Update

.ci/docker/build.sh

@@ -168,18 +168,6 @@ case "$tag" in
     VISION=yes
     TRITON=yes
     ;;
-  pytorch-linux-jammy-py3.11-clang12)
-    ANACONDA_PYTHON_VERSION=3.11
-    CLANG_VERSION=12
-    VISION=no
-    TRITON=no
-    ;;
-  pytorch-linux-jammy-py3.12-clang12)
-    ANACONDA_PYTHON_VERSION=3.12
-    CLANG_VERSION=12
-    VISION=no
-    TRITON=no
-    ;;
   pytorch-linux-jammy-rocm-n-py3 | pytorch-linux-jammy-rocm-n-py3-benchmarks | pytorch-linux-noble-rocm-n-py3)
     if [[ $tag =~ "jammy" ]]; then
       ANACONDA_PYTHON_VERSION=3.10

.ci/docker/common/install_cuda.sh

@@ -129,7 +129,7 @@ function install_129 {
 }
 
 function install_128 {
-  CUDNN_VERSION=9.10.2.21
+  CUDNN_VERSION=9.8.0.87
   echo "Installing CUDA 12.8.1 and cuDNN ${CUDNN_VERSION} and NVSHMEM and NCCL and cuSparseLt-0.7.1"
   # install CUDA 12.8.1 in the same container
   install_cuda 12.8.1 cuda_12.8.1_570.124.06_linux

.ci/docker/requirements-docs.txt

@@ -1,15 +1,11 @@
-sphinx==5.3.0
+sphinx==7.2.6
 #Description: This is used to generate PyTorch docs
-#Pinned versions: 5.3.0
+#Pinned versions: 7.2.6
 
-standard-imghdr==3.13.0; python_version >= "3.13"
-#Description: This is needed by Sphinx, so it needs to be added here.
-# The reasons are as follows:
-# 1) This module has been removed from the Python standard library since Python 3.13(https://peps.python.org/pep-0594/#imghdr);
-# 2) The current version of Sphinx (5.3.0) is not compatible with Python 3.13.
-# Once Sphinx is upgraded to a version compatible with Python 3.13 or later, we can remove this dependency.
+pytorch_sphinx_theme2==0.2.0
+#Description: This is needed to generate PyTorch docs
+#Pinned versions: 0.2.0
 
--e git+https://github.com/pytorch/pytorch_sphinx_theme.git@71e55749be14ceb56e7f8211a9fb649866b87ad4#egg=pytorch_sphinx_theme2
 # TODO: sphinxcontrib.katex 0.9.0 adds a local KaTeX server to speed up pre-rendering
 # but it doesn't seem to work and hangs around idly. The initial thought that it is probably
 # something related to Docker setup. We can investigate this later.
@@ -36,17 +32,17 @@ tensorboard==2.18.0 ; python_version >= "3.13"
 #Description: This is used to generate PyTorch docs
 #Pinned versions: 2.13.0
 
-breathe==4.34.0
+breathe==4.36.0
 #Description: This is used to generate PyTorch C++ docs
-#Pinned versions: 4.34.0
+#Pinned versions: 4.36.0
 
-exhale==0.2.3
+exhale==0.3.7
 #Description: This is used to generate PyTorch C++ docs
-#Pinned versions: 0.2.3
+#Pinned versions: 0.3.7
 
-docutils==0.16
+docutils==0.20
 #Description: This is used to generate PyTorch C++ docs
-#Pinned versions: 0.16
+#Pinned versions: 0.20
 
 bs4==0.0.1
 #Description: This is used to generate PyTorch C++ docs
@@ -56,13 +52,13 @@ IPython==8.12.0
 #Description: This is used to generate PyTorch functorch docs
 #Pinned versions: 8.12.0
 
-myst-nb==0.17.2
+myst-nb==1.3.0
 #Description: This is used to generate PyTorch functorch and torch.compile docs.
-#Pinned versions: 0.17.2
+#Pinned versions: 1.3.0
 
 # The following are required to build torch.distributed.elastic.rendezvous.etcd* docs
 python-etcd==0.4.5
 sphinx-copybutton==0.5.0
-sphinx-design==0.4.0
+sphinx-design==0.6.1
 sphinxcontrib-mermaid==1.0.0
-myst-parser==0.18.1
+myst-parser==4.0.1

.ci/pytorch/python_doc_push_script.sh

@@ -89,23 +89,41 @@ if [ "$is_main_doc" = true ]; then
 
   make coverage
   # Now we have the coverage report, we need to make sure it is empty.
-  # Count the number of lines in the file and turn that number into a variable
-  # $lines. The `cut -f1 ...` is to only parse the number, not the filename
-  # Skip the report header by subtracting 2: the header will be output even if
-  # there are no undocumented items.
+  # Sphinx 7.2.6+ format: python.txt contains a statistics table with a TOTAL row
+  # showing the undocumented count in the third column.
+  # Example: | TOTAL | 99.83% | 2 |
   #
   # Also: see docs/source/conf.py for "coverage_ignore*" items, which should
   # be documented then removed from there.
-  lines=$(wc -l build/coverage/python.txt 2>/dev/null |cut -f1 -d' ')
-  undocumented=$((lines - 2))
-  if [ $undocumented -lt 0 ]; then
+
+  # Extract undocumented count from TOTAL row in Sphinx 7.2.6 statistics table
+  # The table format is: | Module | Coverage | Undocumented |
+  # Extract the third column (undocumented count) from the TOTAL row
+  undocumented=$(grep "| TOTAL" build/coverage/python.txt | awk -F'|' '{print $4}' | tr -d ' ')
+
+  if [ -z "$undocumented" ] || ! [[ "$undocumented" =~ ^[0-9]+$ ]]; then
     echo coverage output not found
     exit 1
-  elif [ $undocumented -gt 0 ]; then
-    echo undocumented objects found:
-    cat build/coverage/python.txt
+  elif [ "$undocumented" -gt 0 ]; then
+    set +x  # Disable command echoing for cleaner output
+    echo ""
+    echo "====================="
+    echo "UNDOCUMENTED OBJECTS:"
+    echo "====================="
+    echo ""
+    # Find the line number of the TOTAL row and print only what comes after it
+    total_line=$(grep -n "| TOTAL" build/coverage/python.txt | cut -d: -f1)
+    if [ -n "$total_line" ]; then
+      # Print only the detailed list (skip the statistics table)
+      tail -n +$((total_line + 2)) build/coverage/python.txt
+    else
+      # Fallback to showing entire file if TOTAL line not found
+      cat build/coverage/python.txt
+    fi
+    echo ""
     echo "Make sure you've updated relevant .rsts in docs/source!"
-    echo "You can reproduce locally by running 'cd docs && make coverage && cat build/coverage/python.txt'"
+    echo "You can reproduce locally by running 'cd docs && make coverage && tail -n +\$((grep -n \"| TOTAL\" build/coverage/python.txt | cut -d: -f1) + 2)) build/coverage/python.txt'"
+    set -x  # Re-enable command echoing
     exit 1
   fi
 else

.ci/pytorch/smoke_test/smoke_test.py

@@ -272,18 +272,6 @@ def smoke_test_cuda(
         torch_cudnn_version = cudnn_to_version_str(torch.backends.cudnn.version())
         print(f"Torch cuDNN version: {torch_cudnn_version}")
 
-        torch_cudnn_compile_version = torch._C._cudnn.getCompileVersion()
-        print(f"Torch cuDNN compile-time version: {torch_cudnn_compile_version}")
-        torch_cudnn_runtime_version = tuple(
-            [int(x) for x in torch_cudnn_version.split(".")]
-        )
-        if torch_cudnn_runtime_version != torch_cudnn_compile_version:
-            raise RuntimeError(
-                "cuDNN runtime version doesn't match comple version. "
-                f"Loaded: {torch_cudnn_runtime_version} "
-                f"Expected: {torch_cudnn_compile_version}"
-            )
-
         if sys.platform in ["linux", "linux2"]:
             torch_nccl_version = ".".join(str(v) for v in torch.cuda.nccl.version())
             print(f"Torch nccl; version: {torch_nccl_version}")

.github/workflows/_rocm-test.yml

@@ -97,8 +97,8 @@ jobs:
         shell: bash
         run: |
           ngpu=$(rocminfo | grep -c -E 'Name:.*\sgfx')
-          if [[ $ngpu -lt 2 ]]; then #We are temporarily reducing this down to 2 from 4 so that we can run tests on nodes with less gpus.
-            echo "Error: only $ngpu GPU(s) detected, at least 2 GPUs are needed for distributed jobs"
+          if [[ $ngpu -lt 4 ]]; then
+            echo "Error: only $ngpu GPU(s) detected, at least 4 GPUs are needed for distributed jobs"
             exit 1
           fi
 

.github/workflows/docker-builds.yml

@@ -56,8 +56,6 @@ jobs:
           pytorch-linux-jammy-cuda12.8-cudnn9-py3-gcc9,
           pytorch-linux-jammy-cuda12.4-cudnn9-py3-gcc11,
           pytorch-linux-jammy-py3.10-clang12,
-          pytorch-linux-jammy-py3.11-clang12,
-          pytorch-linux-jammy-py3.12-clang12,
           pytorch-linux-jammy-py3.13-clang12,
           pytorch-linux-jammy-py3.14-clang12,
           pytorch-linux-jammy-rocm-n-py3,

docs/source/conf.py

@@ -206,6 +206,41 @@ templates_path = [
     os.path.join(os.path.dirname(pytorch_sphinx_theme2.__file__), "templates"),
 ]
 # TODO: document these and remove them from here.
+# Fixes the duplicated
+autosummary_filename_map = {
+    "torch.nn.utils.prune.identity": "torch.nn.utils.prune.identity_function",
+    "torch.nn.utils.prune.Identity": "torch.nn.utils.prune.Identity_class",
+    "torch.optim.adamw.adamw": "torch.optim.adamw.adamw_function",
+    "torch.optim.adamw.AdamW": "torch.optim.adamw.AdamW_class",
+    "torch.optim.asgd.asgd": "torch.optim.asgd.asgd_function",
+    "torch.optim.asgd.ASGD": "torch.optim.asgd.ASGD_class",
+    "torch.optim.nadam.nadam": "torch.optim.nadam.nadam_function",
+    "torch.optim.nadam.NAdam": "torch.optim.nadam.NAdam_class",
+    "torch.optim.radam.radam": "torch.optim.radam.radam_function",
+    "torch.optim.radam.RAdam": "torch.optim.radam.RAdam_class",
+    "torch.optim.rmsprop.rmsprop": "torch.optim.rmsprop.rmsprop_function",
+    "torch.optim.rmsprop.RMSprop": "torch.optim.rmsprop.RMSprop_class",
+    "torch.optim.rprop.rprop": "torch.optim.rprop.rprop_function",
+    "torch.optim.rprop.Rprop": "torch.optim.rprop.Rprop_class",
+    "torch.optim.sgd.sgd": "torch.optim.sgd.sgd_function",
+    "torch.optim.sgd.SGD": "torch.optim.sgd.SGD_class",
+    "torch.optim.adadelta.adadelta": "torch.optim.adadelta.adadelta_function",
+    "torch.optim.adadelta.Adadelta": "torch.optim.adadelta.Adadelta_class",
+    "torch.optim.adagrad.adagrad": "torch.optim.adagrad.adagrad_function",
+    "torch.optim.adagrad.Adagrad": "torch.optim.adagrad.Adagrad_class",
+    "torch.optim.adam.adam": "torch.optim.adam.adam_function",
+    "torch.optim.adam.Adam": "torch.optim.adam.Adam_class",
+    "torch.optim.adamax.adamax": "torch.optim.adamax.adamax_function",
+    "torch.optim.adamax.Adamax": "torch.optim.adamax.Adamax_class",
+    "torch.mtia.stream": "torch.mtia.stream_function",
+    "torch.mtia.Stream": "torch.mtia.Stream_class",
+    "torch.cpu.stream": "torch.cpu.stream_function",
+    "torch.cpu.Stream": "torch.cpu.Stream_class",
+    "torch.cuda.stream": "torch.cuda.stream_function",
+    "torch.cuda.Stream": "torch.cuda.Stream_class",
+    "torch.xpu.stream": "torch.xpu.stream_function",
+    "torch.xpu.Stream": "torch.xpu.Stream_class",
+}
 
 coverage_ignore_functions = [
     # torch
@@ -3195,6 +3230,11 @@ autodoc_type_aliases = {
 # Enable overriding of function signatures in the first line of the docstring.
 autodoc_docstring_signature = True
 
+# Exclude inherited IntEnum methods that have RST formatting issues in their docstrings
+autodoc_default_options = {
+    "exclude-members": "from_bytes, to_bytes",
+}
+
 # -- katex javascript in header
 #
 #    def setup(app):

docs/source/quantization-support.md

@@ -253,7 +253,6 @@ regular full-precision tensor.
 .. autosummary::
     :toctree: generated
     :nosignatures:
-    :template: classtemplate.rst
 
     view
     as_strided

test/functorch/test_aotdispatch.py

@@ -167,14 +167,6 @@ def _pack_fp8_wrap(x):
     if not x.dtype.is_floating_point:
         return x
 
-    if type(x) is not torch.Tensor:
-        # Check only during compilation
-        # Test calls hooks to get reference output
-        ctx = torch._functorch._aot_autograd.graph_compile._get_saved_tensor_hook_context()
-        assert ctx["_fw_graph"] is not None
-        assert ctx["_bw_graph"] is not None
-        assert ctx["_node"] is not None
-
     return (x.dtype, x.to(torch.float8_e5m2))
 
 
@@ -184,13 +176,6 @@ def _unpack_fp8_wrap(x):
         return x
 
     dtype, tensor = x
-    if type(tensor) is not torch.Tensor:
-        # Check only during compilation
-        # Test calls hooks to get reference output
-        ctx = torch._functorch._aot_autograd.graph_compile._get_saved_tensor_hook_context()
-        assert ctx["_fw_graph"] is not None
-        assert ctx["_bw_graph"] is not None
-        assert ctx["_node"] is not None
     return tensor.to(dtype)
 
 

torch/_functorch/_aot_autograd/graph_compile.py

@@ -25,9 +25,6 @@ from typing import Any, Optional, TYPE_CHECKING, Union
 if TYPE_CHECKING:
     from collections.abc import Sequence
 
-import threading
-from contextlib import contextmanager
-
 import torch
 import torch.utils._pytree as pytree
 import torch.utils.dlpack
@@ -100,43 +97,6 @@ from .utils import (
 )
 
 
-_thread_local = threading.local()
-
-
-# Saved tensor hooks context
-# Compiled saved tensor hooks are convenient way to inline some logic in the graphs
-# for saved nodes from forward to backward. (E.g. activations quantization)
-# In base implementation user does not have any additional information about saved value
-# in the hook, except FakeTensor shape, dtype, device etc.
-# _get_saved_tensor_hook_context gives additional graph information about that saved value,
-# that can be used to make a decisions which pack/unpack to apply for particular saved value.
-# This allows user to reuse saved tensors hooks api to apply selective pack/unpack in
-# graph aware way.
-# Alternative to this will be making user to write a custom pass that mucks with forward outputs,
-# backward input metadata, which requires significantly more effort.
-#
-# As for now in context we expose forward graph, backward graph and current saved node,
-# which contains node.meta with additional information about that fx.Node.
-# Warning: This API may change without backward compatibility.
-@contextmanager
-def _saved_tensor_hook_context(state: dict[str, Any]):
-    previous_state = getattr(_thread_local, "state", None)
-    try:
-        _thread_local.state = state
-        yield
-    finally:
-        # Clean up: restore previous state or remove attribute
-        if previous_state is not None:
-            _thread_local.state = previous_state
-        else:
-            if hasattr(_thread_local, "state"):
-                delattr(_thread_local, "state")
-
-
-def _get_saved_tensor_hook_context() -> dict[str, Any] | None:
-    return getattr(_thread_local, "state", None)
-
-
 zip = strict_zip
 
 log = logging.getLogger(__name__)
@@ -1137,11 +1097,7 @@ def maybe_inline_graph_saved_tensors_hooks(
         if not isinstance(val, torch.Tensor):
             continue
 
-        def _get_extra_info() -> dict[str, Any]:
-            return {"_fw_graph": fw_g, "_bw_graph": bw_g, "_node": saved}
-
-        with _saved_tensor_hook_context(_get_extra_info()):
-            pack_out_val = pack_hook_gm(val)
+        pack_out_val = pack_hook_gm(val)
 
         requires_sc_handling = any(
             is_traceable_wrapper_subclass(x) for x in pytree.tree_leaves(pack_out_val)
@@ -1153,17 +1109,16 @@ def maybe_inline_graph_saved_tensors_hooks(
                 " in the pack hook, and reconstructing the subclass in the unpack hook"
             )
 
-        with _saved_tensor_hook_context(_get_extra_info()):
-            pack_gm = prepare_hook_gm(aot_config, pack_hook_gm, (val,))
-            pack_g = pack_gm.graph
-            maybe_log_graph(
-                pack_gm,
-                f"saved_tensors_pack_hook {saved.name}",
-                aot_config,
-                lambda: f"aot_saved_tensors_hooks_pack {saved.name}",
-                structured_logs,
-            )
-            pack_out_val = pack_gm(val)
+        pack_gm = prepare_hook_gm(aot_config, pack_hook_gm, (val,))
+        pack_g = pack_gm.graph
+        maybe_log_graph(
+            pack_gm,
+            f"saved_tensors_pack_hook {saved.name}",
+            aot_config,
+            lambda: f"aot_saved_tensors_hooks_pack {saved.name}",
+            structured_logs,
+        )
+        pack_out_val = pack_gm(val)
 
         # Install pack hook graph as eiplogue of fw_module.
         # Saved tensor output becomes input of pack hook graph.
@@ -1233,16 +1188,15 @@ def maybe_inline_graph_saved_tensors_hooks(
         # Install unpack hook graph as a prologue of backward graph
         # Saved tensors inputs are replaced with packed tensors and packed sym scalars.
         # The saved tensors inputs usages in the graph are replaced with unpack hook graph outputs.
-        with _saved_tensor_hook_context(_get_extra_info()):
-            unpack_gm = prepare_hook_gm(aot_config, unpack_hook_gm, (pack_out_val,))
-            unpack_g = unpack_gm.graph
-            maybe_log_graph(
-                unpack_gm,
-                f"saved_tensors_unpack_hook {saved.name}",
-                aot_config,
-                lambda: f"aot_saved_tensors_hooks_unpack {saved.name}",
-                structured_logs,
-            )
+        unpack_gm = prepare_hook_gm(aot_config, unpack_hook_gm, (pack_out_val,))
+        unpack_g = unpack_gm.graph
+        maybe_log_graph(
+            unpack_gm,
+            f"saved_tensors_unpack_hook {saved.name}",
+            aot_config,
+            lambda: f"aot_saved_tensors_hooks_unpack {saved.name}",
+            structured_logs,
+        )
 
         def find_saved_in_bw_inputs(bw_inputs):
             for n in bw_inputs:

torch/_higher_order_ops/triton_kernel_wrap.py

@@ -498,7 +498,6 @@ def generate_ttir(
         # pyrefly: ignore  # missing-attribute
         codegen_fns = backend.get_codegen_implementation(*codegen_args)
         module_map = backend.get_module_map()
-        # pyrefly: ignore[missing-argument,bad-argument-type]
         ttir_module = src.make_ir(options, codegen_fns, module_map, context)
     else:
         codegen_args = [options] if get_codegen_implementation_sig_params == 1 else []

torch/cuda/__init__.py

@@ -1228,7 +1228,7 @@ def _get_pynvml_handler(device: "Device" = None):
             "nvidia-ml-py does not seem to be installed or it can't be imported."
             # pyrefly: ignore [invalid-inheritance]
         ) from _PYNVML_ERR
-    # pyrefly: ignore [import-error,missing-module-attribute]
+    # pyrefly: ignore [import-error]
     from pynvml import NVMLError_DriverNotLoaded
 
     try:

torch/cuda/memory.py

@@ -828,7 +828,7 @@ def list_gpu_processes(device: "Device" = None) -> str:
             import pynvml  # type: ignore[import]
         except ModuleNotFoundError:
             return "pynvml module not found, please install nvidia-ml-py"
-        # pyrefly: ignore [import-error,missing-module-attribute]
+        # pyrefly: ignore [import-error]
         from pynvml import NVMLError_DriverNotLoaded
 
         try:

torch/package/_stdlib.py

@@ -17,5 +17,230 @@ def is_stdlib_module(module: str) -> bool:
 
 
 def _get_stdlib_modules():
-    assert sys.version_info >= (3, 10)
-    return sys.stdlib_module_names
+    if sys.version_info.major == 3:  # noqa: UP036
+        if sys.version_info.minor == 9:
+            return stdlib3_9
+        if sys.version_info.minor >= 10:  # noqa: YTT204
+            return sys.stdlib_module_names  # type: ignore[attr-defined]
+    elif sys.version_info.major > 3:  # noqa: UP036
+        return sys.stdlib_module_names  # type: ignore[attr-defined]
+
+    raise RuntimeError(f"Unsupported Python version: {sys.version_info}")
+
+
+stdlib3_9 = {
+    "_thread",
+    "abc",
+    "aifc",
+    "argparse",
+    "array",
+    "ast",
+    "asynchat",
+    "asyncio",
+    "asyncore",
+    "atexit",
+    "audioop",
+    "base64",
+    "bdb",
+    "binascii",
+    "binhex",
+    "bisect",
+    "builtins",
+    "bz2",
+    "cProfile",
+    "calendar",
+    "cgi",
+    "cgitb",
+    "chunk",
+    "cmath",
+    "cmd",
+    "code",
+    "codecs",
+    "codeop",
+    "collections",
+    "colorsys",
+    "compileall",
+    "concurrent",
+    "configparser",
+    "contextlib",
+    "contextvars",
+    "copy",
+    "copyreg",
+    "crypt",
+    "csv",
+    "ctypes",
+    "curses",
+    "dataclasses",
+    "datetime",
+    "dbm",
+    "decimal",
+    "difflib",
+    "dis",
+    "distutils",
+    "doctest",
+    "email",
+    "encodings",
+    "ensurepip",
+    "enum",
+    "errno",
+    "faulthandler",
+    "fcntl",
+    "filecmp",
+    "fileinput",
+    "fnmatch",
+    "formatter",
+    "fractions",
+    "ftplib",
+    "functools",
+    "gc",
+    "getopt",
+    "getpass",
+    "gettext",
+    "glob",
+    "graphlib",
+    "grp",
+    "gzip",
+    "hashlib",
+    "heapq",
+    "hmac",
+    "html",
+    "http",
+    "imaplib",
+    "imghdr",
+    "imp",
+    "importlib",
+    "inspect",
+    "io",
+    "ipaddress",
+    "itertools",
+    "json",
+    "keyword",
+    "lib2to3",
+    "linecache",
+    "locale",
+    "logging",
+    "lzma",
+    "mailbox",
+    "mailcap",
+    "marshal",
+    "math",
+    "mimetypes",
+    "mmap",
+    "modulefinder",
+    "msilib",
+    "msvcrt",
+    "multiprocessing",
+    "netrc",
+    "nis",
+    "nntplib",
+    "ntpath",
+    "numbers",
+    "operator",
+    "optparse",
+    "os",
+    "ossaudiodev",
+    "parser",
+    "pathlib",
+    "pdb",
+    "pickle",
+    "pickletools",
+    "pipes",
+    "pkgutil",
+    "platform",
+    "plistlib",
+    "poplib",
+    "posix",
+    "posixpath",
+    "pprint",
+    "profile",
+    "pstats",
+    "pty",
+    "pwd",
+    "py_compile",
+    "pyclbr",
+    "pydoc",
+    "queue",
+    "quopri",
+    "random",
+    "re",
+    "readline",
+    "reprlib",
+    "resource",
+    "rlcompleter",
+    "runpy",
+    "sched",
+    "secrets",
+    "select",
+    "selectors",
+    "shelve",
+    "shlex",
+    "shutil",
+    "signal",
+    "site",
+    "smtpd",
+    "smtplib",
+    "sndhdr",
+    "socket",
+    "socketserver",
+    "spwd",
+    "sqlite3",
+    "sre",
+    "sre_compile",
+    "sre_constants",
+    "sre_parse",
+    "ssl",
+    "stat",
+    "statistics",
+    "string",
+    "stringprep",
+    "struct",
+    "subprocess",
+    "sunau",
+    "symbol",
+    "symtable",
+    "sys",
+    "sysconfig",
+    "syslog",
+    "tabnanny",
+    "tarfile",
+    "telnetlib",
+    "tempfile",
+    "termios",
+    "test",
+    "textwrap",
+    "threading",
+    "time",
+    "timeit",
+    "tkinter",
+    "token",
+    "tokenize",
+    "trace",
+    "traceback",
+    "tracemalloc",
+    "tty",
+    "turtle",
+    "turtledemo",
+    "types",
+    "typing",
+    "unicodedata",
+    "unittest",
+    "urllib",
+    "uu",
+    "uuid",
+    "venv",
+    "warnings",
+    "wave",
+    "weakref",
+    "webbrowser",
+    "winreg",
+    "winsound",
+    "wsgiref",
+    "xdrlib",
+    "xml",
+    "xmlrpc",
+    "zipapp",
+    "zipfile",
+    "zipimport",
+    "zlib",
+    "zoneinfo",
+}