8th day of python challenges 111-117

venv/lib/python3.6/site-packages/astroid/brain/brain_functools.py

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+# Copyright (c) 2016, 2018 Claudiu Popa <pcmanticore@gmail.com>
+# Copyright (c) 2018 Bryce Guinta <bryce.paul.guinta@gmail.com>
+
+"""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
+from astroid import objects
+
+
+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 attr___wrapped__(self):
+        return self._instance
+
+    @property
+    def attr_cache_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 attr_cache_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"
+        )
+
+    partial_function = objects.PartialFunction(
+        call,
+        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,
+)