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File Name : brain_namedtuple_enum.py

# Licensed under the LGPL: https://www.gnu.org/licenses/old-licenses/lgpl-2.1.en.html
# For details: https://github.com/pylint-dev/astroid/blob/main/LICENSE
# Copyright (c) https://github.com/pylint-dev/astroid/blob/main/CONTRIBUTORS.txt

"""Astroid hooks for the Python standard library."""

from __future__ import annotations

import functools
import keyword
from collections.abc import Iterator
from textwrap import dedent
from typing import Final

import astroid
from astroid import arguments, bases, inference_tip, nodes, util
from astroid.builder import AstroidBuilder, _extract_single_node, extract_node
from astroid.context import InferenceContext
from astroid.exceptions import (
    AstroidTypeError,
    AstroidValueError,
    InferenceError,
    UseInferenceDefault,
)
from astroid.manager import AstroidManager

ENUM_QNAME: Final[str] = "enum.Enum"
TYPING_NAMEDTUPLE_QUALIFIED: Final = {
    "typing.NamedTuple",
    "typing_extensions.NamedTuple",
}
TYPING_NAMEDTUPLE_BASENAMES: Final = {
    "NamedTuple",
    "typing.NamedTuple",
    "typing_extensions.NamedTuple",
}

def _infer_first(node, context):
    if isinstance(node, util.UninferableBase):
        raise UseInferenceDefault
    try:
        value = next(node.infer(context=context))
    except StopIteration as exc:
        raise InferenceError from exc
    if isinstance(value, util.UninferableBase):
        raise UseInferenceDefault()
    return value

def _find_func_form_arguments(node, context):
    def _extract_namedtuple_arg_or_keyword(  # pylint: disable=inconsistent-return-statements
        position, key_name=None
    ):
        if len(args) > position:
            return _infer_first(args[position], context)
        if key_name and key_name in found_keywords:
            return _infer_first(found_keywords[key_name], context)

args = node.args
    keywords = node.keywords
    found_keywords = (
        {keyword.arg: keyword.value for keyword in keywords} if keywords else {}
    )

name = _extract_namedtuple_arg_or_keyword(position=0, key_name="typename")
    names = _extract_namedtuple_arg_or_keyword(position=1, key_name="field_names")
    if name and names:
        return name.value, names

raise UseInferenceDefault()

def infer_func_form(
    node: nodes.Call,
    base_type: list[nodes.NodeNG],
    context: InferenceContext | None = None,
    enum: bool = False,
) -> tuple[nodes.ClassDef, str, list[str]]:
    """Specific inference function for namedtuple or Python 3 enum."""
    # node is a Call node, class name as first argument and generated class
    # attributes as second argument

# namedtuple or enums list of attributes can be a list of strings or a
    # whitespace-separate string
    try:
        name, names = _find_func_form_arguments(node, context)
        try:
            attributes: list[str] = names.value.replace(",", " ").split()
        except AttributeError as exc:
            # Handle attributes of NamedTuples
            if not enum:
                attributes = []
                fields = _get_namedtuple_fields(node)
                if fields:
                    fields_node = extract_node(fields)
                    attributes = [
                        _infer_first(const, context).value for const in fields_node.elts
                    ]

# Handle attributes of Enums
            else:
                # Enums supports either iterator of (name, value) pairs
                # or mappings.
                if hasattr(names, "items") and isinstance(names.items, list):
                    attributes = [
                        _infer_first(const[0], context).value
                        for const in names.items
                        if isinstance(const[0], nodes.Const)
                    ]
                elif hasattr(names, "elts"):
                    # Enums can support either ["a", "b", "c"]
                    # or [("a", 1), ("b", 2), ...], but they can't
                    # be mixed.
                    if all(isinstance(const, nodes.Tuple) for const in names.elts):
                        attributes = [
                            _infer_first(const.elts[0], context).value
                            for const in names.elts
                            if isinstance(const, nodes.Tuple)
                        ]
                    else:
                        attributes = [
                            _infer_first(const, context).value for const in names.elts
                        ]
                else:
                    raise AttributeError from exc
                if not attributes:
                    raise AttributeError from exc
    except (AttributeError, InferenceError) as exc:
        raise UseInferenceDefault from exc

if not enum:
        # namedtuple maps sys.intern(str()) over over field_names
        attributes = [str(attr) for attr in attributes]
        # XXX this should succeed *unless* __str__/__repr__ is incorrect or throws
        # in which case we should not have inferred these values and raised earlier
    attributes = [attr for attr in attributes if " " not in attr]

# If we can't infer the name of the class, don't crash, up to this point
    # we know it is a namedtuple anyway.
    name = name or "Uninferable"
    # we want to return a Class node instance with proper attributes set
    class_node = nodes.ClassDef(
        name,
        lineno=node.lineno,
        col_offset=node.col_offset,
        end_lineno=node.end_lineno,
        end_col_offset=node.end_col_offset,
        parent=nodes.Unknown(),
    )
    # A typical ClassDef automatically adds its name to the parent scope,
    # but doing so causes problems, so defer setting parent until after init
    # see: https://github.com/pylint-dev/pylint/issues/5982
    class_node.parent = node.parent
    class_node.postinit(
        # set base class=tuple
        bases=base_type,
        body=[],
        decorators=None,
    )
    # XXX add __init__(*attributes) method
    for attr in attributes:
        fake_node = nodes.EmptyNode()
        fake_node.parent = class_node
        fake_node.attrname = attr
        class_node.instance_attrs[attr] = [fake_node]
    return class_node, name, attributes

def _has_namedtuple_base(node):
    """Predicate for class inference tip.

:type node: ClassDef
    :rtype: bool
    """
    return set(node.basenames) & TYPING_NAMEDTUPLE_BASENAMES

def _looks_like(node, name) -> bool:
    func = node.func
    if isinstance(func, nodes.Attribute):
        return func.attrname == name
    if isinstance(func, nodes.Name):
        return func.name == name
    return False

_looks_like_namedtuple = functools.partial(_looks_like, name="namedtuple")
_looks_like_enum = functools.partial(_looks_like, name="Enum")
_looks_like_typing_namedtuple = functools.partial(_looks_like, name="NamedTuple")

def infer_named_tuple(
    node: nodes.Call, context: InferenceContext | None = None
) -> Iterator[nodes.ClassDef]:
    """Specific inference function for namedtuple Call node."""
    tuple_base_name: list[nodes.NodeNG] = [
        nodes.Name(
            name="tuple",
            parent=node.root(),
            lineno=0,
            col_offset=0,
            end_lineno=None,
            end_col_offset=None,
        )
    ]
    class_node, name, attributes = infer_func_form(
        node, tuple_base_name, context=context
    )
    call_site = arguments.CallSite.from_call(node, context=context)
    node = extract_node("import collections; collections.namedtuple")
    try:
        func = next(node.infer())
    except StopIteration as e:
        raise InferenceError(node=node) from e
    try:
        rename = next(
            call_site.infer_argument(func, "rename", context or InferenceContext())
        ).bool_value()
    except (InferenceError, StopIteration):
        rename = False

try:
        attributes = _check_namedtuple_attributes(name, attributes, rename)
    except AstroidTypeError as exc:
        raise UseInferenceDefault("TypeError: " + str(exc)) from exc
    except AstroidValueError as exc:
        raise UseInferenceDefault("ValueError: " + str(exc)) from exc

replace_args = ", ".join(f"{arg}=None" for arg in attributes)
    field_def = (
        "    {name} = property(lambda self: self[{index:d}], "
        "doc='Alias for field number {index:d}')"
    )
    field_defs = "\n".join(
        field_def.format(name=name, index=index)
        for index, name in enumerate(attributes)
    )
    fake = AstroidBuilder(AstroidManager()).string_build(
        f"""
class {name}(tuple):
    __slots__ = ()
    _fields = {attributes!r}
    def _asdict(self):
        return self.__dict__
    @classmethod
    def _make(cls, iterable, new=tuple.__new__, len=len):
        return new(cls, iterable)
    def _replace(self, {replace_args}):
        return self
    def __getnewargs__(self):
        return tuple(self)
{field_defs}
    """
    )
    class_node.locals["_asdict"] = fake.body[0].locals["_asdict"]
    class_node.locals["_make"] = fake.body[0].locals["_make"]
    class_node.locals["_replace"] = fake.body[0].locals["_replace"]
    class_node.locals["_fields"] = fake.body[0].locals["_fields"]
    for attr in attributes:
        class_node.locals[attr] = fake.body[0].locals[attr]
    # we use UseInferenceDefault, we can't be a generator so return an iterator
    return iter([class_node])

def _get_renamed_namedtuple_attributes(field_names):
    names = list(field_names)
    seen = set()
    for i, name in enumerate(field_names):
        if (
            not all(c.isalnum() or c == "_" for c in name)
            or keyword.iskeyword(name)
            or not name
            or name[0].isdigit()
            or name.startswith("_")
            or name in seen
        ):
            names[i] = "_%d" % i
        seen.add(name)
    return tuple(names)

def _check_namedtuple_attributes(typename, attributes, rename=False):
    attributes = tuple(attributes)
    if rename:
        attributes = _get_renamed_namedtuple_attributes(attributes)

# The following snippet is derived from the CPython Lib/collections/__init__.py sources
    # <snippet>
    for name in (typename, *attributes):
        if not isinstance(name, str):
            raise AstroidTypeError("Type names and field names must be strings")
        if not name.isidentifier():
            raise AstroidValueError(
                "Type names and field names must be valid" + f"identifiers: {name!r}"
            )
        if keyword.iskeyword(name):
            raise AstroidValueError(
                f"Type names and field names cannot be a keyword: {name!r}"
            )

seen = set()
    for name in attributes:
        if name.startswith("_") and not rename:
            raise AstroidValueError(
                f"Field names cannot start with an underscore: {name!r}"
            )
        if name in seen:
            raise AstroidValueError(f"Encountered duplicate field name: {name!r}")
        seen.add(name)
    # </snippet>

return attributes

def infer_enum(
    node: nodes.Call, context: InferenceContext | None = None
) -> Iterator[bases.Instance]:
    """Specific inference function for enum Call node."""
    # Raise `UseInferenceDefault` if `node` is a call to a a user-defined Enum.
    try:
        inferred = node.func.infer(context)
    except (InferenceError, StopIteration) as exc:
        raise UseInferenceDefault from exc

if not any(
        isinstance(item, nodes.ClassDef) and item.qname() == ENUM_QNAME
        for item in inferred
    ):
        raise UseInferenceDefault

enum_meta = _extract_single_node(
        """
    class EnumMeta(object):
        'docstring'
        def __call__(self, node):
            class EnumAttribute(object):
                name = ''
                value = 0
            return EnumAttribute()
        def __iter__(self):
            class EnumAttribute(object):
                name = ''
                value = 0
            return [EnumAttribute()]
        def __reversed__(self):
            class EnumAttribute(object):
                name = ''
                value = 0
            return (EnumAttribute, )
        def __next__(self):
            return next(iter(self))
        def __getitem__(self, attr):
            class Value(object):
                @property
                def name(self):
                    return ''
                @property
                def value(self):
                    return attr

return Value()
        __members__ = ['']
    """
    )
    class_node = infer_func_form(node, [enum_meta], context=context, enum=True)[0]
    return iter([class_node.instantiate_class()])

INT_FLAG_ADDITION_METHODS = """
    def __or__(self, other):
        return {name}(self.value | other.value)
    def __and__(self, other):
        return {name}(self.value & other.value)
    def __xor__(self, other):
        return {name}(self.value ^ other.value)
    def __add__(self, other):
        return {name}(self.value + other.value)
    def __div__(self, other):
        return {name}(self.value / other.value)
    def __invert__(self):
        return {name}(~self.value)
    def __mul__(self, other):
        return {name}(self.value * other.value)
"""

def infer_enum_class(node: nodes.ClassDef) -> nodes.ClassDef:
    """Specific inference for enums."""
    for basename in (b for cls in node.mro() for b in cls.basenames):
        if node.root().name == "enum":
            # Skip if the class is directly from enum module.
            break
        dunder_members = {}
        target_names = set()
        for local, values in node.locals.items():
            if (
                any(not isinstance(value, nodes.AssignName) for value in values)
                or local == "_ignore_"
            ):
                continue

stmt = values[0].statement()
            if isinstance(stmt, nodes.Assign):
                if isinstance(stmt.targets[0], nodes.Tuple):
                    targets = stmt.targets[0].itered()
                else:
                    targets = stmt.targets
            elif isinstance(stmt, nodes.AnnAssign):
                targets = [stmt.target]
            else:
                continue

inferred_return_value = None
            if stmt.value is not None:
                if isinstance(stmt.value, nodes.Const):
                    if isinstance(stmt.value.value, str):
                        inferred_return_value = repr(stmt.value.value)
                    else:
                        inferred_return_value = stmt.value.value
                else:
                    inferred_return_value = stmt.value.as_string()

new_targets = []
            for target in targets:
                if isinstance(target, nodes.Starred):
                    continue
                target_names.add(target.name)
                # Replace all the assignments with our mocked class.
                classdef = dedent(
                    """
                class {name}({types}):
                    @property
                    def value(self):
                        return {return_value}
                    @property
                    def _value_(self):
                        return {return_value}
                    @property
                    def name(self):
                        return "{name}"
                    @property
                    def _name_(self):
                        return "{name}"
                """.format(
                        name=target.name,
                        types=", ".join(node.basenames),
                        return_value=inferred_return_value,
                    )
                )
                if "IntFlag" in basename:
                    # Alright, we need to add some additional methods.
                    # Unfortunately we still can't infer the resulting objects as
                    # Enum members, but once we'll be able to do that, the following
                    # should result in some nice symbolic execution
                    classdef += INT_FLAG_ADDITION_METHODS.format(name=target.name)

fake = AstroidBuilder(
                    AstroidManager(), apply_transforms=False
                ).string_build(classdef)[target.name]
                fake.parent = target.parent
                for method in node.mymethods():
                    fake.locals[method.name] = [method]
                new_targets.append(fake.instantiate_class())
                if stmt.value is None:
                    continue
                dunder_members[local] = fake
            node.locals[local] = new_targets

# The undocumented `_value2member_map_` member:
        node.locals["_value2member_map_"] = [
            nodes.Dict(
                parent=node,
                lineno=node.lineno,
                col_offset=node.col_offset,
                end_lineno=node.end_lineno,
                end_col_offset=node.end_col_offset,
            )
        ]

members = nodes.Dict(
            parent=node,
            lineno=node.lineno,
            col_offset=node.col_offset,
            end_lineno=node.end_lineno,
            end_col_offset=node.end_col_offset,
        )
        members.postinit(
            [
                (
                    nodes.Const(k, parent=members),
                    nodes.Name(
                        v.name,
                        parent=members,
                        lineno=v.lineno,
                        col_offset=v.col_offset,
                        end_lineno=v.end_lineno,
                        end_col_offset=v.end_col_offset,
                    ),
                )
                for k, v in dunder_members.items()
            ]
        )
        node.locals["__members__"] = [members]
        # The enum.Enum class itself defines two @DynamicClassAttribute data-descriptors
        # "name" and "value" (which we override in the mocked class for each enum member
        # above). When dealing with inference of an arbitrary instance of the enum
        # class, e.g. in a method defined in the class body like:
        #     class SomeEnum(enum.Enum):
        #         def method(self):
        #             self.name  # <- here
        # In the absence of an enum member called "name" or "value", these attributes
        # should resolve to the descriptor on that particular instance, i.e. enum member.
        # For "value", we have no idea what that should be, but for "name", we at least
        # know that it should be a string, so infer that as a guess.
        if "name" not in target_names:
            code = dedent(
                """
            @property
            def name(self):
                return ''
            """
            )
            name_dynamicclassattr = AstroidBuilder(AstroidManager()).string_build(code)[
                "name"
            ]
            node.locals["name"] = [name_dynamicclassattr]
        break
    return node

def infer_typing_namedtuple_class(class_node, context: InferenceContext | None = None):
    """Infer a subclass of typing.NamedTuple."""
    # Check if it has the corresponding bases
    annassigns_fields = [
        annassign.target.name
        for annassign in class_node.body
        if isinstance(annassign, nodes.AnnAssign)
    ]
    code = dedent(
        """
    from collections import namedtuple
    namedtuple({typename!r}, {fields!r})
    """
    ).format(typename=class_node.name, fields=",".join(annassigns_fields))
    node = extract_node(code)
    try:
        generated_class_node = next(infer_named_tuple(node, context))
    except StopIteration as e:
        raise InferenceError(node=node, context=context) from e
    for method in class_node.mymethods():
        generated_class_node.locals[method.name] = [method]

for body_node in class_node.body:
        if isinstance(body_node, nodes.Assign):
            for target in body_node.targets:
                attr = target.name
                generated_class_node.locals[attr] = class_node.locals[attr]
        elif isinstance(body_node, nodes.ClassDef):
            generated_class_node.locals[body_node.name] = [body_node]

return iter((generated_class_node,))

def infer_typing_namedtuple_function(node, context: InferenceContext | None = None):
    """
    Starting with python3.9, NamedTuple is a function of the typing module.
    The class NamedTuple is build dynamically through a call to `type` during
    initialization of the `_NamedTuple` variable.
    """
    klass = extract_node(
        """
        from typing import _NamedTuple
        _NamedTuple
        """
    )
    return klass.infer(context)

def infer_typing_namedtuple(
    node: nodes.Call, context: InferenceContext | None = None
) -> Iterator[nodes.ClassDef]:
    """Infer a typing.NamedTuple(...) call."""
    # This is essentially a namedtuple with different arguments
    # so we extract the args and infer a named tuple.
    try:
        func = next(node.func.infer())
    except (InferenceError, StopIteration) as exc:
        raise UseInferenceDefault from exc

if func.qname() not in TYPING_NAMEDTUPLE_QUALIFIED:
        raise UseInferenceDefault

if len(node.args) != 2:
        raise UseInferenceDefault

if not isinstance(node.args[1], (nodes.List, nodes.Tuple)):
        raise UseInferenceDefault

return infer_named_tuple(node, context)

def _get_namedtuple_fields(node: nodes.Call) -> str:
    """Get and return fields of a NamedTuple in code-as-a-string.

Because the fields are represented in their code form we can
    extract a node from them later on.
    """
    names = []
    container = None
    try:
        container = next(node.args[1].infer())
    except (InferenceError, StopIteration) as exc:
        raise UseInferenceDefault from exc
    # We pass on IndexError as we'll try to infer 'field_names' from the keywords
    except IndexError:
        pass
    if not container:
        for keyword_node in node.keywords:
            if keyword_node.arg == "field_names":
                try:
                    container = next(keyword_node.value.infer())
                except (InferenceError, StopIteration) as exc:
                    raise UseInferenceDefault from exc
                break
    if not isinstance(container, nodes.BaseContainer):
        raise UseInferenceDefault
    for elt in container.elts:
        if isinstance(elt, nodes.Const):
            names.append(elt.as_string())
            continue
        if not isinstance(elt, (nodes.List, nodes.Tuple)):
            raise UseInferenceDefault
        if len(elt.elts) != 2:
            raise UseInferenceDefault
        names.append(elt.elts[0].as_string())

if names:
        field_names = f"({','.join(names)},)"
    else:
        field_names = ""
    return field_names

def _is_enum_subclass(cls: astroid.ClassDef) -> bool:
    """Return whether cls is a subclass of an Enum."""
    return cls.is_subtype_of("enum.Enum")

def register(manager: AstroidManager) -> None:
    manager.register_transform(
        nodes.Call, inference_tip(infer_named_tuple), _looks_like_namedtuple
    )
    manager.register_transform(nodes.Call, inference_tip(infer_enum), _looks_like_enum)
    manager.register_transform(
        nodes.ClassDef, infer_enum_class, predicate=_is_enum_subclass
    )
    manager.register_transform(
        nodes.ClassDef,
        inference_tip(infer_typing_namedtuple_class),
        _has_namedtuple_base,
    )
    manager.register_transform(
        nodes.FunctionDef,
        inference_tip(infer_typing_namedtuple_function),
        lambda node: node.name == "NamedTuple"
        and getattr(node.root(), "name", None) == "typing",
    )
    manager.register_transform(
        nodes.Call,
        inference_tip(infer_typing_namedtuple),
        _looks_like_typing_namedtuple,
    )

Name	Type	Size	Owner/Group	Permission	Last Modified	Actions
.	dir	-	root/root	0755	2025-01-07 04:51:56
..	dir	-	root/root	0755	2025-01-07 04:51:35
__pycache__	dir	-	root/root	0755	2025-01-07 04:51:56
__init__.py	application/x-empty	0 B	root/root	0644	2025-01-06 04:11:14
brain_argparse.py	text/x-python	1.77 KB	root/root	0644	2025-01-06 04:11:14
brain_attrs.py	text/x-python	3.27 KB	root/root	0644	2025-01-06 04:11:14
brain_boto3.py	text/x-python	1.05 KB	root/root	0644	2025-01-06 04:11:14
brain_builtin_inference.py	text/x-python	36.56 KB	root/root	0644	2025-01-06 04:11:14
brain_collections.py	text/x-python	4.67 KB	root/root	0644	2025-01-06 04:11:14
brain_crypt.py	text/x-python	915 B	root/root	0644	2025-01-06 04:11:14
brain_ctypes.py	text/x-python	2.66 KB	root/root	0644	2025-01-06 04:11:14
brain_curses.py	text/x-python	3.45 KB	root/root	0644	2025-01-06 04:11:14
brain_dataclasses.py	text/x-python	21.84 KB	root/root	0644	2025-01-06 04:11:14
brain_datetime.py	text/x-python	771 B	root/root	0644	2025-01-06 04:11:14
brain_dateutil.py	text/x-python	819 B	root/root	0644	2025-01-06 04:11:14
brain_functools.py	text/x-python	6.24 KB	root/root	0644	2025-01-06 04:11:14
brain_gi.py	text/x-python	7.42 KB	root/root	0644	2025-01-06 04:11:14
brain_hashlib.py	text/x-python	2.69 KB	root/root	0644	2025-01-06 04:11:14
brain_http.py	text/x-python	10.44 KB	root/root	0644	2025-01-06 04:11:14
brain_hypothesis.py	text/x-python	1.76 KB	root/root	0644	2025-01-06 04:11:14
brain_io.py	text/x-python	1.55 KB	root/root	0644	2025-01-06 04:11:14
brain_mechanize.py	text/x-python	2.63 KB	root/root	0644	2025-01-06 04:11:14
brain_multiprocessing.py	text/x-python	3.18 KB	root/root	0644	2025-01-06 04:11:14
brain_namedtuple_enum.py	text/x-python	23.35 KB	root/root	0644	2025-01-06 04:11:14
brain_nose.py	text/x-python	2.35 KB	root/root	0644	2025-01-06 04:11:14
brain_numpy_core_einsumfunc.py	text/x-python	885 B	root/root	0644	2025-01-06 04:11:14
brain_numpy_core_fromnumeric.py	text/x-python	792 B	root/root	0644	2025-01-06 04:11:14
brain_numpy_core_function_base.py	text/x-python	1.38 KB	root/root	0644	2025-01-06 04:11:14
brain_numpy_core_multiarray.py	text/x-python	4.3 KB	root/root	0644	2025-01-06 04:11:14
brain_numpy_core_numeric.py	text/x-python	1.7 KB	root/root	0644	2025-01-06 04:11:14
brain_numpy_core_numerictypes.py	text/x-python	8.4 KB	root/root	0644	2025-01-06 04:11:14
brain_numpy_core_umath.py	text/x-python	4.82 KB	root/root	0644	2025-01-06 04:11:14
brain_numpy_ma.py	text/x-python	948 B	root/root	0644	2025-01-06 04:11:14
brain_numpy_ndarray.py	text/x-python	8.85 KB	root/root	0644	2025-01-06 04:11:14
brain_numpy_random_mtrand.py	text/x-python	3.41 KB	root/root	0644	2025-01-06 04:11:14
brain_numpy_utils.py	text/x-python	2.49 KB	root/root	0644	2025-01-06 04:11:14
brain_pathlib.py	text/x-python	1.67 KB	root/root	0644	2025-01-06 04:11:14
brain_pkg_resources.py	text/x-python	2.2 KB	root/root	0644	2025-01-06 04:11:14
brain_pytest.py	text/x-python	2.22 KB	root/root	0644	2025-01-06 04:11:14
brain_qt.py	text/x-python	2.81 KB	root/root	0644	2025-01-06 04:11:14
brain_random.py	text/x-python	3.1 KB	root/root	0644	2025-01-06 04:11:14
brain_re.py	text/x-python	2.9 KB	root/root	0644	2025-01-06 04:11:14
brain_regex.py	text/x-python	3.35 KB	root/root	0644	2025-01-06 04:11:14
brain_responses.py	text/x-python	1.88 KB	root/root	0644	2025-01-06 04:11:14
brain_scipy_signal.py	text/x-python	2.27 KB	root/root	0644	2025-01-06 04:11:14
brain_signal.py	text/x-python	3.84 KB	root/root	0644	2025-01-06 04:11:14
brain_six.py	text/x-python	7.49 KB	root/root	0644	2025-01-06 04:11:14
brain_sqlalchemy.py	text/x-python	1.04 KB	root/root	0644	2025-01-06 04:11:14
brain_ssl.py	text/x-python	6.56 KB	root/root	0644	2025-01-06 04:11:14
brain_subprocess.py	text/x-python	2.9 KB	root/root	0644	2025-01-06 04:11:14
brain_threading.py	text/x-python	922 B	root/root	0644	2025-01-06 04:11:14
brain_type.py	text/x-python	2.42 KB	root/root	0644	2025-01-06 04:11:14
brain_typing.py	text/x-python	16.33 KB	root/root	0644	2025-01-06 04:11:14
brain_unittest.py	text/x-python	1.11 KB	root/root	0644	2025-01-06 04:11:14
brain_uuid.py	text/x-python	727 B	root/root	0644	2025-01-06 04:11:14
helpers.py	text/x-python	4.2 KB	root/root	0644	2025-01-06 04:11:14