Mercurial > cpython-lru_cache-weakref

annotate Include/objimpl.h @ 13163:a09e8635534d legacy-trunk

Find changesets by keywords (author, files, the commit message), revision number or hash, or revset expression.
Don't try to guess the name of a .def file -- if one is supplied, use it, otherwise just generate an '/export:' option.
author Greg Ward <gward@python.net>
date Thu, 29 Jun 2000 23:09:20 +0000
parents 8f79c5b69e0a
children 54bffdddc0ef
Ignore whitespace changes - Everywhere: Within whitespace: At end of lines:
rev   line source
1329
519c25039c6a * Added support for X11 modules.
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1 #ifndef Py_OBJIMPL_H
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2 #define Py_OBJIMPL_H
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3 #ifdef __cplusplus
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4 extern "C" {
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5 #endif
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6
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7 /***********************************************************
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8 Copyright 1991-1995 by Stichting Mathematisch Centrum, Amsterdam,
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9 The Netherlands.
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10
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11 All Rights Reserved
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12
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13 Permission to use, copy, modify, and distribute this software and its
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14 documentation for any purpose and without fee is hereby granted,
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15 provided that the above copyright notice appear in all copies and that
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16 both that copyright notice and this permission notice appear in
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17 supporting documentation, and that the names of Stichting Mathematisch
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18 Centrum or CWI or Corporation for National Research Initiatives or
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19 CNRI not be used in advertising or publicity pertaining to
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20 distribution of the software without specific, written prior
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21 permission.
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22
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23 While CWI is the initial source for this software, a modified version
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24 is made available by the Corporation for National Research Initiatives
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25 (CNRI) at the Internet address ftp://ftp.python.org.
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26
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27 STICHTING MATHEMATISCH CENTRUM AND CNRI DISCLAIM ALL WARRANTIES WITH
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28 REGARD TO THIS SOFTWARE, INCLUDING ALL IMPLIED WARRANTIES OF
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29 MERCHANTABILITY AND FITNESS, IN NO EVENT SHALL STICHTING MATHEMATISCH
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30 CENTRUM OR CNRI BE LIABLE FOR ANY SPECIAL, INDIRECT OR CONSEQUENTIAL
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31 DAMAGES OR ANY DAMAGES WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR
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32 PROFITS, WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR OTHER
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33 TORTIOUS ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE OR
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34 PERFORMANCE OF THIS SOFTWARE.
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35
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36 ******************************************************************/
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37
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38 #include "mymalloc.h"
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39
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40 /*
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41 Functions and macros for modules that implement new object types.
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42 You must first include "object.h".
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43
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44 - PyObject_New(type, typeobj) allocates memory for a new object of
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45 the given type; here 'type' must be the C structure type used to
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46 represent the object and 'typeobj' the address of the corresponding
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47 type object. Reference count and type pointer are filled in; the
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48 rest of the bytes of the object are *undefined*! The resulting
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49 expression type is 'type *'. The size of the object is actually
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50 determined by the tp_basicsize field of the type object.
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51
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52 - PyObject_NewVar(type, typeobj, n) is similar but allocates a
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53 variable-size object with n extra items. The size is computed as
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54 tp_basicsize plus n * tp_itemsize. This fills in the ob_size field
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55 as well.
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56
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57 - PyObject_Del(op) releases the memory allocated for an object.
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58
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59 - PyObject_Init(op, typeobj) and PyObject_InitVar(op, typeobj, n) are
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60 similar to PyObject_{New, NewVar} except that they don't allocate
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61 the memory needed for an object. Instead of the 'type' parameter,
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62 they accept the pointer of a new object (allocated by an arbitrary
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63 allocator) and initialize its object header fields.
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64
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65 Note that objects created with PyObject_{New, NewVar} are allocated
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66 within the Python heap by an object allocator, the latter being
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67 implemented (by default) on top of the Python raw memory
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68 allocator. This ensures that Python keeps control on the user's
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69 objects regarding their memory management; for instance, they may be
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70 subject to automatic garbage collection.
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71
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72 In case a specific form of memory management is needed, implying that
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73 the objects would not reside in the Python heap (for example standard
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74 malloc heap(s) are mandatory, use of shared memory, C++ local storage
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75 or operator new), you must first allocate the object with your custom
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76 allocator, then pass its pointer to PyObject_{Init, InitVar} for
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77 filling in its Python-specific fields: reference count, type pointer,
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78 possibly others. You should be aware that Python has very limited
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79 control over these objects because they don't cooperate with the
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80 Python memory manager. Such objects may not be eligible for automatic
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81 garbage collection and you have to make sure that they are released
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82 accordingly whenever their destructor gets called (cf. the specific
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83 form of memory management you're using).
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84
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85 Unless you have specific memory management requirements, it is
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86 recommended to use PyObject_{New, NewVar, Del}. */
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87
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88 /*
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89 * Core object memory allocator
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90 * ============================
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91 */
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92
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93 /* The purpose of the object allocator is to make make the distinction
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94 between "object memory" and the rest within the Python heap.
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95
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96 Object memory is the one allocated by PyObject_{New, NewVar}, i.e.
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97 the one that holds the object's representation defined by its C
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98 type structure, *excluding* any object-specific memory buffers that
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99 might be referenced by the structure (for type structures that have
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100 pointer fields). By default, the object memory allocator is
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101 implemented on top of the raw memory allocator.
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102
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103 The PyCore_* macros can be defined to make the interpreter use a
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104 custom object memory allocator. They are reserved for internal
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105 memory management purposes exclusively. Both the core and extension
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106 modules should use the PyObject_* API. */
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107
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108 #ifndef PyCore_OBJECT_MALLOC_FUNC
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109 #undef PyCore_OBJECT_REALLOC_FUNC
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110 #undef PyCore_OBJECT_FREE_FUNC
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111 #define PyCore_OBJECT_MALLOC_FUNC PyCore_MALLOC_FUNC
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112 #define PyCore_OBJECT_REALLOC_FUNC PyCore_REALLOC_FUNC
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113 #define PyCore_OBJECT_FREE_FUNC PyCore_FREE_FUNC
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114 #endif
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115
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116 #ifndef PyCore_OBJECT_MALLOC_PROTO
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117 #undef PyCore_OBJECT_REALLOC_PROTO
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118 #undef PyCore_OBJECT_FREE_PROTO
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119 #define PyCore_OBJECT_MALLOC_PROTO PyCore_MALLOC_PROTO
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120 #define PyCore_OBJECT_REALLOC_PROTO PyCore_REALLOC_PROTO
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121 #define PyCore_OBJECT_FREE_PROTO PyCore_FREE_PROTO
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122 #endif
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123
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124 #ifdef NEED_TO_DECLARE_OBJECT_MALLOC_AND_FRIEND
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125 extern ANY *PyCore_OBJECT_MALLOC_FUNC PyCore_OBJECT_MALLOC_PROTO;
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126 extern ANY *PyCore_OBJECT_REALLOC_FUNC PyCore_OBJECT_REALLOC_PROTO;
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127 extern void PyCore_OBJECT_FREE_FUNC PyCore_OBJECT_FREE_PROTO;
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128 #endif
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129
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130 #ifndef PyCore_OBJECT_MALLOC
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131 #undef PyCore_OBJECT_REALLOC
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132 #undef PyCore_OBJECT_FREE
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133 #define PyCore_OBJECT_MALLOC(n) PyCore_OBJECT_MALLOC_FUNC(n)
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134 #define PyCore_OBJECT_REALLOC(p, n) PyCore_OBJECT_REALLOC_FUNC((p), (n))
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135 #define PyCore_OBJECT_FREE(p) PyCore_OBJECT_FREE_FUNC(p)
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136 #endif
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137
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138 /*
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139 * Raw object memory interface
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140 * ===========================
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141 */
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142
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143 /* The use of this API should be avoided, unless a builtin object
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144 constructor inlines PyObject_{New, NewVar}, either because the
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145 latter functions cannot allocate the exact amount of needed memory,
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146 either for speed. This situation is exceptional, but occurs for
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147 some object constructors (PyBuffer_New, PyList_New...). Inlining
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148 PyObject_{New, NewVar} for objects that are supposed to belong to
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149 the Python heap is discouraged. If you really have to, make sure
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150 the object is initialized with PyObject_{Init, InitVar}. Do *not*
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151 inline PyObject_{Init, InitVar} for user-extension types or you
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152 might seriously interfere with Python's memory management. */
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153
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154 /* Functions */
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155
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156 /* Wrappers around PyCore_OBJECT_MALLOC and friends; useful if you
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157 need to be sure that you are using the same object memory allocator
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158 as Python. These wrappers *do not* make sure that allocating 0
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159 bytes returns a non-NULL pointer. Returned pointers must be checked
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160 for NULL explicitly; no action is performed on failure. */
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161 extern DL_IMPORT(ANY *) PyObject_Malloc Py_PROTO((size_t));
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162 extern DL_IMPORT(ANY *) PyObject_Realloc Py_PROTO((ANY *, size_t));
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163 extern DL_IMPORT(void) PyObject_Free Py_PROTO((ANY *));
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164
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165 /* Macros */
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166 #define PyObject_MALLOC(n) PyCore_OBJECT_MALLOC(n)
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167 #define PyObject_REALLOC(op, n) PyCore_OBJECT_REALLOC((ANY *)(op), (n))
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168 #define PyObject_FREE(op) PyCore_OBJECT_FREE((ANY *)(op))
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169
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170 /*
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171 * Generic object allocator interface
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172 * ==================================
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173 */
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174
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175 /* Functions */
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176 extern DL_IMPORT(PyObject *) PyObject_Init Py_PROTO((PyObject *, PyTypeObject *));
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177 extern DL_IMPORT(PyVarObject *) PyObject_InitVar Py_PROTO((PyVarObject *, PyTypeObject *, int));
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178 extern DL_IMPORT(PyObject *) _PyObject_New Py_PROTO((PyTypeObject *));
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179 extern DL_IMPORT(PyVarObject *) _PyObject_NewVar Py_PROTO((PyTypeObject *, int));
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180 extern DL_IMPORT(void) _PyObject_Del Py_PROTO((PyObject *));
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181
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182 #define PyObject_New(type, typeobj) \
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183 ( (type *) _PyObject_New(typeobj) )
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184 #define PyObject_NewVar(type, typeobj, n) \
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185 ( (type *) _PyObject_NewVar((typeobj), (n)) )
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186 #define PyObject_Del(op) _PyObject_Del((PyObject *)(op))
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187
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188 /* Macros trading binary compatibility for speed. See also mymalloc.h.
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189 Note that these macros expect non-NULL object pointers.*/
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190 #define PyObject_INIT(op, typeobj) \
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191 ( (op)->ob_type = (typeobj), _Py_NewReference((PyObject *)(op)), (op) )
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192 #define PyObject_INIT_VAR(op, typeobj, size) \
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193 ( (op)->ob_size = (size), PyObject_INIT((op), (typeobj)) )
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194
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195 #define _PyObject_SIZE(typeobj) ( (typeobj)->tp_basicsize )
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196 #define _PyObject_VAR_SIZE(typeobj, n) \
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197 ( (typeobj)->tp_basicsize + (n) * (typeobj)->tp_itemsize )
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198
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199 #define PyObject_NEW(type, typeobj) \
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200 ( (type *) PyObject_Init( \
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201 (PyObject *) PyObject_MALLOC( _PyObject_SIZE(typeobj) ), (typeobj)) )
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202 #define PyObject_NEW_VAR(type, typeobj, n) \
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203 ( (type *) PyObject_InitVar( \
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204 (PyVarObject *) PyObject_MALLOC( _PyObject_VAR_SIZE((typeobj),(n)) ),\
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205 (typeobj), (n)) )
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206 #define PyObject_DEL(op) PyObject_FREE(op)
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207
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208 /* This example code implements an object constructor with a custom
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209 allocator, where PyObject_New is inlined, and shows the important
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210 distinction between two steps (at least):
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211 1) the actual allocation of the object storage;
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212 2) the initialization of the Python specific fields
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213 in this storage with PyObject_{Init, InitVar}.
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214
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215 PyObject *
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216 YourObject_New(...)
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217 {
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218 PyObject *op;
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219
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220 op = (PyObject *) Your_Allocator(_PyObject_SIZE(YourTypeStruct));
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221 if (op == NULL)
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222 return PyErr_NoMemory();
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223
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224 op = PyObject_Init(op, &YourTypeStruct);
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225 if (op == NULL)
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226 return NULL;
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227
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228 op->ob_field = value;
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229 ...
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230 return op;
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231 }
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232
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233 Note that in C++, the use of the new operator usually implies that
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234 the 1st step is performed automatically for you, so in a C++ class
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235 constructor you would start directly with PyObject_Init/InitVar. */
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236
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237
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238
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239 #ifndef WITH_CYCLE_GC
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240 #define PyGC_INFO_SIZE 0
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241 #endif
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242
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243 #ifdef __cplusplus
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244 }
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245 #endif
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246 #endif /* !Py_OBJIMPL_H */