines
8.0 KiB
Python
227 lines
8.0 KiB
Python
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"""
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One of the really important features of |jedi| is to have an option to
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understand code like this::
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def foo(bar):
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bar. # completion here
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foo(1)
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There's no doubt wheter bar is an ``int`` or not, but if there's also a call
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like ``foo('str')``, what would happen? Well, we'll just show both. Because
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that's what a human would expect.
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It works as follows:
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- |Jedi| sees a param
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- search for function calls named ``foo``
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- execute these calls and check the input.
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"""
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from jedi import settings
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from jedi import debug
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from jedi.parser_utils import get_parent_scope
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from jedi.inference.cache import inference_state_method_cache
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from jedi.inference.arguments import TreeArguments
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from jedi.inference.param import get_executed_param_names
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from jedi.inference.helpers import is_stdlib_path
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from jedi.inference.utils import to_list
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from jedi.inference.value import instance
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from jedi.inference.base_value import ValueSet, NO_VALUES
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from jedi.inference.references import get_module_contexts_containing_name
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from jedi.inference import recursion
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MAX_PARAM_SEARCHES = 20
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def _avoid_recursions(func):
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def wrapper(function_value, param_index):
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inf = function_value.inference_state
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with recursion.execution_allowed(inf, function_value.tree_node) as allowed:
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# We need to catch recursions that may occur, because an
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# anonymous functions can create an anonymous parameter that is
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# more or less self referencing.
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if allowed:
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inf.dynamic_params_depth += 1
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try:
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return func(function_value, param_index)
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finally:
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inf.dynamic_params_depth -= 1
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return NO_VALUES
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return wrapper
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@debug.increase_indent
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@_avoid_recursions
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def dynamic_param_lookup(function_value, param_index):
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"""
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A dynamic search for param values. If you try to complete a type:
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>>> def func(foo):
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... foo
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>>> func(1)
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>>> func("")
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It is not known what the type ``foo`` without analysing the whole code. You
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have to look for all calls to ``func`` to find out what ``foo`` possibly
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is.
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"""
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funcdef = function_value.tree_node
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if not settings.dynamic_params:
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return NO_VALUES
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path = function_value.get_root_context().py__file__()
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if path is not None and is_stdlib_path(path):
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# We don't want to search for references in the stdlib. Usually people
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# don't work with it (except if you are a core maintainer, sorry).
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# This makes everything slower. Just disable it and run the tests,
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# you will see the slowdown, especially in 3.6.
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return NO_VALUES
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if funcdef.type == 'lambdef':
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string_name = _get_lambda_name(funcdef)
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if string_name is None:
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return NO_VALUES
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else:
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string_name = funcdef.name.value
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debug.dbg('Dynamic param search in %s.', string_name, color='MAGENTA')
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module_context = function_value.get_root_context()
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arguments_list = _search_function_arguments(module_context, funcdef, string_name)
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values = ValueSet.from_sets(
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get_executed_param_names(
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function_value, arguments
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)[param_index].infer()
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for arguments in arguments_list
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)
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debug.dbg('Dynamic param result finished', color='MAGENTA')
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return values
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@inference_state_method_cache(default=None)
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@to_list
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def _search_function_arguments(module_context, funcdef, string_name):
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"""
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Returns a list of param names.
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"""
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compare_node = funcdef
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if string_name == '__init__':
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cls = get_parent_scope(funcdef)
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if cls.type == 'classdef':
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string_name = cls.name.value
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compare_node = cls
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found_arguments = False
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i = 0
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inference_state = module_context.inference_state
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if settings.dynamic_params_for_other_modules:
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module_contexts = get_module_contexts_containing_name(
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inference_state, [module_context], string_name,
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# Limit the amounts of files to be opened massively.
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limit_reduction=5,
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)
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else:
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module_contexts = [module_context]
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for for_mod_context in module_contexts:
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for name, trailer in _get_potential_nodes(for_mod_context, string_name):
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i += 1
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# This is a simple way to stop Jedi's dynamic param recursion
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# from going wild: The deeper Jedi's in the recursion, the less
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# code should be inferred.
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if i * inference_state.dynamic_params_depth > MAX_PARAM_SEARCHES:
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return
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random_context = for_mod_context.create_context(name)
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for arguments in _check_name_for_execution(
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inference_state, random_context, compare_node, name, trailer):
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found_arguments = True
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yield arguments
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# If there are results after processing a module, we're probably
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# good to process. This is a speed optimization.
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if found_arguments:
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return
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def _get_lambda_name(node):
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stmt = node.parent
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if stmt.type == 'expr_stmt':
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first_operator = next(stmt.yield_operators(), None)
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if first_operator == '=':
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first = stmt.children[0]
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if first.type == 'name':
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return first.value
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return None
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def _get_potential_nodes(module_value, func_string_name):
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try:
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names = module_value.tree_node.get_used_names()[func_string_name]
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except KeyError:
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return
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for name in names:
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bracket = name.get_next_leaf()
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trailer = bracket.parent
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if trailer.type == 'trailer' and bracket == '(':
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yield name, trailer
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def _check_name_for_execution(inference_state, context, compare_node, name, trailer):
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from jedi.inference.value.function import BaseFunctionExecutionContext
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def create_args(value):
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arglist = trailer.children[1]
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if arglist == ')':
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arglist = None
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args = TreeArguments(inference_state, context, arglist, trailer)
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from jedi.inference.value.instance import InstanceArguments
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if value.tree_node.type == 'classdef':
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created_instance = instance.TreeInstance(
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inference_state,
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value.parent_context,
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value,
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args
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)
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return InstanceArguments(created_instance, args)
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else:
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if value.is_bound_method():
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args = InstanceArguments(value.instance, args)
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return args
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for value in inference_state.infer(context, name):
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value_node = value.tree_node
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if compare_node == value_node:
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yield create_args(value)
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elif isinstance(value.parent_context, BaseFunctionExecutionContext) \
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and compare_node.type == 'funcdef':
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# Here we're trying to find decorators by checking the first
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# parameter. It's not very generic though. Should find a better
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# solution that also applies to nested decorators.
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param_names = value.parent_context.get_param_names()
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if len(param_names) != 1:
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continue
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values = param_names[0].infer()
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if [v.tree_node for v in values] == [compare_node]:
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# Found a decorator.
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module_context = context.get_root_context()
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execution_context = value.as_context(create_args(value))
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potential_nodes = _get_potential_nodes(module_context, param_names[0].string_name)
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for name, trailer in potential_nodes:
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if value_node.start_pos < name.start_pos < value_node.end_pos:
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random_context = execution_context.create_context(name)
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iterator = _check_name_for_execution(
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inference_state,
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random_context,
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compare_node,
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name,
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trailer
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)
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for arguments in iterator:
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yield arguments
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