Merge branch 'main' into hex-format

Author: skirpichev

.github/workflows/build.yml

@@ -142,6 +142,8 @@ jobs:
         run: echo "PATH=/usr/lib/ccache:$PATH" >> $GITHUB_ENV
       - name: Configure ccache action
         uses: hendrikmuhs/[email protected]
+        with:
+          save: ${{ github.event_name == 'push' }}
       - name: Check Autoconf and aclocal versions
         run: |
           grep "Generated by GNU Autoconf 2.71" configure
@@ -284,6 +286,8 @@ jobs:
         echo "PATH=/usr/lib/ccache:$PATH" >> $GITHUB_ENV
     - name: Configure ccache action
       uses: hendrikmuhs/[email protected]
+      with:
+        save: ${{ github.event_name == 'push' }}
     - name: Configure CPython
       run: ./configure --config-cache --with-pydebug --with-openssl=$OPENSSL_DIR
     - name: Build CPython
@@ -327,6 +331,8 @@ jobs:
         echo "PATH=/usr/lib/ccache:$PATH" >> $GITHUB_ENV
     - name: Configure ccache action
       uses: hendrikmuhs/[email protected]
+      with:
+        save: ${{ github.event_name == 'push' }}
     - name: Setup directory envs for out-of-tree builds
       run: |
         echo "CPYTHON_RO_SRCDIR=$(realpath -m ${GITHUB_WORKSPACE}/../cpython-ro-srcdir)" >> $GITHUB_ENV
@@ -446,6 +452,8 @@ jobs:
         echo "PATH=/usr/lib/ccache:$PATH" >> $GITHUB_ENV
     - name: Configure ccache action
       uses: hendrikmuhs/[email protected]
+      with:
+        save: ${{ github.event_name == 'push' }}
     - name: Configure CPython
       run: ./configure --config-cache --with-address-sanitizer --without-pymalloc
     - name: Build CPython

.github/workflows/reusable-ubuntu.yml

@@ -41,6 +41,8 @@ jobs:
         echo "PATH=/usr/lib/ccache:$PATH" >> $GITHUB_ENV
     - name: Configure ccache action
       uses: hendrikmuhs/[email protected]
+      with:
+        save: ${{ github.event_name == 'push' }}
     - name: Setup directory envs for out-of-tree builds
       run: |
         echo "CPYTHON_RO_SRCDIR=$(realpath -m ${GITHUB_WORKSPACE}/../cpython-ro-srcdir)" >> $GITHUB_ENV

Doc/c-api/object.rst

@@ -47,9 +47,8 @@ Object Protocol
 
 .. c:function:: int PyObject_HasAttr(PyObject *o, PyObject *attr_name)
 
-   Returns ``1`` if *o* has the attribute *attr_name*, and ``0`` otherwise.  This
-   is equivalent to the Python expression ``hasattr(o, attr_name)``.  This function
-   always succeeds.
+   Returns ``1`` if *o* has the attribute *attr_name*, and ``0`` otherwise.
+   This function always succeeds.
 
    .. note::
 

Doc/faq/design.rst

@@ -451,7 +451,7 @@ on the key and a per-process seed; for example, ``'Python'`` could hash to
 to ``1142331976``.  The hash code is then used to calculate a location in an
 internal array where the value will be stored.  Assuming that you're storing
 keys that all have different hash values, this means that dictionaries take
-constant time -- O(1), in Big-O notation -- to retrieve a key.
+constant time -- *O*\ (1), in Big-O notation -- to retrieve a key.
 
 
 Why must dictionary keys be immutable?

Doc/glossary.rst

@@ -742,7 +742,7 @@ Glossary
    list
       A built-in Python :term:`sequence`.  Despite its name it is more akin
       to an array in other languages than to a linked list since access to
-      elements is O(1).
+      elements is *O*\ (1).
 
    list comprehension
       A compact way to process all or part of the elements in a sequence and

Doc/howto/descriptor.rst

@@ -1250,7 +1250,7 @@ instance::
     <function D.f at 0x00C45070>
 
     >>> d.f.__self__
-    <__main__.D object at 0x1012e1f98>
+    <__main__.D object at 0x00B18C90>
 
 If you have ever wondered where *self* comes from in regular methods or where
 *cls* comes from in class methods, this is it!

Doc/library/asyncio-policy.rst

@@ -237,7 +237,7 @@ implementation used by the asyncio event loop:
 
    It works reliably even when the asyncio event loop is run in a non-main OS thread.
 
-   There is no noticeable overhead when handling a big number of children (*O(1)* each
+   There is no noticeable overhead when handling a big number of children (*O*\ (1) each
    time a child terminates), but starting a thread per process requires extra memory.
 
    This watcher is used by default.
@@ -257,7 +257,7 @@ implementation used by the asyncio event loop:
    watcher is installed.
 
    The solution is safe but it has a significant overhead when
-   handling a big number of processes (*O(n)* each time a
+   handling a big number of processes (*O*\ (*n*) each time a
    :py:data:`SIGCHLD` is received).
 
    .. versionadded:: 3.8
@@ -273,7 +273,7 @@ implementation used by the asyncio event loop:
    The watcher avoids disrupting other code spawning processes
    by polling every process explicitly on a :py:data:`SIGCHLD` signal.
 
-   This solution is as safe as :class:`MultiLoopChildWatcher` and has the same *O(N)*
+   This solution is as safe as :class:`MultiLoopChildWatcher` and has the same *O*\ (*n*)
    complexity but requires a running event loop in the main thread to work.
 
    .. deprecated:: 3.12
@@ -285,7 +285,7 @@ implementation used by the asyncio event loop:
    processes and waiting for their termination.
 
    There is no noticeable overhead when handling a big number of
-   children (*O(1)* each time a child terminates).
+   children (*O*\ (1) each time a child terminates).
 
    This solution requires a running event loop in the main thread to work, as
    :class:`SafeChildWatcher`.

Doc/library/bisect.rst

@@ -79,7 +79,7 @@ The following functions are provided:
    To support inserting records in a table, the *key* function (if any) is
    applied to *x* for the search step but not for the insertion step.
 
-   Keep in mind that the ``O(log n)`` search is dominated by the slow O(n)
+   Keep in mind that the *O*\ (log *n*) search is dominated by the slow *O*\ (*n*)
    insertion step.
 
    .. versionchanged:: 3.10
@@ -99,7 +99,7 @@ The following functions are provided:
    To support inserting records in a table, the *key* function (if any) is
    applied to *x* for the search step but not for the insertion step.
 
-   Keep in mind that the ``O(log n)`` search is dominated by the slow O(n)
+   Keep in mind that the *O*\ (log *n*) search is dominated by the slow *O*\ (*n*)
    insertion step.
 
    .. versionchanged:: 3.10
@@ -115,7 +115,7 @@ thoughts in mind:
 * Bisection is effective for searching ranges of values.
   For locating specific values, dictionaries are more performant.
 
-* The *insort()* functions are ``O(n)`` because the logarithmic search step
+* The *insort()* functions are *O*\ (*n*) because the logarithmic search step
   is dominated by the linear time insertion step.
 
 * The search functions are stateless and discard key function results after

Doc/library/collections.rst

@@ -458,10 +458,10 @@ or subtracting from an empty counter.
     Deques are a generalization of stacks and queues (the name is pronounced "deck"
     and is short for "double-ended queue").  Deques support thread-safe, memory
     efficient appends and pops from either side of the deque with approximately the
-    same O(1) performance in either direction.
+    same *O*\ (1) performance in either direction.
 
     Though :class:`list` objects support similar operations, they are optimized for
-    fast fixed-length operations and incur O(n) memory movement costs for
+    fast fixed-length operations and incur *O*\ (*n*) memory movement costs for
     ``pop(0)`` and ``insert(0, v)`` operations which change both the size and
     position of the underlying data representation.
 
@@ -585,7 +585,7 @@ or subtracting from an empty counter.
 In addition to the above, deques support iteration, pickling, ``len(d)``,
 ``reversed(d)``, ``copy.copy(d)``, ``copy.deepcopy(d)``, membership testing with
 the :keyword:`in` operator, and subscript references such as ``d[0]`` to access
-the first element.  Indexed access is O(1) at both ends but slows to O(n) in
+the first element.  Indexed access is *O*\ (1) at both ends but slows to *O*\ (*n*) in
 the middle.  For fast random access, use lists instead.
 
 Starting in version 3.5, deques support ``__add__()``, ``__mul__()``,

Doc/library/contextvars.rst

@@ -131,7 +131,7 @@ Manual Context Management
       ctx: Context = copy_context()
       print(list(ctx.items()))
 
-   The function has an O(1) complexity, i.e. works equally fast for
+   The function has an *O*\ (1) complexity, i.e. works equally fast for
    contexts with a few context variables and for contexts that have
    a lot of them.