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308 lines
10 KiB
308 lines
10 KiB
"""
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Docstrings are another source of information for functions and classes.
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:mod:`jedi.evaluate.dynamic` tries to find all executions of functions, while
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the docstring parsing is much easier. There are three different types of
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docstrings that |jedi| understands:
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- `Sphinx <http://sphinx-doc.org/markup/desc.html#info-field-lists>`_
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- `Epydoc <http://epydoc.sourceforge.net/manual-fields.html>`_
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- `Numpydoc <https://github.com/numpy/numpy/blob/master/doc/HOWTO_DOCUMENT.rst.txt>`_
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For example, the sphinx annotation ``:type foo: str`` clearly states that the
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type of ``foo`` is ``str``.
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As an addition to parameter searching, this module also provides return
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annotations.
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"""
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import re
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from textwrap import dedent
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from parso import parse, ParserSyntaxError
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from jedi._compatibility import u
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from jedi.evaluate.utils import indent_block
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from jedi.evaluate.cache import evaluator_method_cache
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from jedi.evaluate.base_context import iterator_to_context_set, ContextSet, \
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NO_CONTEXTS
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from jedi.evaluate.lazy_context import LazyKnownContexts
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DOCSTRING_PARAM_PATTERNS = [
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r'\s*:type\s+%s:\s*([^\n]+)', # Sphinx
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r'\s*:param\s+(\w+)\s+%s:[^\n]*', # Sphinx param with type
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r'\s*@type\s+%s:\s*([^\n]+)', # Epydoc
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]
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DOCSTRING_RETURN_PATTERNS = [
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re.compile(r'\s*:rtype:\s*([^\n]+)', re.M), # Sphinx
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re.compile(r'\s*@rtype:\s*([^\n]+)', re.M), # Epydoc
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]
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REST_ROLE_PATTERN = re.compile(r':[^`]+:`([^`]+)`')
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_numpy_doc_string_cache = None
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def _get_numpy_doc_string_cls():
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global _numpy_doc_string_cache
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if isinstance(_numpy_doc_string_cache, ImportError):
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raise _numpy_doc_string_cache
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try:
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from numpydoc.docscrape import NumpyDocString
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_numpy_doc_string_cache = NumpyDocString
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except ImportError as e:
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_numpy_doc_string_cache = e
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raise
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return _numpy_doc_string_cache
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def _search_param_in_numpydocstr(docstr, param_str):
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"""Search `docstr` (in numpydoc format) for type(-s) of `param_str`."""
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try:
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# This is a non-public API. If it ever changes we should be
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# prepared and return gracefully.
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params = _get_numpy_doc_string_cls()(docstr)._parsed_data['Parameters']
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except (KeyError, AttributeError, ImportError):
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return []
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for p_name, p_type, p_descr in params:
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if p_name == param_str:
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m = re.match(r'([^,]+(,[^,]+)*?)(,[ ]*optional)?$', p_type)
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if m:
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p_type = m.group(1)
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return list(_expand_typestr(p_type))
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return []
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def _search_return_in_numpydocstr(docstr):
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"""
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Search `docstr` (in numpydoc format) for type(-s) of function returns.
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"""
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try:
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doc = _get_numpy_doc_string_cls()(docstr)
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except ImportError:
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return
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try:
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# This is a non-public API. If it ever changes we should be
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# prepared and return gracefully.
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returns = doc._parsed_data['Returns']
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returns += doc._parsed_data['Yields']
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except (KeyError, AttributeError):
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return
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for r_name, r_type, r_descr in returns:
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# Return names are optional and if so the type is in the name
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if not r_type:
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r_type = r_name
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for type_ in _expand_typestr(r_type):
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yield type_
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def _expand_typestr(type_str):
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"""
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Attempts to interpret the possible types in `type_str`
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"""
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# Check if alternative types are specified with 'or'
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if re.search(r'\bor\b', type_str):
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for t in type_str.split('or'):
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yield t.split('of')[0].strip()
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# Check if like "list of `type`" and set type to list
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elif re.search(r'\bof\b', type_str):
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yield type_str.split('of')[0]
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# Check if type has is a set of valid literal values eg: {'C', 'F', 'A'}
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elif type_str.startswith('{'):
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node = parse(type_str, version='3.6').children[0]
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if node.type == 'atom':
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for leaf in node.children[1].children:
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if leaf.type == 'number':
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if '.' in leaf.value:
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yield 'float'
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else:
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yield 'int'
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elif leaf.type == 'string':
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if 'b' in leaf.string_prefix.lower():
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yield 'bytes'
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else:
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yield 'str'
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# Ignore everything else.
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# Otherwise just work with what we have.
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else:
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yield type_str
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def _search_param_in_docstr(docstr, param_str):
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"""
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Search `docstr` for type(-s) of `param_str`.
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>>> _search_param_in_docstr(':type param: int', 'param')
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['int']
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>>> _search_param_in_docstr('@type param: int', 'param')
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['int']
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>>> _search_param_in_docstr(
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... ':type param: :class:`threading.Thread`', 'param')
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['threading.Thread']
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>>> bool(_search_param_in_docstr('no document', 'param'))
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False
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>>> _search_param_in_docstr(':param int param: some description', 'param')
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['int']
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"""
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# look at #40 to see definitions of those params
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patterns = [re.compile(p % re.escape(param_str))
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for p in DOCSTRING_PARAM_PATTERNS]
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for pattern in patterns:
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match = pattern.search(docstr)
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if match:
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return [_strip_rst_role(match.group(1))]
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return _search_param_in_numpydocstr(docstr, param_str)
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def _strip_rst_role(type_str):
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"""
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Strip off the part looks like a ReST role in `type_str`.
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>>> _strip_rst_role(':class:`ClassName`') # strip off :class:
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'ClassName'
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>>> _strip_rst_role(':py:obj:`module.Object`') # works with domain
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'module.Object'
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>>> _strip_rst_role('ClassName') # do nothing when not ReST role
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'ClassName'
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See also:
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http://sphinx-doc.org/domains.html#cross-referencing-python-objects
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"""
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match = REST_ROLE_PATTERN.match(type_str)
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if match:
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return match.group(1)
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else:
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return type_str
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def _evaluate_for_statement_string(module_context, string):
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code = dedent(u("""
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def pseudo_docstring_stuff():
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'''
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Create a pseudo function for docstring statements.
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Need this docstring so that if the below part is not valid Python this
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is still a function.
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'''
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{}
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"""))
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if string is None:
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return []
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for element in re.findall(r'((?:\w+\.)*\w+)\.', string):
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# Try to import module part in dotted name.
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# (e.g., 'threading' in 'threading.Thread').
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string = 'import %s\n' % element + string
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# Take the default grammar here, if we load the Python 2.7 grammar here, it
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# will be impossible to use `...` (Ellipsis) as a token. Docstring types
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# don't need to conform with the current grammar.
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grammar = module_context.evaluator.latest_grammar
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try:
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module = grammar.parse(code.format(indent_block(string)), error_recovery=False)
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except ParserSyntaxError:
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return []
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try:
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funcdef = next(module.iter_funcdefs())
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# First pick suite, then simple_stmt and then the node,
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# which is also not the last item, because there's a newline.
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stmt = funcdef.children[-1].children[-1].children[-2]
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except (AttributeError, IndexError):
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return []
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if stmt.type not in ('name', 'atom', 'atom_expr'):
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return []
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from jedi.evaluate.context import FunctionContext
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function_context = FunctionContext(
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module_context.evaluator,
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module_context,
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funcdef
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)
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func_execution_context = function_context.get_function_execution()
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# Use the module of the param.
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# TODO this module is not the module of the param in case of a function
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# call. In that case it's the module of the function call.
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# stuffed with content from a function call.
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return list(_execute_types_in_stmt(func_execution_context, stmt))
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def _execute_types_in_stmt(module_context, stmt):
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"""
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Executing all types or general elements that we find in a statement. This
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doesn't include tuple, list and dict literals, because the stuff they
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contain is executed. (Used as type information).
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"""
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definitions = module_context.eval_node(stmt)
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return ContextSet.from_sets(
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_execute_array_values(module_context.evaluator, d)
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for d in definitions
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)
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def _execute_array_values(evaluator, array):
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"""
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Tuples indicate that there's not just one return value, but the listed
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ones. `(str, int)` means that it returns a tuple with both types.
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"""
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from jedi.evaluate.context.iterable import SequenceLiteralContext, FakeSequence
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if isinstance(array, SequenceLiteralContext):
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values = []
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for lazy_context in array.py__iter__():
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objects = ContextSet.from_sets(
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_execute_array_values(evaluator, typ)
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for typ in lazy_context.infer()
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)
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values.append(LazyKnownContexts(objects))
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return {FakeSequence(evaluator, array.array_type, values)}
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else:
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return array.execute_evaluated()
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@evaluator_method_cache()
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def infer_param(execution_context, param):
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from jedi.evaluate.context.instance import InstanceArguments
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from jedi.evaluate.context import FunctionExecutionContext
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def eval_docstring(docstring):
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return ContextSet.from_iterable(
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p
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for param_str in _search_param_in_docstr(docstring, param.name.value)
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for p in _evaluate_for_statement_string(module_context, param_str)
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)
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module_context = execution_context.get_root_context()
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func = param.get_parent_function()
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if func.type == 'lambdef':
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return NO_CONTEXTS
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types = eval_docstring(execution_context.py__doc__())
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if isinstance(execution_context, FunctionExecutionContext) \
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and isinstance(execution_context.var_args, InstanceArguments) \
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and execution_context.function_context.py__name__() == '__init__':
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class_context = execution_context.var_args.instance.class_context
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types |= eval_docstring(class_context.py__doc__())
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return types
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@evaluator_method_cache()
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@iterator_to_context_set
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def infer_return_types(function_context):
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def search_return_in_docstr(code):
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for p in DOCSTRING_RETURN_PATTERNS:
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match = p.search(code)
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if match:
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yield _strip_rst_role(match.group(1))
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# Check for numpy style return hint
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for type_ in _search_return_in_numpydocstr(code):
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yield type_
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for type_str in search_return_in_docstr(function_context.py__doc__()):
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for type_eval in _evaluate_for_statement_string(function_context.get_root_context(), type_str):
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yield type_eval
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