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Name	Size	Permission
__pycache__	[ DIR ]	drwxr-xr-x
brain_argparse.py	1 KB	-rw-r--r--
brain_attrs.py	1.86 KB	-rw-r--r--
brain_builtin_inference.py	26.28 KB	-rw-r--r--
brain_collections.py	2.76 KB	-rw-r--r--
brain_curses.py	3.23 KB	-rw-r--r--
brain_dateutil.py	795 B	-rw-r--r--
brain_fstrings.py	2.07 KB	-rw-r--r--
brain_functools.py	6.16 KB	-rw-r--r--
brain_gi.py	6.65 KB	-rw-r--r--
brain_hashlib.py	2.11 KB	-rw-r--r--
brain_io.py	1.44 KB	-rw-r--r--
brain_mechanize.py	889 B	-rw-r--r--
brain_multiprocessing.py	3.04 KB	-rw-r--r--
brain_namedtuple_enum.py	13.7 KB	-rw-r--r--
brain_nose.py	2.16 KB	-rw-r--r--
brain_numpy.py	14.44 KB	-rw-r--r--
brain_pkg_resources.py	2.21 KB	-rw-r--r--
brain_pytest.py	2.33 KB	-rw-r--r--
brain_qt.py	2.38 KB	-rw-r--r--
brain_random.py	2.38 KB	-rw-r--r--
brain_re.py	1.1 KB	-rw-r--r--
brain_six.py	6 KB	-rw-r--r--
brain_ssl.py	3.47 KB	-rw-r--r--
brain_subprocess.py	3.55 KB	-rw-r--r--
brain_threading.py	767 B	-rw-r--r--
brain_typing.py	2.66 KB	-rw-r--r--
brain_uuid.py	564 B	-rw-r--r--

Code Editor : brain_functools.py

"""Astroid hooks for understanding functools library module."""
from functools import partial
from itertools import chain

import astroid
from astroid import arguments
from astroid import BoundMethod
from astroid import extract_node
from astroid import helpers
from astroid.interpreter import objectmodel
from astroid import MANAGER

LRU_CACHE = "functools.lru_cache"

class LruWrappedModel(objectmodel.FunctionModel):
    """Special attribute model for functions decorated with functools.lru_cache.

The said decorators patches at decoration time some functions onto
    the decorated function.
    """

@property
    def py__wrapped__(self):
        return self._instance

@property
    def pycache_info(self):
        cache_info = extract_node(
            """
        from functools import _CacheInfo
        _CacheInfo(0, 0, 0, 0)
        """
        )

class CacheInfoBoundMethod(BoundMethod):
            def infer_call_result(self, caller, context=None):
                yield helpers.safe_infer(cache_info)

return CacheInfoBoundMethod(proxy=self._instance, bound=self._instance)

@property
    def pycache_clear(self):
        node = extract_node("""def cache_clear(self): pass""")
        return BoundMethod(proxy=node, bound=self._instance.parent.scope())

def _transform_lru_cache(node, context=None):
    # TODO: this is not ideal, since the node should be immutable,
    # but due to https://github.com/PyCQA/astroid/issues/354,
    # there's not much we can do now.
    # Replacing the node would work partially, because,
    # in pylint, the old node would still be available, leading
    # to spurious false positives.
    node.special_attributes = LruWrappedModel()(node)
    return

def _functools_partial_inference(node, context=None):
    call = arguments.CallSite.from_call(node)
    number_of_positional = len(call.positional_arguments)
    if number_of_positional < 1:
        raise astroid.UseInferenceDefault(
            "functools.partial takes at least one argument"
        )
    if number_of_positional == 1 and not call.keyword_arguments:
        raise astroid.UseInferenceDefault(
            "functools.partial needs at least to have some filled arguments"
        )

partial_function = call.positional_arguments[0]
    try:
        inferred_wrapped_function = next(partial_function.infer(context=context))
    except astroid.InferenceError as exc:
        raise astroid.UseInferenceDefault from exc
    if inferred_wrapped_function is astroid.Uninferable:
        raise astroid.UseInferenceDefault("Cannot infer the wrapped function")
    if not isinstance(inferred_wrapped_function, astroid.FunctionDef):
        raise astroid.UseInferenceDefault("The wrapped function is not a function")

# Determine if the passed keywords into the callsite are supported
    # by the wrapped function.
    function_parameters = chain(
        inferred_wrapped_function.args.args or (),
        inferred_wrapped_function.args.kwonlyargs or (),
    )
    parameter_names = set(
        param.name
        for param in function_parameters
        if isinstance(param, astroid.AssignName)
    )
    if set(call.keyword_arguments) - parameter_names:
        raise astroid.UseInferenceDefault(
            "wrapped function received unknown parameters"
        )

# Return a wrapped() object that can be used further for inference
    class PartialFunction(astroid.FunctionDef):

filled_positionals = len(call.positional_arguments[1:])
        filled_keywords = list(call.keyword_arguments)

def infer_call_result(self, caller=None, context=None):
            nonlocal call
            filled_args = call.positional_arguments[1:]
            filled_keywords = call.keyword_arguments

if context:
                current_passed_keywords = {
                    keyword for (keyword, _) in context.callcontext.keywords
                }
                for keyword, value in filled_keywords.items():
                    if keyword not in current_passed_keywords:
                        context.callcontext.keywords.append((keyword, value))

call_context_args = context.callcontext.args or []
                context.callcontext.args = filled_args + call_context_args

return super().infer_call_result(caller=caller, context=context)

partial_function = PartialFunction(
        name=inferred_wrapped_function.name,
        doc=inferred_wrapped_function.doc,
        lineno=inferred_wrapped_function.lineno,
        col_offset=inferred_wrapped_function.col_offset,
        parent=inferred_wrapped_function.parent,
    )
    partial_function.postinit(
        args=inferred_wrapped_function.args,
        body=inferred_wrapped_function.body,
        decorators=inferred_wrapped_function.decorators,
        returns=inferred_wrapped_function.returns,
        type_comment_returns=inferred_wrapped_function.type_comment_returns,
        type_comment_args=inferred_wrapped_function.type_comment_args,
    )
    return iter((partial_function,))

def _looks_like_lru_cache(node):
    """Check if the given function node is decorated with lru_cache."""
    if not node.decorators:
        return False
    for decorator in node.decorators.nodes:
        if not isinstance(decorator, astroid.Call):
            continue
        if _looks_like_functools_member(decorator, "lru_cache"):
            return True
    return False

def _looks_like_functools_member(node, member):
    """Check if the given Call node is a functools.partial call"""
    if isinstance(node.func, astroid.Name):
        return node.func.name == member
    elif isinstance(node.func, astroid.Attribute):
        return (
            node.func.attrname == member
            and isinstance(node.func.expr, astroid.Name)
            and node.func.expr.name == "functools"
        )

_looks_like_partial = partial(_looks_like_functools_member, member="partial")

MANAGER.register_transform(
    astroid.FunctionDef, _transform_lru_cache, _looks_like_lru_cache
)

MANAGER.register_transform(
    astroid.Call,
    astroid.inference_tip(_functools_partial_inference),
    _looks_like_partial,
)

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Code Editor : brain_functools.py