# Wprowadzenie

Interfejs programowania aplikacji w Pythonie daje programistom języków C i C++ dostęp do programu interpretującego polecenia języka pytonowskiego na wielu poziomach. Sprzęg (API) jest równo użyteczny z poziomu C++ ale dla porządku jest zwykle określany mianem sprzęgu pomiędzy językami pytonowskim a C (z ang. - Python/C API). Istnieją dwie zasadniczo różne przyczyny dla użycia interfejsu między językami Python i C. Pierwszą przyczyną jest pisanie modułów rozszerzających dla szczególnych powodów; są to moduły języka C, które rozszerzają interpreter Pythona. To jest zwykle najczęstsze użycie. Drugą przyczyną jest użycie Pythona jako komponentu większego programu; ta technika jest zwykle określana mianem załączania - z ang. - embedding w aplikacji.

Napisanie moduł rozszerzeń jest stosunkowo dobrze zrozumiałym procesem, w którym dobrze sprawdza się podejście „książki kucharskiej”. Istnieje kilka narzędzi, które w pewnym stopniu automatyzują ten proces. Ludzie wbudowali Pythona w innych aplikacja od początku jego istnienia, proces wbudowania Pythona jest mniej prosty niż pisanie rozszerzenia.

Wiele zadań sprzęgu (API) jest użytecznych niezależnie od tego czy załączasz, czy też rozszerzasz program interpretujący język pytonowski; co więcej, większość aplikacji które załącza program interpretujący polecenia jezyka pytonowskiego potrzebuje także szczególnych rozszerzeń, więc prawdopodobnie jest dobrym pomysłem zaznajomienie się z pisaniem rozszerzenia przed próbą załączenia języka pytonowskiego w prawdziwej aplikacji.

## Language version compatibility

Python’s C API is compatible with C11 and C++11 versions of C and C++.

This is a lower limit: the C API does not require features from later C/C++ versions. You do not need to enable your compiler’s „c11 mode”.

## Standardy kodowania

Jeśli piszesz C do umieszczenia w CPythonie musisz przestrzegać wytycznych standardów zdefiniowanych w PEP 7. Wytyczne te obowiązują niezależnie od tego, do której wersji Pythona wnosisz swój wkład. Przestrzeganie tych konwencji nie jest konieczne dla własnych rozszerzeń, chyba że ostatecznie spodziewasz się wnieść je do Python.

## Pliki Włączania - z ang. Include

Wszystkie zadania, definicje typu i makropoleceń konieczne do użycia sprzęgu między językami pytonowskim i C są włączane do źródeł w kodzie użytkownika przez następującą linijkę:

    #define PY_SSIZE_T_CLEAN
    #include <Python.h>

Oznacza to włączenie następujących standardowych nagłówek: `<stdio.h>`, `<string.h>`, `<errno.h>`, `<limits.h>`, `<assert.h>` i `<stdlib.h>` (jeśli są dostępne).

Informacja

Jako że Python może definiować pewne definicje preprocesora, które wpływają na pliki nagłówkowe na niektórych systemach, musisz załączyć plik `Python.h` przed jakimikolwiek standardowymi nagłówkami.

Zaleca się, aby zawsze definiować `PY_SSIZE_T_CLEAN` przed włączeniem `Python.h`. Zobacz Pobieranie kolejnych rzeczy podanych na wejściu i konstruowanie wartości. dla opisu tego makro.

Wszystkie widoczne dla użytkownika nazwy określone w Python.h ( z wyjątkiem tych określonych przez załączone standardowe pliki nagłówkowe ) mają jeden z przedrostków `Py` lub `_Py`. Nazwy rozpoczynające się od `_Py` służą do wewnętrznego użytku przez urzeczywistnienie programu interpretującego języka pytonowskiego i nie powinno być używane przez piszących rozszerzenia. Nazwy członków struktury nie mają zarezerwowanych przedrostków.

Informacja

User code should never define names that begin with `Py` or `_Py`. This confuses the reader, and jeopardizes the portability of the user code to future Python versions, which may define additional names beginning with one of these prefixes.

The header files are typically installed with Python. On Unix, these are located in the directories `prefix/include/pythonversion/` and `exec_prefix/include/pythonversion/`, where `prefix` and `exec_prefix` are defined by the corresponding parameters to Python’s configure script and version is `'%d.%d' % sys.version_info[:2]`. On Windows, the headers are installed in `prefix/include`, where `prefix` is the installation directory specified to the installer.

To include the headers, place both directories (if different) on your compiler’s search path for includes. Do not place the parent directories on the search path and then use `#include <pythonX.Y/Python.h>`; this will break on multi-platform builds since the platform independent headers under `prefix` include the platform specific headers from `exec_prefix`.

C++ users should note that although the API is defined entirely using C, the header files properly declare the entry points to be `extern "C"`. As a result, there is no need to do anything special to use the API from C++.

### System includes

> `Python.h` includes several standard header files. C extensions should include the standard headers that they use, and should not rely on these implicit includes. The implicit includes are:

> - `<assert.h>`

> - `<intrin.h>` (on Windows)

> - `<inttypes.h>`

> - `<limits.h>`

> - `<math.h>`

> - `<stdarg.h>`

> - `<string.h>`

> - `<wchar.h>`

> - `<sys/types.h>` (if present)

> The following are included for backwards compatibility, unless using Limited API 3.13 or newer:

> - `<ctype.h>`

> - `<unistd.h>` (on POSIX)

> The following are included for backwards compatibility, unless using Limited API 3.11 or newer:

> - `<errno.h>`

> - `<stdio.h>`

> - `<stdlib.h>`

Informacja

Jako że Python może definiować pewne definicje preprocesora, które wpływają na pliki nagłówkowe na niektórych systemach, musisz załączyć plik `Python.h` przed jakimikolwiek standardowymi nagłówkami.

## Useful macros

Several useful macros are defined in the Python header files. Many are defined closer to where they are useful (for example, `Py_RETURN_NONE`, `PyMODINIT_FUNC`). Others of a more general utility are defined here. This is not necessarily a complete listing.

Py_CAN_START_THREADS

  If this macro is defined, then the current system is able to start threads.

  Currently, all systems supported by CPython (per PEP 11), with the exception of some WebAssembly platforms, support starting threads.

  Dodane w wersji 3.13.

Py_GETENV(s)

  Like `getenv(s)`, but returns `NULL` if `-E` was passed on the command line (see `PyConfig.use_environment`).

### Docstring macros

PyDoc_STRVAR(name, str)

  Creates a variable with name name that can be used in docstrings. If Python is built without docstrings (`--without-doc-strings`), the value will be an empty string.

  Przykład:

      PyDoc_STRVAR(pop_doc, "Remove and return the rightmost element.");

      static PyMethodDef deque_methods[] = {
          // ...
          {"pop", (PyCFunction)deque_pop, METH_NOARGS, pop_doc},
          // ...
      }

  Expands to `PyDoc_VAR(name) = PyDoc_STR(str)`.

PyDoc_STR(str)

  Expands to the given input string, or an empty string if docstrings are disabled (`--without-doc-strings`).

  Przykład:

      static PyMethodDef pysqlite_row_methods[] = {
          {"keys", (PyCFunction)pysqlite_row_keys, METH_NOARGS,
              PyDoc_STR("Returns the keys of the row.")},
          {NULL, NULL}
      };

PyDoc_VAR(name)

  Declares a static character array variable with the given name. Expands to `static const char name[]`

  Dla przykładu:

      PyDoc_VAR(python_doc) = PyDoc_STR(
         "A genus of constricting snakes in the Pythonidae family native "
         "to the tropics and subtropics of the Eastern Hemisphere.");

### General utility macros

The following macros are for common tasks not specific to Python.

Py_UNUSED(arg)

  Use this for unused arguments in a function definition to silence compiler warnings. Example: `int func(int a, int Py_UNUSED(b)) { return a; }`.

  Dodane w wersji 3.4.

Py_GCC_ATTRIBUTE(name)

  Use a GCC attribute name, hiding it from compilers that don’t support GCC attributes (such as MSVC).

  This expands to `__attribute__((name))` on a GCC compiler, and expands to nothing on compilers that don’t support GCC attributes.

#### Numeric utilities

Py_ABS(x)

  Return the absolute value of `x`.

  The argument may be evaluated more than once. Consequently, do not pass an expression with side-effects directly to this macro.

  If the result cannot be represented (for example, if `x` has `INT_MIN` value for int type), the behavior is undefined.

  Corresponds roughly to `((x) < 0 ? -(x) : (x))`

  Dodane w wersji 3.3.

Py_MAX(x, y)
Py_MIN(x, y)

  Return the larger or smaller of the arguments, respectively.

  Any arguments may be evaluated more than once. Consequently, do not pass an expression with side-effects directly to this macro.

  `Py_MAX` corresponds roughly to `(((x) > (y)) ? (x) : (y))`.

  Dodane w wersji 3.3.

Py_ARITHMETIC_RIGHT_SHIFT(type, integer, positions)

  Similar to `integer >> positions`, but forces sign extension, as the C standard does not define whether a right-shift of a signed integer will perform sign extension or a zero-fill.

  integer should be any signed integer type. positions is the number of positions to shift to the right.

  Both integer and positions can be evaluated more than once; consequently, avoid directly passing a function call or some other operation with side-effects to this macro. Instead, store the result as a variable and then pass it.

  type is unused and only kept for backwards compatibility. Historically, type was used to cast integer.

  Zmienione w wersji 3.1: This macro is now valid for all signed integer types, not just those for which `unsigned type` is legal. As a result, type is no longer used.

Py_CHARMASK(c)

  Argument must be a character or an integer in the range [-128, 127] or [0, 255]. This macro returns `c` cast to an `unsigned char`.

#### Assertion utilities

Py_UNREACHABLE()

  Use this when you have a code path that cannot be reached by design. For example, in the `default:` clause in a `switch` statement for which all possible values are covered in `case` statements. Use this in places where you might be tempted to put an `assert(0)` or `abort()` call.

  In release mode, the macro helps the compiler to optimize the code, and avoids a warning about unreachable code. For example, the macro is implemented with `__builtin_unreachable()` on GCC in release mode.

  In debug mode, and on unsupported compilers, the macro expands to a call to `Py_FatalError()`.

  A use for `Py_UNREACHABLE()` is following a call to a function that never returns but that is not declared `_Noreturn`.

  If a code path is very unlikely code but can be reached under exceptional case, this macro must not be used. For example, under low memory condition or if a system call returns a value out of the expected range. In this case, it’s better to report the error to the caller. If the error cannot be reported to caller, `Py_FatalError()` can be used.

  Dodane w wersji 3.7.

Py_SAFE_DOWNCAST(value, larger, smaller)

  Cast value to type smaller from type larger, validating that no information was lost.

  On release builds of Python, this is roughly equivalent to `((smaller) value)` (in C++, `static_cast<smaller>(value)` will be used instead).

  On debug builds (implying that `Py_DEBUG` is defined), this asserts that no information was lost with the cast from larger to smaller.

  value, larger, and smaller may all be evaluated more than once in the expression; consequently, do not pass an expression with side-effects directly to this macro.

Py_BUILD_ASSERT(cond)

  Asserts a compile-time condition cond, as a statement. The build will fail if the condition is false or cannot be evaluated at compile time.

  Corresponds roughly to `static_assert(cond)` on C23 and above.

  Dla przykładu:

      Py_BUILD_ASSERT(sizeof(PyTime_t) == sizeof(int64_t));

  Dodane w wersji 3.3.

Py_BUILD_ASSERT_EXPR(cond)

  Asserts a compile-time condition cond, as an expression that evaluates to `0`. The build will fail if the condition is false or cannot be evaluated at compile time.

  Dla przykładu:

      #define foo_to_char(foo) \
          ((char *)(foo) + Py_BUILD_ASSERT_EXPR(offsetof(struct foo, string) == 0))

  Dodane w wersji 3.3.

#### Type size utilities

Py_ARRAY_LENGTH(array)

  Compute the length of a statically allocated C array at compile time.

  The array argument must be a C array with a size known at compile time. Passing an array with an unknown size, such as a heap-allocated array, will result in a compilation error on some compilers, or otherwise produce incorrect results.

  This is roughly equivalent to:

      sizeof(array) / sizeof((array)[0])

Py_MEMBER_SIZE(type, member)

  Return the size of a structure (type) member in bytes.

  Corresponds roughly to `sizeof(((type *)NULL)->member)`.

  Dodane w wersji 3.6.

#### Macro definition utilities

Py_FORCE_EXPANSION(X)

  This is equivalent to `X`, which is useful for token-pasting in macros, as macro expansions in X are forcefully evaluated by the preprocessor.

Py_STRINGIFY(x)

  Convert `x` to a C string. For example, `Py_STRINGIFY(123)` returns `"123"`.

  Dodane w wersji 3.4.

### Declaration utilities

The following macros can be used in declarations. They are most useful for defining the C API itself, and have limited use for extension authors. Most of them expand to compiler-specific spellings of common extensions to the C language.

Py_ALWAYS_INLINE

  Ask the compiler to always inline a static inline function. The compiler can ignore it and decide to not inline the function.

  Corresponds to `always_inline` attribute in GCC and `__forceinline` in MSVC.

  It can be used to inline performance critical static inline functions when building Python in debug mode with function inlining disabled. For example, MSC disables function inlining when building in debug mode.

  Marking blindly a static inline function with Py_ALWAYS_INLINE can result in worse performances (due to increased code size for example). The compiler is usually smarter than the developer for the cost/benefit analysis.

  If Python is built in debug mode (if the `Py_DEBUG` macro is defined), the `Py_ALWAYS_INLINE` macro does nothing.

  It must be specified before the function return type. Usage:

      static inline Py_ALWAYS_INLINE int random(void) { return 4; }

  Dodane w wersji 3.11.

Py_NO_INLINE

  Disable inlining on a function. For example, it reduces the C stack consumption: useful on LTO+PGO builds which heavily inline code (see bpo-33720).

  Corresponds to the `noinline` attribute/specification on GCC and MSVC.

  Usage:

      Py_NO_INLINE static int random(void) { return 4; }

  Dodane w wersji 3.11.

Py_DEPRECATED(version)

  Use this to declare APIs that were deprecated in a specific CPython version. The macro must be placed before the symbol name.

  Przykład:

      Py_DEPRECATED(3.8) PyAPI_FUNC(int) Py_OldFunction(void);

  Zmienione w wersji 3.8: MSVC support was added.

Py_LOCAL(type)

  Declare a function returning the specified type using a fast-calling qualifier for functions that are local to the current file. Semantically, this is equivalent to `static type`.

Py_LOCAL_INLINE(type)

  Equivalent to `Py_LOCAL` but additionally requests the function be inlined.

Py_LOCAL_SYMBOL

  Macro used to declare a symbol as local to the shared library (hidden). On supported platforms, it ensures the symbol is not exported.

  On compatible versions of GCC/Clang, it expands to `__attribute__((visibility("hidden")))`.

Py_EXPORTED_SYMBOL

  Macro used to declare a symbol (function or data) as exported. On Windows, this expands to `__declspec(dllexport)`. On compatible versions of GCC/Clang, it expands to `__attribute__((visibility("default")))`. This macro is for defining the C API itself; extension modules should not use it.

Py_IMPORTED_SYMBOL

  Macro used to declare a symbol as imported. On Windows, this expands to `__declspec(dllimport)`. This macro is for defining the C API itself; extension modules should not use it.

PyAPI_FUNC(type)

  Macro used by CPython to declare a function as part of the C API. Its expansion depends on the platform and build configuration. This macro is intended for defining CPython’s C API itself; extension modules should not use it for their own symbols.

PyAPI_DATA(type)

  Macro used by CPython to declare a public global variable as part of the C API. Its expansion depends on the platform and build configuration. This macro is intended for defining CPython’s C API itself; extension modules should not use it for their own symbols.

### Outdated macros

The following soft deprecated macros have been used to features that have been standardized in C11 (or previous standards).

Py_ALIGNED(num)

  On some GCC-like compilers, specify alignment to num bytes. This does nothing on other compilers.

  Use the standard `alignas` specifier rather than this macro.

  Soft deprecated since version 3.15.

PY_FORMAT_SIZE_T

  The `printf()` formatting modifier for `size_t`. Use `"z"` directly instead.

  Soft deprecated since version 3.15.

Py_LL(number)
Py_ULL(number)

  Use number as a `long long` or `unsigned long long` integer literal, respectively.

  Expands to number followed by `LL` or `LLU`, respectively, but will expand to some compiler-specific suffixes on some older compilers.

  Consider using the C99 standard suffixes `LL` and `LLU` directly.

  Soft deprecated since version 3.15.

PY_LONG_LONG
PY_INT32_T
PY_UINT32_T
PY_INT64_T
PY_UINT64_T

  Aliases for the types `long long`, `int32_t`, `uint32_t`. `int64_t` and `uint64_t`, respectively. Historically, these types needed compiler-specific extensions.

  Soft deprecated since version 3.15.

PY_LLONG_MIN
PY_LLONG_MAX
PY_ULLONG_MAX
PY_SIZE_MAX

  Aliases for the values `LLONG_MIN`, `LLONG_MAX`, `ULLONG_MAX`, and `SIZE_MAX`, respectively. Use these standard names instead.

  The required header, `<limits.h>`, is included in `Python.h`.

  Soft deprecated since version 3.15.

Py_MEMCPY(dest, src, n)

  This is an alias to `memcpy()`.

  Soft deprecated since version 3.14: Use `memcpy()` directly instead.

Py_UNICODE_SIZE

  Size of the `wchar_t` type. Use `sizeof(wchar_t)` or `WCHAR_WIDTH/8` instead.

  The required header for the latter, `<limits.h>`, is included in `Python.h`.

  Soft deprecated since version 3.15.

Py_UNICODE_WIDE

  Defined if `wchar_t` can hold a Unicode character (UCS-4). Use `sizeof(wchar_t) >= 4` instead

  Soft deprecated since version 3.15.

Py_VA_COPY

  This is an alias to the C99-standard `va_copy` function.

  Historically, this would use a compiler-specific method to copy a `va_list`.

  Zmienione w wersji 3.6: This is now an alias to `va_copy`.

  Soft deprecated since version 3.15.

## Przedmioty, ich Rodzaje i Liczby Odwołań

Most Python/C API functions have one or more arguments as well as a return value of type PyObject*. This type is a pointer to an opaque data type representing an arbitrary Python object. Since all Python object types are treated the same way by the Python language in most situations (e.g., assignments, scope rules, and argument passing), it is only fitting that they should be represented by a single C type. Almost all Python objects live on the heap: you never declare an automatic or static variable of type `PyObject`, only pointer variables of type PyObject* can be declared. The sole exception are the type objects; since these must never be deallocated, they are typically static `PyTypeObject` objects.

Wszystkie przedmioty języka pytonowskiego (nawet liczby całkowite języka pytonowskiego) mają rodzaj i liczbę odniesień. Typ przedmiotu określa jakiego rodzaju przedmiot to jest (np. liczba całkowita, lista, lub zadanie zdefiniowane przez użytkownika; jest wiele więcej jak wyjaśniono w The standard type hierarchy). Dla każdego z dobrze-znanych rodzajów istnieje makropolecenie sprawdzające czy przedmiot jest tego rodzaju; na przykład, `PyList_Check(a)` jest prawdziwe wtedy (i tylko wtedy) gdy przedmiot na który wskazuje a jest lista z języka pytonowskiego.

### Liczby odniesień

The reference count is important because today’s computers have a finite (and often severely limited) memory size; it counts how many different places there are that have a strong reference to an object. Such a place could be another object, or a global (or static) C variable, or a local variable in some C function. When the last strong reference to an object is released (i.e. its reference count becomes zero), the object is deallocated. If it contains references to other objects, those references are released. Those other objects may be deallocated in turn, if there are no more references to them, and so on. (There’s an obvious problem with objects that reference each other here; for now, the solution is „don’t do that.”)

Reference counts are always manipulated explicitly. The normal way is to use the macro `Py_INCREF()` to take a new reference to an object (i.e. increment its reference count by one), and `Py_DECREF()` to release that reference (i.e. decrement the reference count by one). The `Py_DECREF()` macro is considerably more complex than the incref one, since it must check whether the reference count becomes zero and then cause the object’s deallocator to be called. The deallocator is a function pointer contained in the object’s type structure. The type-specific deallocator takes care of releasing references for other objects contained in the object if this is a compound object type, such as a list, as well as performing any additional finalization that’s needed. There’s no chance that the reference count can overflow; at least as many bits are used to hold the reference count as there are distinct memory locations in virtual memory (assuming `sizeof(Py_ssize_t) >= sizeof(void*)`). Thus, the reference count increment is a simple operation.

It is not necessary to hold a strong reference (i.e. increment the reference count) for every local variable that contains a pointer to an object. In theory, the object’s reference count goes up by one when the variable is made to point to it and it goes down by one when the variable goes out of scope. However, these two cancel each other out, so at the end the reference count hasn’t changed. The only real reason to use the reference count is to prevent the object from being deallocated as long as our variable is pointing to it. If we know that there is at least one other reference to the object that lives at least as long as our variable, there is no need to take a new strong reference (i.e. increment the reference count) temporarily. An important situation where this arises is in objects that are passed as arguments to C functions in an extension module that are called from Python; the call mechanism guarantees to hold a reference to every argument for the duration of the call.

However, a common pitfall is to extract an object from a list and hold on to it for a while without taking a new reference. Some other operation might conceivably remove the object from the list, releasing that reference, and possibly deallocating it. The real danger is that innocent-looking operations may invoke arbitrary Python code which could do this; there is a code path which allows control to flow back to the user from a `Py_DECREF()`, so almost any operation is potentially dangerous.

A safe approach is to always use the generic operations (functions whose name begins with `PyObject_`, `PyNumber_`, `PySequence_` or `PyMapping_`). These operations always create a new strong reference (i.e. increment the reference count) of the object they return. This leaves the caller with the responsibility to call `Py_DECREF()` when they are done with the result; this soon becomes second nature.

#### Szczegóły Liczby Odniesień

The reference count behavior of functions in the Python/C API is best explained in terms of ownership of references. Ownership pertains to references, never to objects (objects are not owned: they are always shared). „Owning a reference” means being responsible for calling Py_DECREF on it when the reference is no longer needed. Ownership can also be transferred, meaning that the code that receives ownership of the reference then becomes responsible for eventually releasing it by calling `Py_DECREF()` or `Py_XDECREF()` when it’s no longer needed—or passing on this responsibility (usually to its caller). When a function passes ownership of a reference on to its caller, the caller is said to receive a new reference. When no ownership is transferred, the caller is said to borrow the reference. Nothing needs to be done for a borrowed reference.

Idąc dalej, gdy wywołujące zadanie przekazuje odniesienie do przedmiotu, istnieją dwie możliwości: zadanie kradnie odniesienie do przedmiotu, lub nie kradnie go. Kradnięcie odniesienia oznacza, że gdy przekazujesz odniesienie do zadania, to zadanie przyjmuje, że teraz ono posiada odniesienie i nie jesteś za nie odpowiedzialny ani chwili dłużej.

Few functions steal references; the two notable exceptions are `PyList_SetItem()` and `PyTuple_SetItem()`, which steal a reference to the item (but not to the tuple or list into which the item is put!). These functions were designed to steal a reference because of a common idiom for populating a tuple or list with newly created objects; for example, the code to create the tuple `(1, 2, "three")` could look like this (forgetting about error handling for the moment; a better way to code this is shown below):

    PyObject *t;

    t = PyTuple_New(3);
    PyTuple_SetItem(t, 0, PyLong_FromLong(1L));
    PyTuple_SetItem(t, 1, PyLong_FromLong(2L));
    PyTuple_SetItem(t, 2, PyUnicode_FromString("three"));

Here, `PyLong_FromLong()` returns a new reference which is immediately stolen by `PyTuple_SetItem()`. When you want to keep using an object although the reference to it will be stolen, use `Py_INCREF()` to grab another reference before calling the reference-stealing function.

Incidentally, `PyTuple_SetItem()` is the only way to set tuple items; `PySequence_SetItem()` and `PyObject_SetItem()` refuse to do this since tuples are an immutable data type. You should only use `PyTuple_SetItem()` for tuples that you are creating yourself.

Equivalent code for populating a list can be written using `PyList_New()` and `PyList_SetItem()`.

However, in practice, you will rarely use these ways of creating and populating a tuple or list. There’s a generic function, `Py_BuildValue()`, that can create most common objects from C values, directed by a format string. For example, the above two blocks of code could be replaced by the following (which also takes care of the error checking):

    PyObject *tuple, *list;

    tuple = Py_BuildValue("(iis)", 1, 2, "three");
    list = Py_BuildValue("[iis]", 1, 2, "three");

It is much more common to use `PyObject_SetItem()` and friends with items whose references you are only borrowing, like arguments that were passed in to the function you are writing. In that case, their behaviour regarding references is much saner, since you don’t have to take a new reference just so you can give that reference away („have it be stolen”). For example, this function sets all items of a list (actually, any mutable sequence) to a given item:

    int
    set_all(PyObject *target, PyObject *item)
    {
        Py_ssize_t i, n;

        n = PyObject_Length(target);
        if (n < 0)
            return -1;
        for (i = 0; i < n; i++) {
            PyObject *index = PyLong_FromSsize_t(i);
            if (!index)
                return -1;
            if (PyObject_SetItem(target, index, item) < 0) {
                Py_DECREF(index);
                return -1;
            }
            Py_DECREF(index);
        }
        return 0;
    }

The situation is slightly different for function return values. While passing a reference to most functions does not change your ownership responsibilities for that reference, many functions that return a reference to an object give you ownership of the reference. The reason is simple: in many cases, the returned object is created on the fly, and the reference you get is the only reference to the object. Therefore, the generic functions that return object references, like `PyObject_GetItem()` and `PySequence_GetItem()`, always return a new reference (the caller becomes the owner of the reference).

It is important to realize that whether you own a reference returned by a function depends on which function you call only — the plumage (the type of the object passed as an argument to the function) doesn’t enter into it! Thus, if you extract an item from a list using `PyList_GetItem()`, you don’t own the reference — but if you obtain the same item from the same list using `PySequence_GetItem()` (which happens to take exactly the same arguments), you do own a reference to the returned object.

Here is an example of how you could write a function that computes the sum of the items in a list of integers; once using `PyList_GetItem()`, and once using `PySequence_GetItem()`.

    long
    sum_list(PyObject *list)
    {
        Py_ssize_t i, n;
        long total = 0, value;
        PyObject *item;

        n = PyList_Size(list);
        if (n < 0)
            return -1; /* Not a list */
        for (i = 0; i < n; i++) {
            item = PyList_GetItem(list, i); /* Can't fail */
            if (!PyLong_Check(item)) continue; /* Skip non-integers */
            value = PyLong_AsLong(item);
            if (value == -1 && PyErr_Occurred())
                /* Integer too big to fit in a C long, bail out */
                return -1;
            total += value;
        }
        return total;
    }

    long
    sum_sequence(PyObject *sequence)
    {
        Py_ssize_t i, n;
        long total = 0, value;
        PyObject *item;
        n = PySequence_Length(sequence);
        if (n < 0)
            return -1; /* Has no length */
        for (i = 0; i < n; i++) {
            item = PySequence_GetItem(sequence, i);
            if (item == NULL)
                return -1; /* Not a sequence, or other failure */
            if (PyLong_Check(item)) {
                value = PyLong_AsLong(item);
                Py_DECREF(item);
                if (value == -1 && PyErr_Occurred())
                    /* Integer too big to fit in a C long, bail out */
                    return -1;
                total += value;
            }
            else {
                Py_DECREF(item); /* Discard reference ownership */
            }
        }
        return total;
    }

### Typy

There are few other data types that play a significant role in the Python/C API; most are simple C types such as int, long, double and char*. A few structure types are used to describe static tables used to list the functions exported by a module or the data attributes of a new object type, and another is used to describe the value of a complex number. These will be discussed together with the functions that use them.

type Py_ssize_t
  Część stabilnego ABI.

  A signed integral type such that `sizeof(Py_ssize_t) == sizeof(size_t)`. C99 doesn’t define such a thing directly (size_t is an unsigned integral type). See PEP 353 for details. `PY_SSIZE_T_MAX` is the largest positive value of type `Py_ssize_t`.

## Wyjątki

Programujący komputer w języku pytonowskim musi sobie zaprzątać głowę tylko sytuacjami wyjątkowymi tylko jeśli szczególna obsługa błędów jest konieczna; Nieobsłużone wyjątki są automatycznie przesyłane do zadania wywołującego, potem do zadania które wywołało tamto zadanie, i tak dalej, dopóki nie natrafi na program interpretujący najwyższego poziomu, gdzie są przekazywane użytkownikowi wraz z wypisem kolejnych wywołań odłożonych na stercie.

For C programmers, however, error checking always has to be explicit. All functions in the Python/C API can raise exceptions, unless an explicit claim is made otherwise in a function’s documentation. In general, when a function encounters an error, it sets an exception, discards any object references that it owns, and returns an error indicator. If not documented otherwise, this indicator is either `NULL` or `-1`, depending on the function’s return type. A few functions return a Boolean true/false result, with false indicating an error. Very few functions return no explicit error indicator or have an ambiguous return value, and require explicit testing for errors with `PyErr_Occurred()`. These exceptions are always explicitly documented.

Exception state is maintained in per-thread storage (this is equivalent to using global storage in an unthreaded application). A thread can be in one of two states: an exception has occurred, or not. The function `PyErr_Occurred()` can be used to check for this: it returns a borrowed reference to the exception type object when an exception has occurred, and `NULL` otherwise. There are a number of functions to set the exception state: `PyErr_SetString()` is the most common (though not the most general) function to set the exception state, and `PyErr_Clear()` clears the exception state.

The full exception state consists of three objects (all of which can be `NULL`): the exception type, the corresponding exception value, and the traceback. These have the same meanings as the Python result of `sys.exc_info()`; however, they are not the same: the Python objects represent the last exception being handled by a Python `try` … `except` statement, while the C level exception state only exists while an exception is being passed on between C functions until it reaches the Python bytecode interpreter’s main loop, which takes care of transferring it to `sys.exc_info()` and friends.

Zauważ że poczynając od języka pytonowskiego w wersji 1.5 preferowaną, bezpiecznym dla wątków sposobem na dostęp do stanu wyjątku z poziomu kodu napisanego w języku pytonowskim jest wezwanie zadania `sys.exc_info()`, które zwraca określony-dla-wątku stan wyjątku dla kodu napisanego w języku pytonowskim. Poza tym składnia obu sposobów na dostęp do stanu sytuacji wyjątkowej zmieniła się tak, że zadanie które złapie wyjątek zachowa i przywróci swój stan wyjątku tak, aby zachować stan wyjątku wywołujacego zadanie. To działanie zapobiega typowym błędom w obsłudze sytuacji wyjątkowych powodowanych przez niewinnie-wyglądające zadania nadpisujące sytuacje wyjątkowe które aktualnie są obsługiwane; to także redukuje często niechciane wydłużanie czasu życia przedmiotów do których odnosi się ramka stosu w wypisie śladu wywołań.

Jako nadrzędną zasadę, przyjmuje się że zadanie które wywołuje inne zadanie do wykonania pewnych operacji powinno sprawdzić czy wywołane zadanie zgłosiło wyjątek, a jeśli tak, to przekazać stan wyjątku do wywołującego. Powinno też odrzucić jakiekolwiek odniesienia do przedmiotów, które posiada, i zwrócić sygnalizator błędu, ale nie powinno ustawiać innego wyjątku — który nadpisywałby wyjątek, który właśnie został zgłoszony i tracić istotne informacje o dokładnym powodzie błędu.

A simple example of detecting exceptions and passing them on is shown in the `sum_sequence()` example above. It so happens that this example doesn’t need to clean up any owned references when it detects an error. The following example function shows some error cleanup. First, to remind you why you like Python, we show the equivalent Python code:

    def incr_item(dict, key):
        try:
            item = dict[key]
        except KeyError:
            item = 0
        dict[key] = item + 1

Tu następuje odpowiadający kod w języku C, w całej pełni okazałości:

    int
    incr_item(PyObject *dict, PyObject *key)
    {
        /* Objects all initialized to NULL for Py_XDECREF */
        PyObject *item = NULL, *const_one = NULL, *incremented_item = NULL;
        int rv = -1; /* Return value initialized to -1 (failure) */

        item = PyObject_GetItem(dict, key);
        if (item == NULL) {
            /* Handle KeyError only: */
            if (!PyErr_ExceptionMatches(PyExc_KeyError))
                goto error;

            /* Clear the error and use zero: */
            PyErr_Clear();
            item = PyLong_FromLong(0L);
            if (item == NULL)
                goto error;
        }
        const_one = PyLong_FromLong(1L);
        if (const_one == NULL)
            goto error;

        incremented_item = PyNumber_Add(item, const_one);
        if (incremented_item == NULL)
            goto error;

        if (PyObject_SetItem(dict, key, incremented_item) < 0)
            goto error;
        rv = 0; /* Success */
        /* Continue with cleanup code */

     error:
        /* Cleanup code, shared by success and failure path */

        /* Use Py_XDECREF() to ignore NULL references */
        Py_XDECREF(item);
        Py_XDECREF(const_one);
        Py_XDECREF(incremented_item);

        return rv; /* -1 for error, 0 for success */
    }

This example represents an endorsed use of the `goto` statement in C! It illustrates the use of `PyErr_ExceptionMatches()` and `PyErr_Clear()` to handle specific exceptions, and the use of `Py_XDECREF()` to dispose of owned references that may be `NULL` (note the `'X'` in the name; `Py_DECREF()` would crash when confronted with a `NULL` reference). It is important that the variables used to hold owned references are initialized to `NULL` for this to work; likewise, the proposed return value is initialized to `-1` (failure) and only set to success after the final call made is successful.

## Załączanie programu interpretującego język pytonowski

Jedno istotne zadanie, o które załączający (w przeciwieństwie do piszących rozszerzenia) program interpretujący język pytonowski muszą się martwić jest zainicjowanie i prawdopodobne zakończenie programu interpretującego polecenia języka pytonowskiego. Większość użyteczności programu interpretującego polecenia języka pytonowskiego może tylko być użyta po jego zainicjowaniu.

The basic initialization function is `Py_Initialize()`. This initializes the table of loaded modules, and creates the fundamental modules `builtins`, `__main__`, and `sys`. It also initializes the module search path (`sys.path`).

`Py_Initialize()` does not set the „script argument list” (`sys.argv`). If this variable is needed by Python code that will be executed later, setting `PyConfig.argv` and `PyConfig.parse_argv` must be set: see Python Initialization Configuration.

On most systems (in particular, on Unix and Windows, although the details are slightly different), `Py_Initialize()` calculates the module search path based upon its best guess for the location of the standard Python interpreter executable, assuming that the Python library is found in a fixed location relative to the Python interpreter executable. In particular, it looks for a directory named `lib/pythonX.Y` relative to the parent directory where the executable named `python` is found on the shell command search path (the environment variable `PATH`).

Na przykład, jeśli plik wykonywalny programu interpretującego polecenia języka pytonowskiego znajduje się w katalogu `/usr/local/bin/python`, będzie zakładał, że biblioteki są w katalogu `/usr/local/lib/pythonX.Y` (Faktycznie, ta szczególna ścieżka jest także „ratunkowym” położeniem, używanym gdy żaden plik wykonywalny nazwany `python` nie znajdzie się w katalogach znajdujących się w zmiennej środowiskowej `PATH`.) Użytkownik może podmienić to zachowanie przez ustawienie zmiennej środowiskowej `PYTHONHOME`, lub wstawić dodatkowe katalogi przed sztandarową ścieżką przez ustawienie zmiennej środowiskowej `PYTHONPATH`.

The embedding application can steer the search by setting `PyConfig.program_name` before calling `Py_InitializeFromConfig()`. Note that `PYTHONHOME` still overrides this and `PYTHONPATH` is still inserted in front of the standard path.

Sometimes, it is desirable to „uninitialize” Python. For instance, the application may want to start over (make another call to `Py_Initialize()`) or the application is simply done with its use of Python and wants to free memory allocated by Python. This can be accomplished by calling `Py_FinalizeEx()`. The function `Py_IsInitialized()` returns true if Python is currently in the initialized state. More information about these functions is given in a later chapter. Notice that `Py_FinalizeEx()` does not free all memory allocated by the Python interpreter, e.g. memory allocated by extension modules currently cannot be released.

## Odpluskwiające Budowy

Program interpretujący język pytonowski może być zbudowany z kilkoma makropoleceniami do załączenia dodatkowych sprawdzeń programu interpretującego polecenia języka pytonowskiego i modułów rozszerzających. Te sprawdzenia mają zwyczaj dodawać duży narzut czasu wykonania poleceń programu więc nie są załączane domyślnie.

A full list of the various types of debugging builds is in the file `Misc/SpecialBuilds.txt` in the Python source distribution. Builds are available that support tracing of reference counts, debugging the memory allocator, or low-level profiling of the main interpreter loop. Only the most frequently used builds will be described in the remainder of this section.

Py_DEBUG

Compiling the interpreter with the `Py_DEBUG` macro defined produces what is generally meant by a debug build of Python.

On Unix, `Py_DEBUG` can be enabled by adding `--with-pydebug` to the `./configure` command. This will also disable compiler optimization.

On Windows, selecting a debug build (e.g., by passing the `-d` option to `PCbuild/build.bat`) automatically enables `Py_DEBUG`. Additionally, the presence of the not-Python-specific `_DEBUG` macro, when defined by the compiler, will also implicitly enable `Py_DEBUG`.

In addition to the reference count debugging described below, extra checks are performed. See Python Debug Build for more details.

Defining `Py_TRACE_REFS` enables reference tracing (see the `configure --with-trace-refs option`). When defined, a circular doubly linked list of active objects is maintained by adding two extra fields to every `PyObject`. Total allocations are tracked as well. Upon exit, all existing references are printed. (In interactive mode this happens after every statement run by the interpreter.)

Odwołaj się do `Misc/SpecialBuilds.txt` w źródłowym pakiecie języka pytonowskiego po więcej szczegółów.

## Rekomendowane zewnętrzne narzędzia.

The following third party tools offer both simpler and more sophisticated approaches to creating C, C++ and Rust extensions for Python:

- Cython

- cffi

- HPy

- nanobind (C++)

- Numba

- pybind11 (C++)

- PyO3 (Rust)

- SWIG

Using tools such as these can help avoid writing code that is tightly bound to a particular version of CPython, avoid reference counting errors, and focus more on your own code than on using the CPython API. In general, new versions of Python can be supported by updating the tool, and your code will often use newer and more efficient APIs automatically. Some tools also support compiling for other implementations of Python from a single set of sources.

These projects are not supported by the same people who maintain Python, and issues need to be raised with the projects directly. Remember to check that the project is still maintained and supported, as the list above may become outdated.

Zobacz także

Pakiety Pythona Podręcznik Użytkownika: Rozszerzenia Binarne <https://packaging.python.org/guides/packaging-binary-extensions/

  Przewodnik Użytkownika Python Packaging nie tylko obejmuje kilka dostępnych narzędzi, które upraszczają tworzenie rozszerzeń binarnych, ale także omawia różne powody, dla których tworzenie modułu rozszerzeń może być pożądane w pierwszej kolejności.