summaryrefslogtreecommitdiffstats
path: root/doc/trace.n
blob: 6eba97495f936179946394e00516eb80c548a951 (plain)
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
'\"
'\" Copyright (c) 1993 The Regents of the University of California.
'\" Copyright (c) 1994-1996 Sun Microsystems, Inc.
'\" Copyright (c) 2000 Ajuba Solutions.
'\"
'\" See the file "license.terms" for information on usage and redistribution
'\" of this file, and for a DISCLAIMER OF ALL WARRANTIES.
'\"
.TH trace n "8.4" Tcl "Tcl Built-In Commands"
.so man.macros
.BS
'\" Note:  do not modify the .SH NAME line immediately below!
.SH NAME
trace \- Monitor variable accesses, command usages and command executions
.SH SYNOPSIS
\fBtrace \fIoption\fR ?\fIarg arg ...\fR?
.BE
.SH DESCRIPTION
.PP
This command causes Tcl commands to be executed whenever certain operations are
invoked.  The legal \fIoption\fRs (which may be abbreviated) are:
.\" METHOD: add
.TP
\fBtrace add \fItype name ops\fR ?\fIargs\fR?
.
Where \fItype\fR is \fBcommand\fR, \fBexecution\fR, or \fBvariable\fR.
.RS
.TP
\fBtrace add command\fI name ops commandPrefix\fR
.
Arrange for \fIcommandPrefix\fR to be executed (with additional arguments)
whenever command \fIname\fR is modified in one of the ways given by the list
\fIops\fR. \fIName\fR will be resolved using the usual namespace resolution
rules used by commands. If the command does not exist, an error will be
thrown.
.RS
.PP
\fIOps\fR indicates which operations are of interest, and is a list of
one or more of the following items:
.TP
\fBrename\fR
.
Invoke \fIcommandPrefix\fR whenever the traced command is renamed.  Note that
renaming to the empty string is considered deletion, and will not be traced
with
.QW \fBrename\fR .
.TP
\fBdelete\fR
.
Invoke \fIcommandPrefix\fR when the traced command is deleted. Commands can be
deleted explicitly by using the \fBrename\fR command to rename the command to
an empty string. Commands are also deleted when the interpreter is deleted,
but traces will not be invoked because there is no interpreter in which to
execute them.
.PP
When the trace triggers, depending on the operations being traced, a number of
arguments are appended to \fIcommandPrefix\fR so that the actual command is as
follows:
.PP
.CS
\fIcommandPrefix oldName newName op\fR
.CE
.PP
\fIOldName\fR and \fInewName\fR give the traced command's current (old) name,
and the name to which it is being renamed (the empty string if this is a
.QW delete
operation).
\fIOp\fR indicates what operation is being performed on the
command, and is one of \fBrename\fR or \fBdelete\fR as
defined above.  The trace operation cannot be used to stop a command
from being deleted.  Tcl will always remove the command once the trace
is complete.  Recursive renaming or deleting will not cause further traces
of the same type to be evaluated, so a delete trace which itself
deletes the command, or a rename trace which itself renames the
command will not cause further trace evaluations to occur.
Both \fIoldName\fR and \fInewName\fR are fully qualified with any namespace(s)
in which they appear.
.RE
.TP
\fBtrace add execution\fI name ops commandPrefix\fR
.
Arrange for \fIcommandPrefix\fR to be executed (with additional arguments)
whenever command \fIname\fR is executed, with traces occurring at the points
indicated by the list \fIops\fR.  \fIName\fR will be resolved using the usual
namespace resolution rules used by commands.  If the command does not exist,
an error will be thrown.
.RS
.PP
\fIOps\fR indicates which operations are of interest, and is a list of
one or more of the following items:
.TP
\fBenter\fR
.
Invoke \fIcommandPrefix\fR whenever the command \fIname\fR is executed,
just before the actual execution takes place.
.TP
\fBleave\fR
.
Invoke \fIcommandPrefix\fR whenever the command \fIname\fR is executed,
just after the actual execution takes place.
.TP
\fBenterstep\fR
.
Invoke \fIcommandPrefix\fR for every Tcl command which is executed from the
start of the execution of the procedure \fIname\fR until that
procedure finishes. \fICommandPrefix\fR is invoked just before the actual
execution of the Tcl command being reported takes place.  For example
if we have
.QW "proc foo {} { puts \N'34'hello\N'34' }" ,
then an \fIenterstep\fR trace would be invoked just before
.QW "\fIputs \N'34'hello\N'34'\fR"
is executed.
Setting an \fIenterstep\fR trace on a command \fIname\fR that does not refer
to a procedure will not result in an error and is simply ignored.
.TP
\fBleavestep\fR
.
Invoke \fIcommandPrefix\fR for every Tcl command which is executed from the
start of the execution of the procedure \fIname\fR until that
procedure finishes. \fICommandPrefix\fR is invoked just after the actual
execution of the Tcl command being reported takes place.
Setting a \fIleavestep\fR trace on a command \fIname\fR that does not refer to
a procedure will not result in an error and is simply ignored.
.PP
When the trace triggers, depending on the operations being traced, a
number of arguments are appended to \fIcommandPrefix\fR so that the actual
command is as follows:
.PP
For \fBenter\fR and \fBenterstep\fR operations:
.PP
.CS
\fIcommandPrefix command-string op\fR
.CE
.PP
\fICommand-string\fR gives the complete current command being
executed (the traced command for a \fBenter\fR operation, an
arbitrary command for a \fBenterstep\fR operation), including
all arguments in their fully expanded form.
\fIOp\fR indicates what operation is being performed on the
command execution, and is one of \fBenter\fR or \fBenterstep\fR as
defined above.  The trace operation can be used to stop the
command from executing, by deleting the command in question.  Of
course when the command is subsequently executed, an
.QW "invalid command"
error will occur.
.PP
For \fBleave\fR and \fBleavestep\fR operations:
.PP
.CS
\fIcommandPrefix command-string code result op\fR
.CE
.PP
\fICommand-string\fR gives the complete current command being
executed (the traced command for a \fBenter\fR operation, an
arbitrary command for a \fBenterstep\fR operation), including
all arguments in their fully expanded form.
\fICode\fR gives the result code of that execution, and \fIresult\fR
the result string.
\fIOp\fR indicates what operation is being performed on the
command execution, and is one of \fBleave\fR or \fBleavestep\fR as
defined above.
.PP
Note that the creation of many \fBenterstep\fR or
\fBleavestep\fR traces can lead to unintuitive results, since the
invoked commands from one trace can themselves lead to further
command invocations for other traces.
.PP
\fICommandPrefix\fR executes in the same context as the code that invoked
the traced operation: thus the \fIcommandPrefix\fR, if invoked from a
procedure, will have access to the same local variables as code in the
procedure. This context may be different than the context in which the trace
was created. If \fIcommandPrefix\fR invokes a procedure (which it normally
does) then the procedure will have to use \fBupvar\fR or \fBuplevel\fR
commands if it wishes to access the local variables of the code which invoked
the trace operation.
.PP
While \fIcommandPrefix\fR is executing during an execution trace, traces
on \fIname\fR are temporarily disabled. This allows the \fIcommandPrefix\fR
to execute \fIname\fR in its body without invoking any other traces again.
If an error occurs while executing the \fIcommandPrefix\fR, then the
command \fIname\fR as a whole will return that same error.
.PP
When multiple traces are set on \fIname\fR, then for \fIenter\fR
and \fIenterstep\fR operations, the traced commands are invoked
in the reverse order of how the traces were originally created;
and for \fIleave\fR and \fIleavestep\fR operations, the traced
commands are invoked in the original order of creation.
.PP
The behavior of execution traces is currently undefined for a command
\fIname\fR imported into another namespace.
.RE
.TP
\fBtrace add variable\fI name ops commandPrefix\fR
.
Arrange for \fIcommandPrefix\fR to be executed whenever variable \fIname\fR
is accessed in one of the ways given by the list \fIops\fR.  \fIName\fR may
refer to a normal variable, an element of an array, or to an array
as a whole (i.e. \fIname\fR may be just the name of an array, with no
parenthesized index).  If \fIname\fR refers to a whole array, then
\fIcommandPrefix\fR is invoked whenever any element of the array is
manipulated.  If the variable does not exist, it will be created but
will not be given a value, so it will be visible to \fBnamespace which\fR
queries, but not to \fBinfo exists\fR queries.
.RS
.PP
\fIOps\fR indicates which operations are of interest, and is a list of
one or more of the following items:
.TP
\fBarray\fR
.
Invoke \fIcommandPrefix\fR whenever the variable is accessed or modified via
the \fBarray\fR command, provided that \fIname\fR is not a scalar
variable at the time that the \fBarray\fR command is invoked.  If
\fIname\fR is a scalar variable, the access via the \fBarray\fR
command will not trigger the trace.
.TP
\fBread\fR
.
Invoke \fIcommandPrefix\fR whenever the variable is read.
.TP
\fBwrite\fR
.
Invoke \fIcommandPrefix\fR whenever the variable is written.
.TP
\fBunset\fR
.
Invoke \fIcommandPrefix\fR whenever the variable is unset.  Variables
can be unset explicitly with the \fBunset\fR command, or
implicitly when procedures return (all of their local variables
are unset).  Variables are also unset when interpreters are
deleted, but traces will not be invoked because there is no
interpreter in which to execute them.
.PP
When the trace triggers, three arguments are appended to
\fIcommandPrefix\fR so that the actual command is as follows:
.PP
.CS
\fIcommandPrefix name1 name2 op\fR
.CE
.PP
\fIName1\fR gives the name for the variable being accessed.
This is not necessarily the same as the name used in the
\fBtrace add variable\fR command:  the \fBupvar\fR command allows a
procedure to reference a variable under a different name.
If the trace was originally set on an array or array element,
\fIname2\fR provides which index into the array was affected.
This information is present even when \fIname1\fR refers to a
scalar, which may happen if the \fBupvar\fR command was used to
create a reference to a single array element.
If an entire array is being deleted and the trace was registered
on the overall array, rather than a single element, then \fIname1\fR
gives the array name and \fIname2\fR is an empty string.
\fIOp\fR indicates what operation is being performed on the
variable, and is one of \fBread\fR, \fBwrite\fR, or \fBunset\fR as
defined above.
.PP
\fICommandPrefix\fR executes in the same context as the code that invoked
the traced operation:  if the variable was accessed as part of a Tcl
procedure, then \fIcommandPrefix\fR will have access to the same local
variables as code in the procedure.  This context may be different
than the context in which the trace was created. If \fIcommandPrefix\fR
invokes a procedure (which it normally does) then the procedure will
have to use \fBupvar\fR or \fBuplevel\fR if it wishes to access the
traced variable.  Note also that \fIname1\fR may not necessarily be
the same as the name used to set the trace on the variable;
differences can occur if the access is made through a variable defined
with the \fBupvar\fR command.
.PP
For read and write traces, \fIcommandPrefix\fR can modify the variable to
affect the result of the traced operation.  If \fIcommandPrefix\fR modifies
the value of a variable during a read or write trace, then the new
value will be returned as the result of the traced operation.  The
return value from  \fIcommandPrefix\fR is ignored except that if it returns
an error of any sort then the traced operation also returns an error
with the same error message returned by the trace command (this
mechanism can be used to implement read-only variables, for example).
For write traces, \fIcommandPrefix\fR is invoked after the variable's value
has been changed; it can write a new value into the variable to
override the original value specified in the write operation.  To
implement read-only variables, \fIcommandPrefix\fR will have to restore the
old value of the variable.
.PP
While \fIcommandPrefix\fR is executing during a read or write trace, traces
on the variable are temporarily disabled.  This means that reads and
writes invoked by \fIcommandPrefix\fR will occur directly, without invoking
\fIcommandPrefix\fR (or any other traces) again.  However, if
\fIcommandPrefix\fR unsets the variable then unset traces will be invoked.
.PP
When an unset trace is invoked, the variable has already been deleted:
it will appear to be undefined with no traces.  If an unset occurs
because of a procedure return, then the trace will be invoked in the
variable context of the procedure being returned to:  the stack frame
of the returning procedure will no longer exist.  Traces are not
disabled during unset traces, so if an unset trace command creates a
new trace and accesses the variable, the trace will be invoked.  Any
errors in unset traces are ignored.
.PP
If there are multiple traces on a variable they are invoked in order
of creation, most-recent first.  If one trace returns an error, then
no further traces are invoked for the variable.  If an array element
has a trace set, and there is also a trace set on the array as a
whole, the trace on the overall array is invoked before the one on the
element.
.PP
Once created, the trace remains in effect either until the trace is
removed with the \fBtrace remove variable\fR command described below,
until the variable is unset, or until the interpreter is deleted.
Unsetting an element of array will remove any traces on that element,
but will not remove traces on the overall array.
.PP
This command returns an empty string.
.RE
.RE
.\" METHOD: remove
.TP
\fBtrace remove \fItype name opList commandPrefix\fR
.
Where \fItype\fR is either \fBcommand\fR, \fBexecution\fR or \fBvariable\fR.
.RS
.TP
\fBtrace remove command\fI name opList commandPrefix\fR
.
If there is a trace set on command \fIname\fR with the operations and
command given by \fIopList\fR and \fIcommandPrefix\fR, then the trace is
removed, so that \fIcommandPrefix\fR will never again be invoked.  Returns
an empty string.   If \fIname\fR does not exist, the command will throw
an error.
.TP
\fBtrace remove execution\fI name opList commandPrefix\fR
.
If there is a trace set on command \fIname\fR with the operations and
command given by \fIopList\fR and \fIcommandPrefix\fR, then the trace is
removed, so that \fIcommandPrefix\fR will never again be invoked.  Returns
an empty string.   If \fIname\fR does not exist, the command will throw
an error.
.TP
\fBtrace remove variable\fI name opList commandPrefix\fR
.
If there is a trace set on variable \fIname\fR with the operations and
command given by \fIopList\fR and \fIcommandPrefix\fR, then the trace is
removed, so that \fIcommandPrefix\fR will never again be invoked.  Returns
an empty string.
.RE
.\" METHOD: info
.TP
\fBtrace info \fItype name\fR
.
Where \fItype\fR is either \fBcommand\fR, \fBexecution\fR or \fBvariable\fR.
.RS
.TP
\fBtrace info command\fI name\fR
.
Returns a list containing one element for each trace currently set on
command \fIname\fR. Each element of the list is itself a list
containing two elements, which are the \fIopList\fR and \fIcommandPrefix\fR
associated with the trace.  If \fIname\fR does not have any traces set,
then the result of the command will be an empty string.  If \fIname\fR
does not exist, the command will throw an error.
.TP
\fBtrace info execution\fI name\fR
.
Returns a list containing one element for each trace currently set on
command \fIname\fR. Each element of the list is itself a list
containing two elements, which are the \fIopList\fR and \fIcommandPrefix\fR
associated with the trace.  If \fIname\fR does not have any traces set,
then the result of the command will be an empty string.  If \fIname\fR
does not exist, the command will throw an error.
.TP
\fBtrace info variable\fI name\fR
.
Returns a list containing one element for each trace currently set on
variable \fIname\fR.  Each element of the list is itself a list
containing two elements, which are the \fIopList\fR and \fIcommandPrefix\fR
associated with the trace.  If \fIname\fR does not exist or does not
have any traces set, then the result of the command will be an empty
string.
.RE
.SH EXAMPLES
.PP
Print a message whenever either of the global variables \fBfoo\fR and
\fBbar\fR are updated, even if they have a different local name at the
time (which can be done with the \fBupvar\fR command):
.PP
.CS
proc tracer {varname args} {
    upvar #0 $varname var
    puts "$varname was updated to be \e"$var\e""
}
\fBtrace add\fR variable foo write "tracer foo"
\fBtrace add\fR variable bar write "tracer bar"
.CE
.PP
Ensure that the global variable \fBfoobar\fR always contains the
product of the global variables \fBfoo\fR and \fBbar\fR:
.PP
.CS
proc doMult args {
    global foo bar foobar
    set foobar [expr {$foo * $bar}]
}
\fBtrace add\fR variable foo write doMult
\fBtrace add\fR variable bar write doMult
.CE
.PP
Print a trace of what commands are executed during the processing of a Tcl
procedure:
.PP
.CS
proc x {} { y }
proc y {} { z }
proc z {} { puts hello }
proc report args {puts [info level 0]}
\fBtrace add\fR execution x enterstep report
x
  \(-> \fIreport y enterstep\fR
    \fIreport z enterstep\fR
    \fIreport {puts hello} enterstep\fR
    \fIhello\fR
.CE
.SH "SEE ALSO"
set(n), unset(n)
.SH KEYWORDS
read, command, rename, variable, write, trace, unset
.\" Local Variables:
.\" mode: nroff
.\" End:
f='#n2846'>2846 2847 2848 2849 2850 2851 2852 2853 2854 2855 2856 2857 2858 2859 2860 2861 2862 2863 2864 2865 2866 2867 2868 2869 2870 2871 2872 2873 2874 2875 2876 2877 2878 2879 2880 2881 2882 2883 2884 2885 2886 2887 2888 2889 2890 2891 2892 2893 2894 2895 2896 2897 2898 2899 2900 2901 2902 2903 2904 2905 2906 2907 2908 2909 2910 2911 2912 2913 2914 2915 2916 2917 2918 2919 2920 2921 2922 2923 2924 2925 2926 2927 2928 2929 2930 2931 2932 2933 2934 2935 2936 2937 2938 2939 2940 2941 2942 2943 2944 2945 2946 2947 2948 2949 2950 2951 2952 2953 2954 2955 2956 2957 2958 2959 2960 2961 2962 2963 2964 2965 2966 2967 2968 2969 2970 2971 2972 2973 2974 2975 2976 2977 2978 2979 2980 2981 2982 2983 2984 2985 2986 2987 2988 2989 2990 2991 2992 2993 2994 2995 2996 2997 2998 2999 3000 3001 3002 3003 3004 3005 3006 3007 3008 3009 3010 3011 3012 3013 3014 3015 3016 3017 3018 3019 3020 3021 3022 3023 3024 3025 3026 3027 3028 3029 3030 3031 3032 3033 3034 3035 3036 3037 3038 3039 3040 3041 3042 3043 3044 3045 3046 3047 3048 3049 3050 3051 3052 3053 3054 3055 3056 3057 3058 3059 3060 3061 3062 3063 3064 3065 3066 3067 3068 3069 3070 3071 3072 3073 3074 3075 3076 3077 3078 3079 3080 3081 3082 3083 3084 3085 3086 3087 3088 3089 3090 3091 3092 3093 3094 3095 3096 3097 3098 3099 3100 3101 3102 3103 3104 3105 3106 3107 3108 3109 3110 3111 3112 3113 3114 3115 3116 3117 3118 3119 3120 3121 3122 3123 3124 3125 3126 3127 3128 3129 3130 3131 3132 3133 3134 3135 3136 3137 3138 3139 3140 3141 3142 3143 3144 3145 3146 3147 3148 3149 3150 3151 3152 3153 3154 3155 3156 3157 3158 3159 3160 3161 3162 3163 3164 3165 3166 3167 3168 3169 3170 3171 3172 3173 3174 3175 3176 3177 3178 3179 3180 3181 3182 3183 3184 3185 3186 3187 3188 3189 3190 3191 3192 3193 3194 3195 3196 3197 3198 3199 3200 3201 3202 3203 3204 3205 3206 3207 3208 3209 3210 3211 3212 3213 3214 3215 3216 3217 3218 3219 3220 3221 3222 3223 3224 3225 3226 3227 3228 3229 3230 3231 3232 3233 3234 3235 3236 3237 3238 3239 3240 3241 3242 3243 3244 3245 3246 3247 3248 3249 3250 3251 3252 3253 3254 3255 3256 3257 3258 3259 3260 3261 3262 3263 3264 3265 3266 3267 3268 3269 3270 3271 3272 3273 3274 3275 3276 3277 3278 3279 3280 3281 3282 3283 3284 3285 3286 3287 3288 3289 3290 3291 3292 3293 3294 3295 3296 3297 3298 3299 3300 3301 3302 3303 3304 3305 3306 3307 3308 3309 3310 3311 3312 3313 3314 3315 3316 3317 3318 3319 3320 3321 3322 3323 3324 3325 3326 3327 3328 3329 3330 3331 3332 3333 3334 3335 3336 3337 3338 3339 3340 3341 3342 3343 3344 3345 3346 3347 3348 3349 3350 3351 3352 3353 3354 3355 3356 3357 3358 3359 3360 3361 3362 3363 3364 3365 3366 3367 3368 3369 3370 3371 3372 3373 3374 3375 3376 3377 3378 3379 3380 3381 3382 3383 3384 3385 3386 3387 3388 3389 3390 3391 3392 3393 3394 3395 3396 3397 3398 3399 3400 3401 3402 3403 3404 3405 3406 3407 3408 3409 3410 3411 3412 3413 3414 3415 3416 3417 3418 3419 3420 3421 3422 3423 3424 3425 3426 3427 3428 3429 3430 3431 3432 3433 3434 3435 3436 3437 3438 3439 3440 3441 3442 3443 3444 3445 3446 3447 3448 3449 3450 3451 3452 3453 3454 3455 3456 3457 3458 3459 3460 3461 3462 3463 3464 3465 3466 3467 3468 3469 3470 3471 3472 3473 3474 3475 3476 3477 3478 3479 3480 3481 3482 3483 3484 3485 3486 3487 3488 3489 3490 3491 3492 3493 3494 3495 3496 3497 3498 3499 3500 3501 3502 3503 3504 3505 3506 3507 3508 3509 3510 3511 3512 3513 3514 3515 3516 3517 3518 3519 3520 3521 3522 3523 3524 3525 3526 3527 3528 3529 3530 3531 3532 3533 3534 3535 3536 3537 3538 3539 3540 3541 3542 3543 3544 3545 3546 3547 3548 3549 3550 3551 3552 3553 3554 3555 3556 3557 3558 3559 3560 3561 3562 3563 3564 3565 3566 3567 3568 3569 3570 3571 3572 3573 3574 3575 3576 3577 3578 3579 3580 3581 3582 3583 3584 3585 3586 3587 3588 3589 3590 3591 3592 3593 3594 3595 3596 3597 3598 3599 3600 3601 3602 3603 3604 3605 3606 3607 3608 3609 3610 3611 3612 3613 3614 3615 3616 3617 3618 3619 3620 3621 3622 3623 3624 3625 3626 3627 3628 3629 3630 3631 3632 3633 3634 3635 3636 3637 3638 3639 3640 3641 3642 3643 3644 3645 3646 3647 3648 3649 3650 3651 3652 3653 3654 3655 3656 3657 3658 3659 3660 3661 3662 3663 3664 3665 3666 3667 3668 3669 3670 3671 3672 3673 3674 3675 3676 3677 3678 3679 3680 3681 3682 3683 3684 3685 3686 3687 3688 3689 3690 3691 3692 3693 3694 3695 3696 3697 3698 3699 3700 3701 3702 3703 3704 3705 3706 3707 3708 3709 3710 3711 3712 3713 3714 3715 3716 3717 3718 3719 3720 3721 3722 3723 3724 3725 3726 3727 3728 3729 3730 3731 3732 3733 3734 3735 3736 3737 3738 3739 3740 3741 3742 3743 3744 3745 3746 3747 3748 3749 3750 3751 3752 3753 3754 3755 3756 3757 3758 3759 3760 3761 3762 3763 3764 3765 3766 3767 3768 3769 3770 3771 3772 3773 3774 3775 3776 3777 3778 3779 3780 3781 3782 3783 3784 3785 3786 3787 3788 3789 3790 3791 3792 3793 3794 3795 3796 3797 3798 3799 3800 3801 3802 3803 3804 3805 3806 3807 3808 3809 3810 3811 3812 3813 3814 3815 3816 3817 3818 3819 3820 3821 3822 3823 3824 3825 3826 3827 3828 3829 3830 3831 3832 3833 3834 3835 3836 3837 3838 3839 3840 3841 3842 3843 3844 3845 3846 3847 3848 3849 3850 3851 3852 3853 3854 3855 3856 3857 3858 3859 3860 3861 3862 3863 3864 3865 3866 3867 3868 3869 3870 3871 3872 3873 3874 3875 3876 3877 3878 3879 3880 3881 3882 3883 3884 3885 3886 3887 3888 3889 3890 3891 3892 3893 3894 3895 3896 3897 3898 3899 3900 3901 3902 3903 3904 3905 3906 3907 3908 3909 3910 3911 3912 3913 3914 3915 3916 3917 3918 3919 3920 3921 3922 3923 3924 3925 3926 3927 3928 3929 3930 3931 3932 3933 3934 3935 3936 3937 3938 3939 3940 3941 3942 3943 3944 3945 3946 3947 3948 3949 3950 3951 3952 3953 3954 3955 3956 3957 3958 3959 3960 3961 3962 3963 3964 3965 3966 3967 3968 3969 3970 3971 3972 3973 3974 3975 3976 3977 3978 3979 3980 3981 3982 3983 3984 3985 3986 3987 3988 3989 3990 3991 3992 3993 3994 3995 3996 3997 3998 3999 4000 4001 4002 4003 4004 4005 4006 4007 4008 4009 4010 4011 4012 4013 4014 4015 4016 4017 4018 4019 4020 4021 4022 4023 4024 4025 4026 4027 4028 4029 4030 4031 4032 4033 4034 4035 4036 4037 4038 4039 4040 4041 4042 4043 4044 4045 4046 4047 4048 4049 4050 4051 4052 4053 4054 4055 4056 4057 4058 4059 4060 4061 4062 4063 4064 4065 4066 4067 4068 4069 4070 4071 4072 4073 4074 4075 4076 4077 4078 4079 4080 4081 4082 4083 4084 4085 4086 4087 4088 4089 4090 4091 4092 4093 4094 4095 4096 4097 4098 4099 4100 4101 4102 4103 4104 4105 4106 4107 4108 4109 4110 4111 4112 4113 4114 4115 4116 4117 4118 4119 4120 4121 4122 4123 4124 4125 4126 4127 4128 4129 4130 4131 4132 4133 4134 4135 4136 4137 4138 4139 4140 4141 4142 4143 4144 4145 4146 4147 4148 4149 4150 4151 4152 4153 4154 4155 4156 4157 4158 4159 4160 4161 4162 4163 4164 4165 4166 4167 4168 4169 4170 4171 4172 4173 4174 4175 4176 4177 4178 4179 4180 4181 4182 4183 4184 4185 4186 4187 4188 4189 4190 4191 4192 4193 4194 4195 4196 4197 4198 4199 4200 4201 4202 4203 4204 4205 4206 4207 4208 4209 4210 4211 4212 4213 4214 4215 4216 4217 4218 4219 4220 4221 4222 4223 4224 4225 4226 4227 4228 4229 4230 4231 4232 4233 4234 4235 4236 4237 4238 4239 4240 4241 4242 4243 4244 4245 4246 4247 4248 4249 4250 4251 4252 4253 4254 4255 4256 4257 4258 4259 4260 4261 4262 4263 4264 4265 4266 4267 4268 4269 4270 4271 4272 4273 4274 4275 4276 4277 4278 4279 4280 4281 4282 4283 4284 4285 4286 4287 4288 4289 4290 4291
#
# The ndarray object from _testbuffer.c is a complete implementation of
# a PEP-3118 buffer provider. It is independent from NumPy's ndarray
# and the tests don't require NumPy.
#
# If NumPy is present, some tests check both ndarray implementations
# against each other.
#
# Most ndarray tests also check that memoryview(ndarray) behaves in
# the same way as the original. Thus, a substantial part of the
# memoryview tests is now in this module.
#

import unittest
from test import support
from itertools import permutations, product
from random import randrange, sample, choice
from sysconfig import get_config_var
import warnings
import sys, array, io
from decimal import Decimal
from fractions import Fraction

try:
    from _testbuffer import *
except ImportError:
    ndarray = None

try:
    import struct
except ImportError:
    struct = None

try:
    import ctypes
except ImportError:
    ctypes = None

try:
    with warnings.catch_warnings():
        from numpy import ndarray as numpy_array
except ImportError:
    numpy_array = None


SHORT_TEST = True


# ======================================================================
#                    Random lists by format specifier
# ======================================================================

# Native format chars and their ranges.
NATIVE = {
    '?':0, 'c':0, 'b':0, 'B':0,
    'h':0, 'H':0, 'i':0, 'I':0,
    'l':0, 'L':0, 'n':0, 'N':0,
    'f':0, 'd':0, 'P':0
}

# NumPy does not have 'n' or 'N':
if numpy_array:
    del NATIVE['n']
    del NATIVE['N']

if struct:
    try:
        # Add "qQ" if present in native mode.
        struct.pack('Q', 2**64-1)
        NATIVE['q'] = 0
        NATIVE['Q'] = 0
    except struct.error:
        pass

# Standard format chars and their ranges.
STANDARD = {
    '?':(0, 2),            'c':(0, 1<<8),
    'b':(-(1<<7), 1<<7),   'B':(0, 1<<8),
    'h':(-(1<<15), 1<<15), 'H':(0, 1<<16),
    'i':(-(1<<31), 1<<31), 'I':(0, 1<<32),
    'l':(-(1<<31), 1<<31), 'L':(0, 1<<32),
    'q':(-(1<<63), 1<<63), 'Q':(0, 1<<64),
    'f':(-(1<<63), 1<<63), 'd':(-(1<<1023), 1<<1023)
}

def native_type_range(fmt):
    """Return range of a native type."""
    if fmt == 'c':
        lh = (0, 256)
    elif fmt == '?':
        lh = (0, 2)
    elif fmt == 'f':
        lh = (-(1<<63), 1<<63)
    elif fmt == 'd':
        lh = (-(1<<1023), 1<<1023)
    else:
        for exp in (128, 127, 64, 63, 32, 31, 16, 15, 8, 7):
            try:
                struct.pack(fmt, (1<<exp)-1)
                break
            except struct.error:
                pass
        lh = (-(1<<exp), 1<<exp) if exp & 1 else (0, 1<<exp)
    return lh

fmtdict = {
    '':NATIVE,
    '@':NATIVE,
    '<':STANDARD,
    '>':STANDARD,
    '=':STANDARD,
    '!':STANDARD
}

if struct:
    for fmt in fmtdict['@']:
        fmtdict['@'][fmt] = native_type_range(fmt)

MEMORYVIEW = NATIVE.copy()
ARRAY = NATIVE.copy()
for k in NATIVE:
    if not k in "bBhHiIlLfd":
        del ARRAY[k]

BYTEFMT = NATIVE.copy()
for k in NATIVE:
    if not k in "Bbc":
        del BYTEFMT[k]

fmtdict['m']  = MEMORYVIEW
fmtdict['@m'] = MEMORYVIEW
fmtdict['a']  = ARRAY
fmtdict['b']  = BYTEFMT
fmtdict['@b']  = BYTEFMT

# Capabilities of the test objects:
MODE = 0
MULT = 1
cap = {         # format chars                  # multiplier
  'ndarray':    (['', '@', '<', '>', '=', '!'], ['', '1', '2', '3']),
  'array':      (['a'],                         ['']),
  'numpy':      ([''],                          ['']),
  'memoryview': (['@m', 'm'],                   ['']),
  'bytefmt':    (['@b', 'b'],                   ['']),
}

def randrange_fmt(mode, char, obj):
    """Return random item for a type specified by a mode and a single
       format character."""
    x = randrange(*fmtdict[mode][char])
    if char == 'c':
        x = bytes(chr(x), 'latin1')
    if char == '?':
        x = bool(x)
    if char == 'f' or char == 'd':
        x = struct.pack(char, x)
        x = struct.unpack(char, x)[0]
    if obj == 'numpy' and x == b'\x00':
        # http://projects.scipy.org/numpy/ticket/1925
        x = b'\x01'
    return x

def gen_item(fmt, obj):
    """Return single random item."""
    mode, chars = fmt.split('#')
    x = []
    for c in chars:
        x.append(randrange_fmt(mode, c, obj))
    return x[0] if len(x) == 1 else tuple(x)

def gen_items(n, fmt, obj):
    """Return a list of random items (or a scalar)."""
    if n == 0:
        return gen_item(fmt, obj)
    lst = [0] * n
    for i in range(n):
        lst[i] = gen_item(fmt, obj)
    return lst

def struct_items(n, obj):
    mode = choice(cap[obj][MODE])
    xfmt = mode + '#'
    fmt = mode.strip('amb')
    nmemb = randrange(2, 10) # number of struct members
    for _ in range(nmemb):
        char = choice(tuple(fmtdict[mode]))
        multiplier = choice(cap[obj][MULT])
        xfmt += (char * int(multiplier if multiplier else 1))
        fmt += (multiplier + char)
    items = gen_items(n, xfmt, obj)
    item = gen_item(xfmt, obj)
    return fmt, items, item

def randitems(n, obj='ndarray', mode=None, char=None):
    """Return random format, items, item."""
    if mode is None:
        mode = choice(cap[obj][MODE])
    if char is None:
        char = choice(tuple(fmtdict[mode]))
    multiplier = choice(cap[obj][MULT])
    fmt = mode + '#' + char * int(multiplier if multiplier else 1)
    items = gen_items(n, fmt, obj)
    item = gen_item(fmt, obj)
    fmt = mode.strip('amb') + multiplier + char
    return fmt, items, item

def iter_mode(n, obj='ndarray'):
    """Iterate through supported mode/char combinations."""
    for mode in cap[obj][MODE]:
        for char in fmtdict[mode]:
            yield randitems(n, obj, mode, char)

def iter_format(nitems, testobj='ndarray'):
    """Yield (format, items, item) for all possible modes and format
       characters plus one random compound format string."""
    for t in iter_mode(nitems, testobj):
        yield t
    if testobj != 'ndarray':
        raise StopIteration
    yield struct_items(nitems, testobj)


def is_byte_format(fmt):
    return 'c' in fmt or 'b' in fmt or 'B' in fmt

def is_memoryview_format(fmt):
    """format suitable for memoryview"""
    x = len(fmt)
    return ((x == 1 or (x == 2 and fmt[0] == '@')) and
            fmt[x-1] in MEMORYVIEW)

NON_BYTE_FORMAT = [c for c in fmtdict['@'] if not is_byte_format(c)]


# ======================================================================
#       Multi-dimensional tolist(), slicing and slice assignments
# ======================================================================

def atomp(lst):
    """Tuple items (representing structs) are regarded as atoms."""
    return not isinstance(lst, list)

def listp(lst):
    return isinstance(lst, list)

def prod(lst):
    """Product of list elements."""
    if len(lst) == 0:
        return 0
    x = lst[0]
    for v in lst[1:]:
        x *= v
    return x

def strides_from_shape(ndim, shape, itemsize, layout):
    """Calculate strides of a contiguous array. Layout is 'C' or
       'F' (Fortran)."""
    if ndim == 0:
        return ()
    if layout == 'C':
        strides = list(shape[1:]) + [itemsize]
        for i in range(ndim-2, -1, -1):
            strides[i] *= strides[i+1]
    else:
        strides = [itemsize] + list(shape[:-1])
        for i in range(1, ndim):
            strides[i] *= strides[i-1]
    return strides

def _ca(items, s):
    """Convert flat item list to the nested list representation of a
       multidimensional C array with shape 's'."""
    if atomp(items):
        return items
    if len(s) == 0:
        return items[0]
    lst = [0] * s[0]
    stride = len(items) // s[0] if s[0] else 0
    for i in range(s[0]):
        start = i*stride
        lst[i] = _ca(items[start:start+stride], s[1:])
    return lst

def _fa(items, s):
    """Convert flat item list to the nested list representation of a
       multidimensional Fortran array with shape 's'."""
    if atomp(items):
        return items
    if len(s) == 0:
        return items[0]
    lst = [0] * s[0]
    stride = s[0]
    for i in range(s[0]):
        lst[i] = _fa(items[i::stride], s[1:])
    return lst

def carray(items, shape):
    if listp(items) and not 0 in shape and prod(shape) != len(items):
        raise ValueError("prod(shape) != len(items)")
    return _ca(items, shape)

def farray(items, shape):
    if listp(items) and not 0 in shape and prod(shape) != len(items):
        raise ValueError("prod(shape) != len(items)")
    return _fa(items, shape)

def indices(shape):
    """Generate all possible tuples of indices."""
    iterables = [range(v) for v in shape]
    return product(*iterables)

def getindex(ndim, ind, strides):
    """Convert multi-dimensional index to the position in the flat list."""
    ret = 0
    for i in range(ndim):
        ret += strides[i] * ind[i]
    return ret

def transpose(src, shape):
    """Transpose flat item list that is regarded as a multi-dimensional
       matrix defined by shape: dest...[k][j][i] = src[i][j][k]...  """
    if not shape:
        return src
    ndim = len(shape)
    sstrides = strides_from_shape(ndim, shape, 1, 'C')
    dstrides = strides_from_shape(ndim, shape[::-1], 1, 'C')
    dest = [0] * len(src)
    for ind in indices(shape):
        fr = getindex(ndim, ind, sstrides)
        to = getindex(ndim, ind[::-1], dstrides)
        dest[to] = src[fr]
    return dest

def _flatten(lst):
    """flatten list"""
    if lst == []:
        return lst
    if atomp(lst):
        return [lst]
    return _flatten(lst[0]) + _flatten(lst[1:])

def flatten(lst):
    """flatten list or return scalar"""
    if atomp(lst): # scalar
        return lst
    return _flatten(lst)

def slice_shape(lst, slices):
    """Get the shape of lst after slicing: slices is a list of slice
       objects."""
    if atomp(lst):
        return []
    return [len(lst[slices[0]])] + slice_shape(lst[0], slices[1:])

def multislice(lst, slices):
    """Multi-dimensional slicing: slices is a list of slice objects."""
    if atomp(lst):
        return lst
    return [multislice(sublst, slices[1:]) for sublst in lst[slices[0]]]

def m_assign(llst, rlst, lslices, rslices):
    """Multi-dimensional slice assignment: llst and rlst are the operands,
       lslices and rslices are lists of slice objects. llst and rlst must
       have the same structure.

       For a two-dimensional example, this is not implemented in Python:

         llst[0:3:2, 0:3:2] = rlst[1:3:1, 1:3:1]

       Instead we write:

         lslices = [slice(0,3,2), slice(0,3,2)]
         rslices = [slice(1,3,1), slice(1,3,1)]
         multislice_assign(llst, rlst, lslices, rslices)
    """
    if atomp(rlst):
        return rlst
    rlst = [m_assign(l, r, lslices[1:], rslices[1:])
            for l, r in zip(llst[lslices[0]], rlst[rslices[0]])]
    llst[lslices[0]] = rlst
    return llst

def cmp_structure(llst, rlst, lslices, rslices):
    """Compare the structure of llst[lslices] and rlst[rslices]."""
    lshape = slice_shape(llst, lslices)
    rshape = slice_shape(rlst, rslices)
    if (len(lshape) != len(rshape)):
        return -1
    for i in range(len(lshape)):
        if lshape[i] != rshape[i]:
            return -1
        if lshape[i] == 0:
            return 0
    return 0

def multislice_assign(llst, rlst, lslices, rslices):
    """Return llst after assigning: llst[lslices] = rlst[rslices]"""
    if cmp_structure(llst, rlst, lslices, rslices) < 0:
        raise ValueError("lvalue and rvalue have different structures")
    return m_assign(llst, rlst, lslices, rslices)


# ======================================================================
#                          Random structures
# ======================================================================

#
# PEP-3118 is very permissive with respect to the contents of a
# Py_buffer. In particular:
#
#   - shape can be zero
#   - strides can be any integer, including zero
#   - offset can point to any location in the underlying
#     memory block, provided that it is a multiple of
#     itemsize.
#
# The functions in this section test and verify random structures
# in full generality. A structure is valid iff it fits in the
# underlying memory block.
#
# The structure 't' (short for 'tuple') is fully defined by:
#
#   t = (memlen, itemsize, ndim, shape, strides, offset)
#

def verify_structure(memlen, itemsize, ndim, shape, strides, offset):
    """Verify that the parameters represent a valid array within
       the bounds of the allocated memory:
           char *mem: start of the physical memory block
           memlen: length of the physical memory block
           offset: (char *)buf - mem
    """
    if offset % itemsize:
        return False
    if offset < 0 or offset+itemsize > memlen:
        return False
    if any(v % itemsize for v in strides):
        return False

    if ndim <= 0:
        return ndim == 0 and not shape and not strides
    if 0 in shape:
        return True

    imin = sum(strides[j]*(shape[j]-1) for j in range(ndim)
               if strides[j] <= 0)
    imax = sum(strides[j]*(shape[j]-1) for j in range(ndim)
               if strides[j] > 0)

    return 0 <= offset+imin and offset+imax+itemsize <= memlen

def get_item(lst, indices):
    for i in indices:
        lst = lst[i]
    return lst

def memory_index(indices, t):
    """Location of an item in the underlying memory."""
    memlen, itemsize, ndim, shape, strides, offset = t
    p = offset
    for i in range(ndim):
        p += strides[i]*indices[i]
    return p

def is_overlapping(t):
    """The structure 't' is overlapping if at least one memory location
       is visited twice while iterating through all possible tuples of
       indices."""
    memlen, itemsize, ndim, shape, strides, offset = t
    visited = 1<<memlen
    for ind in indices(shape):
        i = memory_index(ind, t)
        bit = 1<<i
        if visited & bit:
            return True
        visited |= bit
    return False

def rand_structure(itemsize, valid, maxdim=5, maxshape=16, shape=()):
    """Return random structure:
           (memlen, itemsize, ndim, shape, strides, offset)
       If 'valid' is true, the returned structure is valid, otherwise invalid.
       If 'shape' is given, use that instead of creating a random shape.
    """
    if not shape:
        ndim = randrange(maxdim+1)
        if (ndim == 0):
            if valid:
                return itemsize, itemsize, ndim, (), (), 0
            else:
                nitems = randrange(1, 16+1)
                memlen = nitems * itemsize
                offset = -itemsize if randrange(2) == 0 else memlen
                return memlen, itemsize, ndim, (), (), offset

        minshape = 2
        n = randrange(100)
        if n >= 95 and valid:
            minshape = 0
        elif n >= 90:
            minshape = 1
        shape = [0] * ndim

        for i in range(ndim):
            shape[i] = randrange(minshape, maxshape+1)
    else:
        ndim = len(shape)

    maxstride = 5
    n = randrange(100)
    zero_stride = True if n >= 95 and n & 1 else False

    strides = [0] * ndim
    strides[ndim-1] = itemsize * randrange(-maxstride, maxstride+1)
    if not zero_stride and strides[ndim-1] == 0:
        strides[ndim-1] = itemsize

    for i in range(ndim-2, -1, -1):
        maxstride *= shape[i+1] if shape[i+1] else 1
        if zero_stride:
            strides[i] = itemsize * randrange(-maxstride, maxstride+1)
        else:
            strides[i] = ((1,-1)[randrange(2)] *
                          itemsize * randrange(1, maxstride+1))

    imin = imax = 0
    if not 0 in shape:
        imin = sum(strides[j]*(shape[j]-1) for j in range(ndim)
                   if strides[j] <= 0)
        imax = sum(strides[j]*(shape[j]-1) for j in range(ndim)
                   if strides[j] > 0)

    nitems = imax - imin
    if valid:
        offset = -imin * itemsize
        memlen = offset + (imax+1) * itemsize
    else:
        memlen = (-imin + imax) * itemsize
        offset = -imin-itemsize if randrange(2) == 0 else memlen
    return memlen, itemsize, ndim, shape, strides, offset

def randslice_from_slicelen(slicelen, listlen):
    """Create a random slice of len slicelen that fits into listlen."""
    maxstart = listlen - slicelen
    start = randrange(maxstart+1)
    maxstep = (listlen - start) // slicelen if slicelen else 1
    step = randrange(1, maxstep+1)
    stop = start + slicelen * step
    s = slice(start, stop, step)
    _, _, _, control = slice_indices(s, listlen)
    if control != slicelen:
        raise RuntimeError
    return s

def randslice_from_shape(ndim, shape):
    """Create two sets of slices for an array x with shape 'shape'
       such that shapeof(x[lslices]) == shapeof(x[rslices])."""
    lslices = [0] * ndim
    rslices = [0] * ndim
    for n in range(ndim):
        l = shape[n]
        slicelen = randrange(1, l+1) if l > 0 else 0
        lslices[n] = randslice_from_slicelen(slicelen, l)
        rslices[n] = randslice_from_slicelen(slicelen, l)
    return tuple(lslices), tuple(rslices)

def rand_aligned_slices(maxdim=5, maxshape=16):
    """Create (lshape, rshape, tuple(lslices), tuple(rslices)) such that
       shapeof(x[lslices]) == shapeof(y[rslices]), where x is an array
       with shape 'lshape' and y is an array with shape 'rshape'."""
    ndim = randrange(1, maxdim+1)
    minshape = 2
    n = randrange(100)
    if n >= 95:
        minshape = 0
    elif n >= 90:
        minshape = 1
    all_random = True if randrange(100) >= 80 else False
    lshape = [0]*ndim; rshape = [0]*ndim
    lslices = [0]*ndim; rslices = [0]*ndim

    for n in range(ndim):
        small = randrange(minshape, maxshape+1)
        big = randrange(minshape, maxshape+1)
        if big < small:
            big, small = small, big

        # Create a slice that fits the smaller value.
        if all_random:
            start = randrange(-small, small+1)
            stop = randrange(-small, small+1)
            step = (1,-1)[randrange(2)] * randrange(1, small+2)
            s_small = slice(start, stop, step)
            _, _, _, slicelen = slice_indices(s_small, small)
        else:
            slicelen = randrange(1, small+1) if small > 0 else 0
            s_small = randslice_from_slicelen(slicelen, small)

        # Create a slice of the same length for the bigger value.
        s_big = randslice_from_slicelen(slicelen, big)
        if randrange(2) == 0:
            rshape[n], lshape[n] = big, small
            rslices[n], lslices[n] = s_big, s_small
        else:
            rshape[n], lshape[n] = small, big
            rslices[n], lslices[n] = s_small, s_big

    return lshape, rshape, tuple(lslices), tuple(rslices)

def randitems_from_structure(fmt, t):
    """Return a list of random items for structure 't' with format
       'fmtchar'."""
    memlen, itemsize, _, _, _, _ = t
    return gen_items(memlen//itemsize, '#'+fmt, 'numpy')

def ndarray_from_structure(items, fmt, t, flags=0):
    """Return ndarray from the tuple returned by rand_structure()"""
    memlen, itemsize, ndim, shape, strides, offset = t
    return ndarray(items, shape=shape, strides=strides, format=fmt,
                   offset=offset, flags=ND_WRITABLE|flags)

def numpy_array_from_structure(items, fmt, t):
    """Return numpy_array from the tuple returned by rand_structure()"""
    memlen, itemsize, ndim, shape, strides, offset = t
    buf = bytearray(memlen)
    for j, v in enumerate(items):
        struct.pack_into(fmt, buf, j*itemsize, v)
    return numpy_array(buffer=buf, shape=shape, strides=strides,
                       dtype=fmt, offset=offset)


# ======================================================================
#                          memoryview casts
# ======================================================================

def cast_items(exporter, fmt, itemsize, shape=None):
    """Interpret the raw memory of 'exporter' as a list of items with
       size 'itemsize'. If shape=None, the new structure is assumed to
       be 1-D with n * itemsize = bytelen. If shape is given, the usual
       constraint for contiguous arrays prod(shape) * itemsize = bytelen
       applies. On success, return (items, shape). If the constraints
       cannot be met, return (None, None). If a chunk of bytes is interpreted
       as NaN as a result of float conversion, return ('nan', None)."""
    bytelen = exporter.nbytes
    if shape:
        if prod(shape) * itemsize != bytelen:
            return None, shape
    elif shape == []:
        if exporter.ndim == 0 or itemsize != bytelen:
            return None, shape
    else:
        n, r = divmod(bytelen, itemsize)
        shape = [n]
        if r != 0:
            return None, shape

    mem = exporter.tobytes()
    byteitems = [mem[i:i+itemsize] for i in range(0, len(mem), itemsize)]

    items = []
    for v in byteitems:
        item = struct.unpack(fmt, v)[0]
        if item != item:
            return 'nan', shape
        items.append(item)

    return (items, shape) if shape != [] else (items[0], shape)

def gencastshapes():
    """Generate shapes to test casting."""
    for n in range(32):
        yield [n]
    ndim = randrange(4, 6)
    minshape = 1 if randrange(100) > 80 else 2
    yield [randrange(minshape, 5) for _ in range(ndim)]
    ndim = randrange(2, 4)
    minshape = 1 if randrange(100) > 80 else 2
    yield [randrange(minshape, 5) for _ in range(ndim)]


# ======================================================================
#                              Actual tests
# ======================================================================

def genslices(n):
    """Generate all possible slices for a single dimension."""
    return product(range(-n, n+1), range(-n, n+1), range(-n, n+1))

def genslices_ndim(ndim, shape):
    """Generate all possible slice tuples for 'shape'."""
    iterables = [genslices(shape[n]) for n in range(ndim)]
    return product(*iterables)

def rslice(n, allow_empty=False):
    """Generate random slice for a single dimension of length n.
       If zero=True, the slices may be empty, otherwise they will
       be non-empty."""
    minlen = 0 if allow_empty or n == 0 else 1
    slicelen = randrange(minlen, n+1)
    return randslice_from_slicelen(slicelen, n)

def rslices(n, allow_empty=False):
    """Generate random slices for a single dimension."""
    for _ in range(5):
        yield rslice(n, allow_empty)

def rslices_ndim(ndim, shape, iterations=5):
    """Generate random slice tuples for 'shape'."""
    # non-empty slices
    for _ in range(iterations):
        yield tuple(rslice(shape[n]) for n in range(ndim))
    # possibly empty slices
    for _ in range(iterations):
        yield tuple(rslice(shape[n], allow_empty=True) for n in range(ndim))
    # invalid slices
    yield tuple(slice(0,1,0) for _ in range(ndim))

def rpermutation(iterable, r=None):
    pool = tuple(iterable)
    r = len(pool) if r is None else r
    yield tuple(sample(pool, r))

def ndarray_print(nd):
    """Print ndarray for debugging."""
    try:
        x = nd.tolist()
    except (TypeError, NotImplementedError):
        x = nd.tobytes()
    if isinstance(nd, ndarray):
        offset = nd.offset
        flags = nd.flags
    else:
        offset = 'unknown'
        flags = 'unknown'
    print("ndarray(%s, shape=%s, strides=%s, suboffsets=%s, offset=%s, "
          "format='%s', itemsize=%s, flags=%s)" %
          (x, nd.shape, nd.strides, nd.suboffsets, offset,
           nd.format, nd.itemsize, flags))
    sys.stdout.flush()


ITERATIONS = 100
MAXDIM = 5
MAXSHAPE = 10

if SHORT_TEST:
    ITERATIONS = 10
    MAXDIM = 3
    MAXSHAPE = 4
    genslices = rslices
    genslices_ndim = rslices_ndim
    permutations = rpermutation


@unittest.skipUnless(struct, 'struct module required for this test.')
@unittest.skipUnless(ndarray, 'ndarray object required for this test')
class TestBufferProtocol(unittest.TestCase):

    def setUp(self):
        # The suboffsets tests need sizeof(void *).
        self.sizeof_void_p = get_sizeof_void_p()

    def verify(self, result, obj=-1,
                     itemsize={1}, fmt=-1, readonly={1},
                     ndim={1}, shape=-1, strides=-1,
                     lst=-1, sliced=False, cast=False):
        # Verify buffer contents against expected values. Default values
        # are deliberately initialized to invalid types.
        if shape:
            expected_len = prod(shape)*itemsize
        else:
            if not fmt: # array has been implicitly cast to unsigned bytes
                expected_len = len(lst)
            else: # ndim = 0
                expected_len = itemsize

        # Reconstruct suboffsets from strides. Support for slicing
        # could be added, but is currently only needed for test_getbuf().
        suboffsets = ()
        if result.suboffsets:
            self.assertGreater(ndim, 0)

            suboffset0 = 0
            for n in range(1, ndim):
                if shape[n] == 0:
                    break
                if strides[n] <= 0:
                    suboffset0 += -strides[n] * (shape[n]-1)

            suboffsets = [suboffset0] + [-1 for v in range(ndim-1)]

            # Not correct if slicing has occurred in the first dimension.
            stride0 = self.sizeof_void_p
            if strides[0] < 0:
                stride0 = -stride0
            strides = [stride0] + list(strides[1:])

        self.assertIs(result.obj, obj)
        self.assertEqual(result.nbytes, expected_len)
        self.assertEqual(result.itemsize, itemsize)
        self.assertEqual(result.format, fmt)
        self.assertEqual(result.readonly, readonly)
        self.assertEqual(result.ndim, ndim)
        self.assertEqual(result.shape, tuple(shape))
        if not (sliced and suboffsets):
            self.assertEqual(result.strides, tuple(strides))
        self.assertEqual(result.suboffsets, tuple(suboffsets))

        if isinstance(result, ndarray) or is_memoryview_format(fmt):
            rep = result.tolist() if fmt else result.tobytes()
            self.assertEqual(rep, lst)

        if not fmt: # array has been cast to unsigned bytes,
            return  # the remaining tests won't work.

        # PyBuffer_GetPointer() is the definition how to access an item.
        # If PyBuffer_GetPointer(indices) is correct for all possible
        # combinations of indices, the buffer is correct.
        #
        # Also test tobytes() against the flattened 'lst', with all items
        # packed to bytes.
        if not cast: # casts chop up 'lst' in different ways
            b = bytearray()
            buf_err = None
            for ind in indices(shape):
                try:
                    item1 = get_pointer(result, ind)
                    item2 = get_item(lst, ind)
                    if isinstance(item2, tuple):
                        x = struct.pack(fmt, *item2)
                    else:
                        x = struct.pack(fmt, item2)
                    b.extend(x)
                except BufferError:
                    buf_err = True # re-exporter does not provide full buffer
                    break
                self.assertEqual(item1, item2)

            if not buf_err:
                # test tobytes()
                self.assertEqual(result.tobytes(), b)

                # lst := expected multi-dimensional logical representation
                # flatten(lst) := elements in C-order
                ff = fmt if fmt else 'B'
                flattened = flatten(lst)

                # Rules for 'A': if the array is already contiguous, return
                # the array unaltered. Otherwise, return a contiguous 'C'
                # representation.
                for order in ['C', 'F', 'A']:
                    expected = result
                    if order == 'F':
                        if not is_contiguous(result, 'A') or \
                           is_contiguous(result, 'C'):
                            # For constructing the ndarray, convert the
                            # flattened logical representation to Fortran order.
                            trans = transpose(flattened, shape)
                            expected = ndarray(trans, shape=shape, format=ff,
                                               flags=ND_FORTRAN)
                    else: # 'C', 'A'
                        if not is_contiguous(result, 'A') or \
                           is_contiguous(result, 'F') and order == 'C':
                            # The flattened list is already in C-order.
                            expected = ndarray(flattened, shape=shape, format=ff)

                    contig = get_contiguous(result, PyBUF_READ, order)
                    self.assertEqual(contig.tobytes(), b)
                    self.assertTrue(cmp_contig(contig, expected))

                    if ndim == 0:
                        continue

                    nmemb = len(flattened)
                    ro = 0 if readonly else ND_WRITABLE

                    ### See comment in test_py_buffer_to_contiguous for an
                    ### explanation why these tests are valid.

                    # To 'C'
                    contig = py_buffer_to_contiguous(result, 'C', PyBUF_FULL_RO)
                    self.assertEqual(len(contig), nmemb * itemsize)
                    initlst = [struct.unpack_from(fmt, contig, n*itemsize)
                               for n in range(nmemb)]
                    if len(initlst[0]) == 1:
                        initlst = [v[0] for v in initlst]

                    y = ndarray(initlst, shape=shape, flags=ro, format=fmt)
                    self.assertEqual(memoryview(y), memoryview(result))

                    # To 'F'
                    contig = py_buffer_to_contiguous(result, 'F', PyBUF_FULL_RO)
                    self.assertEqual(len(contig), nmemb * itemsize)
                    initlst = [struct.unpack_from(fmt, contig, n*itemsize)
                               for n in range(nmemb)]
                    if len(initlst[0]) == 1:
                        initlst = [v[0] for v in initlst]

                    y = ndarray(initlst, shape=shape, flags=ro|ND_FORTRAN,
                                format=fmt)
                    self.assertEqual(memoryview(y), memoryview(result))

                    # To 'A'
                    contig = py_buffer_to_contiguous(result, 'A', PyBUF_FULL_RO)
                    self.assertEqual(len(contig), nmemb * itemsize)
                    initlst = [struct.unpack_from(fmt, contig, n*itemsize)
                               for n in range(nmemb)]
                    if len(initlst[0]) == 1:
                        initlst = [v[0] for v in initlst]

                    f = ND_FORTRAN if is_contiguous(result, 'F') else 0
                    y = ndarray(initlst, shape=shape, flags=f|ro, format=fmt)
                    self.assertEqual(memoryview(y), memoryview(result))

        if is_memoryview_format(fmt):
            try:
                m = memoryview(result)
            except BufferError: # re-exporter does not provide full information
                return
            ex = result.obj if isinstance(result, memoryview) else result
            self.assertIs(m.obj, ex)
            self.assertEqual(m.nbytes, expected_len)
            self.assertEqual(m.itemsize, itemsize)
            self.assertEqual(m.format, fmt)
            self.assertEqual(m.readonly, readonly)
            self.assertEqual(m.ndim, ndim)
            self.assertEqual(m.shape, tuple(shape))
            if not (sliced and suboffsets):
                self.assertEqual(m.strides, tuple(strides))
            self.assertEqual(m.suboffsets, tuple(suboffsets))

            n = 1 if ndim == 0 else len(lst)
            self.assertEqual(len(m), n)

            rep = result.tolist() if fmt else result.tobytes()
            self.assertEqual(rep, lst)
            self.assertEqual(m, result)

    def verify_getbuf(self, orig_ex, ex, req, sliced=False):
        def simple_fmt(ex):
            return ex.format == '' or ex.format == 'B'
        def match(req, flag):
            return ((req&flag) == flag)

        if (# writable request to read-only exporter
            (ex.readonly and match(req, PyBUF_WRITABLE)) or
            # cannot match explicit contiguity request
            (match(req, PyBUF_C_CONTIGUOUS) and not ex.c_contiguous) or
            (match(req, PyBUF_F_CONTIGUOUS) and not ex.f_contiguous) or
            (match(req, PyBUF_ANY_CONTIGUOUS) and not ex.contiguous) or
            # buffer needs suboffsets
            (not match(req, PyBUF_INDIRECT) and ex.suboffsets) or
            # buffer without strides must be C-contiguous
            (not match(req, PyBUF_STRIDES) and not ex.c_contiguous) or
            # PyBUF_SIMPLE|PyBUF_FORMAT and PyBUF_WRITABLE|PyBUF_FORMAT
            (not match(req, PyBUF_ND) and match(req, PyBUF_FORMAT))):

            self.assertRaises(BufferError, ndarray, ex, getbuf=req)
            return

        if isinstance(ex, ndarray) or is_memoryview_format(ex.format):
            lst = ex.tolist()
        else:
            nd = ndarray(ex, getbuf=PyBUF_FULL_RO)
            lst = nd.tolist()

        # The consumer may have requested default values or a NULL format.
        ro = 0 if match(req, PyBUF_WRITABLE) else ex.readonly
        fmt = ex.format
        itemsize = ex.itemsize
        ndim = ex.ndim
        if not match(req, PyBUF_FORMAT):
            # itemsize refers to the original itemsize before the cast.
            # The equality product(shape) * itemsize = len still holds.
            # The equality calcsize(format) = itemsize does _not_ hold.
            fmt = ''
            lst = orig_ex.tobytes() # Issue 12834
        if not match(req, PyBUF_ND):
            ndim = 1
        shape = orig_ex.shape if match(req, PyBUF_ND) else ()
        strides = orig_ex.strides if match(req, PyBUF_STRIDES) else ()

        nd = ndarray(ex, getbuf=req)
        self.verify(nd, obj=ex,
                    itemsize=itemsize, fmt=fmt, readonly=ro,
                    ndim=ndim, shape=shape, strides=strides,
                    lst=lst, sliced=sliced)

    def test_ndarray_getbuf(self):
        requests = (
            # distinct flags
            PyBUF_INDIRECT, PyBUF_STRIDES, PyBUF_ND, PyBUF_SIMPLE,
            PyBUF_C_CONTIGUOUS, PyBUF_F_CONTIGUOUS, PyBUF_ANY_CONTIGUOUS,
            # compound requests
            PyBUF_FULL, PyBUF_FULL_RO,
            PyBUF_RECORDS, PyBUF_RECORDS_RO,
            PyBUF_STRIDED, PyBUF_STRIDED_RO,
            PyBUF_CONTIG, PyBUF_CONTIG_RO,
        )
        # items and format
        items_fmt = (
            ([True if x % 2 else False for x in range(12)], '?'),
            ([1,2,3,4,5,6,7,8,9,10,11,12], 'b'),
            ([1,2,3,4,5,6,7,8,9,10,11,12], 'B'),
            ([(2**31-x) if x % 2 else (-2**31+x) for x in range(12)], 'l')
        )
        # shape, strides, offset
        structure = (
            ([], [], 0),
            ([12], [], 0),
            ([12], [-1], 11),
            ([6], [2], 0),
            ([6], [-2], 11),
            ([3, 4], [], 0),
            ([3, 4], [-4, -1], 11),
            ([2, 2], [4, 1], 4),
            ([2, 2], [-4, -1], 8)
        )
        # ndarray creation flags
        ndflags = (
            0, ND_WRITABLE, ND_FORTRAN, ND_FORTRAN|ND_WRITABLE,
            ND_PIL, ND_PIL|ND_WRITABLE
        )
        # flags that can actually be used as flags
        real_flags = (0, PyBUF_WRITABLE, PyBUF_FORMAT,
                      PyBUF_WRITABLE|PyBUF_FORMAT)

        for items, fmt in items_fmt:
            itemsize = struct.calcsize(fmt)
            for shape, strides, offset in structure:
                strides = [v * itemsize for v in strides]
                offset *= itemsize
                for flags in ndflags:

                    if strides and (flags&ND_FORTRAN):
                        continue
                    if not shape and (flags&ND_PIL):
                        continue

                    _items = items if shape else items[0]
                    ex1 = ndarray(_items, format=fmt, flags=flags,
                                  shape=shape, strides=strides, offset=offset)
                    ex2 = ex1[::-2] if shape else None

                    m1 = memoryview(ex1)
                    if ex2:
                        m2 = memoryview(ex2)
                    if ex1.ndim == 0 or (ex1.ndim == 1 and shape and strides):
                        self.assertEqual(m1, ex1)
                    if ex2 and ex2.ndim == 1 and shape and strides:
                        self.assertEqual(m2, ex2)

                    for req in requests:
                        for bits in real_flags:
                            self.verify_getbuf(ex1, ex1, req|bits)
                            self.verify_getbuf(ex1, m1, req|bits)
                            if ex2:
                                self.verify_getbuf(ex2, ex2, req|bits,
                                                   sliced=True)
                                self.verify_getbuf(ex2, m2, req|bits,
                                                   sliced=True)

        items = [1,2,3,4,5,6,7,8,9,10,11,12]

        # ND_GETBUF_FAIL
        ex = ndarray(items, shape=[12], flags=ND_GETBUF_FAIL)
        self.assertRaises(BufferError, ndarray, ex)

        # Request complex structure from a simple exporter. In this
        # particular case the test object is not PEP-3118 compliant.
        base = ndarray([9], [1])
        ex = ndarray(base, getbuf=PyBUF_SIMPLE)
        self.assertRaises(BufferError, ndarray, ex, getbuf=PyBUF_WRITABLE)
        self.assertRaises(BufferError, ndarray, ex, getbuf=PyBUF_ND)
        self.assertRaises(BufferError, ndarray, ex, getbuf=PyBUF_STRIDES)
        self.assertRaises(BufferError, ndarray, ex, getbuf=PyBUF_C_CONTIGUOUS)
        self.assertRaises(BufferError, ndarray, ex, getbuf=PyBUF_F_CONTIGUOUS)
        self.assertRaises(BufferError, ndarray, ex, getbuf=PyBUF_ANY_CONTIGUOUS)
        nd = ndarray(ex, getbuf=PyBUF_SIMPLE)

    def test_ndarray_exceptions(self):
        nd = ndarray([9], [1])
        ndm = ndarray([9], [1], flags=ND_VAREXPORT)

        # Initialization of a new ndarray or mutation of an existing array.
        for c in (ndarray, nd.push, ndm.push):
            # Invalid types.
            self.assertRaises(TypeError, c, {1,2,3})
            self.assertRaises(TypeError, c, [1,2,'3'])
            self.assertRaises(TypeError, c, [1,2,(3,4)])
            self.assertRaises(TypeError, c, [1,2,3], shape={3})
            self.assertRaises(TypeError, c, [1,2,3], shape=[3], strides={1})
            self.assertRaises(TypeError, c, [1,2,3], shape=[3], offset=[])
            self.assertRaises(TypeError, c, [1], shape=[1], format={})
            self.assertRaises(TypeError, c, [1], shape=[1], flags={})
            self.assertRaises(TypeError, c, [1], shape=[1], getbuf={})

            # ND_FORTRAN flag is only valid without strides.
            self.assertRaises(TypeError, c, [1], shape=[1], strides=[1],
                              flags=ND_FORTRAN)

            # ND_PIL flag is only valid with ndim > 0.
            self.assertRaises(TypeError, c, [1], shape=[], flags=ND_PIL)

            # Invalid items.
            self.assertRaises(ValueError, c, [], shape=[1])
            self.assertRaises(ValueError, c, ['XXX'], shape=[1], format="L")
            # Invalid combination of items and format.
            self.assertRaises(struct.error, c, [1000], shape=[1], format="B")
            self.assertRaises(ValueError, c, [1,(2,3)], shape=[2], format="B")
            self.assertRaises(ValueError, c, [1,2,3], shape=[3], format="QL")

            # Invalid ndim.
            n = ND_MAX_NDIM+1
            self.assertRaises(ValueError, c, [1]*n, shape=[1]*n)

            # Invalid shape.
            self.assertRaises(ValueError, c, [1], shape=[-1])
            self.assertRaises(ValueError, c, [1,2,3], shape=['3'])
            self.assertRaises(OverflowError, c, [1], shape=[2**128])
            # prod(shape) * itemsize != len(items)
            self.assertRaises(ValueError, c, [1,2,3,4,5], shape=[2,2], offset=3)

            # Invalid strides.
            self.assertRaises(ValueError, c, [1,2,3], shape=[3], strides=['1'])
            self.assertRaises(OverflowError, c, [1], shape=[1],
                              strides=[2**128])

            # Invalid combination of strides and shape.
            self.assertRaises(ValueError, c, [1,2], shape=[2,1], strides=[1])
            # Invalid combination of strides and format.
            self.assertRaises(ValueError, c, [1,2,3,4], shape=[2], strides=[3],
                              format="L")

            # Invalid offset.
            self.assertRaises(ValueError, c, [1,2,3], shape=[3], offset=4)
            self.assertRaises(ValueError, c, [1,2,3], shape=[1], offset=3,
                              format="L")

            # Invalid format.
            self.assertRaises(ValueError, c, [1,2,3], shape=[3], format="")
            self.assertRaises(struct.error, c, [(1,2,3)], shape=[1],
                              format="@#$")

            # Striding out of the memory bounds.
            items = [1,2,3,4,5,6,7,8,9,10]
            self.assertRaises(ValueError, c, items, shape=[2,3],
                              strides=[-3, -2], offset=5)

            # Constructing consumer: format argument invalid.
            self.assertRaises(TypeError, c, bytearray(), format="Q")

            # Constructing original base object: getbuf argument invalid.
            self.assertRaises(TypeError, c, [1], shape=[1], getbuf=PyBUF_FULL)

            # Shape argument is mandatory for original base objects.
            self.assertRaises(TypeError, c, [1])


        # PyBUF_WRITABLE request to read-only provider.
        self.assertRaises(BufferError, ndarray, b'123', getbuf=PyBUF_WRITABLE)

        # ND_VAREXPORT can only be specified during construction.
        nd = ndarray([9], [1], flags=ND_VAREXPORT)
        self.assertRaises(ValueError, nd.push, [1], [1], flags=ND_VAREXPORT)

        # Invalid operation for consumers: push/pop
        nd = ndarray(b'123')
        self.assertRaises(BufferError, nd.push, [1], [1])
        self.assertRaises(BufferError, nd.pop)

        # ND_VAREXPORT not set: push/pop fail with exported buffers
        nd = ndarray([9], [1])
        nd.push([1], [1])
        m = memoryview(nd)
        self.assertRaises(BufferError, nd.push, [1], [1])
        self.assertRaises(BufferError, nd.pop)
        m.release()
        nd.pop()

        # Single remaining buffer: pop fails
        self.assertRaises(BufferError, nd.pop)
        del nd

        # get_pointer()
        self.assertRaises(TypeError, get_pointer, {}, [1,2,3])
        self.assertRaises(TypeError, get_pointer, b'123', {})

        nd = ndarray(list(range(100)), shape=[1]*100)
        self.assertRaises(ValueError, get_pointer, nd, [5])

        nd = ndarray(list(range(12)), shape=[3,4])
        self.assertRaises(ValueError, get_pointer, nd, [2,3,4])
        self.assertRaises(ValueError, get_pointer, nd, [3,3])
        self.assertRaises(ValueError, get_pointer, nd, [-3,3])
        self.assertRaises(OverflowError, get_pointer, nd, [1<<64,3])

        # tolist() needs format
        ex = ndarray([1,2,3], shape=[3], format='L')
        nd = ndarray(ex, getbuf=PyBUF_SIMPLE)
        self.assertRaises(ValueError, nd.tolist)

        # memoryview_from_buffer()
        ex1 = ndarray([1,2,3], shape=[3], format='L')
        ex2 = ndarray(ex1)
        nd = ndarray(ex2)
        self.assertRaises(TypeError, nd.memoryview_from_buffer)

        nd = ndarray([(1,)*200], shape=[1], format='L'*200)
        self.assertRaises(TypeError, nd.memoryview_from_buffer)

        n = ND_MAX_NDIM
        nd = ndarray(list(range(n)), shape=[1]*n)
        self.assertRaises(ValueError, nd.memoryview_from_buffer)

        # get_contiguous()
        nd = ndarray([1], shape=[1])
        self.assertRaises(TypeError, get_contiguous, 1, 2, 3, 4, 5)
        self.assertRaises(TypeError, get_contiguous, nd, "xyz", 'C')
        self.assertRaises(OverflowError, get_contiguous, nd, 2**64, 'C')
        self.assertRaises(TypeError, get_contiguous, nd, PyBUF_READ, 961)
        self.assertRaises(UnicodeEncodeError, get_contiguous, nd, PyBUF_READ,
                          '\u2007')
        self.assertRaises(ValueError, get_contiguous, nd, PyBUF_READ, 'Z')
        self.assertRaises(ValueError, get_contiguous, nd, 255, 'A')

        # cmp_contig()
        nd = ndarray([1], shape=[1])
        self.assertRaises(TypeError, cmp_contig, 1, 2, 3, 4, 5)
        self.assertRaises(TypeError, cmp_contig, {}, nd)
        self.assertRaises(TypeError, cmp_contig, nd, {})

        # is_contiguous()
        nd = ndarray([1], shape=[1])
        self.assertRaises(TypeError, is_contiguous, 1, 2, 3, 4, 5)
        self.assertRaises(TypeError, is_contiguous, {}, 'A')
        self.assertRaises(TypeError, is_contiguous, nd, 201)

    def test_ndarray_linked_list(self):
        for perm in permutations(range(5)):
            m = [0]*5
            nd = ndarray([1,2,3], shape=[3], flags=ND_VAREXPORT)
            m[0] = memoryview(nd)

            for i in range(1, 5):
                nd.push([1,2,3], shape=[3])
                m[i] = memoryview(nd)

            for i in range(5):
                m[perm[i]].release()

            self.assertRaises(BufferError, nd.pop)
            del nd

    def test_ndarray_format_scalar(self):
        # ndim = 0: scalar
        for fmt, scalar, _ in iter_format(0):
            itemsize = struct.calcsize(fmt)
            nd = ndarray(scalar, shape=(), format=fmt)
            self.verify(nd, obj=None,
                        itemsize=itemsize, fmt=fmt, readonly=1,
                        ndim=0, shape=(), strides=(),
                        lst=scalar)

    def test_ndarray_format_shape(self):
        # ndim = 1, shape = [n]
        nitems =  randrange(1, 10)
        for fmt, items, _ in iter_format(nitems):
            itemsize = struct.calcsize(fmt)
            for flags in (0, ND_PIL):
                nd = ndarray(items, shape=[nitems], format=fmt, flags=flags)
                self.verify(nd, obj=None,
                            itemsize=itemsize, fmt=fmt, readonly=1,
                            ndim=1, shape=(nitems,), strides=(itemsize,),
                            lst=items)

    def test_ndarray_format_strides(self):
        # ndim = 1, strides
        nitems = randrange(1, 30)
        for fmt, items, _ in iter_format(nitems):
            itemsize = struct.calcsize(fmt)
            for step in range(-5, 5):
                if step == 0:
                    continue

                shape = [len(items[::step])]
                strides = [step*itemsize]
                offset = itemsize*(nitems-1) if step < 0 else 0

                for flags in (0, ND_PIL):
                    nd = ndarray(items, shape=shape, strides=strides,
                                 format=fmt, offset=offset, flags=flags)
                    self.verify(nd, obj=None,
                                itemsize=itemsize, fmt=fmt, readonly=1,
                                ndim=1, shape=shape, strides=strides,
                                lst=items[::step])

    def test_ndarray_fortran(self):
        items = [1,2,3,4,5,6,7,8,9,10,11,12]
        ex = ndarray(items, shape=(3, 4), strides=(1, 3))
        nd = ndarray(ex, getbuf=PyBUF_F_CONTIGUOUS|PyBUF_FORMAT)
        self.assertEqual(nd.tolist(), farray(items, (3, 4)))

    def test_ndarray_multidim(self):
        for ndim in range(5):
            shape_t = [randrange(2, 10) for _ in range(ndim)]
            nitems = prod(shape_t)
            for shape in permutations(shape_t):

                fmt, items, _ = randitems(nitems)
                itemsize = struct.calcsize(fmt)

                for flags in (0, ND_PIL):
                    if ndim == 0 and flags == ND_PIL:
                        continue

                    # C array
                    nd = ndarray(items, shape=shape, format=fmt, flags=flags)

                    strides = strides_from_shape(ndim, shape, itemsize, 'C')
                    lst = carray(items, shape)
                    self.verify(nd, obj=None,
                                itemsize=itemsize, fmt=fmt, readonly=1,
                                ndim=ndim, shape=shape, strides=strides,
                                lst=lst)

                    if is_memoryview_format(fmt):
                        # memoryview: reconstruct strides
                        ex = ndarray(items, shape=shape, format=fmt)
                        nd = ndarray(ex, getbuf=PyBUF_CONTIG_RO|PyBUF_FORMAT)
                        self.assertTrue(nd.strides == ())
                        mv = nd.memoryview_from_buffer()
                        self.verify(mv, obj=None,
                                    itemsize=itemsize, fmt=fmt, readonly=1,
                                    ndim=ndim, shape=shape, strides=strides,
                                    lst=lst)

                    # Fortran array
                    nd = ndarray(items, shape=shape, format=fmt,
                                 flags=flags|ND_FORTRAN)

                    strides = strides_from_shape(ndim, shape, itemsize, 'F')
                    lst = farray(items, shape)
                    self.verify(nd, obj=None,
                                itemsize=itemsize, fmt=fmt, readonly=1,
                                ndim=ndim, shape=shape, strides=strides,
                                lst=lst)

    def test_ndarray_index_invalid(self):
        # not writable
        nd = ndarray([1], shape=[1])
        self.assertRaises(TypeError, nd.__setitem__, 1, 8)
        mv = memoryview(nd)
        self.assertEqual(mv, nd)
        self.assertRaises(TypeError, mv.__setitem__, 1, 8)

        # cannot be deleted
        nd = ndarray([1], shape=[1], flags=ND_WRITABLE)
        self.assertRaises(TypeError, nd.__delitem__, 1)
        mv = memoryview(nd)
        self.assertEqual(mv, nd)
        self.assertRaises(TypeError, mv.__delitem__, 1)

        # overflow
        nd = ndarray([1], shape=[1], flags=ND_WRITABLE)
        self.assertRaises(OverflowError, nd.__getitem__, 1<<64)
        self.assertRaises(OverflowError, nd.__setitem__, 1<<64, 8)
        mv = memoryview(nd)
        self.assertEqual(mv, nd)
        self.assertRaises(IndexError, mv.__getitem__, 1<<64)
        self.assertRaises(IndexError, mv.__setitem__, 1<<64, 8)

        # format
        items = [1,2,3,4,5,6,7,8]
        nd = ndarray(items, shape=[len(items)], format="B", flags=ND_WRITABLE)
        self.assertRaises(struct.error, nd.__setitem__, 2, 300)
        self.assertRaises(ValueError, nd.__setitem__, 1, (100, 200))
        mv = memoryview(nd)
        self.assertEqual(mv, nd)
        self.assertRaises(ValueError, mv.__setitem__, 2, 300)
        self.assertRaises(TypeError, mv.__setitem__, 1, (100, 200))

        items = [(1,2), (3,4), (5,6)]
        nd = ndarray(items, shape=[len(items)], format="LQ", flags=ND_WRITABLE)
        self.assertRaises(ValueError, nd.__setitem__, 2, 300)
        self.assertRaises(struct.error, nd.__setitem__, 1, (b'\x001', 200))

    def test_ndarray_index_scalar(self):
        # scalar
        nd = ndarray(1, shape=(), flags=ND_WRITABLE)
        mv = memoryview(nd)
        self.assertEqual(mv, nd)

        x = nd[()];  self.assertEqual(x, 1)
        x = nd[...]; self.assertEqual(x.tolist(), nd.tolist())

        x = mv[()];  self.assertEqual(x, 1)
        x = mv[...]; self.assertEqual(x.tolist(), nd.tolist())

        self.assertRaises(TypeError, nd.__getitem__, 0)
        self.assertRaises(TypeError, mv.__getitem__, 0)
        self.assertRaises(TypeError, nd.__setitem__, 0, 8)
        self.assertRaises(TypeError, mv.__setitem__, 0, 8)

        self.assertEqual(nd.tolist(), 1)
        self.assertEqual(mv.tolist(), 1)

        nd[()] = 9; self.assertEqual(nd.tolist(), 9)
        mv[()] = 9; self.assertEqual(mv.tolist(), 9)

        nd[...] = 5; self.assertEqual(nd.tolist(), 5)
        mv[...] = 5; self.assertEqual(mv.tolist(), 5)

    def test_ndarray_index_null_strides(self):
        ex = ndarray(list(range(2*4)), shape=[2, 4], flags=ND_WRITABLE)
        nd = ndarray(ex, getbuf=PyBUF_CONTIG)

        # Sub-views are only possible for full exporters.
        self.assertRaises(BufferError, nd.__getitem__, 1)
        # Same for slices.
        self.assertRaises(BufferError, nd.__getitem__, slice(3,5,1))

    def test_ndarray_index_getitem_single(self):
        # getitem
        for fmt, items, _ in iter_format(5):
            nd = ndarray(items, shape=[5], format=fmt)
            for i in range(-5, 5):
                self.assertEqual(nd[i], items[i])

            self.assertRaises(IndexError, nd.__getitem__, -6)
            self.assertRaises(IndexError, nd.__getitem__, 5)

            if is_memoryview_format(fmt):
                mv = memoryview(nd)
                self.assertEqual(mv, nd)
                for i in range(-5, 5):
                    self.assertEqual(mv[i], items[i])

                self.assertRaises(IndexError, mv.__getitem__, -6)
                self.assertRaises(IndexError, mv.__getitem__, 5)

        # getitem with null strides
        for fmt, items, _ in iter_format(5):
            ex = ndarray(items, shape=[5], flags=ND_WRITABLE, format=fmt)
            nd = ndarray(ex, getbuf=PyBUF_CONTIG|PyBUF_FORMAT)

            for i in range(-5, 5):
                self.assertEqual(nd[i], items[i])

            if is_memoryview_format(fmt):
                mv = nd.memoryview_from_buffer()
                self.assertIs(mv.__eq__(nd), NotImplemented)
                for i in range(-5, 5):
                    self.assertEqual(mv[i], items[i])

        # getitem with null format
        items = [1,2,3,4,5]
        ex = ndarray(items, shape=[5])
        nd = ndarray(ex, getbuf=PyBUF_CONTIG_RO)
        for i in range(-5, 5):
            self.assertEqual(nd[i], items[i])

        # getitem with null shape/strides/format
        items = [1,2,3,4,5]
        ex = ndarray(items, shape=[5])
        nd = ndarray(ex, getbuf=PyBUF_SIMPLE)

        for i in range(-5, 5):
            self.assertEqual(nd[i], items[i])

    def test_ndarray_index_setitem_single(self):
        # assign single value
        for fmt, items, single_item in iter_format(5):
            nd = ndarray(items, shape=[5], format=fmt, flags=ND_WRITABLE)
            for i in range(5):
                items[i] = single_item
                nd[i] = single_item
            self.assertEqual(nd.tolist(), items)

            self.assertRaises(IndexError, nd.__setitem__, -6, single_item)
            self.assertRaises(IndexError, nd.__setitem__, 5, single_item)

            if not is_memoryview_format(fmt):
                continue

            nd = ndarray(items, shape=[5], format=fmt, flags=ND_WRITABLE)
            mv = memoryview(nd)
            self.assertEqual(mv, nd)
            for i in range(5):
                items[i] = single_item
                mv[i] = single_item
            self.assertEqual(mv.tolist(), items)

            self.assertRaises(IndexError, mv.__setitem__, -6, single_item)
            self.assertRaises(IndexError, mv.__setitem__, 5, single_item)


        # assign single value: lobject = robject
        for fmt, items, single_item in iter_format(5):
            nd = ndarray(items, shape=[5], format=fmt, flags=ND_WRITABLE)
            for i in range(-5, 4):
                items[i] = items[i+1]
                nd[i] = nd[i+1]
            self.assertEqual(nd.tolist(), items)

            if not is_memoryview_format(fmt):
                continue

            nd = ndarray(items, shape=[5], format=fmt, flags=ND_WRITABLE)
            mv = memoryview(nd)
            self.assertEqual(mv, nd)
            for i in range(-5, 4):
                items[i] = items[i+1]
                mv[i] = mv[i+1]
            self.assertEqual(mv.tolist(), items)

    def test_ndarray_index_getitem_multidim(self):
        shape_t = (2, 3, 5)
        nitems = prod(shape_t)
        for shape in permutations(shape_t):

            fmt, items, _ = randitems(nitems)

            for flags in (0, ND_PIL):
                # C array
                nd = ndarray(items, shape=shape, format=fmt, flags=flags)
                lst = carray(items, shape)

                for i in range(-shape[0], shape[0]):
                    self.assertEqual(lst[i], nd[i].tolist())
                    for j in range(-shape[1], shape[1]):
                        self.assertEqual(lst[i][j], nd[i][j].tolist())
                        for k in range(-shape[2], shape[2]):
                            self.assertEqual(lst[i][j][k], nd[i][j][k])

                # Fortran array
                nd = ndarray(items, shape=shape, format=fmt,
                             flags=flags|ND_FORTRAN)
                lst = farray(items, shape)

                for i in range(-shape[0], shape[0]):
                    self.assertEqual(lst[i], nd[i].tolist())
                    for j in range(-shape[1], shape[1]):
                        self.assertEqual(lst[i][j], nd[i][j].tolist())
                        for k in range(shape[2], shape[2]):
                            self.assertEqual(lst[i][j][k], nd[i][j][k])

    def test_ndarray_sequence(self):
        nd = ndarray(1, shape=())
        self.assertRaises(TypeError, eval, "1 in nd", locals())
        mv = memoryview(nd)
        self.assertEqual(mv, nd)
        self.assertRaises(TypeError, eval, "1 in mv", locals())

        for fmt, items, _ in iter_format(5):
            nd = ndarray(items, shape=[5], format=fmt)
            for i, v in enumerate(nd):
                self.assertEqual(v, items[i])
                self.assertTrue(v in nd)

            if is_memoryview_format(fmt):
                mv = memoryview(nd)
                for i, v in enumerate(mv):
                    self.assertEqual(v, items[i])
                    self.assertTrue(v in mv)

    def test_ndarray_slice_invalid(self):
        items = [1,2,3,4,5,6,7,8]

        # rvalue is not an exporter
        xl = ndarray(items, shape=[8], flags=ND_WRITABLE)
        ml = memoryview(xl)
        self.assertRaises(TypeError, xl.__setitem__, slice(0,8,1), items)
        self.assertRaises(TypeError, ml.__setitem__, slice(0,8,1), items)

        # rvalue is not a full exporter
        xl = ndarray(items, shape=[8], flags=ND_WRITABLE)
        ex = ndarray(items, shape=[8], flags=ND_WRITABLE)
        xr = ndarray(ex, getbuf=PyBUF_ND)
        self.assertRaises(BufferError, xl.__setitem__, slice(0,8,1), xr)

        # zero step
        nd = ndarray(items, shape=[8], format="L", flags=ND_WRITABLE)
        mv = memoryview(nd)
        self.assertRaises(ValueError, nd.__getitem__, slice(0,1,0))
        self.assertRaises(ValueError, mv.__getitem__, slice(0,1,0))

        nd = ndarray(items, shape=[2,4], format="L", flags=ND_WRITABLE)
        mv = memoryview(nd)

        self.assertRaises(ValueError, nd.__getitem__,
                          (slice(0,1,1), slice(0,1,0)))
        self.assertRaises(ValueError, nd.__getitem__,
                          (slice(0,1,0), slice(0,1,1)))
        self.assertRaises(TypeError, nd.__getitem__, "@%$")
        self.assertRaises(TypeError, nd.__getitem__, ("@%$", slice(0,1,1)))
        self.assertRaises(TypeError, nd.__getitem__, (slice(0,1,1), {}))

        # memoryview: not implemented
        self.assertRaises(NotImplementedError, mv.__getitem__,
                          (slice(0,1,1), slice(0,1,0)))
        self.assertRaises(TypeError, mv.__getitem__, "@%$")

        # differing format
        xl = ndarray(items, shape=[8], format="B", flags=ND_WRITABLE)
        xr = ndarray(items, shape=[8], format="b")
        ml = memoryview(xl)
        mr = memoryview(xr)
        self.assertRaises(ValueError, xl.__setitem__, slice(0,1,1), xr[7:8])
        self.assertEqual(xl.tolist(), items)
        self.assertRaises(ValueError, ml.__setitem__, slice(0,1,1), mr[7:8])
        self.assertEqual(ml.tolist(), items)

        # differing itemsize
        xl = ndarray(items, shape=[8], format="B", flags=ND_WRITABLE)
        yr = ndarray(items, shape=[8], format="L")
        ml = memoryview(xl)
        mr = memoryview(xr)
        self.assertRaises(ValueError, xl.__setitem__, slice(0,1,1), xr[7:8])
        self.assertEqual(xl.tolist(), items)
        self.assertRaises(ValueError, ml.__setitem__, slice(0,1,1), mr[7:8])
        self.assertEqual(ml.tolist(), items)

        # differing ndim
        xl = ndarray(items, shape=[2, 4], format="b", flags=ND_WRITABLE)
        xr = ndarray(items, shape=[8], format="b")
        ml = memoryview(xl)
        mr = memoryview(xr)
        self.assertRaises(ValueError, xl.__setitem__, slice(0,1,1), xr[7:8])
        self.assertEqual(xl.tolist(), [[1,2,3,4], [5,6,7,8]])
        self.assertRaises(NotImplementedError, ml.__setitem__, slice(0,1,1),
                          mr[7:8])

        # differing shape
        xl = ndarray(items, shape=[8], format="b", flags=ND_WRITABLE)
        xr = ndarray(items, shape=[8], format="b")
        ml = memoryview(xl)
        mr = memoryview(xr)
        self.assertRaises(ValueError, xl.__setitem__, slice(0,2,1), xr[7:8])
        self.assertEqual(xl.tolist(), items)
        self.assertRaises(ValueError, ml.__setitem__, slice(0,2,1), mr[7:8])
        self.assertEqual(ml.tolist(), items)

        # _testbuffer.c module functions
        self.assertRaises(TypeError, slice_indices, slice(0,1,2), {})
        self.assertRaises(TypeError, slice_indices, "###########", 1)
        self.assertRaises(ValueError, slice_indices, slice(0,1,0), 4)

        x = ndarray(items, shape=[8], format="b", flags=ND_PIL)
        self.assertRaises(TypeError, x.add_suboffsets)

        ex = ndarray(items, shape=[8], format="B")
        x = ndarray(ex, getbuf=PyBUF_SIMPLE)
        self.assertRaises(TypeError, x.add_suboffsets)

    def test_ndarray_slice_zero_shape(self):
        items = [1,2,3,4,5,6,7,8,9,10,11,12]

        x = ndarray(items, shape=[12], format="L", flags=ND_WRITABLE)
        y = ndarray(items, shape=[12], format="L")
        x[4:4] = y[9:9]
        self.assertEqual(x.tolist(), items)

        ml = memoryview(x)
        mr = memoryview(y)
        self.assertEqual(ml, x)
        self.assertEqual(ml, y)
        ml[4:4] = mr[9:9]
        self.assertEqual(ml.tolist(), items)

        x = ndarray(items, shape=[3, 4], format="L", flags=ND_WRITABLE)
        y = ndarray(items, shape=[4, 3], format="L")
        x[1:2, 2:2] = y[1:2, 3:3]
        self.assertEqual(x.tolist(), carray(items, [3, 4]))

    def test_ndarray_slice_multidim(self):
        shape_t = (2, 3, 5)
        ndim = len(shape_t)
        nitems = prod(shape_t)
        for shape in permutations(shape_t):

            fmt, items, _ = randitems(nitems)
            itemsize = struct.calcsize(fmt)

            for flags in (0, ND_PIL):
                nd = ndarray(items, shape=shape, format=fmt, flags=flags)
                lst = carray(items, shape)

                for slices in rslices_ndim(ndim, shape):

                    listerr = None
                    try:
                        sliced = multislice(lst, slices)
                    except Exception as e:
                        listerr = e.__class__

                    nderr = None
                    try:
                        ndsliced = nd[slices]
                    except Exception as e:
                        nderr = e.__class__

                    if nderr or listerr:
                        self.assertIs(nderr, listerr)
                    else:
                        self.assertEqual(ndsliced.tolist(), sliced)

    def test_ndarray_slice_redundant_suboffsets(self):
        shape_t = (2, 3, 5, 2)
        ndim = len(shape_t)
        nitems = prod(shape_t)
        for shape in permutations(shape_t):

            fmt, items, _ = randitems(nitems)
            itemsize = struct.calcsize(fmt)

            nd = ndarray(items, shape=shape, format=fmt)
            nd.add_suboffsets()
            ex = ndarray(items, shape=shape, format=fmt)
            ex.add_suboffsets()
            mv = memoryview(ex)
            lst = carray(items, shape)

            for slices in rslices_ndim(ndim, shape):

                listerr = None
                try:
                    sliced = multislice(lst, slices)
                except Exception as e:
                    listerr = e.__class__

                nderr = None
                try:
                    ndsliced = nd[slices]
                except Exception as e:
                    nderr = e.__class__

                if nderr or listerr:
                    self.assertIs(nderr, listerr)
                else:
                    self.assertEqual(ndsliced.tolist(), sliced)

    def test_ndarray_slice_assign_single(self):
        for fmt, items, _ in iter_format(5):
            for lslice in genslices(5):
                for rslice in genslices(5):
                    for flags in (0, ND_PIL):

                        f = flags|ND_WRITABLE
                        nd = ndarray(items, shape=[5], format=fmt, flags=f)
                        ex = ndarray(items, shape=[5], format=fmt, flags=f)
                        mv = memoryview(ex)

                        lsterr = None
                        diff_structure = None
                        lst = items[:]
                        try:
                            lval = lst[lslice]
                            rval = lst[rslice]
                            lst[lslice] = lst[rslice]
                            diff_structure = len(lval) != len(rval)
                        except Exception as e:
                            lsterr = e.__class__

                        nderr = None
                        try:
                            nd[lslice] = nd[rslice]
                        except Exception as e:
                            nderr = e.__class__

                        if diff_structure: # ndarray cannot change shape
                            self.assertIs(nderr, ValueError)
                        else:
                            self.assertEqual(nd.tolist(), lst)
                            self.assertIs(nderr, lsterr)

                        if not is_memoryview_format(fmt):
                            continue

                        mverr = None
                        try:
                            mv[lslice] = mv[rslice]
                        except Exception as e:
                            mverr = e.__class__

                        if diff_structure: # memoryview cannot change shape
                            self.assertIs(mverr, ValueError)
                        else:
                            self.assertEqual(mv.tolist(), lst)
                            self.assertEqual(mv, nd)
                            self.assertIs(mverr, lsterr)
                            self.verify(mv, obj=ex,
                              itemsize=nd.itemsize, fmt=fmt, readonly=0,
                              ndim=nd.ndim, shape=nd.shape, strides=nd.strides,
                              lst=nd.tolist())

    def test_ndarray_slice_assign_multidim(self):
        shape_t = (2, 3, 5)
        ndim = len(shape_t)
        nitems = prod(shape_t)
        for shape in permutations(shape_t):

            fmt, items, _ = randitems(nitems)

            for flags in (0, ND_PIL):
                for _ in range(ITERATIONS):
                    lslices, rslices = randslice_from_shape(ndim, shape)

                    nd = ndarray(items, shape=shape, format=fmt,
                                 flags=flags|ND_WRITABLE)
                    lst = carray(items, shape)

                    listerr = None
                    try:
                        result = multislice_assign(lst, lst, lslices, rslices)
                    except Exception as e:
                        listerr = e.__class__

                    nderr = None
                    try:
                        nd[lslices] = nd[rslices]
                    except Exception as e:
                        nderr = e.__class__

                    if nderr or listerr:
                        self.assertIs(nderr, listerr)
                    else:
                        self.assertEqual(nd.tolist(), result)

    def test_ndarray_random(self):
        # construction of valid arrays
        for _ in range(ITERATIONS):
            for fmt in fmtdict['@']:
                itemsize = struct.calcsize(fmt)

                t = rand_structure(itemsize, True, maxdim=MAXDIM,
                                   maxshape=MAXSHAPE)
                self.assertTrue(verify_structure(*t))
                items = randitems_from_structure(fmt, t)

                x = ndarray_from_structure(items, fmt, t)
                xlist = x.tolist()

                mv = memoryview(x)
                if is_memoryview_format(fmt):
                    mvlist = mv.tolist()
                    self.assertEqual(mvlist, xlist)

                if t[2] > 0:
                    # ndim > 0: test against suboffsets representation.
                    y = ndarray_from_structure(items, fmt, t, flags=ND_PIL)
                    ylist = y.tolist()
                    self.assertEqual(xlist, ylist)

                    mv = memoryview(y)
                    if is_memoryview_format(fmt):
                        self.assertEqual(mv, y)
                        mvlist = mv.tolist()
                        self.assertEqual(mvlist, ylist)

                if numpy_array:
                    shape = t[3]
                    if 0 in shape:
                        continue # http://projects.scipy.org/numpy/ticket/1910
                    z = numpy_array_from_structure(items, fmt, t)
                    self.verify(x, obj=None,
                                itemsize=z.itemsize, fmt=fmt, readonly=0,
                                ndim=z.ndim, shape=z.shape, strides=z.strides,
                                lst=z.tolist())

    def test_ndarray_random_invalid(self):
        # exceptions during construction of invalid arrays
        for _ in range(ITERATIONS):
            for fmt in fmtdict['@']:
                itemsize = struct.calcsize(fmt)

                t = rand_structure(itemsize, False, maxdim=MAXDIM,
                                   maxshape=MAXSHAPE)
                self.assertFalse(verify_structure(*t))
                items = randitems_from_structure(fmt, t)

                nderr = False
                try:
                    x = ndarray_from_structure(items, fmt, t)
                except Exception as e:
                    nderr = e.__class__
                self.assertTrue(nderr)

                if numpy_array:
                    numpy_err = False
                    try:
                        y = numpy_array_from_structure(items, fmt, t)
                    except Exception as e:
                        numpy_err = e.__class__

                    if 0: # http://projects.scipy.org/numpy/ticket/1910
                        self.assertTrue(numpy_err)

    def test_ndarray_random_slice_assign(self):
        # valid slice assignments
        for _ in range(ITERATIONS):
            for fmt in fmtdict['@']:
                itemsize = struct.calcsize(fmt)

                lshape, rshape, lslices, rslices = \
                    rand_aligned_slices(maxdim=MAXDIM, maxshape=MAXSHAPE)
                tl = rand_structure(itemsize, True, shape=lshape)
                tr = rand_structure(itemsize, True, shape=rshape)
                self.assertTrue(verify_structure(*tl))
                self.assertTrue(verify_structure(*tr))
                litems = randitems_from_structure(fmt, tl)
                ritems = randitems_from_structure(fmt, tr)

                xl = ndarray_from_structure(litems, fmt, tl)
                xr = ndarray_from_structure(ritems, fmt, tr)
                xl[lslices] = xr[rslices]
                xllist = xl.tolist()
                xrlist = xr.tolist()

                ml = memoryview(xl)
                mr = memoryview(xr)
                self.assertEqual(ml.tolist(), xllist)
                self.assertEqual(mr.tolist(), xrlist)

                if tl[2] > 0 and tr[2] > 0:
                    # ndim > 0: test against suboffsets representation.
                    yl = ndarray_from_structure(litems, fmt, tl, flags=ND_PIL)
                    yr = ndarray_from_structure(ritems, fmt, tr, flags=ND_PIL)
                    yl[lslices] = yr[rslices]
                    yllist = yl.tolist()
                    yrlist = yr.tolist()
                    self.assertEqual(xllist, yllist)
                    self.assertEqual(xrlist, yrlist)

                    ml = memoryview(yl)
                    mr = memoryview(yr)
                    self.assertEqual(ml.tolist(), yllist)
                    self.assertEqual(mr.tolist(), yrlist)

                if numpy_array:
                    if 0 in lshape or 0 in rshape:
                        continue # http://projects.scipy.org/numpy/ticket/1910

                    zl = numpy_array_from_structure(litems, fmt, tl)
                    zr = numpy_array_from_structure(ritems, fmt, tr)
                    zl[lslices] = zr[rslices]

                    if not is_overlapping(tl) and not is_overlapping(tr):
                        # Slice assignment of overlapping structures
                        # is undefined in NumPy.
                        self.verify(xl, obj=None,
                                    itemsize=zl.itemsize, fmt=fmt, readonly=0,
                                    ndim=zl.ndim, shape=zl.shape,
                                    strides=zl.strides, lst=zl.tolist())

                    self.verify(xr, obj=None,
                                itemsize=zr.itemsize, fmt=fmt, readonly=0,
                                ndim=zr.ndim, shape=zr.shape,
                                strides=zr.strides, lst=zr.tolist())

    def test_ndarray_re_export(self):
        items = [1,2,3,4,5,6,7,8,9,10,11,12]

        nd = ndarray(items, shape=[3,4], flags=ND_PIL)
        ex = ndarray(nd)

        self.assertTrue(ex.flags & ND_PIL)
        self.assertIs(ex.obj, nd)
        self.assertEqual(ex.suboffsets, (0, -1))
        self.assertFalse(ex.c_contiguous)
        self.assertFalse(ex.f_contiguous)
        self.assertFalse(ex.contiguous)

    def test_ndarray_zero_shape(self):
        # zeros in shape
        for flags in (0, ND_PIL):
            nd = ndarray([1,2,3], shape=[0], flags=flags)
            mv = memoryview(nd)
            self.assertEqual(mv, nd)
            self.assertEqual(nd.tolist(), [])
            self.assertEqual(mv.tolist(), [])

            nd = ndarray([1,2,3], shape=[0,3,3], flags=flags)
            self.assertEqual(nd.tolist(), [])

            nd = ndarray([1,2,3], shape=[3,0,3], flags=flags)
            self.assertEqual(nd.tolist(), [[], [], []])

            nd = ndarray([1,2,3], shape=[3,3,0], flags=flags)
            self.assertEqual(nd.tolist(),
                             [[[], [], []], [[], [], []], [[], [], []]])

    def test_ndarray_zero_strides(self):
        # zero strides
        for flags in (0, ND_PIL):
            nd = ndarray([1], shape=[5], strides=[0], flags=flags)
            mv = memoryview(nd)
            self.assertEqual(mv, nd)
            self.assertEqual(nd.tolist(), [1, 1, 1, 1, 1])
            self.assertEqual(mv.tolist(), [1, 1, 1, 1, 1])

    def test_ndarray_offset(self):
        nd = ndarray(list(range(20)), shape=[3], offset=7)
        self.assertEqual(nd.offset, 7)
        self.assertEqual(nd.tolist(), [7,8,9])

    def test_ndarray_memoryview_from_buffer(self):
        for flags in (0, ND_PIL):
            nd = ndarray(list(range(3)), shape=[3], flags=flags)
            m = nd.memoryview_from_buffer()
            self.assertEqual(m, nd)

    def test_ndarray_get_pointer(self):
        for flags in (0, ND_PIL):
            nd = ndarray(list(range(3)), shape=[3], flags=flags)
            for i in range(3):
                self.assertEqual(nd[i], get_pointer(nd, [i]))

    def test_ndarray_tolist_null_strides(self):
        ex = ndarray(list(range(20)), shape=[2,2,5])

        nd = ndarray(ex, getbuf=PyBUF_ND|PyBUF_FORMAT)
        self.assertEqual(nd.tolist(), ex.tolist())

        m = memoryview(ex)
        self.assertEqual(m.tolist(), ex.tolist())

    def test_ndarray_cmp_contig(self):

        self.assertFalse(cmp_contig(b"123", b"456"))

        x = ndarray(list(range(12)), shape=[3,4])
        y = ndarray(list(range(12)), shape=[4,3])
        self.assertFalse(cmp_contig(x, y))

        x = ndarray([1], shape=[1], format="B")
        self.assertTrue(cmp_contig(x, b'\x01'))
        self.assertTrue(cmp_contig(b'\x01', x))

    def test_ndarray_hash(self):

        a = array.array('L', [1,2,3])
        nd = ndarray(a)
        self.assertRaises(ValueError, hash, nd)

        # one-dimensional
        b = bytes(list(range(12)))

        nd = ndarray(list(range(12)), shape=[12])
        self.assertEqual(hash(nd), hash(b))

        # C-contiguous
        nd = ndarray(list(range(12)), shape=[3,4])
        self.assertEqual(hash(nd), hash(b))

        nd = ndarray(list(range(12)), shape=[3,2,2])
        self.assertEqual(hash(nd), hash(b))

        # Fortran contiguous
        b = bytes(transpose(list(range(12)), shape=[4,3]))
        nd = ndarray(list(range(12)), shape=[3,4], flags=ND_FORTRAN)
        self.assertEqual(hash(nd), hash(b))

        b = bytes(transpose(list(range(12)), shape=[2,3,2]))
        nd = ndarray(list(range(12)), shape=[2,3,2], flags=ND_FORTRAN)
        self.assertEqual(hash(nd), hash(b))

        # suboffsets
        b = bytes(list(range(12)))
        nd = ndarray(list(range(12)), shape=[2,2,3], flags=ND_PIL)
        self.assertEqual(hash(nd), hash(b))

        # non-byte formats
        nd = ndarray(list(range(12)), shape=[2,2,3], format='L')
        self.assertEqual(hash(nd), hash(nd.tobytes()))

    def test_py_buffer_to_contiguous(self):

        # The requests are used in _testbuffer.c:py_buffer_to_contiguous
        # to generate buffers without full information for testing.
        requests = (
            # distinct flags
            PyBUF_INDIRECT, PyBUF_STRIDES, PyBUF_ND, PyBUF_SIMPLE,
            # compound requests
            PyBUF_FULL, PyBUF_FULL_RO,
            PyBUF_RECORDS, PyBUF_RECORDS_RO,
            PyBUF_STRIDED, PyBUF_STRIDED_RO,
            PyBUF_CONTIG, PyBUF_CONTIG_RO,
        )

        # no buffer interface
        self.assertRaises(TypeError, py_buffer_to_contiguous, {}, 'F',
                          PyBUF_FULL_RO)

        # scalar, read-only request
        nd = ndarray(9, shape=(), format="L", flags=ND_WRITABLE)
        for order in ['C', 'F', 'A']:
            for request in requests:
                b = py_buffer_to_contiguous(nd, order, request)
                self.assertEqual(b, nd.tobytes())

        # zeros in shape
        nd = ndarray([1], shape=[0], format="L", flags=ND_WRITABLE)
        for order in ['C', 'F', 'A']:
            for request in requests:
                b = py_buffer_to_contiguous(nd, order, request)
                self.assertEqual(b, b'')

        nd = ndarray(list(range(8)), shape=[2, 0, 7], format="L",
                     flags=ND_WRITABLE)
        for order in ['C', 'F', 'A']:
            for request in requests:
                b = py_buffer_to_contiguous(nd, order, request)
                self.assertEqual(b, b'')

        ### One-dimensional arrays are trivial, since Fortran and C order
        ### are the same.

        # one-dimensional
        for f in [0, ND_FORTRAN]:
            nd = ndarray([1], shape=[1], format="h", flags=f|ND_WRITABLE)
            ndbytes = nd.tobytes()
            for order in ['C', 'F', 'A']:
                for request in requests:
                    b = py_buffer_to_contiguous(nd, order, request)
                    self.assertEqual(b, ndbytes)

            nd = ndarray([1, 2, 3], shape=[3], format="b", flags=f|ND_WRITABLE)
            ndbytes = nd.tobytes()
            for order in ['C', 'F', 'A']:
                for request in requests:
                    b = py_buffer_to_contiguous(nd, order, request)
                    self.assertEqual(b, ndbytes)

        # one-dimensional, non-contiguous input
        nd = ndarray([1, 2, 3], shape=[2], strides=[2], flags=ND_WRITABLE)
        ndbytes = nd.tobytes()
        for order in ['C', 'F', 'A']:
            for request in [PyBUF_STRIDES, PyBUF_FULL]:
                b = py_buffer_to_contiguous(nd, order, request)
                self.assertEqual(b, ndbytes)

        nd = nd[::-1]
        ndbytes = nd.tobytes()
        for order in ['C', 'F', 'A']:
            for request in requests:
                try:
                    b = py_buffer_to_contiguous(nd, order, request)
                except BufferError:
                    continue
                self.assertEqual(b, ndbytes)

        ###
        ### Multi-dimensional arrays:
        ###
        ### The goal here is to preserve the logical representation of the
        ### input array but change the physical representation if necessary.
        ###
        ### _testbuffer example:
        ### ====================
        ###
        ###    C input array:
        ###    --------------
        ###       >>> nd = ndarray(list(range(12)), shape=[3, 4])
        ###       >>> nd.tolist()
        ###       [[0, 1, 2, 3],
        ###        [4, 5, 6, 7],
        ###        [8, 9, 10, 11]]
        ###
        ###    Fortran output:
        ###    ---------------
        ###       >>> py_buffer_to_contiguous(nd, 'F', PyBUF_FULL_RO)
        ###       >>> b'\x00\x04\x08\x01\x05\t\x02\x06\n\x03\x07\x0b'
        ###
        ###    The return value corresponds to this input list for
        ###    _testbuffer's ndarray:
        ###       >>> nd = ndarray([0,4,8,1,5,9,2,6,10,3,7,11], shape=[3,4],
        ###                        flags=ND_FORTRAN)
        ###       >>> nd.tolist()
        ###       [[0, 1, 2, 3],
        ###        [4, 5, 6, 7],
        ###        [8, 9, 10, 11]]
        ###
        ###    The logical array is the same, but the values in memory are now
        ###    in Fortran order.
        ###
        ### NumPy example:
        ### ==============
        ###    _testbuffer's ndarray takes lists to initialize the memory.
        ###    Here's the same sequence in NumPy:
        ###
        ###    C input:
        ###    --------
        ###       >>> nd = ndarray(buffer=bytearray(list(range(12))),
        ###                        shape=[3, 4], dtype='B')
        ###       >>> nd
        ###       array([[ 0,  1,  2,  3],
        ###              [ 4,  5,  6,  7],
        ###              [ 8,  9, 10, 11]], dtype=uint8)
        ###
        ###    Fortran output:
        ###    ---------------
        ###       >>> fortran_buf = nd.tostring(order='F')
        ###       >>> fortran_buf
        ###       b'\x00\x04\x08\x01\x05\t\x02\x06\n\x03\x07\x0b'
        ###
        ###       >>> nd = ndarray(buffer=fortran_buf, shape=[3, 4],
        ###                        dtype='B', order='F')
        ###
        ###       >>> nd
        ###       array([[ 0,  1,  2,  3],
        ###              [ 4,  5,  6,  7],
        ###              [ 8,  9, 10, 11]], dtype=uint8)
        ###

        # multi-dimensional, contiguous input
        lst = list(range(12))
        for f in [0, ND_FORTRAN]:
            nd = ndarray(lst, shape=[3, 4], flags=f|ND_WRITABLE)
            if numpy_array:
                na = numpy_array(buffer=bytearray(lst),
                                 shape=[3, 4], dtype='B',
                                 order='C' if f == 0 else 'F')

            # 'C' request
            if f == ND_FORTRAN: # 'F' to 'C'
                x = ndarray(transpose(lst, [4, 3]), shape=[3, 4],
                            flags=ND_WRITABLE)
                expected = x.tobytes()
            else:
                expected = nd.tobytes()
            for request in requests:
                try:
                    b = py_buffer_to_contiguous(nd, 'C', request)
                except BufferError:
                    continue

                self.assertEqual(b, expected)

                # Check that output can be used as the basis for constructing
                # a C array that is logically identical to the input array.
                y = ndarray([v for v in b], shape=[3, 4], flags=ND_WRITABLE)
                self.assertEqual(memoryview(y), memoryview(nd))

                if numpy_array:
                    self.assertEqual(b, na.tostring(order='C'))

            # 'F' request
            if f == 0: # 'C' to 'F'
                x = ndarray(transpose(lst, [3, 4]), shape=[4, 3],
                            flags=ND_WRITABLE)
            else:
                x = ndarray(lst, shape=[3, 4], flags=ND_WRITABLE)
            expected = x.tobytes()
            for request in [PyBUF_FULL, PyBUF_FULL_RO, PyBUF_INDIRECT,
                            PyBUF_STRIDES, PyBUF_ND]:
                try:
                    b = py_buffer_to_contiguous(nd, 'F', request)
                except BufferError:
                    continue
                self.assertEqual(b, expected)

                # Check that output can be used as the basis for constructing
                # a Fortran array that is logically identical to the input array.
                y = ndarray([v for v in b], shape=[3, 4], flags=ND_FORTRAN|ND_WRITABLE)
                self.assertEqual(memoryview(y), memoryview(nd))

                if numpy_array:
                    self.assertEqual(b, na.tostring(order='F'))

            # 'A' request
            if f == ND_FORTRAN:
                x = ndarray(lst, shape=[3, 4], flags=ND_WRITABLE)
                expected = x.tobytes()
            else:
                expected = nd.tobytes()
            for request in [PyBUF_FULL, PyBUF_FULL_RO, PyBUF_INDIRECT,
                            PyBUF_STRIDES, PyBUF_ND]:
                try:
                    b = py_buffer_to_contiguous(nd, 'A', request)
                except BufferError:
                    continue

                self.assertEqual(b, expected)

                # Check that output can be used as the basis for constructing
                # an array with order=f that is logically identical to the input
                # array.
                y = ndarray([v for v in b], shape=[3, 4], flags=f|ND_WRITABLE)
                self.assertEqual(memoryview(y), memoryview(nd))

                if numpy_array:
                    self.assertEqual(b, na.tostring(order='A'))

        # multi-dimensional, non-contiguous input
        nd = ndarray(list(range(12)), shape=[3, 4], flags=ND_WRITABLE|ND_PIL)

        # 'C'
        b = py_buffer_to_contiguous(nd, 'C', PyBUF_FULL_RO)
        self.assertEqual(b, nd.tobytes())
        y = ndarray([v for v in b], shape=[3, 4], flags=ND_WRITABLE)
        self.assertEqual(memoryview(y), memoryview(nd))

        # 'F'
        b = py_buffer_to_contiguous(nd, 'F', PyBUF_FULL_RO)
        x = ndarray(transpose(lst, [3, 4]), shape=[4, 3], flags=ND_WRITABLE)
        self.assertEqual(b, x.tobytes())
        y = ndarray([v for v in b], shape=[3, 4], flags=ND_FORTRAN|ND_WRITABLE)
        self.assertEqual(memoryview(y), memoryview(nd))

        # 'A'
        b = py_buffer_to_contiguous(nd, 'A', PyBUF_FULL_RO)
        self.assertEqual(b, nd.tobytes())
        y = ndarray([v for v in b], shape=[3, 4], flags=ND_WRITABLE)
        self.assertEqual(memoryview(y), memoryview(nd))

    def test_memoryview_construction(self):

        items_shape = [(9, []), ([1,2,3], [3]), (list(range(2*3*5)), [2,3,5])]

        # NumPy style, C-contiguous:
        for items, shape in items_shape:

            # From PEP-3118 compliant exporter:
            ex = ndarray(items, shape=shape)
            m = memoryview(ex)
            self.assertTrue(m.c_contiguous)
            self.assertTrue(m.contiguous)

            ndim = len(shape)
            strides = strides_from_shape(ndim, shape, 1, 'C')
            lst = carray(items, shape)

            self.verify(m, obj=ex,
                        itemsize=1, fmt='B', readonly=1,
                        ndim=ndim, shape=shape, strides=strides,
                        lst=lst)

            # From memoryview:
            m2 = memoryview(m)
            self.verify(m2, obj=ex,
                        itemsize=1, fmt='B', readonly=1,
                        ndim=ndim, shape=shape, strides=strides,
                        lst=lst)

            # PyMemoryView_FromBuffer(): no strides
            nd = ndarray(ex, getbuf=PyBUF_CONTIG_RO|PyBUF_FORMAT)
            self.assertEqual(nd.strides, ())
            m = nd.memoryview_from_buffer()
            self.verify(m, obj=None,
                        itemsize=1, fmt='B', readonly=1,
                        ndim=ndim, shape=shape, strides=strides,
                        lst=lst)

            # PyMemoryView_FromBuffer(): no format, shape, strides
            nd = ndarray(ex, getbuf=PyBUF_SIMPLE)
            self.assertEqual(nd.format, '')
            self.assertEqual(nd.shape, ())
            self.assertEqual(nd.strides, ())
            m = nd.memoryview_from_buffer()

            lst = [items] if ndim == 0 else items
            self.verify(m, obj=None,
                        itemsize=1, fmt='B', readonly=1,
                        ndim=1, shape=[ex.nbytes], strides=(1,),
                        lst=lst)

        # NumPy style, Fortran contiguous:
        for items, shape in items_shape:

            # From PEP-3118 compliant exporter:
            ex = ndarray(items, shape=shape, flags=ND_FORTRAN)
            m = memoryview(ex)
            self.assertTrue(m.f_contiguous)
            self.assertTrue(m.contiguous)

            ndim = len(shape)
            strides = strides_from_shape(ndim, shape, 1, 'F')
            lst = farray(items, shape)

            self.verify(m, obj=ex,
                        itemsize=1, fmt='B', readonly=1,
                        ndim=ndim, shape=shape, strides=strides,
                        lst=lst)

            # From memoryview:
            m2 = memoryview(m)
            self.verify(m2, obj=ex,
                        itemsize=1, fmt='B', readonly=1,
                        ndim=ndim, shape=shape, strides=strides,
                        lst=lst)

        # PIL style:
        for items, shape in items_shape[1:]:

            # From PEP-3118 compliant exporter:
            ex = ndarray(items, shape=shape, flags=ND_PIL)
            m = memoryview(ex)

            ndim = len(shape)
            lst = carray(items, shape)

            self.verify(m, obj=ex,
                        itemsize=1, fmt='B', readonly=1,
                        ndim=ndim, shape=shape, strides=ex.strides,
                        lst=lst)

            # From memoryview:
            m2 = memoryview(m)
            self.verify(m2, obj=ex,
                        itemsize=1, fmt='B', readonly=1,
                        ndim=ndim, shape=shape, strides=ex.strides,
                        lst=lst)

        # Invalid number of arguments:
        self.assertRaises(TypeError, memoryview, b'9', 'x')
        # Not a buffer provider:
        self.assertRaises(TypeError, memoryview, {})
        # Non-compliant buffer provider:
        ex = ndarray([1,2,3], shape=[3])
        nd = ndarray(ex, getbuf=PyBUF_SIMPLE)
        self.assertRaises(BufferError, memoryview, nd)
        nd = ndarray(ex, getbuf=PyBUF_CONTIG_RO|PyBUF_FORMAT)
        self.assertRaises(BufferError, memoryview, nd)

        # ndim > 64
        nd = ndarray([1]*128, shape=[1]*128, format='L')
        self.assertRaises(ValueError, memoryview, nd)
        self.assertRaises(ValueError, nd.memoryview_from_buffer)
        self.assertRaises(ValueError, get_contiguous, nd, PyBUF_READ, 'C')
        self.assertRaises(ValueError, get_contiguous, nd, PyBUF_READ, 'F')
        self.assertRaises(ValueError, get_contiguous, nd[::-1], PyBUF_READ, 'C')

    def test_memoryview_cast_zero_shape(self):
        # Casts are undefined if shape contains zeros. These arrays are
        # regarded as C-contiguous by Numpy and PyBuffer_GetContiguous(),
        # so they are not caught by the test for C-contiguity in memory_cast().
        items = [1,2,3]
        for shape in ([0,3,3], [3,0,3], [0,3,3]):
            ex = ndarray(items, shape=shape)
            self.assertTrue(ex.c_contiguous)
            msrc = memoryview(ex)
            self.assertRaises(TypeError, msrc.cast, 'c')

    def test_memoryview_struct_module(self):

        class INT(object):
            def __init__(self, val):
                self.val = val
            def __int__(self):
                return self.val

        class IDX(object):
            def __init__(self, val):
                self.val = val
            def __index__(self):
                return self.val

        def f(): return 7

        values = [INT(9), IDX(9),
                  2.2+3j, Decimal("-21.1"), 12.2, Fraction(5, 2),
                  [1,2,3], {4,5,6}, {7:8}, (), (9,),
                  True, False, None, NotImplemented,
                  b'a', b'abc', bytearray(b'a'), bytearray(b'abc'),
                  'a', 'abc', r'a', r'abc',
                  f, lambda x: x]

        for fmt, items, item in iter_format(10, 'memoryview'):
            ex = ndarray(items, shape=[10], format=fmt, flags=ND_WRITABLE)
            nd = ndarray(items, shape=[10], format=fmt, flags=ND_WRITABLE)
            m = memoryview(ex)

            struct.pack_into(fmt, nd, 0, item)
            m[0] = item
            self.assertEqual(m[0], nd[0])

            itemsize = struct.calcsize(fmt)
            if 'P' in fmt:
                continue

            for v in values:
                struct_err = None
                try:
                    struct.pack_into(fmt, nd, itemsize, v)
                except struct.error:
                    struct_err = struct.error

                mv_err = None
                try:
                    m[1] = v
                except (TypeError, ValueError) as e:
                    mv_err = e.__class__

                if struct_err or mv_err:
                    self.assertIsNot(struct_err, None)
                    self.assertIsNot(mv_err, None)
                else:
                    self.assertEqual(m[1], nd[1])

    def test_memoryview_cast_zero_strides(self):
        # Casts are undefined if strides contains zeros. These arrays are
        # (sometimes!) regarded as C-contiguous by Numpy, but not by
        # PyBuffer_GetContiguous().
        ex = ndarray([1,2,3], shape=[3], strides=[0])
        self.assertFalse(ex.c_contiguous)
        msrc = memoryview(ex)
        self.assertRaises(TypeError, msrc.cast, 'c')

    def test_memoryview_cast_invalid(self):
        # invalid format
        for sfmt in NON_BYTE_FORMAT:
            sformat = '@' + sfmt if randrange(2) else sfmt
            ssize = struct.calcsize(sformat)
            for dfmt in NON_BYTE_FORMAT:
                dformat = '@' + dfmt if randrange(2) else dfmt
                dsize = struct.calcsize(dformat)
                ex = ndarray(list(range(32)), shape=[32//ssize], format=sformat)
                msrc = memoryview(ex)
                self.assertRaises(TypeError, msrc.cast, dfmt, [32//dsize])

        for sfmt, sitems, _ in iter_format(1):
            ex = ndarray(sitems, shape=[1], format=sfmt)
            msrc = memoryview(ex)
            for dfmt, _, _ in iter_format(1):
                if (not is_memoryview_format(sfmt) or
                    not is_memoryview_format(dfmt)):
                    self.assertRaises(ValueError, msrc.cast, dfmt,
                                      [32//dsize])
                else:
                    if not is_byte_format(sfmt) and not is_byte_format(dfmt):
                        self.assertRaises(TypeError, msrc.cast, dfmt,
                                          [32//dsize])

        # invalid shape
        size_h = struct.calcsize('h')
        size_d = struct.calcsize('d')
        ex = ndarray(list(range(2*2*size_d)), shape=[2,2,size_d], format='h')
        msrc = memoryview(ex)
        self.assertRaises(TypeError, msrc.cast, shape=[2,2,size_h], format='d')

        ex = ndarray(list(range(120)), shape=[1,2,3,4,5])
        m = memoryview(ex)

        # incorrect number of args
        self.assertRaises(TypeError, m.cast)
        self.assertRaises(TypeError, m.cast, 1, 2, 3)

        # incorrect dest format type
        self.assertRaises(TypeError, m.cast, {})

        # incorrect dest format
        self.assertRaises(ValueError, m.cast, "X")
        self.assertRaises(ValueError, m.cast, "@X")
        self.assertRaises(ValueError, m.cast, "@XY")

        # dest format not implemented
        self.assertRaises(ValueError, m.cast, "=B")
        self.assertRaises(ValueError, m.cast, "!L")
        self.assertRaises(ValueError, m.cast, "<P")
        self.assertRaises(ValueError, m.cast, ">l")
        self.assertRaises(ValueError, m.cast, "BI")
        self.assertRaises(ValueError, m.cast, "xBI")

        # src format not implemented
        ex = ndarray([(1,2), (3,4)], shape=[2], format="II")
        m = memoryview(ex)
        self.assertRaises(NotImplementedError, m.__getitem__, 0)
        self.assertRaises(NotImplementedError, m.__setitem__, 0, 8)
        self.assertRaises(NotImplementedError, m.tolist)

        # incorrect shape type
        ex = ndarray(list(range(120)), shape=[1,2,3,4,5])
        m = memoryview(ex)
        self.assertRaises(TypeError, m.cast, "B", shape={})

        # incorrect shape elements
        ex = ndarray(list(range(120)), shape=[2*3*4*5])
        m = memoryview(ex)
        self.assertRaises(OverflowError, m.cast, "B", shape=[2**64])
        self.assertRaises(ValueError, m.cast, "B", shape=[-1])
        self.assertRaises(ValueError, m.cast, "B", shape=[2,3,4,5,6,7,-1])
        self.assertRaises(ValueError, m.cast, "B", shape=[2,3,4,5,6,7,0])
        self.assertRaises(TypeError, m.cast, "B", shape=[2,3,4,5,6,7,'x'])

        # N-D -> N-D cast
        ex = ndarray(list([9 for _ in range(3*5*7*11)]), shape=[3,5,7,11])
        m = memoryview(ex)
        self.assertRaises(TypeError, m.cast, "I", shape=[2,3,4,5])

        # cast with ndim > 64
        nd = ndarray(list(range(128)), shape=[128], format='I')
        m = memoryview(nd)
        self.assertRaises(ValueError, m.cast, 'I', [1]*128)

        # view->len not a multiple of itemsize
        ex = ndarray(list([9 for _ in range(3*5*7*11)]), shape=[3*5*7*11])
        m = memoryview(ex)
        self.assertRaises(TypeError, m.cast, "I", shape=[2,3,4,5])

        # product(shape) * itemsize != buffer size
        ex = ndarray(list([9 for _ in range(3*5*7*11)]), shape=[3*5*7*11])
        m = memoryview(ex)
        self.assertRaises(TypeError, m.cast, "B", shape=[2,3,4,5])

        # product(shape) * itemsize overflow
        nd = ndarray(list(range(128)), shape=[128], format='I')
        m1 = memoryview(nd)
        nd = ndarray(list(range(128)), shape=[128], format='B')
        m2 = memoryview(nd)
        if sys.maxsize == 2**63-1:
            self.assertRaises(TypeError, m1.cast, 'B',
                              [7, 7, 73, 127, 337, 92737, 649657])
            self.assertRaises(ValueError, m1.cast, 'B',
                              [2**20, 2**20, 2**10, 2**10, 2**3])
            self.assertRaises(ValueError, m2.cast, 'I',
                              [2**20, 2**20, 2**10, 2**10, 2**1])
        else:
            self.assertRaises(TypeError, m1.cast, 'B',
                              [1, 2147483647])
            self.assertRaises(ValueError, m1.cast, 'B',
                              [2**10, 2**10, 2**5, 2**5, 2**1])
            self.assertRaises(ValueError, m2.cast, 'I',
                              [2**10, 2**10, 2**5, 2**3, 2**1])

    def test_memoryview_cast(self):
        bytespec = (
          ('B', lambda ex: list(ex.tobytes())),
          ('b', lambda ex: [x-256 if x > 127 else x for x in list(ex.tobytes())]),
          ('c', lambda ex: [bytes(chr(x), 'latin-1') for x in list(ex.tobytes())]),
        )

        def iter_roundtrip(ex, m, items, fmt):
            srcsize = struct.calcsize(fmt)
            for bytefmt, to_bytelist in bytespec:

                m2 = m.cast(bytefmt)
                lst = to_bytelist(ex)
                self.verify(m2, obj=ex,
                            itemsize=1, fmt=bytefmt, readonly=0,
                            ndim=1, shape=[31*srcsize], strides=(1,),
                            lst=lst, cast=True)

                m3 = m2.cast(fmt)
                self.assertEqual(m3, ex)
                lst = ex.tolist()
                self.verify(m3, obj=ex,
                            itemsize=srcsize, fmt=fmt, readonly=0,
                            ndim=1, shape=[31], strides=(srcsize,),
                            lst=lst, cast=True)

        # cast from ndim = 0 to ndim = 1
        srcsize = struct.calcsize('I')
        ex = ndarray(9, shape=[], format='I')
        destitems, destshape = cast_items(ex, 'B', 1)
        m = memoryview(ex)
        m2 = m.cast('B')
        self.verify(m2, obj=ex,
                    itemsize=1, fmt='B', readonly=1,
                    ndim=1, shape=destshape, strides=(1,),
                    lst=destitems, cast=True)

        # cast from ndim = 1 to ndim = 0
        destsize = struct.calcsize('I')
        ex = ndarray([9]*destsize, shape=[destsize], format='B')
        destitems, destshape = cast_items(ex, 'I', destsize, shape=[])
        m = memoryview(ex)
        m2 = m.cast('I', shape=[])
        self.verify(m2, obj=ex,
                    itemsize=destsize, fmt='I', readonly=1,
                    ndim=0, shape=(), strides=(),
                    lst=destitems, cast=True)

        # array.array: roundtrip to/from bytes
        for fmt, items, _ in iter_format(31, 'array'):
            ex = array.array(fmt, items)
            m = memoryview(ex)
            iter_roundtrip(ex, m, items, fmt)

        # ndarray: roundtrip to/from bytes
        for fmt, items, _ in iter_format(31, 'memoryview'):
            ex = ndarray(items, shape=[31], format=fmt, flags=ND_WRITABLE)
            m = memoryview(ex)
            iter_roundtrip(ex, m, items, fmt)

    def test_memoryview_cast_1D_ND(self):
        # Cast between C-contiguous buffers. At least one buffer must
        # be 1D, at least one format must be 'c', 'b' or 'B'.
        for _tshape in gencastshapes():
            for char in fmtdict['@']:
                tfmt = ('', '@')[randrange(2)] + char
                tsize = struct.calcsize(tfmt)
                n = prod(_tshape) * tsize
                obj = 'memoryview' if is_byte_format(tfmt) else 'bytefmt'
                for fmt, items, _ in iter_format(n, obj):
                    size = struct.calcsize(fmt)
                    shape = [n] if n > 0 else []
                    tshape = _tshape + [size]

                    ex = ndarray(items, shape=shape, format=fmt)
                    m = memoryview(ex)

                    titems, tshape = cast_items(ex, tfmt, tsize, shape=tshape)

                    if titems is None:
                        self.assertRaises(TypeError, m.cast, tfmt, tshape)
                        continue
                    if titems == 'nan':
                        continue # NaNs in lists are a recipe for trouble.

                    # 1D -> ND
                    nd = ndarray(titems, shape=tshape, format=tfmt)

                    m2 = m.cast(tfmt, shape=tshape)
                    ndim = len(tshape)
                    strides = nd.strides
                    lst = nd.tolist()
                    self.verify(m2, obj=ex,
                                itemsize=tsize, fmt=tfmt, readonly=1,
                                ndim=ndim, shape=tshape, strides=strides,
                                lst=lst, cast=True)

                    # ND -> 1D
                    m3 = m2.cast(fmt)
                    m4 = m2.cast(fmt, shape=shape)
                    ndim = len(shape)
                    strides = ex.strides
                    lst = ex.tolist()

                    self.verify(m3, obj=ex,
                                itemsize=size, fmt=fmt, readonly=1,
                                ndim=ndim, shape=shape, strides=strides,
                                lst=lst, cast=True)

                    self.verify(m4, obj=ex,
                                itemsize=size, fmt=fmt, readonly=1,
                                ndim=ndim, shape=shape, strides=strides,
                                lst=lst, cast=True)

    def test_memoryview_tolist(self):

        # Most tolist() tests are in self.verify() etc.

        a = array.array('h', list(range(-6, 6)))
        m = memoryview(a)
        self.assertEqual(m, a)
        self.assertEqual(m.tolist(), a.tolist())

        a = a[2::3]
        m = m[2::3]
        self.assertEqual(m, a)
        self.assertEqual(m.tolist(), a.tolist())

        ex = ndarray(list(range(2*3*5*7*11)), shape=[11,2,7,3,5], format='L')
        m = memoryview(ex)
        self.assertEqual(m.tolist(), ex.tolist())

        ex = ndarray([(2, 5), (7, 11)], shape=[2], format='lh')
        m = memoryview(ex)
        self.assertRaises(NotImplementedError, m.tolist)

        ex = ndarray([b'12345'], shape=[1], format="s")
        m = memoryview(ex)
        self.assertRaises(NotImplementedError, m.tolist)

        ex = ndarray([b"a",b"b",b"c",b"d",b"e",b"f"], shape=[2,3], format='s')
        m = memoryview(ex)
        self.assertRaises(NotImplementedError, m.tolist)

    def test_memoryview_repr(self):
        m = memoryview(bytearray(9))
        r = m.__repr__()
        self.assertTrue(r.startswith("<memory"))

        m.release()
        r = m.__repr__()
        self.assertTrue(r.startswith("<released"))

    def test_memoryview_sequence(self):

        for fmt in ('d', 'f'):
            inf = float(3e400)
            ex = array.array(fmt, [1.0, inf, 3.0])
            m = memoryview(ex)
            self.assertIn(1.0, m)
            self.assertIn(5e700, m)
            self.assertIn(3.0, m)

        ex = ndarray(9.0, [], format='f')
        m = memoryview(ex)
        self.assertRaises(TypeError, eval, "9.0 in m", locals())

    def test_memoryview_index(self):

        # ndim = 0
        ex = ndarray(12.5, shape=[], format='d')
        m = memoryview(ex)
        self.assertEqual(m[()], 12.5)
        self.assertEqual(m[...], m)
        self.assertEqual(m[...], ex)
        self.assertRaises(TypeError, m.__getitem__, 0)

        ex = ndarray((1,2,3), shape=[], format='iii')
        m = memoryview(ex)
        self.assertRaises(NotImplementedError, m.__getitem__, ())

        # range
        ex = ndarray(list(range(7)), shape=[7], flags=ND_WRITABLE)
        m = memoryview(ex)

        self.assertRaises(IndexError, m.__getitem__, 2**64)
        self.assertRaises(TypeError, m.__getitem__, 2.0)
        self.assertRaises(TypeError, m.__getitem__, 0.0)

        # out of bounds
        self.assertRaises(IndexError, m.__getitem__, -8)
        self.assertRaises(IndexError, m.__getitem__, 8)

        # Not implemented: multidimensional sub-views
        ex = ndarray(list(range(12)), shape=[3,4], flags=ND_WRITABLE)
        m = memoryview(ex)

        self.assertRaises(NotImplementedError, m.__getitem__, 0)
        self.assertRaises(NotImplementedError, m.__setitem__, 0, 9)
        self.assertRaises(NotImplementedError, m.__getitem__, 0)

    def test_memoryview_assign(self):

        # ndim = 0
        ex = ndarray(12.5, shape=[], format='f', flags=ND_WRITABLE)
        m = memoryview(ex)
        m[()] = 22.5
        self.assertEqual(m[()], 22.5)
        m[...] = 23.5
        self.assertEqual(m[()], 23.5)
        self.assertRaises(TypeError, m.__setitem__, 0, 24.7)

        # read-only
        ex = ndarray(list(range(7)), shape=[7])
        m = memoryview(ex)
        self.assertRaises(TypeError, m.__setitem__, 2, 10)

        # range
        ex = ndarray(list(range(7)), shape=[7], flags=ND_WRITABLE)
        m = memoryview(ex)

        self.assertRaises(IndexError, m.__setitem__, 2**64, 9)
        self.assertRaises(TypeError, m.__setitem__, 2.0, 10)
        self.assertRaises(TypeError, m.__setitem__, 0.0, 11)

        # out of bounds
        self.assertRaises(IndexError, m.__setitem__, -8, 20)
        self.assertRaises(IndexError, m.__setitem__, 8, 25)

        # pack_single() success:
        for fmt in fmtdict['@']:
            if fmt == 'c' or fmt == '?':
                continue
            ex = ndarray([1,2,3], shape=[3], format=fmt, flags=ND_WRITABLE)
            m = memoryview(ex)
            i = randrange(-3, 3)
            m[i] = 8
            self.assertEqual(m[i], 8)
            self.assertEqual(m[i], ex[i])

        ex = ndarray([b'1', b'2', b'3'], shape=[3], format='c',
                     flags=ND_WRITABLE)
        m = memoryview(ex)
        m[2] = b'9'
        self.assertEqual(m[2], b'9')

        ex = ndarray([True, False, True], shape=[3], format='?',
                     flags=ND_WRITABLE)
        m = memoryview(ex)
        m[1] = True
        self.assertEqual(m[1], True)

        # pack_single() exceptions:
        nd = ndarray([b'x'], shape=[1], format='c', flags=ND_WRITABLE)
        m = memoryview(nd)
        self.assertRaises(TypeError, m.__setitem__, 0, 100)

        ex = ndarray(list(range(120)), shape=[1,2,3,4,5], flags=ND_WRITABLE)
        m1 = memoryview(ex)

        for fmt, _range in fmtdict['@'].items():
            if (fmt == '?'): # PyObject_IsTrue() accepts anything
                continue
            if fmt == 'c': # special case tested above
                continue
            m2 = m1.cast(fmt)
            lo, hi = _range
            if fmt == 'd' or fmt == 'f':
                lo, hi = -2**1024, 2**1024
            if fmt != 'P': # PyLong_AsVoidPtr() accepts negative numbers
                self.assertRaises(ValueError, m2.__setitem__, 0, lo-1)
                self.assertRaises(TypeError, m2.__setitem__, 0, "xyz")
            self.assertRaises(ValueError, m2.__setitem__, 0, hi)

        # invalid item
        m2 = m1.cast('c')
        self.assertRaises(ValueError, m2.__setitem__, 0, b'\xff\xff')

        # format not implemented
        ex = ndarray(list(range(1)), shape=[1], format="xL", flags=ND_WRITABLE)
        m = memoryview(ex)
        self.assertRaises(NotImplementedError, m.__setitem__, 0, 1)

        ex = ndarray([b'12345'], shape=[1], format="s", flags=ND_WRITABLE)
        m = memoryview(ex)
        self.assertRaises(NotImplementedError, m.__setitem__, 0, 1)

        # Not implemented: multidimensional sub-views
        ex = ndarray(list(range(12)), shape=[3,4], flags=ND_WRITABLE)
        m = memoryview(ex)

        self.assertRaises(NotImplementedError, m.__setitem__, 0, [2, 3])

    def test_memoryview_slice(self):

        ex = ndarray(list(range(12)), shape=[12], flags=ND_WRITABLE)
        m = memoryview(ex)

        # zero step
        self.assertRaises(ValueError, m.__getitem__, slice(0,2,0))
        self.assertRaises(ValueError, m.__setitem__, slice(0,2,0),
                          bytearray([1,2]))

        # invalid slice key
        self.assertRaises(TypeError, m.__getitem__, ())

        # multidimensional slices
        ex = ndarray(list(range(12)), shape=[12], flags=ND_WRITABLE)
        m = memoryview(ex)

        self.assertRaises(NotImplementedError, m.__getitem__,
                          (slice(0,2,1), slice(0,2,1)))
        self.assertRaises(NotImplementedError, m.__setitem__,
                          (slice(0,2,1), slice(0,2,1)), bytearray([1,2]))

        # invalid slice tuple
        self.assertRaises(TypeError, m.__getitem__, (slice(0,2,1), {}))
        self.assertRaises(TypeError, m.__setitem__, (slice(0,2,1), {}),
                          bytearray([1,2]))

        # rvalue is not an exporter
        self.assertRaises(TypeError, m.__setitem__, slice(0,1,1), [1])

        # non-contiguous slice assignment
        for flags in (0, ND_PIL):
            ex1 = ndarray(list(range(12)), shape=[12], strides=[-1], offset=11,
                          flags=ND_WRITABLE|flags)
            ex2 = ndarray(list(range(24)), shape=[12], strides=[2], flags=flags)
            m1 = memoryview(ex1)
            m2 = memoryview(ex2)

            ex1[2:5] = ex1[2:5]
            m1[2:5] = m2[2:5]

            self.assertEqual(m1, ex1)
            self.assertEqual(m2, ex2)

            ex1[1:3][::-1] = ex2[0:2][::1]
            m1[1:3][::-1] = m2[0:2][::1]

            self.assertEqual(m1, ex1)
            self.assertEqual(m2, ex2)

            ex1[4:1:-2][::-1] = ex1[1:4:2][::1]
            m1[4:1:-2][::-1] = m1[1:4:2][::1]

            self.assertEqual(m1, ex1)
            self.assertEqual(m2, ex2)

    def test_memoryview_array(self):

        def cmptest(testcase, a, b, m, singleitem):
            for i, _ in enumerate(a):
                ai = a[i]
                mi = m[i]
                testcase.assertEqual(ai, mi)
                a[i] = singleitem
                if singleitem != ai:
                    testcase.assertNotEqual(a, m)
                    testcase.assertNotEqual(a, b)
                else:
                    testcase.assertEqual(a, m)
                    testcase.assertEqual(a, b)
                m[i] = singleitem
                testcase.assertEqual(a, m)
                testcase.assertEqual(b, m)
                a[i] = ai
                m[i] = mi

        for n in range(1, 5):
            for fmt, items, singleitem in iter_format(n, 'array'):
                for lslice in genslices(n):
                    for rslice in genslices(n):

                        a = array.array(fmt, items)
                        b = array.array(fmt, items)
                        m = memoryview(b)

                        self.assertEqual(m, a)
                        self.assertEqual(m.tolist(), a.tolist())
                        self.assertEqual(m.tobytes(), a.tobytes())
                        self.assertEqual(len(m), len(a))

                        cmptest(self, a, b, m, singleitem)

                        array_err = None
                        have_resize = None
                        try:
                            al = a[lslice]
                            ar = a[rslice]
                            a[lslice] = a[rslice]
                            have_resize = len(al) != len(ar)
                        except Exception as e:
                            array_err = e.__class__

                        m_err = None
                        try:
                            m[lslice] = m[rslice]
                        except Exception as e:
                            m_err = e.__class__

                        if have_resize: # memoryview cannot change shape
                            self.assertIs(m_err, ValueError)
                        elif m_err or array_err:
                            self.assertIs(m_err, array_err)
                        else:
                            self.assertEqual(m, a)
                            self.assertEqual(m.tolist(), a.tolist())
                            self.assertEqual(m.tobytes(), a.tobytes())
                            cmptest(self, a, b, m, singleitem)

    def test_memoryview_compare_special_cases(self):

        a = array.array('L', [1, 2, 3])
        b = array.array('L', [1, 2, 7])

        # Ordering comparisons raise:
        v = memoryview(a)
        w = memoryview(b)
        for attr in ('__lt__', '__le__', '__gt__', '__ge__'):
            self.assertIs(getattr(v, attr)(w), NotImplemented)
            self.assertIs(getattr(a, attr)(v), NotImplemented)

        # Released views compare equal to themselves:
        v = memoryview(a)
        v.release()
        self.assertEqual(v, v)
        self.assertNotEqual(v, a)
        self.assertNotEqual(a, v)

        v = memoryview(a)
        w = memoryview(a)
        w.release()
        self.assertNotEqual(v, w)
        self.assertNotEqual(w, v)

        # Operand does not implement the buffer protocol:
        v = memoryview(a)
        self.assertNotEqual(v, [1, 2, 3])

        # NaNs
        nd = ndarray([(0, 0)], shape=[1], format='l x d x', flags=ND_WRITABLE)
        nd[0] = (-1, float('nan'))
        self.assertNotEqual(memoryview(nd), nd)

        # Depends on issue #15625: the struct module does not understand 'u'.
        a = array.array('u', 'xyz')
        v = memoryview(a)
        self.assertNotEqual(a, v)
        self.assertNotEqual(v, a)

        # Some ctypes format strings are unknown to the struct module.
        if ctypes:
            # format: "T{>l:x:>l:y:}"
            class BEPoint(ctypes.BigEndianStructure):
                _fields_ = [("x", ctypes.c_long), ("y", ctypes.c_long)]
            point = BEPoint(100, 200)
            a = memoryview(point)
            b = memoryview(point)
            self.assertNotEqual(a, b)
            self.assertNotEqual(a, point)
            self.assertNotEqual(point, a)
            self.assertRaises(NotImplementedError, a.tolist)

    def test_memoryview_compare_ndim_zero(self):

        nd1 = ndarray(1729, shape=[], format='@L')
        nd2 = ndarray(1729, shape=[], format='L', flags=ND_WRITABLE)
        v = memoryview(nd1)
        w = memoryview(nd2)
        self.assertEqual(v, w)
        self.assertEqual(w, v)
        self.assertEqual(v, nd2)
        self.assertEqual(nd2, v)
        self.assertEqual(w, nd1)
        self.assertEqual(nd1, w)

        self.assertFalse(v.__ne__(w))
        self.assertFalse(w.__ne__(v))

        w[()] = 1728
        self.assertNotEqual(v, w)
        self.assertNotEqual(w, v)
        self.assertNotEqual(v, nd2)
        self.assertNotEqual(nd2, v)
        self.assertNotEqual(w, nd1)
        self.assertNotEqual(nd1, w)

        self.assertFalse(v.__eq__(w))
        self.assertFalse(w.__eq__(v))

        nd = ndarray(list(range(12)), shape=[12], flags=ND_WRITABLE|ND_PIL)
        ex = ndarray(list(range(12)), shape=[12], flags=ND_WRITABLE|ND_PIL)
        m = memoryview(ex)

        self.assertEqual(m, nd)
        m[9] = 100
        self.assertNotEqual(m, nd)

        # struct module: equal
        nd1 = ndarray((1729, 1.2, b'12345'), shape=[], format='Lf5s')
        nd2 = ndarray((1729, 1.2, b'12345'), shape=[], format='hf5s',
                      flags=ND_WRITABLE)
        v = memoryview(nd1)
        w = memoryview(nd2)
        self.assertEqual(v, w)
        self.assertEqual(w, v)
        self.assertEqual(v, nd2)
        self.assertEqual(nd2, v)
        self.assertEqual(w, nd1)
        self.assertEqual(nd1, w)

        # struct module: not equal
        nd1 = ndarray((1729, 1.2, b'12345'), shape=[], format='Lf5s')
        nd2 = ndarray((-1729, 1.2, b'12345'), shape=[], format='hf5s',
                      flags=ND_WRITABLE)
        v = memoryview(nd1)
        w = memoryview(nd2)
        self.assertNotEqual(v, w)
        self.assertNotEqual(w, v)
        self.assertNotEqual(v, nd2)
        self.assertNotEqual(nd2, v)
        self.assertNotEqual(w, nd1)
        self.assertNotEqual(nd1, w)
        self.assertEqual(v, nd1)
        self.assertEqual(w, nd2)

    def test_memoryview_compare_ndim_one(self):

        # contiguous
        nd1 = ndarray([-529, 576, -625, 676, -729], shape=[5], format='@h')
        nd2 = ndarray([-529, 576, -625, 676, 729], shape=[5], format='@h')
        v = memoryview(nd1)
        w = memoryview(nd2)

        self.assertEqual(v, nd1)
        self.assertEqual(w, nd2)
        self.assertNotEqual(v, nd2)
        self.assertNotEqual(w, nd1)
        self.assertNotEqual(v, w)

        # contiguous, struct module
        nd1 = ndarray([-529, 576, -625, 676, -729], shape=[5], format='<i')
        nd2 = ndarray([-529, 576, -625, 676, 729], shape=[5], format='>h')
        v = memoryview(nd1)
        w = memoryview(nd2)

        self.assertEqual(v, nd1)
        self.assertEqual(w, nd2)
        self.assertNotEqual(v, nd2)
        self.assertNotEqual(w, nd1)
        self.assertNotEqual(v, w)

        # non-contiguous
        nd1 = ndarray([-529, -625, -729], shape=[3], format='@h')
        nd2 = ndarray([-529, 576, -625, 676, -729], shape=[5], format='@h')
        v = memoryview(nd1)
        w = memoryview(nd2)

        self.assertEqual(v, nd2[::2])
        self.assertEqual(w[::2], nd1)
        self.assertEqual(v, w[::2])
        self.assertEqual(v[::-1], w[::-2])

        # non-contiguous, struct module
        nd1 = ndarray([-529, -625, -729], shape=[3], format='!h')
        nd2 = ndarray([-529, 576, -625, 676, -729], shape=[5], format='<l')
        v = memoryview(nd1)
        w = memoryview(nd2)

        self.assertEqual(v, nd2[::2])
        self.assertEqual(w[::2], nd1)
        self.assertEqual(v, w[::2])
        self.assertEqual(v[::-1], w[::-2])

        # non-contiguous, suboffsets
        nd1 = ndarray([-529, -625, -729], shape=[3], format='@h')
        nd2 = ndarray([-529, 576, -625, 676, -729], shape=[5], format='@h',
                      flags=ND_PIL)
        v = memoryview(nd1)
        w = memoryview(nd2)

        self.assertEqual(v, nd2[::2])
        self.assertEqual(w[::2], nd1)
        self.assertEqual(v, w[::2])
        self.assertEqual(v[::-1], w[::-2])

        # non-contiguous, suboffsets, struct module
        nd1 = ndarray([-529, -625, -729], shape=[3], format='h  0c')
        nd2 = ndarray([-529, 576, -625, 676, -729], shape=[5], format='>  h',
                      flags=ND_PIL)
        v = memoryview(nd1)
        w = memoryview(nd2)

        self.assertEqual(v, nd2[::2])
        self.assertEqual(w[::2], nd1)
        self.assertEqual(v, w[::2])
        self.assertEqual(v[::-1], w[::-2])

    def test_memoryview_compare_zero_shape(self):

        # zeros in shape
        nd1 = ndarray([900, 961], shape=[0], format='@h')
        nd2 = ndarray([-900, -961], shape=[0], format='@h')
        v = memoryview(nd1)
        w = memoryview(nd2)

        self.assertEqual(v, nd1)
        self.assertEqual(w, nd2)
        self.assertEqual(v, nd2)
        self.assertEqual(w, nd1)
        self.assertEqual(v, w)

        # zeros in shape, struct module
        nd1 = ndarray([900, 961], shape=[0], format='= h0c')
        nd2 = ndarray([-900, -961], shape=[0], format='@   i')
        v = memoryview(nd1)
        w = memoryview(nd2)

        self.assertEqual(v, nd1)
        self.assertEqual(w, nd2)
        self.assertEqual(v, nd2)
        self.assertEqual(w, nd1)
        self.assertEqual(v, w)

    def test_memoryview_compare_zero_strides(self):

        # zero strides
        nd1 = ndarray([900, 900, 900, 900], shape=[4], format='@L')
        nd2 = ndarray([900], shape=[4], strides=[0], format='L')
        v = memoryview(nd1)
        w = memoryview(nd2)

        self.assertEqual(v, nd1)
        self.assertEqual(w, nd2)
        self.assertEqual(v, nd2)
        self.assertEqual(w, nd1)
        self.assertEqual(v, w)

        # zero strides, struct module
        nd1 = ndarray([(900, 900)]*4, shape=[4], format='@ Li')
        nd2 = ndarray([(900, 900)], shape=[4], strides=[0], format='!L  h')
        v = memoryview(nd1)
        w = memoryview(nd2)

        self.assertEqual(v, nd1)
        self.assertEqual(w, nd2)
        self.assertEqual(v, nd2)
        self.assertEqual(w, nd1)
        self.assertEqual(v, w)

    def test_memoryview_compare_random_formats(self):

        # random single character native formats
        n = 10
        for char in fmtdict['@m']:
            fmt, items, singleitem = randitems(n, 'memoryview', '@', char)
            for flags in (0, ND_PIL):
                nd = ndarray(items, shape=[n], format=fmt, flags=flags)
                m = memoryview(nd)
                self.assertEqual(m, nd)

                nd = nd[::-3]
                m = memoryview(nd)
                self.assertEqual(m, nd)

        # random formats
        n = 10
        for _ in range(100):
            fmt, items, singleitem = randitems(n)
            for flags in (0, ND_PIL):
                nd = ndarray(items, shape=[n], format=fmt, flags=flags)
                m = memoryview(nd)
                self.assertEqual(m, nd)

                nd = nd[::-3]
                m = memoryview(nd)
                self.assertEqual(m, nd)

    def test_memoryview_compare_multidim_c(self):

        # C-contiguous, different values
        nd1 = ndarray(list(range(-15, 15)), shape=[3, 2, 5], format='@h')
        nd2 = ndarray(list(range(0, 30)), shape=[3, 2, 5], format='@h')
        v = memoryview(nd1)
        w = memoryview(nd2)

        self.assertEqual(v, nd1)
        self.assertEqual(w, nd2)
        self.assertNotEqual(v, nd2)
        self.assertNotEqual(w, nd1)
        self.assertNotEqual(v, w)

        # C-contiguous, different values, struct module
        nd1 = ndarray([(0, 1, 2)]*30, shape=[3, 2, 5], format='=f q xxL')
        nd2 = ndarray([(-1.2, 1, 2)]*30, shape=[3, 2, 5], format='< f 2Q')
        v = memoryview(nd1)
        w = memoryview(nd2)

        self.assertEqual(v, nd1)
        self.assertEqual(w, nd2)
        self.assertNotEqual(v, nd2)
        self.assertNotEqual(w, nd1)
        self.assertNotEqual(v, w)

        # C-contiguous, different shape
        nd1 = ndarray(list(range(30)), shape=[2, 3, 5], format='L')
        nd2 = ndarray(list(range(30)), shape=[3, 2, 5], format='L')
        v = memoryview(nd1)
        w = memoryview(nd2)

        self.assertEqual(v, nd1)
        self.assertEqual(w, nd2)
        self.assertNotEqual(v, nd2)
        self.assertNotEqual(w, nd1)
        self.assertNotEqual(v, w)

        # C-contiguous, different shape, struct module
        nd1 = ndarray([(0, 1, 2)]*21, shape=[3, 7], format='! b B xL')
        nd2 = ndarray([(0, 1, 2)]*21, shape=[7, 3], format='= Qx l xxL')
        v = memoryview(nd1)
        w = memoryview(nd2)

        self.assertEqual(v, nd1)
        self.assertEqual(w, nd2)
        self.assertNotEqual(v, nd2)
        self.assertNotEqual(w, nd1)
        self.assertNotEqual(v, w)

        # C-contiguous, different format, struct module
        nd1 = ndarray(list(range(30)), shape=[2, 3, 5], format='L')
        nd2 = ndarray(list(range(30)), shape=[2, 3, 5], format='l')
        v = memoryview(nd1)
        w = memoryview(nd2)

        self.assertEqual(v, nd1)
        self.assertEqual(w, nd2)
        self.assertEqual(v, nd2)
        self.assertEqual(w, nd1)
        self.assertEqual(v, w)

    def test_memoryview_compare_multidim_fortran(self):

        # Fortran-contiguous, different values
        nd1 = ndarray(list(range(-15, 15)), shape=[5, 2, 3], format='@h',
                      flags=ND_FORTRAN)
        nd2 = ndarray(list(range(0, 30)), shape=[5, 2, 3], format='@h',
                      flags=ND_FORTRAN)
        v = memoryview(nd1)
        w = memoryview(nd2)

        self.assertEqual(v, nd1)
        self.assertEqual(w, nd2)
        self.assertNotEqual(v, nd2)
        self.assertNotEqual(w, nd1)
        self.assertNotEqual(v, w)

        # Fortran-contiguous, different values, struct module
        nd1 = ndarray([(2**64-1, -1)]*6, shape=[2, 3], format='=Qq',
                      flags=ND_FORTRAN)
        nd2 = ndarray([(-1, 2**64-1)]*6, shape=[2, 3], format='=qQ',
                      flags=ND_FORTRAN)
        v = memoryview(nd1)
        w = memoryview(nd2)

        self.assertEqual(v, nd1)
        self.assertEqual(w, nd2)
        self.assertNotEqual(v, nd2)
        self.assertNotEqual(w, nd1)
        self.assertNotEqual(v, w)

        # Fortran-contiguous, different shape
        nd1 = ndarray(list(range(-15, 15)), shape=[2, 3, 5], format='l',
                      flags=ND_FORTRAN)
        nd2 = ndarray(list(range(-15, 15)), shape=[3, 2, 5], format='l',
                      flags=ND_FORTRAN)
        v = memoryview(nd1)
        w = memoryview(nd2)

        self.assertEqual(v, nd1)
        self.assertEqual(w, nd2)
        self.assertNotEqual(v, nd2)
        self.assertNotEqual(w, nd1)
        self.assertNotEqual(v, w)

        # Fortran-contiguous, different shape, struct module
        nd1 = ndarray(list(range(-15, 15)), shape=[2, 3, 5], format='0ll',
                      flags=ND_FORTRAN)
        nd2 = ndarray(list(range(-15, 15)), shape=[3, 2, 5], format='l',
                      flags=ND_FORTRAN)
        v = memoryview(nd1)
        w = memoryview(nd2)

        self.assertEqual(v, nd1)
        self.assertEqual(w, nd2)
        self.assertNotEqual(v, nd2)
        self.assertNotEqual(w, nd1)
        self.assertNotEqual(v, w)

        # Fortran-contiguous, different format, struct module
        nd1 = ndarray(list(range(30)), shape=[5, 2, 3], format='@h',
                      flags=ND_FORTRAN)
        nd2 = ndarray(list(range(30)), shape=[5, 2, 3], format='@b',
                      flags=ND_FORTRAN)
        v = memoryview(nd1)
        w = memoryview(nd2)

        self.assertEqual(v, nd1)
        self.assertEqual(w, nd2)
        self.assertEqual(v, nd2)
        self.assertEqual(w, nd1)
        self.assertEqual(v, w)

    def test_memoryview_compare_multidim_mixed(self):

        # mixed C/Fortran contiguous
        lst1 = list(range(-15, 15))
        lst2 = transpose(lst1, [3, 2, 5])
        nd1 = ndarray(lst1, shape=[3, 2, 5], format='@l')
        nd2 = ndarray(lst2, shape=[3, 2, 5], format='l', flags=ND_FORTRAN)
        v = memoryview(nd1)
        w = memoryview(nd2)

        self.assertEqual(v, nd1)
        self.assertEqual(w, nd2)
        self.assertEqual(v, w)

        # mixed C/Fortran contiguous, struct module
        lst1 = [(-3.3, -22, b'x')]*30
        lst1[5] = (-2.2, -22, b'x')
        lst2 = transpose(lst1, [3, 2, 5])
        nd1 = ndarray(lst1, shape=[3, 2, 5], format='d b c')
        nd2 = ndarray(lst2, shape=[3, 2, 5], format='d h c', flags=ND_FORTRAN)
        v = memoryview(nd1)
        w = memoryview(nd2)

        self.assertEqual(v, nd1)
        self.assertEqual(w, nd2)
        self.assertEqual(v, w)

        # different values, non-contiguous
        ex1 = ndarray(list(range(40)), shape=[5, 8], format='@I')
        nd1 = ex1[3:1:-1, ::-2]
        ex2 = ndarray(list(range(40)), shape=[5, 8], format='I')
        nd2 = ex2[1:3:1, ::-2]
        v = memoryview(nd1)
        w = memoryview(nd2)

        self.assertEqual(v, nd1)
        self.assertEqual(w, nd2)
        self.assertNotEqual(v, nd2)
        self.assertNotEqual(w, nd1)
        self.assertNotEqual(v, w)

        # same values, non-contiguous, struct module
        ex1 = ndarray([(2**31-1, -2**31)]*22, shape=[11, 2], format='=ii')
        nd1 = ex1[3:1:-1, ::-2]
        ex2 = ndarray([(2**31-1, -2**31)]*22, shape=[11, 2], format='>ii')
        nd2 = ex2[1:3:1, ::-2]
        v = memoryview(nd1)
        w = memoryview(nd2)

        self.assertEqual(v, nd1)
        self.assertEqual(w, nd2)
        self.assertEqual(v, nd2)
        self.assertEqual(w, nd1)
        self.assertEqual(v, w)

        # different shape
        ex1 = ndarray(list(range(30)), shape=[2, 3, 5], format='b')
        nd1 = ex1[1:3:, ::-2]
        nd2 = ndarray(list(range(30)), shape=[3, 2, 5], format='b')
        nd2 = ex2[1:3:, ::-2]
        v = memoryview(nd1)
        w = memoryview(nd2)

        self.assertEqual(v, nd1)
        self.assertEqual(w, nd2)
        self.assertNotEqual(v, nd2)
        self.assertNotEqual(w, nd1)
        self.assertNotEqual(v, w)

        # different shape, struct module
        ex1 = ndarray(list(range(30)), shape=[2, 3, 5], format='B')
        nd1 = ex1[1:3:, ::-2]
        nd2 = ndarray(list(range(30)), shape=[3, 2, 5], format='b')
        nd2 = ex2[1:3:, ::-2]
        v = memoryview(nd1)
        w = memoryview(nd2)

        self.assertEqual(v, nd1)
        self.assertEqual(w, nd2)
        self.assertNotEqual(v, nd2)
        self.assertNotEqual(w, nd1)
        self.assertNotEqual(v, w)

        # different format, struct module
        ex1 = ndarray([(2, b'123')]*30, shape=[5, 3, 2], format='b3s')
        nd1 = ex1[1:3:, ::-2]
        nd2 = ndarray([(2, b'123')]*30, shape=[5, 3, 2], format='i3s')
        nd2 = ex2[1:3:, ::-2]
        v = memoryview(nd1)
        w = memoryview(nd2)

        self.assertEqual(v, nd1)
        self.assertEqual(w, nd2)
        self.assertNotEqual(v, nd2)
        self.assertNotEqual(w, nd1)
        self.assertNotEqual(v, w)

    def test_memoryview_compare_multidim_zero_shape(self):

        # zeros in shape
        nd1 = ndarray(list(range(30)), shape=[0, 3, 2], format='i')
        nd2 = ndarray(list(range(30)), shape=[5, 0, 2], format='@i')
        v = memoryview(nd1)
        w = memoryview(nd2)

        self.assertEqual(v, nd1)
        self.assertEqual(w, nd2)
        self.assertNotEqual(v, nd2)
        self.assertNotEqual(w, nd1)
        self.assertNotEqual(v, w)

        # zeros in shape, struct module
        nd1 = ndarray(list(range(30)), shape=[0, 3, 2], format='i')
        nd2 = ndarray(list(range(30)), shape=[5, 0, 2], format='@i')
        v = memoryview(nd1)
        w = memoryview(nd2)

        self.assertEqual(v, nd1)
        self.assertEqual(w, nd2)
        self.assertNotEqual(v, nd2)
        self.assertNotEqual(w, nd1)
        self.assertNotEqual(v, w)

    def test_memoryview_compare_multidim_zero_strides(self):

        # zero strides
        nd1 = ndarray([900]*80, shape=[4, 5, 4], format='@L')
        nd2 = ndarray([900], shape=[4, 5, 4], strides=[0, 0, 0], format='L')
        v = memoryview(nd1)
        w = memoryview(nd2)

        self.assertEqual(v, nd1)
        self.assertEqual(w, nd2)
        self.assertEqual(v, nd2)
        self.assertEqual(w, nd1)
        self.assertEqual(v, w)
        self.assertEqual(v.tolist(), w.tolist())

        # zero strides, struct module
        nd1 = ndarray([(1, 2)]*10, shape=[2, 5], format='=lQ')
        nd2 = ndarray([(1, 2)], shape=[2, 5], strides=[0, 0], format='<lQ')
        v = memoryview(nd1)
        w = memoryview(nd2)

        self.assertEqual(v, nd1)
        self.assertEqual(w, nd2)
        self.assertEqual(v, nd2)
        self.assertEqual(w, nd1)
        self.assertEqual(v, w)

    def test_memoryview_compare_multidim_suboffsets(self):

        # suboffsets
        ex1 = ndarray(list(range(40)), shape=[5, 8], format='@I')
        nd1 = ex1[3:1:-1, ::-2]
        ex2 = ndarray(list(range(40)), shape=[5, 8], format='I', flags=ND_PIL)
        nd2 = ex2[1:3:1, ::-2]
        v = memoryview(nd1)
        w = memoryview(nd2)

        self.assertEqual(v, nd1)
        self.assertEqual(w, nd2)
        self.assertNotEqual(v, nd2)
        self.assertNotEqual(w, nd1)
        self.assertNotEqual(v, w)

        # suboffsets, struct module
        ex1 = ndarray([(2**64-1, -1)]*40, shape=[5, 8], format='=Qq',
                      flags=ND_WRITABLE)
        ex1[2][7] = (1, -2)
        nd1 = ex1[3:1:-1, ::-2]

        ex2 = ndarray([(2**64-1, -1)]*40, shape=[5, 8], format='>Qq',
                      flags=ND_PIL|ND_WRITABLE)
        ex2[2][7] = (1, -2)
        nd2 = ex2[1:3:1, ::-2]

        v = memoryview(nd1)
        w = memoryview(nd2)

        self.assertEqual(v, nd1)
        self.assertEqual(w, nd2)
        self.assertEqual(v, nd2)
        self.assertEqual(w, nd1)
        self.assertEqual(v, w)

        # suboffsets, different shape
        ex1 = ndarray(list(range(30)), shape=[2, 3, 5], format='b',
                      flags=ND_PIL)
        nd1 = ex1[1:3:, ::-2]
        nd2 = ndarray(list(range(30)), shape=[3, 2, 5], format='b')
        nd2 = ex2[1:3:, ::-2]
        v = memoryview(nd1)
        w = memoryview(nd2)

        self.assertEqual(v, nd1)
        self.assertEqual(w, nd2)
        self.assertNotEqual(v, nd2)
        self.assertNotEqual(w, nd1)
        self.assertNotEqual(v, w)

        # suboffsets, different shape, struct module
        ex1 = ndarray([(2**8-1, -1)]*40, shape=[2, 3, 5], format='Bb',
                      flags=ND_PIL|ND_WRITABLE)
        nd1 = ex1[1:2:, ::-2]

        ex2 = ndarray([(2**8-1, -1)]*40, shape=[3, 2, 5], format='Bb')
        nd2 = ex2[1:2:, ::-2]

        v = memoryview(nd1)
        w = memoryview(nd2)

        self.assertEqual(v, nd1)
        self.assertEqual(w, nd2)
        self.assertNotEqual(v, nd2)
        self.assertNotEqual(w, nd1)
        self.assertNotEqual(v, w)

        # suboffsets, different format
        ex1 = ndarray(list(range(30)), shape=[5, 3, 2], format='i', flags=ND_PIL)
        nd1 = ex1[1:3:, ::-2]
        ex2 = ndarray(list(range(30)), shape=[5, 3, 2], format='@I', flags=ND_PIL)
        nd2 = ex2[1:3:, ::-2]
        v = memoryview(nd1)
        w = memoryview(nd2)

        self.assertEqual(v, nd1)
        self.assertEqual(w, nd2)
        self.assertEqual(v, nd2)
        self.assertEqual(w, nd1)
        self.assertEqual(v, w)

        # suboffsets, different format, struct module
        ex1 = ndarray([(b'hello', b'', 1)]*27, shape=[3, 3, 3], format='5s0sP',
                      flags=ND_PIL|ND_WRITABLE)
        ex1[1][2][2] = (b'sushi', b'', 1)
        nd1 = ex1[1:3:, ::-2]

        ex2 = ndarray([(b'hello', b'', 1)]*27, shape=[3, 3, 3], format='5s0sP',
                      flags=ND_PIL|ND_WRITABLE)
        ex1[1][2][2] = (b'sushi', b'', 1)
        nd2 = ex2[1:3:, ::-2]

        v = memoryview(nd1)
        w = memoryview(nd2)

        self.assertEqual(v, nd1)
        self.assertEqual(w, nd2)
        self.assertNotEqual(v, nd2)
        self.assertNotEqual(w, nd1)
        self.assertNotEqual(v, w)

        # initialize mixed C/Fortran + suboffsets
        lst1 = list(range(-15, 15))
        lst2 = transpose(lst1, [3, 2, 5])
        nd1 = ndarray(lst1, shape=[3, 2, 5], format='@l', flags=ND_PIL)
        nd2 = ndarray(lst2, shape=[3, 2, 5], format='l', flags=ND_FORTRAN|ND_PIL)
        v = memoryview(nd1)
        w = memoryview(nd2)

        self.assertEqual(v, nd1)
        self.assertEqual(w, nd2)
        self.assertEqual(v, w)

        # initialize mixed C/Fortran + suboffsets, struct module
        lst1 = [(b'sashimi', b'sliced', 20.05)]*30
        lst1[11] = (b'ramen', b'spicy', 9.45)
        lst2 = transpose(lst1, [3, 2, 5])

        nd1 = ndarray(lst1, shape=[3, 2, 5], format='< 10p 9p d', flags=ND_PIL)
        nd2 = ndarray(lst2, shape=[3, 2, 5], format='> 10p 9p d',
                      flags=ND_FORTRAN|ND_PIL)
        v = memoryview(nd1)
        w = memoryview(nd2)

        self.assertEqual(v, nd1)
        self.assertEqual(w, nd2)
        self.assertEqual(v, w)

    def test_memoryview_compare_not_equal(self):

        # items not equal
        for byteorder in ['=', '<', '>', '!']:
            x = ndarray([2**63]*120, shape=[3,5,2,2,2], format=byteorder+'Q')
            y = ndarray([2**63]*120, shape=[3,5,2,2,2], format=byteorder+'Q',
                        flags=ND_WRITABLE|ND_FORTRAN)
            y[2][3][1][1][1] = 1
            a = memoryview(x)
            b = memoryview(y)
            self.assertEqual(a, x)
            self.assertEqual(b, y)
            self.assertNotEqual(a, b)
            self.assertNotEqual(a, y)
            self.assertNotEqual(b, x)

            x = ndarray([(2**63, 2**31, 2**15)]*120, shape=[3,5,2,2,2],
                        format=byteorder+'QLH')
            y = ndarray([(2**63, 2**31, 2**15)]*120, shape=[3,5,2,2,2],
                        format=byteorder+'QLH', flags=ND_WRITABLE|ND_FORTRAN)
            y[2][3][1][1][1] = (1, 1, 1)
            a = memoryview(x)
            b = memoryview(y)
            self.assertEqual(a, x)
            self.assertEqual(b, y)
            self.assertNotEqual(a, b)
            self.assertNotEqual(a, y)
            self.assertNotEqual(b, x)

    def test_memoryview_check_released(self):

        a = array.array('d', [1.1, 2.2, 3.3])

        m = memoryview(a)
        m.release()

        # PyMemoryView_FromObject()
        self.assertRaises(ValueError, memoryview, m)
        # memoryview.cast()
        self.assertRaises(ValueError, m.cast, 'c')
        # getbuffer()
        self.assertRaises(ValueError, ndarray, m)
        # memoryview.tolist()
        self.assertRaises(ValueError, m.tolist)
        # memoryview.tobytes()
        self.assertRaises(ValueError, m.tobytes)
        # sequence
        self.assertRaises(ValueError, eval, "1.0 in m", locals())
        # subscript
        self.assertRaises(ValueError, m.__getitem__, 0)
        # assignment
        self.assertRaises(ValueError, m.__setitem__, 0, 1)

        for attr in ('obj', 'nbytes', 'readonly', 'itemsize', 'format', 'ndim',
                     'shape', 'strides', 'suboffsets', 'c_contiguous',
                     'f_contiguous', 'contiguous'):
            self.assertRaises(ValueError, m.__getattribute__, attr)

        # richcompare
        b = array.array('d', [1.1, 2.2, 3.3])
        m1 = memoryview(a)
        m2 = memoryview(b)

        self.assertEqual(m1, m2)
        m1.release()
        self.assertNotEqual(m1, m2)
        self.assertNotEqual(m1, a)
        self.assertEqual(m1, m1)

    def test_memoryview_tobytes(self):
        # Many implicit tests are already in self.verify().

        t = (-529, 576, -625, 676, -729)

        nd = ndarray(t, shape=[5], format='@h')
        m = memoryview(nd)
        self.assertEqual(m, nd)
        self.assertEqual(m.tobytes(), nd.tobytes())

        nd = ndarray([t], shape=[1], format='>hQiLl')
        m = memoryview(nd)
        self.assertEqual(m, nd)
        self.assertEqual(m.tobytes(), nd.tobytes())

        nd = ndarray([t for _ in range(12)], shape=[2,2,3], format='=hQiLl')
        m = memoryview(nd)
        self.assertEqual(m, nd)
        self.assertEqual(m.tobytes(), nd.tobytes())

        nd = ndarray([t for _ in range(120)], shape=[5,2,2,3,2],
                     format='<hQiLl')
        m = memoryview(nd)
        self.assertEqual(m, nd)
        self.assertEqual(m.tobytes(), nd.tobytes())

        # Unknown formats are handled: tobytes() purely depends on itemsize.
        if ctypes:
            # format: "T{>l:x:>l:y:}"
            class BEPoint(ctypes.BigEndianStructure):
                _fields_ = [("x", ctypes.c_long), ("y", ctypes.c_long)]
            point = BEPoint(100, 200)
            a = memoryview(point)
            self.assertEqual(a.tobytes(), bytes(point))

    def test_memoryview_get_contiguous(self):
        # Many implicit tests are already in self.verify().

        # no buffer interface
        self.assertRaises(TypeError, get_contiguous, {}, PyBUF_READ, 'F')

        # writable request to read-only object
        self.assertRaises(BufferError, get_contiguous, b'x', PyBUF_WRITE, 'C')

        # writable request to non-contiguous object
        nd = ndarray([1, 2, 3], shape=[2], strides=[2])
        self.assertRaises(BufferError, get_contiguous, nd, PyBUF_WRITE, 'A')

        # scalar, read-only request from read-only exporter
        nd = ndarray(9, shape=(), format="L")
        for order in ['C', 'F', 'A']:
            m = get_contiguous(nd, PyBUF_READ, order)
            self.assertEqual(m, nd)
            self.assertEqual(m[()], 9)

        # scalar, read-only request from writable exporter
        nd = ndarray(9, shape=(), format="L", flags=ND_WRITABLE)
        for order in ['C', 'F', 'A']:
            m = get_contiguous(nd, PyBUF_READ, order)
            self.assertEqual(m, nd)
            self.assertEqual(m[()], 9)

        # scalar, writable request
        for order in ['C', 'F', 'A']:
            nd[()] = 9
            m = get_contiguous(nd, PyBUF_WRITE, order)
            self.assertEqual(m, nd)
            self.assertEqual(m[()], 9)

            m[()] = 10
            self.assertEqual(m[()], 10)
            self.assertEqual(nd[()], 10)

        # zeros in shape
        nd = ndarray([1], shape=[0], format="L", flags=ND_WRITABLE)
        for order in ['C', 'F', 'A']:
            m = get_contiguous(nd, PyBUF_READ, order)
            self.assertRaises(IndexError, m.__getitem__, 0)
            self.assertEqual(m, nd)
            self.assertEqual(m.tolist(), [])

        nd = ndarray(list(range(8)), shape=[2, 0, 7], format="L",
                     flags=ND_WRITABLE)
        for order in ['C', 'F', 'A']:
            m = get_contiguous(nd, PyBUF_READ, order)
            self.assertEqual(ndarray(m).tolist(), [[], []])

        # one-dimensional
        nd = ndarray([1], shape=[1], format="h", flags=ND_WRITABLE)
        for order in ['C', 'F', 'A']:
            m = get_contiguous(nd, PyBUF_WRITE, order)
            self.assertEqual(m, nd)
            self.assertEqual(m.tolist(), nd.tolist())

        nd = ndarray([1, 2, 3], shape=[3], format="b", flags=ND_WRITABLE)
        for order in ['C', 'F', 'A']:
            m = get_contiguous(nd, PyBUF_WRITE, order)
            self.assertEqual(m, nd)
            self.assertEqual(m.tolist(), nd.tolist())

        # one-dimensional, non-contiguous
        nd = ndarray([1, 2, 3], shape=[2], strides=[2], flags=ND_WRITABLE)
        for order in ['C', 'F', 'A']:
            m = get_contiguous(nd, PyBUF_READ, order)
            self.assertEqual(m, nd)
            self.assertEqual(m.tolist(), nd.tolist())
            self.assertRaises(TypeError, m.__setitem__, 1, 20)
            self.assertEqual(m[1], 3)
            self.assertEqual(nd[1], 3)

        nd = nd[::-1]
        for order in ['C', 'F', 'A']:
            m = get_contiguous(nd, PyBUF_READ, order)
            self.assertEqual(m, nd)
            self.assertEqual(m.tolist(), nd.tolist())
            self.assertRaises(TypeError, m.__setitem__, 1, 20)
            self.assertEqual(m[1], 1)
            self.assertEqual(nd[1], 1)

        # multi-dimensional, contiguous input
        nd = ndarray(list(range(12)), shape=[3, 4], flags=ND_WRITABLE)
        for order in ['C', 'A']:
            m = get_contiguous(nd, PyBUF_WRITE, order)
            self.assertEqual(ndarray(m).tolist(), nd.tolist())

        self.assertRaises(BufferError, get_contiguous, nd, PyBUF_WRITE, 'F')
        m = get_contiguous(nd, PyBUF_READ, order)
        self.assertEqual(ndarray(m).tolist(), nd.tolist())

        nd = ndarray(list(range(12)), shape=[3, 4],
                     flags=ND_WRITABLE|ND_FORTRAN)
        for order in ['F', 'A']:
            m = get_contiguous(nd, PyBUF_WRITE, order)
            self.assertEqual(ndarray(m).tolist(), nd.tolist())

        self.assertRaises(BufferError, get_contiguous, nd, PyBUF_WRITE, 'C')
        m = get_contiguous(nd, PyBUF_READ, order)
        self.assertEqual(ndarray(m).tolist(), nd.tolist())

        # multi-dimensional, non-contiguous input
        nd = ndarray(list(range(12)), shape=[3, 4], flags=ND_WRITABLE|ND_PIL)
        for order in ['C', 'F', 'A']:
            self.assertRaises(BufferError, get_contiguous, nd, PyBUF_WRITE,
                              order)
            m = get_contiguous(nd, PyBUF_READ, order)
            self.assertEqual(ndarray(m).tolist(), nd.tolist())

        # flags
        nd = ndarray([1,2,3,4,5], shape=[3], strides=[2])
        m = get_contiguous(nd, PyBUF_READ, 'C')
        self.assertTrue(m.c_contiguous)

    def test_memoryview_serializing(self):

        # C-contiguous
        size = struct.calcsize('i')
        a = array.array('i', [1,2,3,4,5])
        m = memoryview(a)
        buf = io.BytesIO(m)
        b = bytearray(5*size)
        buf.readinto(b)
        self.assertEqual(m.tobytes(), b)

        # C-contiguous, multi-dimensional
        size = struct.calcsize('L')
        nd = ndarray(list(range(12)), shape=[2,3,2], format="L")
        m = memoryview(nd)
        buf = io.BytesIO(m)
        b = bytearray(2*3*2*size)
        buf.readinto(b)
        self.assertEqual(m.tobytes(), b)

        # Fortran contiguous, multi-dimensional
        #size = struct.calcsize('L')
        #nd = ndarray(list(range(12)), shape=[2,3,2], format="L",
        #             flags=ND_FORTRAN)
        #m = memoryview(nd)
        #buf = io.BytesIO(m)
        #b = bytearray(2*3*2*size)
        #buf.readinto(b)
        #self.assertEqual(m.tobytes(), b)

    def test_memoryview_hash(self):

        # bytes exporter
        b = bytes(list(range(12)))
        m = memoryview(b)
        self.assertEqual(hash(b), hash(m))

        # C-contiguous
        mc = m.cast('c', shape=[3,4])
        self.assertEqual(hash(mc), hash(b))

        # non-contiguous
        mx = m[::-2]
        b = bytes(list(range(12))[::-2])
        self.assertEqual(hash(mx), hash(b))

        # Fortran contiguous
        nd = ndarray(list(range(30)), shape=[3,2,5], flags=ND_FORTRAN)
        m = memoryview(nd)
        self.assertEqual(hash(m), hash(nd))

        # multi-dimensional slice
        nd = ndarray(list(range(30)), shape=[3,2,5])
        x = nd[::2, ::, ::-1]
        m = memoryview(x)
        self.assertEqual(hash(m), hash(x))

        # multi-dimensional slice with suboffsets
        nd = ndarray(list(range(30)), shape=[2,5,3], flags=ND_PIL)
        x = nd[::2, ::, ::-1]
        m = memoryview(x)
        self.assertEqual(hash(m), hash(x))

        # equality-hash invariant
        x = ndarray(list(range(12)), shape=[12], format='B')
        a = memoryview(x)

        y = ndarray(list(range(12)), shape=[12], format='b')
        b = memoryview(y)

        self.assertEqual(a, b)
        self.assertEqual(hash(a), hash(b))

        # non-byte formats
        nd = ndarray(list(range(12)), shape=[2,2,3], format='L')
        m = memoryview(nd)
        self.assertRaises(ValueError, m.__hash__)

        nd = ndarray(list(range(-6, 6)), shape=[2,2,3], format='h')
        m = memoryview(nd)
        self.assertRaises(ValueError, m.__hash__)

        nd = ndarray(list(range(12)), shape=[2,2,3], format='= L')
        m = memoryview(nd)
        self.assertRaises(ValueError, m.__hash__)

        nd = ndarray(list(range(-6, 6)), shape=[2,2,3], format='< h')
        m = memoryview(nd)
        self.assertRaises(ValueError, m.__hash__)

    def test_memoryview_release(self):

        # Create re-exporter from getbuffer(memoryview), then release the view.
        a = bytearray([1,2,3])
        m = memoryview(a)
        nd = ndarray(m) # re-exporter
        self.assertRaises(BufferError, m.release)
        del nd
        m.release()

        a = bytearray([1,2,3])
        m = memoryview(a)
        nd1 = ndarray(m, getbuf=PyBUF_FULL_RO, flags=ND_REDIRECT)
        nd2 = ndarray(nd1, getbuf=PyBUF_FULL_RO, flags=ND_REDIRECT)
        self.assertIs(nd2.obj, m)
        self.assertRaises(BufferError, m.release)
        del nd1, nd2
        m.release()

        # chained views
        a = bytearray([1,2,3])
        m1 = memoryview(a)
        m2 = memoryview(m1)
        nd = ndarray(m2) # re-exporter
        m1.release()
        self.assertRaises(BufferError, m2.release)
        del nd
        m2.release()

        a = bytearray([1,2,3])
        m1 = memoryview(a)
        m2 = memoryview(m1)
        nd1 = ndarray(m2, getbuf=PyBUF_FULL_RO, flags=ND_REDIRECT)
        nd2 = ndarray(nd1, getbuf=PyBUF_FULL_RO, flags=ND_REDIRECT)
        self.assertIs(nd2.obj, m2)
        m1.release()
        self.assertRaises(BufferError, m2.release)
        del nd1, nd2
        m2.release()

        # Allow changing layout while buffers are exported.
        nd = ndarray([1,2,3], shape=[3], flags=ND_VAREXPORT)
        m1 = memoryview(nd)

        nd.push([4,5,6,7,8], shape=[5]) # mutate nd
        m2 = memoryview(nd)

        x = memoryview(m1)
        self.assertEqual(x.tolist(), m1.tolist())

        y = memoryview(m2)
        self.assertEqual(y.tolist(), m2.tolist())
        self.assertEqual(y.tolist(), nd.tolist())
        m2.release()
        y.release()

        nd.pop() # pop the current view
        self.assertEqual(x.tolist(), nd.tolist())

        del nd
        m1.release()
        x.release()

        # If multiple memoryviews share the same managed buffer, implicit
        # release() in the context manager's __exit__() method should still
        # work.
        def catch22(b):
            with memoryview(b) as m2:
                pass

        x = bytearray(b'123')
        with memoryview(x) as m1:
            catch22(m1)
            self.assertEqual(m1[0], ord(b'1'))

        x = ndarray(list(range(12)), shape=[2,2,3], format='l')
        y = ndarray(x, getbuf=PyBUF_FULL_RO, flags=ND_REDIRECT)
        z = ndarray(y, getbuf=PyBUF_FULL_RO, flags=ND_REDIRECT)
        self.assertIs(z.obj, x)
        with memoryview(z) as m:
            catch22(m)
            self.assertEqual(m[0:1].tolist(), [[[0, 1, 2], [3, 4, 5]]])

        # Test garbage collection.
        for flags in (0, ND_REDIRECT):
            x = bytearray(b'123')
            with memoryview(x) as m1:
                del x
                y = ndarray(m1, getbuf=PyBUF_FULL_RO, flags=flags)
                with memoryview(y) as m2:
                    del y
                    z = ndarray(m2, getbuf=PyBUF_FULL_RO, flags=flags)
                    with memoryview(z) as m3:
                        del z
                        catch22(m3)
                        catch22(m2)
                        catch22(m1)
                        self.assertEqual(m1[0], ord(b'1'))
                        self.assertEqual(m2[1], ord(b'2'))
                        self.assertEqual(m3[2], ord(b'3'))
                        del m3
                    del m2
                del m1

            x = bytearray(b'123')
            with memoryview(x) as m1:
                del x
                y = ndarray(m1, getbuf=PyBUF_FULL_RO, flags=flags)
                with memoryview(y) as m2:
                    del y
                    z = ndarray(m2, getbuf=PyBUF_FULL_RO, flags=flags)
                    with memoryview(z) as m3:
                        del z
                        catch22(m1)
                        catch22(m2)
                        catch22(m3)
                        self.assertEqual(m1[0], ord(b'1'))
                        self.assertEqual(m2[1], ord(b'2'))
                        self.assertEqual(m3[2], ord(b'3'))
                        del m1, m2, m3

        # memoryview.release() fails if the view has exported buffers.
        x = bytearray(b'123')
        with self.assertRaises(BufferError):
            with memoryview(x) as m:
                ex = ndarray(m)
                m[0] == ord(b'1')

    def test_memoryview_redirect(self):

        nd = ndarray([1.0 * x for x in range(12)], shape=[12], format='d')
        a = array.array('d', [1.0 * x for x in range(12)])

        for x in (nd, a):
            y = ndarray(x, getbuf=PyBUF_FULL_RO, flags=ND_REDIRECT)
            z = ndarray(y, getbuf=PyBUF_FULL_RO, flags=ND_REDIRECT)
            m = memoryview(z)

            self.assertIs(y.obj, x)
            self.assertIs(z.obj, x)
            self.assertIs(m.obj, x)

            self.assertEqual(m, x)
            self.assertEqual(m, y)
            self.assertEqual(m, z)

            self.assertEqual(m[1:3], x[1:3])
            self.assertEqual(m[1:3], y[1:3])
            self.assertEqual(m[1:3], z[1:3])
            del y, z
            self.assertEqual(m[1:3], x[1:3])

    def test_memoryview_from_static_exporter(self):

        fmt = 'B'
        lst = [0,1,2,3,4,5,6,7,8,9,10,11]

        # exceptions
        self.assertRaises(TypeError, staticarray, 1, 2, 3)

        # view.obj==x
        x = staticarray()
        y = memoryview(x)
        self.verify(y, obj=x,
                    itemsize=1, fmt=fmt, readonly=1,
                    ndim=1, shape=[12], strides=[1],
                    lst=lst)
        for i in range(12):
            self.assertEqual(y[i], i)
        del x
        del y

        x = staticarray()
        y = memoryview(x)
        del y
        del x

        x = staticarray()
        y = ndarray(x, getbuf=PyBUF_FULL_RO)
        z = ndarray(y, getbuf=PyBUF_FULL_RO)
        m = memoryview(z)
        self.assertIs(y.obj, x)
        self.assertIs(m.obj, z)
        self.verify(m, obj=z,
                    itemsize=1, fmt=fmt, readonly=1,
                    ndim=1, shape=[12], strides=[1],
                    lst=lst)
        del x, y, z, m

        x = staticarray()
        y = ndarray(x, getbuf=PyBUF_FULL_RO, flags=ND_REDIRECT)
        z = ndarray(y, getbuf=PyBUF_FULL_RO, flags=ND_REDIRECT)
        m = memoryview(z)
        self.assertIs(y.obj, x)
        self.assertIs(z.obj, x)
        self.assertIs(m.obj, x)
        self.verify(m, obj=x,
                    itemsize=1, fmt=fmt, readonly=1,
                    ndim=1, shape=[12], strides=[1],
                    lst=lst)
        del x, y, z, m

        # view.obj==NULL
        x = staticarray(legacy_mode=True)
        y = memoryview(x)
        self.verify(y, obj=None,
                    itemsize=1, fmt=fmt, readonly=1,
                    ndim=1, shape=[12], strides=[1],
                    lst=lst)
        for i in range(12):
            self.assertEqual(y[i], i)
        del x
        del y

        x = staticarray(legacy_mode=True)
        y = memoryview(x)
        del y
        del x

        x = staticarray(legacy_mode=True)
        y = ndarray(x, getbuf=PyBUF_FULL_RO)
        z = ndarray(y, getbuf=PyBUF_FULL_RO)
        m = memoryview(z)
        self.assertIs(y.obj, None)
        self.assertIs(m.obj, z)
        self.verify(m, obj=z,
                    itemsize=1, fmt=fmt, readonly=1,
                    ndim=1, shape=[12], strides=[1],
                    lst=lst)
        del x, y, z, m

        x = staticarray(legacy_mode=True)
        y = ndarray(x, getbuf=PyBUF_FULL_RO, flags=ND_REDIRECT)
        z = ndarray(y, getbuf=PyBUF_FULL_RO, flags=ND_REDIRECT)
        m = memoryview(z)
        # Clearly setting view.obj==NULL is inferior, since it
        # messes up the redirection chain:
        self.assertIs(y.obj, None)
        self.assertIs(z.obj, y)
        self.assertIs(m.obj, y)
        self.verify(m, obj=y,
                    itemsize=1, fmt=fmt, readonly=1,
                    ndim=1, shape=[12], strides=[1],
                    lst=lst)
        del x, y, z, m

    def test_memoryview_getbuffer_undefined(self):

        # getbufferproc does not adhere to the new documentation
        nd = ndarray([1,2,3], [3], flags=ND_GETBUF_FAIL|ND_GETBUF_UNDEFINED)
        self.assertRaises(BufferError, memoryview, nd)

    def test_issue_7385(self):
        x = ndarray([1,2,3], shape=[3], flags=ND_GETBUF_FAIL)
        self.assertRaises(BufferError, memoryview, x)


def test_main():
    support.run_unittest(TestBufferProtocol)


if __name__ == "__main__":
    test_main()