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AP4_AvccAtom::AP4_AvccAtom(AP4_UI32 size, const AP4_UI08* payload) :
AP4_Atom(AP4_ATOM_TYPE_AVCC, size)
{
// make a copy of our configuration bytes
unsigned int payload_size = size-AP4_ATOM_HEADER_SIZE;
m_RawBytes.SetData(payload, payload_size);
// parse the payload
m_ConfigurationVersion = payload[0];
m_Profile = payload[1];
m_ProfileCompatibility = payload[2];
m_Level = payload[3];
m_NaluLengthSize = 1+(payload[4]&3);
AP4_UI08 num_seq_params = payload[5]&31;
m_SequenceParameters.EnsureCapacity(num_seq_params);
unsigned int cursor = 6;
for (unsigned int i=0; i<num_seq_params; i++) {
m_SequenceParameters.Append(AP4_DataBuffer());
AP4_UI16 param_length = AP4_BytesToInt16BE(&payload[cursor]);
m_SequenceParameters[i].SetData(&payload[cursor]+2, param_length);
cursor += 2+param_length;
}
AP4_UI08 num_pic_params = payload[cursor++];
m_PictureParameters.EnsureCapacity(num_pic_params);
for (unsigned int i=0; i<num_pic_params; i++) {
m_PictureParameters.Append(AP4_DataBuffer());
AP4_UI16 param_length = AP4_BytesToInt16BE(&payload[cursor]);
m_PictureParameters[i].SetData(&payload[cursor]+2, param_length);
cursor += 2+param_length;
}
} | CWE-125 | 47 |
TfLiteStatus EvalImpl(TfLiteContext* context, const TfLiteTensor* input,
TfLiteNode* node) {
// Map from value, to index in the unique elements vector.
// Note that we prefer to use map than unordered_map as it showed less
// increase in the binary size.
std::map<T, int> unique_values;
TfLiteTensor* output_indexes = GetOutput(context, node, 1);
std::vector<T> output_values;
I* indexes = GetTensorData<I>(output_indexes);
const T* data = GetTensorData<T>(input);
const int num_elements = NumElements(input);
for (int i = 0; i < num_elements; ++i) {
const auto element_it = unique_values.find(data[i]);
if (element_it != unique_values.end()) {
indexes[i] = element_it->second;
} else {
const int unique_index = unique_values.size();
unique_values[data[i]] = unique_index;
indexes[i] = unique_index;
output_values.push_back(data[i]);
}
}
// Allocate output tensor.
TfLiteTensor* unique_output = GetOutput(context, node, 0);
std::unique_ptr<TfLiteIntArray, void (*)(TfLiteIntArray*)> shape(
TfLiteIntArrayCreate(NumDimensions(input)), TfLiteIntArrayFree);
shape->data[0] = unique_values.size();
TF_LITE_ENSURE_STATUS(
context->ResizeTensor(context, unique_output, shape.release()));
// Set the values in the output tensor.
T* output_unique_values = GetTensorData<T>(unique_output);
for (int i = 0; i < output_values.size(); ++i) {
output_unique_values[i] = output_values[i];
}
return kTfLiteOk;
} | CWE-125 | 47 |
R_API ut64 r_bin_java_element_pair_calc_size(RBinJavaElementValuePair *evp) {
ut64 sz = 0;
if (evp == NULL) {
return sz;
}
// evp->element_name_idx = r_bin_java_read_short(bin, bin->b->cur);
sz += 2;
// evp->value = r_bin_java_element_value_new (bin, offset+2);
if (evp->value) {
sz += r_bin_java_element_value_calc_size (evp->value);
}
return sz;
} | CWE-805 | 63 |
TfLiteStatus GreaterEqualEval(TfLiteContext* context, TfLiteNode* node) {
const TfLiteTensor* input1 = GetInput(context, node, kInputTensor1);
const TfLiteTensor* input2 = GetInput(context, node, kInputTensor2);
TfLiteTensor* output = GetOutput(context, node, kOutputTensor);
bool requires_broadcast = !HaveSameShapes(input1, input2);
switch (input1->type) {
case kTfLiteFloat32:
Comparison<float, reference_ops::GreaterEqualFn>(input1, input2, output,
requires_broadcast);
break;
case kTfLiteInt32:
Comparison<int32_t, reference_ops::GreaterEqualFn>(input1, input2, output,
requires_broadcast);
break;
case kTfLiteInt64:
Comparison<int64_t, reference_ops::GreaterEqualFn>(input1, input2, output,
requires_broadcast);
break;
case kTfLiteUInt8:
ComparisonQuantized<uint8_t, reference_ops::GreaterEqualFn>(
input1, input2, output, requires_broadcast);
break;
case kTfLiteInt8:
ComparisonQuantized<int8_t, reference_ops::GreaterEqualFn>(
input1, input2, output, requires_broadcast);
break;
default:
context->ReportError(context,
"Does not support type %d, requires float|int|uint8",
input1->type);
return kTfLiteError;
}
return kTfLiteOk;
} | CWE-125 | 47 |
TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) {
const TfLiteTensor* input = GetInput(context, node, kInputTensor);
TfLiteTensor* output = GetOutput(context, node, kOutputTensor);
if (type == kGenericOptimized) {
optimized_ops::Floor(GetTensorShape(input), GetTensorData<float>(input),
GetTensorShape(output), GetTensorData<float>(output));
} else {
reference_ops::Floor(GetTensorShape(input), GetTensorData<float>(input),
GetTensorShape(output), GetTensorData<float>(output));
}
return kTfLiteOk;
} | CWE-787 | 24 |
TfLiteRegistration CopyOpRegistration() {
TfLiteRegistration reg = {nullptr, nullptr, nullptr, nullptr};
reg.prepare = [](TfLiteContext* context, TfLiteNode* node) {
// Set output size to input size
const TfLiteTensor* tensor0 = GetInput(context, node, 0);
TfLiteTensor* tensor1 = GetOutput(context, node, 0);
TfLiteIntArray* newSize = TfLiteIntArrayCopy(tensor0->dims);
return context->ResizeTensor(context, tensor1, newSize);
};
reg.invoke = [](TfLiteContext* context, TfLiteNode* node) {
CallReporting* call_reporting =
static_cast<CallReporting*>(node->builtin_data);
// Copy input data to output data.
const TfLiteTensor* a0 = GetInput(context, node, 0);
TfLiteTensor* a1 = GetOutput(context, node, 0);
int num = a0->dims->data[0];
for (int i = 0; i < num; i++) {
a1->data.f[i] = a0->data.f[i];
}
call_reporting->Record();
return kTfLiteOk;
};
return reg;
} | CWE-787 | 24 |
R_API RBinJavaAttrInfo *r_bin_java_annotation_default_attr_new(RBinJavaObj *bin, ut8 *buffer, ut64 sz, ut64 buf_offset) {
ut64 offset = 0;
RBinJavaAttrInfo *attr = NULL;
attr = r_bin_java_default_attr_new (bin, buffer, sz, buf_offset);
offset += 6;
if (attr && sz >= offset) {
attr->type = R_BIN_JAVA_ATTR_TYPE_ANNOTATION_DEFAULT_ATTR;
attr->info.annotation_default_attr.default_value = r_bin_java_element_value_new (buffer + offset, sz - offset, buf_offset + offset);
if (attr->info.annotation_default_attr.default_value) {
offset += attr->info.annotation_default_attr.default_value->size;
}
}
r_bin_java_print_annotation_default_attr_summary (attr);
return attr;
} | CWE-805 | 63 |
TEST(DefaultCertValidatorTest, TestMatchSubjectAltNameNotMatched) {
bssl::UniquePtr<X509> cert = readCertFromFile(TestEnvironment::substitute(
"{{ test_rundir }}/test/extensions/transport_sockets/tls/test_data/san_dns_cert.pem"));
envoy::type::matcher::v3::StringMatcher matcher;
matcher.MergeFrom(TestUtility::createRegexMatcher(".*.foo.com"));
std::vector<Matchers::StringMatcherImpl<envoy::type::matcher::v3::StringMatcher>>
subject_alt_name_matchers;
subject_alt_name_matchers.push_back(Matchers::StringMatcherImpl(matcher));
EXPECT_FALSE(DefaultCertValidator::matchSubjectAltName(cert.get(), subject_alt_name_matchers));
} | CWE-295 | 52 |
void Logger::addMessage(const QString &message, const Log::MsgType &type)
{
QWriteLocker locker(&lock);
Log::Msg temp = { msgCounter++, QDateTime::currentMSecsSinceEpoch(), type, message };
m_messages.push_back(temp);
if (m_messages.size() >= MAX_LOG_MESSAGES)
m_messages.pop_front();
emit newLogMessage(temp);
} | CWE-79 | 1 |
TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) {
// Check that the inputs and outputs have the right sizes and types.
TF_LITE_ENSURE_EQ(context, NumInputs(node), 2);
TF_LITE_ENSURE_EQ(context, NumOutputs(node), 2);
const TfLiteTensor* input = GetInput(context, node, kInputTensor);
TfLiteTensor* output_values = GetOutput(context, node, kOutputValues);
TF_LITE_ENSURE_TYPES_EQ(context, input->type, output_values->type);
const TfLiteTensor* top_k = GetInput(context, node, kInputTopK);
TF_LITE_ENSURE_TYPES_EQ(context, top_k->type, kTfLiteInt32);
// Set output dynamic if the input is not const.
if (IsConstantTensor(top_k)) {
TF_LITE_ENSURE_OK(context, ResizeOutput(context, node));
} else {
TfLiteTensor* output_indexes = GetOutput(context, node, kOutputIndexes);
TfLiteTensor* output_values = GetOutput(context, node, kOutputValues);
SetTensorToDynamic(output_indexes);
SetTensorToDynamic(output_values);
}
return kTfLiteOk;
} | CWE-125 | 47 |
void ImplPolygon::ImplSplit( sal_uInt16 nPos, sal_uInt16 nSpace, ImplPolygon const * pInitPoly )
{
//Can't fit this in :-(, throw ?
if (mnPoints + nSpace > USHRT_MAX)
return;
const sal_uInt16 nNewSize = mnPoints + nSpace;
const std::size_t nSpaceSize = static_cast<std::size_t>(nSpace) * sizeof(Point);
if( nPos >= mnPoints )
{
// Append at the back
nPos = mnPoints;
ImplSetSize( nNewSize );
if( pInitPoly )
{
memcpy( mpPointAry + nPos, pInitPoly->mpPointAry, nSpaceSize );
if( pInitPoly->mpFlagAry )
memcpy( mpFlagAry + nPos, pInitPoly->mpFlagAry, nSpace );
}
}
else
{
const sal_uInt16 nSecPos = nPos + nSpace;
const sal_uInt16 nRest = mnPoints - nPos;
Point* pNewAry = reinterpret_cast<Point*>(new char[ static_cast<std::size_t>(nNewSize) * sizeof(Point) ]);
memcpy( pNewAry, mpPointAry, nPos * sizeof( Point ) );
if( pInitPoly )
memcpy( pNewAry + nPos, pInitPoly->mpPointAry, nSpaceSize );
else
memset( pNewAry + nPos, 0, nSpaceSize );
memcpy( pNewAry + nSecPos, mpPointAry + nPos, nRest * sizeof( Point ) );
delete[] reinterpret_cast<char*>(mpPointAry);
// consider FlagArray
if( mpFlagAry )
{
PolyFlags* pNewFlagAry = new PolyFlags[ nNewSize ];
memcpy( pNewFlagAry, mpFlagAry, nPos );
if( pInitPoly && pInitPoly->mpFlagAry )
memcpy( pNewFlagAry + nPos, pInitPoly->mpFlagAry, nSpace );
else
memset( pNewFlagAry + nPos, 0, nSpace );
memcpy( pNewFlagAry + nSecPos, mpFlagAry + nPos, nRest );
delete[] mpFlagAry;
mpFlagAry = pNewFlagAry;
}
mpPointAry = pNewAry;
mnPoints = nNewSize;
}
} | CWE-787 | 24 |
int pnm_validate(jas_stream_t *in)
{
uchar buf[2];
int i;
int n;
assert(JAS_STREAM_MAXPUTBACK >= 2);
/* Read the first two characters that constitute the signature. */
if ((n = jas_stream_read(in, buf, 2)) < 0) {
return -1;
}
/* Put these characters back to the stream. */
for (i = n - 1; i >= 0; --i) {
if (jas_stream_ungetc(in, buf[i]) == EOF) {
return -1;
}
}
/* Did we read enough data? */
if (n < 2) {
return -1;
}
/* Is this the correct signature for a PNM file? */
if (buf[0] == 'P' && isdigit(buf[1])) {
return 0;
}
return -1;
} | CWE-190 | 19 |
TEST_P(SslSocketTest, Ipv4San) {
const std::string client_ctx_yaml = R"EOF(
common_tls_context:
validation_context:
trusted_ca:
filename: "{{ test_rundir }}/test/config/integration/certs/upstreamcacert.pem"
match_subject_alt_names:
exact: "127.0.0.1"
)EOF";
const std::string server_ctx_yaml = R"EOF(
common_tls_context:
tls_certificates:
certificate_chain:
filename: "{{ test_rundir }}/test/config/integration/certs/upstreamlocalhostcert.pem"
private_key:
filename: "{{ test_rundir }}/test/config/integration/certs/upstreamlocalhostkey.pem"
)EOF";
TestUtilOptions test_options(client_ctx_yaml, server_ctx_yaml, true, GetParam());
testUtil(test_options);
} | CWE-295 | 52 |
TfLiteRegistration AddOpRegistration() {
TfLiteRegistration reg = {nullptr, nullptr, nullptr, nullptr};
reg.custom_name = "my_add";
reg.builtin_code = tflite::BuiltinOperator_CUSTOM;
reg.prepare = [](TfLiteContext* context, TfLiteNode* node) {
// Set output size to input size
const TfLiteTensor* input1 = GetInput(context, node, 0);
const TfLiteTensor* input2 = GetInput(context, node, 1);
TfLiteTensor* output = GetOutput(context, node, 0);
TF_LITE_ENSURE_EQ(context, input1->dims->size, input2->dims->size);
for (int i = 0; i < input1->dims->size; ++i) {
TF_LITE_ENSURE_EQ(context, input1->dims->data[i], input2->dims->data[i]);
}
TF_LITE_ENSURE_STATUS(context->ResizeTensor(
context, output, TfLiteIntArrayCopy(input1->dims)));
return kTfLiteOk;
};
reg.invoke = [](TfLiteContext* context, TfLiteNode* node) {
// Copy input data to output data.
const TfLiteTensor* a0 = GetInput(context, node, 0);
TF_LITE_ENSURE(context, a0);
TF_LITE_ENSURE(context, a0->data.f);
const TfLiteTensor* a1 = GetInput(context, node, 1);
TF_LITE_ENSURE(context, a1);
TF_LITE_ENSURE(context, a1->data.f);
TfLiteTensor* out = GetOutput(context, node, 0);
TF_LITE_ENSURE(context, out);
TF_LITE_ENSURE(context, out->data.f);
int num = a0->dims->data[0];
for (int i = 0; i < num; i++) {
out->data.f[i] = a0->data.f[i] + a1->data.f[i];
}
return kTfLiteOk;
};
return reg;
} | CWE-787 | 24 |
ResponsePtr Server::ServeStatic(RequestPtr request) {
assert(request->method() == methods::kGet);
if (doc_root_.empty()) {
LOG_INFO("The doc root was not specified");
return {};
}
fs::path path = doc_root_ / request->url().path();
try {
// NOTE: FileBody might throw Error::kFileError.
auto body = std::make_shared<FileBody>(path, file_chunk_size_);
auto response = std::make_shared<Response>(Status::kOK);
std::string extension = path.extension().string();
response->SetContentType(media_types::FromExtension(extension), "");
// NOTE: Gzip compression is not supported.
response->SetBody(body, true);
return response;
} catch (const Error& error) {
LOG_ERRO("File error: %s", error.message().c_str());
return {};
}
} | CWE-22 | 2 |
void PngImg::InitStorage_() {
rowPtrs_.resize(info_.height, nullptr);
data_ = new png_byte[info_.height * info_.rowbytes];
for(size_t i = 0; i < info_.height; ++i) {
rowPtrs_[i] = data_ + i * info_.rowbytes;
}
} | CWE-787 | 24 |
void AverageEvalQuantizedUint8(TfLiteContext* context, TfLiteNode* node,
TfLitePoolParams* params, OpData* data,
const TfLiteTensor* input,
TfLiteTensor* output) {
int32_t activation_min;
int32_t activation_max;
(void)CalculateActivationRangeQuantized(context, params->activation, output,
&activation_min, &activation_max);
#define TF_LITE_AVERAGE_POOL(type) \
tflite::PoolParams op_params; \
op_params.stride_height = params->stride_height; \
op_params.stride_width = params->stride_width; \
op_params.filter_height = params->filter_height; \
op_params.filter_width = params->filter_width; \
op_params.padding_values.height = data->padding.height; \
op_params.padding_values.width = data->padding.width; \
op_params.quantized_activation_min = activation_min; \
op_params.quantized_activation_max = activation_max; \
type::AveragePool(op_params, GetTensorShape(input), \
GetTensorData<uint8_t>(input), GetTensorShape(output), \
GetTensorData<uint8_t>(output))
if (kernel_type == kReference) {
TF_LITE_AVERAGE_POOL(reference_ops);
} else {
TF_LITE_AVERAGE_POOL(optimized_ops);
}
#undef TF_LITE_AVERAGE_POOL
} | CWE-835 | 42 |
static __forceinline void draw_line(float *output, int x0, int y0, int x1, int y1, int n)
{
int dy = y1 - y0;
int adx = x1 - x0;
int ady = abs(dy);
int base;
int x=x0,y=y0;
int err = 0;
int sy;
#ifdef STB_VORBIS_DIVIDE_TABLE
if (adx < DIVTAB_DENOM && ady < DIVTAB_NUMER) {
if (dy < 0) {
base = -integer_divide_table[ady][adx];
sy = base-1;
} else {
base = integer_divide_table[ady][adx];
sy = base+1;
}
} else {
base = dy / adx;
if (dy < 0)
sy = base - 1;
else
sy = base+1;
}
#else
base = dy / adx;
if (dy < 0)
sy = base - 1;
else
sy = base+1;
#endif
ady -= abs(base) * adx;
if (x1 > n) x1 = n;
if (x < x1) {
LINE_OP(output[x], inverse_db_table[y]);
for (++x; x < x1; ++x) {
err += ady;
if (err >= adx) {
err -= adx;
y += sy;
} else
y += base;
LINE_OP(output[x], inverse_db_table[y]);
}
}
} | CWE-369 | 60 |
TfLiteStatus MaxEval(TfLiteContext* context, TfLiteNode* node) {
auto* params = reinterpret_cast<TfLitePoolParams*>(node->builtin_data);
OpData* data = reinterpret_cast<OpData*>(node->user_data);
TfLiteTensor* output = GetOutput(context, node, 0);
const TfLiteTensor* input = GetInput(context, node, 0);
switch (input->type) { // Already know in/out types are same.
case kTfLiteFloat32:
MaxEvalFloat<kernel_type>(context, node, params, data, input, output);
break;
case kTfLiteUInt8:
MaxEvalQuantizedUInt8<kernel_type>(context, node, params, data, input,
output);
break;
case kTfLiteInt8:
MaxEvalQuantizedInt8<kernel_type>(context, node, params, data, input,
output);
break;
case kTfLiteInt16:
MaxEvalQuantizedInt16<kernel_type>(context, node, params, data, input,
output);
break;
default:
TF_LITE_KERNEL_LOG(context, "Type %s not currently supported.",
TfLiteTypeGetName(input->type));
return kTfLiteError;
}
return kTfLiteOk;
} | CWE-787 | 24 |
bool MemFile::seek(int64_t offset, int whence /* = SEEK_SET */) {
assertx(m_len != -1);
if (whence == SEEK_CUR) {
if (offset > 0 && offset < bufferedLen()) {
setReadPosition(getReadPosition() + offset);
setPosition(getPosition() + offset);
return true;
}
offset += getPosition();
whence = SEEK_SET;
}
// invalidate the current buffer
setWritePosition(0);
setReadPosition(0);
if (whence == SEEK_SET) {
m_cursor = offset;
} else {
assertx(whence == SEEK_END);
m_cursor = m_len + offset;
}
setPosition(m_cursor);
return true;
} | CWE-190 | 19 |
void DefaultEnv::Initialize()
{
sLog = new Log();
SetUpLog();
sEnv = new DefaultEnv();
sForkHandler = new ForkHandler();
sFileTimer = new FileTimer();
sPlugInManager = new PlugInManager();
sPlugInManager->ProcessEnvironmentSettings();
sForkHandler->RegisterFileTimer( sFileTimer );
//--------------------------------------------------------------------------
// MacOSX library loading is completely moronic. We cannot dlopen a library
// from a thread other than a main thread, so we-pre dlopen all the
// libraries that we may potentially want.
//--------------------------------------------------------------------------
#ifdef __APPLE__
char *errBuff = new char[1024];
const char *libs[] =
{
"libXrdSeckrb5.so",
"libXrdSecgsi.so",
"libXrdSecgsiAuthzVO.so",
"libXrdSecgsiGMAPDN.so",
"libXrdSecgsiGMAPLDAP.so",
"libXrdSecpwd.so",
"libXrdSecsss.so",
"libXrdSecunix.so",
0
};
for( int i = 0; libs[i]; ++i )
{
sLog->Debug( UtilityMsg, "Attempting to pre-load: %s", libs[i] );
bool ok = XrdOucPreload( libs[i], errBuff, 1024 );
if( !ok )
sLog->Error( UtilityMsg, "Unable to pre-load %s: %s", libs[i], errBuff );
}
delete [] errBuff;
#endif
} | CWE-78 | 6 |
TEST_P(SslSocketTest, FailedClientAuthSanVerificationNoClientCert) {
const std::string client_ctx_yaml = R"EOF(
common_tls_context:
)EOF";
const std::string server_ctx_yaml = R"EOF(
common_tls_context:
tls_certificates:
certificate_chain:
filename: "{{ test_rundir }}/test/extensions/transport_sockets/tls/test_data/unittest_cert.pem"
private_key:
filename: "{{ test_rundir }}/test/extensions/transport_sockets/tls/test_data/unittest_key.pem"
validation_context:
trusted_ca:
filename: "{{ test_rundir }}/test/extensions/transport_sockets/tls/test_data/ca_cert.pem"
match_subject_alt_names:
exact: "example.com"
)EOF";
TestUtilOptions test_options(client_ctx_yaml, server_ctx_yaml, false, GetParam());
testUtil(test_options.setExpectedServerStats("ssl.fail_verify_no_cert"));
} | CWE-295 | 52 |
MONGO_EXPORT int bson_append_symbol_n( bson *b, const char *name, const char *value, int len ) {
return bson_append_string_base( b, name, value, len, BSON_SYMBOL );
} | CWE-190 | 19 |
jas_image_t *jas_image_create(int numcmpts, jas_image_cmptparm_t *cmptparms,
int clrspc)
{
jas_image_t *image;
uint_fast32_t rawsize;
uint_fast32_t inmem;
int cmptno;
jas_image_cmptparm_t *cmptparm;
if (!(image = jas_image_create0())) {
return 0;
}
image->clrspc_ = clrspc;
image->maxcmpts_ = numcmpts;
image->inmem_ = true;
/* Allocate memory for the per-component information. */
if (!(image->cmpts_ = jas_alloc2(image->maxcmpts_,
sizeof(jas_image_cmpt_t *)))) {
jas_image_destroy(image);
return 0;
}
/* Initialize in case of failure. */
for (cmptno = 0; cmptno < image->maxcmpts_; ++cmptno) {
image->cmpts_[cmptno] = 0;
}
/* Compute the approximate raw size of the image. */
rawsize = 0;
for (cmptno = 0, cmptparm = cmptparms; cmptno < numcmpts; ++cmptno,
++cmptparm) {
rawsize += cmptparm->width * cmptparm->height *
(cmptparm->prec + 7) / 8;
}
/* Decide whether to buffer the image data in memory, based on the
raw size of the image. */
inmem = (rawsize < JAS_IMAGE_INMEMTHRESH);
/* Create the individual image components. */
for (cmptno = 0, cmptparm = cmptparms; cmptno < numcmpts; ++cmptno,
++cmptparm) {
if (!(image->cmpts_[cmptno] = jas_image_cmpt_create(cmptparm->tlx,
cmptparm->tly, cmptparm->hstep, cmptparm->vstep,
cmptparm->width, cmptparm->height, cmptparm->prec,
cmptparm->sgnd, inmem))) {
jas_image_destroy(image);
return 0;
}
++image->numcmpts_;
}
/* Determine the bounding box for all of the components on the
reference grid (i.e., the image area) */
jas_image_setbbox(image);
return image;
} | CWE-190 | 19 |
int TLSOutStream::overrun(int itemSize, int nItems)
{
if (itemSize > bufSize)
throw Exception("TLSOutStream overrun: max itemSize exceeded");
flush();
if (itemSize * nItems > end - ptr)
nItems = (end - ptr) / itemSize;
return nItems;
} | CWE-787 | 24 |
TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) {
TfLiteTensor* output = GetOutput(context, node, 0);
TfLiteTensor* hits = GetOutput(context, node, 1);
const TfLiteTensor* lookup = GetInput(context, node, 0);
const TfLiteTensor* key = GetInput(context, node, 1);
const TfLiteTensor* value = GetInput(context, node, 2);
const int num_rows = SizeOfDimension(value, 0);
const int row_bytes = value->bytes / num_rows;
void* pointer = nullptr;
DynamicBuffer buf;
for (int i = 0; i < SizeOfDimension(lookup, 0); i++) {
int idx = -1;
pointer = bsearch(&(lookup->data.i32[i]), key->data.i32, num_rows,
sizeof(int32_t), greater);
if (pointer != nullptr) {
idx = (reinterpret_cast<char*>(pointer) - (key->data.raw)) /
sizeof(int32_t);
}
if (idx >= num_rows || idx < 0) {
if (output->type == kTfLiteString) {
buf.AddString(nullptr, 0);
} else {
memset(output->data.raw + i * row_bytes, 0, row_bytes);
}
hits->data.uint8[i] = 0;
} else {
if (output->type == kTfLiteString) {
buf.AddString(GetString(value, idx));
} else {
memcpy(output->data.raw + i * row_bytes,
value->data.raw + idx * row_bytes, row_bytes);
}
hits->data.uint8[i] = 1;
}
}
if (output->type == kTfLiteString) {
buf.WriteToTensorAsVector(output);
}
return kTfLiteOk;
} | CWE-787 | 24 |
TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) {
auto* params = reinterpret_cast<TfLiteDivParams*>(node->builtin_data);
OpData* data = reinterpret_cast<OpData*>(node->user_data);
TF_LITE_ENSURE_EQ(context, NumInputs(node), 2);
TF_LITE_ENSURE_EQ(context, NumOutputs(node), 1);
const TfLiteTensor* input1 = GetInput(context, node, kInputTensor1);
const TfLiteTensor* input2 = GetInput(context, node, kInputTensor2);
TfLiteTensor* output = GetOutput(context, node, kOutputTensor);
TF_LITE_ENSURE_TYPES_EQ(context, input1->type, input2->type);
output->type = input2->type;
data->requires_broadcast = !HaveSameShapes(input1, input2);
TfLiteIntArray* output_size = nullptr;
if (data->requires_broadcast) {
TF_LITE_ENSURE_OK(context, CalculateShapeForBroadcast(
context, input1, input2, &output_size));
} else {
output_size = TfLiteIntArrayCopy(input1->dims);
}
if (output->type == kTfLiteUInt8) {
TF_LITE_ENSURE_STATUS(CalculateActivationRangeQuantized(
context, params->activation, output, &data->output_activation_min,
&data->output_activation_max));
const double real_multiplier =
input1->params.scale / (input2->params.scale * output->params.scale);
QuantizeMultiplier(real_multiplier, &data->output_multiplier,
&data->output_shift);
}
return context->ResizeTensor(context, output, output_size);
} | CWE-787 | 24 |
Status CalculateOutputIndex(OpKernelContext* context, int dimension,
const vector<INDEX_TYPE>& parent_output_index,
INDEX_TYPE output_index_multiplier,
INDEX_TYPE output_size,
vector<INDEX_TYPE>* result) {
const RowPartitionTensor row_partition_tensor =
GetRowPartitionTensor(context, dimension);
auto partition_type = GetRowPartitionTypeByDimension(dimension);
switch (partition_type) {
case RowPartitionType::VALUE_ROWIDS:
CalculateOutputIndexValueRowID(
context, row_partition_tensor, parent_output_index,
output_index_multiplier, output_size, result);
return tensorflow::Status::OK();
case RowPartitionType::ROW_SPLITS:
if (row_partition_tensor.size() - 1 > parent_output_index.size()) {
return errors::InvalidArgument(
"Row partition size is greater than output size: ",
row_partition_tensor.size() - 1, " > ",
parent_output_index.size());
}
CalculateOutputIndexRowSplit(
context, row_partition_tensor, parent_output_index,
output_index_multiplier, output_size, result);
return tensorflow::Status::OK();
default:
return errors::InvalidArgument(
"Unsupported partition type:",
RowPartitionTypeToString(partition_type));
}
} | CWE-131 | 88 |
String UTF16BEDecoder::to_utf8(const StringView& input)
{
StringBuilder builder(input.length() / 2);
for (size_t i = 0; i < input.length(); i += 2) {
u16 code_point = (input[i] << 8) | input[i + 1];
builder.append_code_point(code_point);
}
return builder.to_string();
} | CWE-120 | 44 |
Result ZipFile::uncompressEntry (int index, const File& targetDirectory, bool shouldOverwriteFiles)
{
auto* zei = entries.getUnchecked (index);
#if JUCE_WINDOWS
auto entryPath = zei->entry.filename;
#else
auto entryPath = zei->entry.filename.replaceCharacter ('\\', '/');
#endif
if (entryPath.isEmpty())
return Result::ok();
auto targetFile = targetDirectory.getChildFile (entryPath);
if (entryPath.endsWithChar ('/') || entryPath.endsWithChar ('\\'))
return targetFile.createDirectory(); // (entry is a directory, not a file)
std::unique_ptr<InputStream> in (createStreamForEntry (index));
if (in == nullptr)
return Result::fail ("Failed to open the zip file for reading");
if (targetFile.exists())
{
if (! shouldOverwriteFiles)
return Result::ok();
if (! targetFile.deleteFile())
return Result::fail ("Failed to write to target file: " + targetFile.getFullPathName());
}
if (! targetFile.getParentDirectory().createDirectory())
return Result::fail ("Failed to create target folder: " + targetFile.getParentDirectory().getFullPathName());
if (zei->entry.isSymbolicLink)
{
String originalFilePath (in->readEntireStreamAsString()
.replaceCharacter (L'/', File::getSeparatorChar()));
if (! File::createSymbolicLink (targetFile, originalFilePath, true))
return Result::fail ("Failed to create symbolic link: " + originalFilePath);
}
else
{
FileOutputStream out (targetFile);
if (out.failedToOpen())
return Result::fail ("Failed to write to target file: " + targetFile.getFullPathName());
out << *in;
}
targetFile.setCreationTime (zei->entry.fileTime);
targetFile.setLastModificationTime (zei->entry.fileTime);
targetFile.setLastAccessTime (zei->entry.fileTime);
return Result::ok();
}
| CWE-22 | 2 |
TfLiteStatus PrepareHashtable(TfLiteContext* context, TfLiteNode* node) {
TF_LITE_ENSURE_EQ(context, NumInputs(node), 0);
TF_LITE_ENSURE_EQ(context, NumOutputs(node), 1);
TF_LITE_ENSURE(context, node->user_data != nullptr);
const auto* params =
reinterpret_cast<const TfLiteHashtableParams*>(node->user_data);
TF_LITE_ENSURE(context, !params->table_name.empty());
TF_LITE_ENSURE(context, (params->key_dtype == kTfLiteInt64 &&
params->value_dtype == kTfLiteString) ||
(params->key_dtype == kTfLiteString &&
params->value_dtype == kTfLiteInt64));
TfLiteTensor* resource_handle_tensor =
GetOutput(context, node, kResourceHandleTensor);
TF_LITE_ENSURE(context, resource_handle_tensor != nullptr);
TF_LITE_ENSURE_EQ(context, resource_handle_tensor->type, kTfLiteInt32);
TfLiteIntArray* outputSize = TfLiteIntArrayCreate(1);
outputSize->data[0] = 1;
return context->ResizeTensor(context, resource_handle_tensor, outputSize);
} | CWE-125 | 47 |
TEST_F(QuantizedConv2DTest, OddPadding) {
const int stride = 2;
TF_ASSERT_OK(NodeDefBuilder("quantized_conv_op", "QuantizedConv2D")
.Input(FakeInput(DT_QUINT8))
.Input(FakeInput(DT_QUINT8))
.Input(FakeInput(DT_FLOAT))
.Input(FakeInput(DT_FLOAT))
.Input(FakeInput(DT_FLOAT))
.Input(FakeInput(DT_FLOAT))
.Attr("out_type", DataTypeToEnum<qint32>::v())
.Attr("strides", {1, stride, stride, 1})
.Attr("padding", "SAME")
.Finalize(node_def()));
TF_ASSERT_OK(InitOp());
const int depth = 1;
const int image_width = 4;
const int image_height = 4;
const int image_batch_count = 1;
AddInputFromArray<quint8>(
TensorShape({image_batch_count, image_height, image_width, depth}),
{1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16});
const int filter_size = 3;
const int filter_count = 1;
AddInputFromArray<quint8>(
TensorShape({filter_size, filter_size, depth, filter_count}),
{1, 2, 3, 4, 5, 6, 7, 8, 9});
AddInputFromArray<float>(TensorShape({1}), {0});
AddInputFromArray<float>(TensorShape({1}), {255.0f});
AddInputFromArray<float>(TensorShape({1}), {0});
AddInputFromArray<float>(TensorShape({1}), {255.0f});
TF_ASSERT_OK(RunOpKernel());
const int expected_width = image_width / stride;
const int expected_height = (image_height * filter_count) / stride;
Tensor expected(DT_QINT32, TensorShape({image_batch_count, expected_height,
expected_width, filter_count}));
test::FillValues<qint32>(&expected, {348, 252, 274, 175});
test::ExpectTensorEqual<qint32>(expected, *GetOutput(0));
} | CWE-476 | 46 |
void AverageEvalQuantizedInt16(TfLiteContext* context, TfLiteNode* node,
TfLitePoolParams* params, OpData* data,
const TfLiteTensor* input,
TfLiteTensor* output) {
int32_t activation_min;
int32_t activation_max;
CalculateActivationRangeQuantized(context, params->activation, output,
&activation_min, &activation_max);
#define TF_LITE_AVERAGE_POOL(type) \
tflite::PoolParams op_params; \
op_params.stride_height = params->stride_height; \
op_params.stride_width = params->stride_width; \
op_params.filter_height = params->filter_height; \
op_params.filter_width = params->filter_width; \
op_params.padding_values.height = data->padding.height; \
op_params.padding_values.width = data->padding.width; \
op_params.quantized_activation_min = activation_min; \
op_params.quantized_activation_max = activation_max; \
type::AveragePool(op_params, GetTensorShape(input), \
GetTensorData<int16_t>(input), GetTensorShape(output), \
GetTensorData<int16_t>(output))
TF_LITE_AVERAGE_POOL(reference_integer_ops);
#undef TF_LITE_AVERAGE_POOL
} | CWE-835 | 42 |
inline bool ShapeIsVector(TfLiteContext* context, TfLiteNode* node) {
const TfLiteTensor* shape = GetInput(context, node, kShapeTensor);
return (shape->dims->size == 1 && shape->type == kTfLiteInt32);
} | CWE-125 | 47 |
TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) {
const CTCBeamSearchDecoderParams* option =
reinterpret_cast<CTCBeamSearchDecoderParams*>(node->user_data);
const int top_paths = option->top_paths;
TF_LITE_ENSURE(context, option->beam_width >= top_paths);
TF_LITE_ENSURE_EQ(context, NumInputs(node), 2);
// The outputs should be top_paths * 3 + 1.
TF_LITE_ENSURE_EQ(context, NumOutputs(node), 3 * top_paths + 1);
const TfLiteTensor* inputs = GetInput(context, node, kInputsTensor);
TF_LITE_ENSURE_EQ(context, NumDimensions(inputs), 3);
// TensorFlow only supports float.
TF_LITE_ENSURE_EQ(context, inputs->type, kTfLiteFloat32);
const int batch_size = SizeOfDimension(inputs, 1);
const TfLiteTensor* sequence_length =
GetInput(context, node, kSequenceLengthTensor);
TF_LITE_ENSURE_EQ(context, NumDimensions(sequence_length), 1);
TF_LITE_ENSURE_EQ(context, NumElements(sequence_length), batch_size);
// TensorFlow only supports int32.
TF_LITE_ENSURE_EQ(context, sequence_length->type, kTfLiteInt32);
// Resize decoded outputs.
// Do not resize indices & values cause we don't know the values yet.
for (int i = 0; i < top_paths; ++i) {
TfLiteTensor* indices = GetOutput(context, node, i);
SetTensorToDynamic(indices);
TfLiteTensor* values = GetOutput(context, node, i + top_paths);
SetTensorToDynamic(values);
TfLiteTensor* output_shape = GetOutput(context, node, i + 2 * top_paths);
SetTensorToDynamic(output_shape);
}
// Resize log probability outputs.
TfLiteTensor* log_probability_output =
GetOutput(context, node, top_paths * 3);
TfLiteIntArray* log_probability_output_shape_array = TfLiteIntArrayCreate(2);
log_probability_output_shape_array->data[0] = batch_size;
log_probability_output_shape_array->data[1] = top_paths;
return context->ResizeTensor(context, log_probability_output,
log_probability_output_shape_array);
} | CWE-125 | 47 |
TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) {
TfLiteTensor* output = GetOutput(context, node, kOutputTensor);
const TfLiteTensor* input = GetInput(context, node, kInputTensor);
const TfLiteTensor* diag = GetInput(context, node, kDiagonalTensor);
FillDiagHelper(input, diag, output);
return kTfLiteOk;
} | CWE-787 | 24 |
TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) {
TF_LITE_ENSURE_EQ(context, NumInputs(node), 1);
TF_LITE_ENSURE_EQ(context, NumOutputs(node), 1);
const TfLiteTensor* input = GetInput(context, node, kInputTensor);
TfLiteTensor* output = GetOutput(context, node, kOutputTensor);
auto* params = reinterpret_cast<TfLiteShapeParams*>(node->builtin_data);
switch (params->out_type) {
case kTfLiteInt32:
output->type = kTfLiteInt32;
break;
case kTfLiteInt64:
output->type = kTfLiteInt64;
break;
default:
context->ReportError(context, "Unknown shape output data type: %d",
params->out_type);
return kTfLiteError;
}
// By design, the input shape is always known at the time of Prepare, even
// if the preceding op that generates |input| is dynamic. Thus, we can
// always compute the shape immediately, without waiting for Eval.
SetTensorToPersistentRo(output);
// Shape always produces a 1-dimensional output tensor, where each output
// element is the length of the corresponding input tensor's dimension.
TfLiteIntArray* output_size = TfLiteIntArrayCreate(1);
output_size->data[0] = NumDimensions(input);
TF_LITE_ENSURE_STATUS(context->ResizeTensor(context, output, output_size));
TFLITE_DCHECK_EQ(NumDimensions(output), 1);
TFLITE_DCHECK_EQ(SizeOfDimension(output, 0), NumDimensions(input));
// Immediately propagate the known shape to the output tensor. This allows
// downstream ops that rely on the value to use it during prepare.
switch (output->type) {
case kTfLiteInt32:
ExtractShape(input, GetTensorData<int32_t>(output));
break;
case kTfLiteInt64:
ExtractShape(input, GetTensorData<int64_t>(output));
break;
default:
return kTfLiteError;
}
return kTfLiteOk;
} | CWE-125 | 47 |
CharArray(int len) {
buf = new char[len]();
} | CWE-787 | 24 |
TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) {
static const int kOutputUniqueTensor = 0;
static const int kOutputIndexTensor = 1;
TF_LITE_ENSURE_EQ(context, NumInputs(node), 1);
TF_LITE_ENSURE_EQ(context, NumOutputs(node), 2);
const TfLiteTensor* input = GetInput(context, node, 0);
TfLiteTensor* output_unique_tensor =
GetOutput(context, node, kOutputUniqueTensor);
TfLiteTensor* output_index_tensor =
GetOutput(context, node, kOutputIndexTensor);
// The op only supports 1D input.
TF_LITE_ENSURE_EQ(context, NumDimensions(input), 1);
TfLiteIntArray* output_index_shape = TfLiteIntArrayCopy(input->dims);
// The unique values are determined during evaluation, so we don't know yet
// the size of the output tensor.
SetTensorToDynamic(output_unique_tensor);
return context->ResizeTensor(context, output_index_tensor,
output_index_shape);
} | CWE-787 | 24 |
static int lookup1_values(int entries, int dim)
{
int r = (int) floor(exp((float) log((float) entries) / dim));
if ((int) floor(pow((float) r+1, dim)) <= entries) // (int) cast for MinGW warning;
++r; // floor() to avoid _ftol() when non-CRT
assert(pow((float) r+1, dim) > entries);
assert((int) floor(pow((float) r, dim)) <= entries); // (int),floor() as above
return r;
} | CWE-125 | 47 |
int PackLinuxElf32::canUnpack()
{
if (super::canUnpack()) {
return true;
}
if (Elf32_Ehdr::ET_DYN==get_te16(&ehdri.e_type)) {
PackLinuxElf32help1(fi);
}
return false;
} | CWE-476 | 46 |
void ResourceHandle::FromProto(const ResourceHandleProto& proto) {
set_device(proto.device());
set_container(proto.container());
set_name(proto.name());
set_hash_code(proto.hash_code());
set_maybe_type_name(proto.maybe_type_name());
std::vector<DtypeAndPartialTensorShape> dtypes_and_shapes;
for (const auto& dtype_and_shape : proto.dtypes_and_shapes()) {
DataType dtype = dtype_and_shape.dtype();
PartialTensorShape shape(dtype_and_shape.shape());
dtypes_and_shapes.push_back(DtypeAndPartialTensorShape{dtype, shape});
}
dtypes_and_shapes_ = std::move(dtypes_and_shapes);
} | CWE-617 | 51 |
int jas_seq2d_output(jas_matrix_t *matrix, FILE *out)
{
#define MAXLINELEN 80
int i;
int j;
jas_seqent_t x;
char buf[MAXLINELEN + 1];
char sbuf[MAXLINELEN + 1];
int n;
fprintf(out, "%"PRIiFAST32" %"PRIiFAST32"\n", jas_seq2d_xstart(matrix),
jas_seq2d_ystart(matrix));
fprintf(out, "%"PRIiFAST32" %"PRIiFAST32"\n", jas_matrix_numcols(matrix),
jas_matrix_numrows(matrix));
buf[0] = '\0';
for (i = 0; i < jas_matrix_numrows(matrix); ++i) {
for (j = 0; j < jas_matrix_numcols(matrix); ++j) {
x = jas_matrix_get(matrix, i, j);
sprintf(sbuf, "%s%4ld", (strlen(buf) > 0) ? " " : "",
JAS_CAST(long, x));
n = JAS_CAST(int, strlen(buf));
if (n + JAS_CAST(int, strlen(sbuf)) > MAXLINELEN) {
fputs(buf, out);
fputs("\n", out);
buf[0] = '\0';
}
strcat(buf, sbuf);
if (j == jas_matrix_numcols(matrix) - 1) {
fputs(buf, out);
fputs("\n", out);
buf[0] = '\0';
}
}
}
fputs(buf, out);
return 0;
} | CWE-190 | 19 |
size_t jsuGetFreeStack() {
#ifdef ARM
void *frame = __builtin_frame_address(0);
size_t stackPos = (size_t)((char*)frame);
size_t stackEnd = (size_t)((char*)&LINKER_END_VAR);
if (stackPos < stackEnd) return 0; // should never happen, but just in case of overflow!
return stackPos - stackEnd;
#elif defined(LINUX)
// On linux, we set STACK_BASE from `main`.
char ptr; // this is on the stack
extern void *STACK_BASE;
uint32_t count = (uint32_t)((size_t)STACK_BASE - (size_t)&ptr);
return 1000000 - count; // give it 1 megabyte of stack
#else
// stack depth seems pretty platform-specific :( Default to a value that disables it
return 1000000; // no stack depth check on this platform
#endif
} | CWE-190 | 19 |
EntropyParser::EntropyParser(class Frame *frame,class Scan *scan)
: JKeeper(scan->EnvironOf()), m_pScan(scan), m_pFrame(frame)
{
m_ucCount = scan->ComponentsInScan();
// The residual scan uses all components here, not just for, but
// it does not require the component count either.
for(volatile UBYTE i = 0;i < m_ucCount && i < 4;i++) {
JPG_TRY {
m_pComponent[i] = scan->ComponentOf(i);
} JPG_CATCH {
m_pComponent[i] = NULL;
} JPG_ENDTRY;
}
m_ulRestartInterval = m_pFrame->TablesOf()->RestartIntervalOf();
m_usNextRestartMarker = 0xffd0;
m_ulMCUsToGo = m_ulRestartInterval;
m_bSegmentIsValid = true;
m_bScanForDNL = (m_pFrame->HeightOf() == 0)?true:false;
m_bDNLFound = false;
} | CWE-476 | 46 |
void ZlibInStream::setUnderlying(InStream* is, int bytesIn_)
{
underlying = is;
bytesIn = bytesIn_;
ptr = end = start;
} | CWE-787 | 24 |
bool load_face(Face & face, unsigned int options)
{
#ifdef GRAPHITE2_TELEMETRY
telemetry::category _misc_cat(face.tele.misc);
#endif
Face::Table silf(face, Tag::Silf, 0x00050000);
if (silf) options &= ~gr_face_dumbRendering;
else if (!(options & gr_face_dumbRendering))
return false;
if (!face.readGlyphs(options))
return false;
if (silf)
{
if (!face.readFeatures() || !face.readGraphite(silf))
{
#if !defined GRAPHITE2_NTRACING
if (global_log)
{
*global_log << json::object
<< "type" << "fontload"
<< "failure" << face.error()
<< "context" << face.error_context()
<< json::close;
}
#endif
return false;
}
else
return true;
}
else
return options & gr_face_dumbRendering;
} | CWE-476 | 46 |
void* TFE_HandleToDLPack(TFE_TensorHandle* h, TF_Status* status) {
const Tensor* tensor = GetTensorFromHandle(h, status);
TF_DataType data_type = static_cast<TF_DataType>(tensor->dtype());
TensorReference tensor_ref(*tensor); // This will call buf_->Ref()
auto* tf_dlm_tensor_ctx = new TfDlManagedTensorCtx(tensor_ref);
tf_dlm_tensor_ctx->reference = tensor_ref;
DLManagedTensor* dlm_tensor = &tf_dlm_tensor_ctx->tensor;
dlm_tensor->manager_ctx = tf_dlm_tensor_ctx;
dlm_tensor->deleter = &DLManagedTensorDeleter;
dlm_tensor->dl_tensor.ctx = GetDlContext(h, status);
int ndim = tensor->dims();
dlm_tensor->dl_tensor.ndim = ndim;
dlm_tensor->dl_tensor.data = TFE_TensorHandleDevicePointer(h, status);
dlm_tensor->dl_tensor.dtype = GetDlDataType(data_type, status);
std::vector<int64_t>* shape_arr = &tf_dlm_tensor_ctx->shape;
std::vector<int64_t>* stride_arr = &tf_dlm_tensor_ctx->strides;
shape_arr->resize(ndim);
stride_arr->resize(ndim, 1);
for (int i = 0; i < ndim; i++) {
(*shape_arr)[i] = tensor->dim_size(i);
}
for (int i = ndim - 2; i >= 0; --i) {
(*stride_arr)[i] = (*shape_arr)[i + 1] * (*stride_arr)[i + 1];
}
dlm_tensor->dl_tensor.shape = &(*shape_arr)[0];
// There are two ways to represent compact row-major data
// 1) nullptr indicates tensor is compact and row-majored.
// 2) fill in the strides array as the real case for compact row-major data.
// Here we choose option 2, since some frameworks didn't handle the strides
// argument properly.
dlm_tensor->dl_tensor.strides = &(*stride_arr)[0];
dlm_tensor->dl_tensor.byte_offset =
0; // TF doesn't handle the strides and byte_offsets here
return static_cast<void*>(dlm_tensor);
} | CWE-908 | 48 |
TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) {
TF_LITE_ENSURE_EQ(context, NumInputs(node), 1);
TF_LITE_ENSURE_EQ(context, NumOutputs(node), 1);
TF_LITE_ENSURE_TYPES_EQ(context, GetInput(context, node, 0)->type,
kTfLiteString);
TF_LITE_ENSURE_TYPES_EQ(context, GetOutput(context, node, 0)->type,
kTfLiteString);
return kTfLiteOk;
} | CWE-125 | 47 |
TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) {
const TfLiteTensor* input = GetInput(context, node, kInputTensor);
TfLiteTensor* output = GetOutput(context, node, kOutputTensor);
TF_LITE_ENSURE_EQ(context, NumInputs(node), 1);
TF_LITE_ENSURE_EQ(context, NumOutputs(node), 1);
TF_LITE_ENSURE_TYPES_EQ(context, input->type, kTfLiteFloat32);
output->type = input->type;
TfLiteIntArray* output_size = TfLiteIntArrayCopy(input->dims);
return context->ResizeTensor(context, output, output_size);
} | CWE-787 | 24 |
otError Commissioner::AddJoiner(const Mac::ExtAddress *aEui64, const char *aPskd, uint32_t aTimeout)
{
otError error = OT_ERROR_NO_BUFS;
VerifyOrExit(mState == OT_COMMISSIONER_STATE_ACTIVE, error = OT_ERROR_INVALID_STATE);
VerifyOrExit(strlen(aPskd) <= Dtls::kPskMaxLength, error = OT_ERROR_INVALID_ARGS);
RemoveJoiner(aEui64, 0); // remove immediately
for (Joiner *joiner = &mJoiners[0]; joiner < OT_ARRAY_END(mJoiners); joiner++)
{
if (joiner->mValid)
{
continue;
}
if (aEui64 != NULL)
{
joiner->mEui64 = *aEui64;
joiner->mAny = false;
}
else
{
joiner->mAny = true;
}
(void)strlcpy(joiner->mPsk, aPskd, sizeof(joiner->mPsk));
joiner->mValid = true;
joiner->mExpirationTime = TimerMilli::GetNow() + Time::SecToMsec(aTimeout);
UpdateJoinerExpirationTimer();
SendCommissionerSet();
otLogInfoMeshCoP("Added Joiner (%s, %s)", (aEui64 != NULL) ? aEui64->ToString().AsCString() : "*", aPskd);
ExitNow(error = OT_ERROR_NONE);
}
exit:
return error;
} | CWE-787 | 24 |
TfLiteStatus ResizeOutputTensor(TfLiteContext* context,
const TfLiteTensor* data,
const TfLiteTensor* segment_ids,
TfLiteTensor* output) {
int max_index = -1;
const int segment_id_size = segment_ids->dims->data[0];
if (segment_id_size > 0) {
max_index = segment_ids->data.i32[segment_id_size - 1];
}
const int data_rank = NumDimensions(data);
TfLiteIntArray* output_shape = TfLiteIntArrayCreate(NumDimensions(data));
output_shape->data[0] = max_index + 1;
for (int i = 1; i < data_rank; ++i) {
output_shape->data[i] = data->dims->data[i];
}
return context->ResizeTensor(context, output, output_shape);
} | CWE-770 | 37 |
TfLiteStatus EluEval(TfLiteContext* context, TfLiteNode* node) {
const TfLiteTensor* input = GetInput(context, node, 0);
TfLiteTensor* output = GetOutput(context, node, 0);
switch (input->type) {
case kTfLiteFloat32: {
optimized_ops::Elu(GetTensorShape(input), GetTensorData<float>(input),
GetTensorShape(output), GetTensorData<float>(output));
return kTfLiteOk;
} break;
case kTfLiteInt8: {
OpData* data = reinterpret_cast<OpData*>(node->user_data);
EvalUsingLookupTable(data, input, output);
return kTfLiteOk;
} break;
default:
TF_LITE_KERNEL_LOG(
context, "Only float32 and int8 is supported currently, got %s.",
TfLiteTypeGetName(input->type));
return kTfLiteError;
}
} | CWE-787 | 24 |
int FdInStream::overrun(int itemSize, int nItems, bool wait)
{
if (itemSize > bufSize)
throw Exception("FdInStream overrun: max itemSize exceeded");
if (end - ptr != 0)
memmove(start, ptr, end - ptr);
offset += ptr - start;
end -= ptr - start;
ptr = start;
int bytes_to_read;
while (end < start + itemSize) {
bytes_to_read = start + bufSize - end;
if (!timing) {
// When not timing, we must be careful not to read too much
// extra data into the buffer. Otherwise, the line speed
// estimation might stay at zero for a long time: All reads
// during timing=1 can be satisfied without calling
// readWithTimeoutOrCallback. However, reading only 1 or 2 bytes
// bytes is ineffecient.
bytes_to_read = vncmin(bytes_to_read, vncmax(itemSize*nItems, 8));
}
int n = readWithTimeoutOrCallback((U8*)end, bytes_to_read, wait);
if (n == 0) return 0;
end += n;
}
if (itemSize * nItems > end - ptr)
nItems = (end - ptr) / itemSize;
return nItems;
} | CWE-787 | 24 |
Variant HHVM_FUNCTION(mcrypt_generic_init, const Resource& td,
const String& key,
const String& iv) {
auto pm = get_valid_mcrypt_resource(td);
if (!pm) {
return false;
}
int max_key_size = mcrypt_enc_get_key_size(pm->m_td);
int iv_size = mcrypt_enc_get_iv_size(pm->m_td);
if (key.empty()) {
raise_warning("Key size is 0");
}
unsigned char *key_s = (unsigned char *)malloc(key.size());
memset(key_s, 0, key.size());
unsigned char *iv_s = (unsigned char *)malloc(iv_size + 1);
memset(iv_s, 0, iv_size + 1);
int key_size;
if (key.size() > max_key_size) {
raise_warning("Key size too large; supplied length: %d, max: %d",
key.size(), max_key_size);
key_size = max_key_size;
} else {
key_size = key.size();
}
memcpy(key_s, key.data(), key.size());
if (iv.size() != iv_size) {
raise_warning("Iv size incorrect; supplied length: %d, needed: %d",
iv.size(), iv_size);
}
memcpy(iv_s, iv.data(), std::min(iv_size, iv.size()));
mcrypt_generic_deinit(pm->m_td);
int result = mcrypt_generic_init(pm->m_td, key_s, key_size, iv_s);
/* If this function fails, close the mcrypt module to prevent crashes
* when further functions want to access this resource */
if (result < 0) {
pm->close();
switch (result) {
case -3:
raise_warning("Key length incorrect");
break;
case -4:
raise_warning("Memory allocation error");
break;
case -1:
default:
raise_warning("Unknown error");
break;
}
} else {
pm->m_init = true;
}
free(iv_s);
free(key_s);
return result;
} | CWE-190 | 19 |
TfLiteStatus GreaterEqualEval(TfLiteContext* context, TfLiteNode* node) {
const TfLiteTensor* input1 = GetInput(context, node, kInputTensor1);
const TfLiteTensor* input2 = GetInput(context, node, kInputTensor2);
TfLiteTensor* output = GetOutput(context, node, kOutputTensor);
bool requires_broadcast = !HaveSameShapes(input1, input2);
switch (input1->type) {
case kTfLiteFloat32:
Comparison<float, reference_ops::GreaterEqualFn>(input1, input2, output,
requires_broadcast);
break;
case kTfLiteInt32:
Comparison<int32_t, reference_ops::GreaterEqualFn>(input1, input2, output,
requires_broadcast);
break;
case kTfLiteInt64:
Comparison<int64_t, reference_ops::GreaterEqualFn>(input1, input2, output,
requires_broadcast);
break;
case kTfLiteUInt8:
ComparisonQuantized<uint8_t, reference_ops::GreaterEqualFn>(
input1, input2, output, requires_broadcast);
break;
case kTfLiteInt8:
ComparisonQuantized<int8_t, reference_ops::GreaterEqualFn>(
input1, input2, output, requires_broadcast);
break;
default:
context->ReportError(context,
"Does not support type %d, requires float|int|uint8",
input1->type);
return kTfLiteError;
}
return kTfLiteOk;
} | CWE-787 | 24 |
explicit UnravelIndexOp(OpKernelConstruction* ctx) : OpKernel(ctx) {} | CWE-190 | 19 |
TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) {
auto* params = reinterpret_cast<TfLiteDivParams*>(node->builtin_data);
OpData* data = reinterpret_cast<OpData*>(node->user_data);
const TfLiteTensor* input1 = GetInput(context, node, kInputTensor1);
const TfLiteTensor* input2 = GetInput(context, node, kInputTensor2);
TfLiteTensor* output = GetOutput(context, node, kOutputTensor);
if (output->type == kTfLiteFloat32 || output->type == kTfLiteInt32) {
EvalDiv<kernel_type>(context, node, params, data, input1, input2, output);
} else if (output->type == kTfLiteUInt8) {
TF_LITE_ENSURE_OK(
context, EvalQuantized<kernel_type>(context, node, params, data, input1,
input2, output));
} else {
context->ReportError(
context,
"Div only supports FLOAT32, INT32 and quantized UINT8 now, got %d.",
output->type);
return kTfLiteError;
}
return kTfLiteOk;
} | CWE-125 | 47 |
Java_org_tensorflow_lite_InterpreterTest_getNativeHandleForDelegate(
JNIEnv* env, jclass clazz) {
// A simple op which outputs a tensor with values of 7.
static TfLiteRegistration registration = {
.init = nullptr,
.free = nullptr,
.prepare =
[](TfLiteContext* context, TfLiteNode* node) {
const TfLiteTensor* input = tflite::GetInput(context, node, 0);
TfLiteTensor* output = tflite::GetOutput(context, node, 0);
TfLiteIntArray* output_dims = TfLiteIntArrayCopy(input->dims);
output->type = kTfLiteFloat32;
return context->ResizeTensor(context, output, output_dims);
},
.invoke =
[](TfLiteContext* context, TfLiteNode* node) {
TfLiteTensor* output = tflite::GetOutput(context, node, 0);
std::fill(output->data.f,
output->data.f + tflite::NumElements(output), 7.0f);
return kTfLiteOk;
},
.profiling_string = nullptr,
.builtin_code = 0,
.custom_name = "",
.version = 1,
};
static TfLiteDelegate delegate = {
.data_ = nullptr,
.Prepare = [](TfLiteContext* context,
TfLiteDelegate* delegate) -> TfLiteStatus {
TfLiteIntArray* execution_plan;
TF_LITE_ENSURE_STATUS(
context->GetExecutionPlan(context, &execution_plan));
context->ReplaceNodeSubsetsWithDelegateKernels(
context, registration, execution_plan, delegate);
// Now bind delegate buffer handles for all tensors.
for (size_t i = 0; i < context->tensors_size; ++i) {
context->tensors[i].delegate = delegate;
context->tensors[i].buffer_handle = static_cast<int>(i);
}
return kTfLiteOk;
},
.CopyFromBufferHandle = nullptr,
.CopyToBufferHandle = nullptr,
.FreeBufferHandle = nullptr,
.flags = kTfLiteDelegateFlagsAllowDynamicTensors,
};
return reinterpret_cast<jlong>(&delegate);
} | CWE-125 | 47 |
TfLiteStatus Relu6Eval(TfLiteContext* context, TfLiteNode* node) {
const TfLiteTensor* input = GetInput(context, node, 0);
TfLiteTensor* output = GetOutput(context, node, 0);
ReluOpData* data = reinterpret_cast<ReluOpData*>(node->user_data);
switch (input->type) {
case kTfLiteFloat32: {
size_t elements = input->bytes / sizeof(float);
const float* in = GetTensorData<float>(input);
const float* in_end = in + elements;
float* out = GetTensorData<float>(output);
for (; in < in_end; in++, out++) *out = std::min(std::max(0.f, *in), 6.f);
return kTfLiteOk;
} break;
case kTfLiteUInt8:
QuantizedReluX<uint8_t>(0.0f, 6.0f, input, output, data);
return kTfLiteOk;
case kTfLiteInt8: {
QuantizedReluX<int8_t>(0.0f, 6.0f, input, output, data);
return kTfLiteOk;
} break;
default:
TF_LITE_KERNEL_LOG(
context,
"Only float32, uint8 and int8 are supported currently, got %s.",
TfLiteTypeGetName(input->type));
return kTfLiteError;
}
} | CWE-787 | 24 |
bool IsFullyConnectedOpSupported(const TfLiteRegistration* registration,
const TfLiteNode* node,
TfLiteContext* context) {
if (node->builtin_data == nullptr) return false;
const auto* fc_params =
reinterpret_cast<const TfLiteFullyConnectedParams*>(node->builtin_data);
const int kInput = 0;
const int kWeights = 1;
const int kBias = 2;
if (fc_params->weights_format != kTfLiteFullyConnectedWeightsFormatDefault) {
return false;
}
const TfLiteTensor* input = GetInput(context, node, kInput);
const TfLiteTensor* weights = GetInput(context, node, kWeights);
if (!IsFloatType(input->type)) {
return false;
}
if (!IsFloatType(weights->type) || !IsConstantTensor(weights)) {
return false;
}
// Core ML 2 only supports single-batch fully connected layer, thus dimensions
// except the last one should be 1.
if (input->dims->data[input->dims->size - 1] != NumElements(input)) {
return false;
}
if (node->inputs->size > 2) {
const TfLiteTensor* bias = GetInput(context, node, kBias);
if (!IsFloatType(bias->type) || !IsConstantTensor(bias)) {
return false;
}
}
TfLiteFusedActivation activation = fc_params->activation;
if (activation == kTfLiteActSignBit) {
return false;
}
return true;
} | CWE-125 | 47 |
gdImagePtr gdImageCreateTrueColor (int sx, int sy)
{
int i;
gdImagePtr im;
if (overflow2(sx, sy)) {
return NULL;
}
if (overflow2(sizeof(unsigned char *), sy)) {
return NULL;
}
if (overflow2(sizeof(int) + sizeof(unsigned char), sx * sy)) {
return NULL;
}
// Check for OOM before doing a potentially large allocation.
auto allocsz = sizeof(gdImage)
+ sy * (sizeof(int *) + sizeof(unsigned char *))
+ sx * sy * (sizeof(int) + sizeof(unsigned char));
if (UNLIKELY(precheckOOM(allocsz))) {
// Don't throw here because GD might need to do its own cleanup.
return NULL;
}
im = (gdImage *) gdMalloc(sizeof(gdImage));
memset(im, 0, sizeof(gdImage));
im->tpixels = (int **) gdMalloc(sizeof(int *) * sy);
im->AA_opacity = (unsigned char **) gdMalloc(sizeof(unsigned char *) * sy);
im->polyInts = 0;
im->polyAllocated = 0;
im->brush = 0;
im->tile = 0;
im->style = 0;
for (i = 0; i < sy; i++) {
im->tpixels[i] = (int *) gdCalloc(sx, sizeof(int));
im->AA_opacity[i] = (unsigned char *) gdCalloc(sx, sizeof(unsigned char));
}
im->sx = sx;
im->sy = sy;
im->transparent = (-1);
im->interlace = 0;
im->trueColor = 1;
/* 2.0.2: alpha blending is now on by default, and saving of alpha is
* off by default. This allows font antialiasing to work as expected
* on the first try in JPEGs -- quite important -- and also allows
* for smaller PNGs when saving of alpha channel is not really
* desired, which it usually isn't!
*/
im->saveAlphaFlag = 0;
im->alphaBlendingFlag = 1;
im->thick = 1;
im->AA = 0;
im->AA_polygon = 0;
im->cx1 = 0;
im->cy1 = 0;
im->cx2 = im->sx - 1;
im->cy2 = im->sy - 1;
im->interpolation = NULL;
im->interpolation_id = GD_BILINEAR_FIXED;
return im;
} | CWE-22 | 2 |
bool read(ReadonlyBytes buffer)
{
auto fields_size = sizeof(EndOfCentralDirectory) - sizeof(u8*);
if (buffer.size() < fields_size)
return false;
if (memcmp(buffer.data(), end_of_central_directory_signature, sizeof(end_of_central_directory_signature)) != 0)
return false;
memcpy(reinterpret_cast<void*>(&disk_number), buffer.data() + sizeof(end_of_central_directory_signature), fields_size);
comment = buffer.data() + sizeof(end_of_central_directory_signature) + fields_size;
return true;
} | CWE-120 | 44 |
Variant HHVM_FUNCTION(mcrypt_generic_init, const Resource& td,
const String& key,
const String& iv) {
auto pm = get_valid_mcrypt_resource(td);
if (!pm) {
return false;
}
int max_key_size = mcrypt_enc_get_key_size(pm->m_td);
int iv_size = mcrypt_enc_get_iv_size(pm->m_td);
if (key.empty()) {
raise_warning("Key size is 0");
}
unsigned char *key_s = (unsigned char *)malloc(key.size());
memset(key_s, 0, key.size());
unsigned char *iv_s = (unsigned char *)malloc(iv_size + 1);
memset(iv_s, 0, iv_size + 1);
int key_size;
if (key.size() > max_key_size) {
raise_warning("Key size too large; supplied length: %d, max: %d",
key.size(), max_key_size);
key_size = max_key_size;
} else {
key_size = key.size();
}
memcpy(key_s, key.data(), key.size());
if (iv.size() != iv_size) {
raise_warning("Iv size incorrect; supplied length: %d, needed: %d",
iv.size(), iv_size);
}
memcpy(iv_s, iv.data(), std::min(iv_size, iv.size()));
mcrypt_generic_deinit(pm->m_td);
int result = mcrypt_generic_init(pm->m_td, key_s, key_size, iv_s);
/* If this function fails, close the mcrypt module to prevent crashes
* when further functions want to access this resource */
if (result < 0) {
pm->close();
switch (result) {
case -3:
raise_warning("Key length incorrect");
break;
case -4:
raise_warning("Memory allocation error");
break;
case -1:
default:
raise_warning("Unknown error");
break;
}
} else {
pm->m_init = true;
}
free(iv_s);
free(key_s);
return result;
} | CWE-787 | 24 |
TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) {
const CTCBeamSearchDecoderParams* option =
reinterpret_cast<CTCBeamSearchDecoderParams*>(node->user_data);
const int top_paths = option->top_paths;
TF_LITE_ENSURE(context, option->beam_width >= top_paths);
TF_LITE_ENSURE_EQ(context, NumInputs(node), 2);
// The outputs should be top_paths * 3 + 1.
TF_LITE_ENSURE_EQ(context, NumOutputs(node), 3 * top_paths + 1);
const TfLiteTensor* inputs = GetInput(context, node, kInputsTensor);
TF_LITE_ENSURE_EQ(context, NumDimensions(inputs), 3);
// TensorFlow only supports float.
TF_LITE_ENSURE_EQ(context, inputs->type, kTfLiteFloat32);
const int batch_size = SizeOfDimension(inputs, 1);
const TfLiteTensor* sequence_length =
GetInput(context, node, kSequenceLengthTensor);
TF_LITE_ENSURE_EQ(context, NumDimensions(sequence_length), 1);
TF_LITE_ENSURE_EQ(context, NumElements(sequence_length), batch_size);
// TensorFlow only supports int32.
TF_LITE_ENSURE_EQ(context, sequence_length->type, kTfLiteInt32);
// Resize decoded outputs.
// Do not resize indices & values cause we don't know the values yet.
for (int i = 0; i < top_paths; ++i) {
TfLiteTensor* indices = GetOutput(context, node, i);
SetTensorToDynamic(indices);
TfLiteTensor* values = GetOutput(context, node, i + top_paths);
SetTensorToDynamic(values);
TfLiteTensor* output_shape = GetOutput(context, node, i + 2 * top_paths);
SetTensorToDynamic(output_shape);
}
// Resize log probability outputs.
TfLiteTensor* log_probability_output =
GetOutput(context, node, top_paths * 3);
TfLiteIntArray* log_probability_output_shape_array = TfLiteIntArrayCreate(2);
log_probability_output_shape_array->data[0] = batch_size;
log_probability_output_shape_array->data[1] = top_paths;
return context->ResizeTensor(context, log_probability_output,
log_probability_output_shape_array);
} | CWE-787 | 24 |
TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) {
const TfLiteTensor* input = GetInput(context, node, 0);
TfLiteTensor* output_index_tensor = GetOutput(context, node, 1);
TF_LITE_ENSURE_EQ(context, NumElements(output_index_tensor),
NumElements(input));
switch (input->type) {
case kTfLiteInt8:
TF_LITE_ENSURE_STATUS(EvalImpl<int8_t>(context, input, node));
break;
case kTfLiteInt16:
TF_LITE_ENSURE_STATUS(EvalImpl<int16_t>(context, input, node));
break;
case kTfLiteInt32:
TF_LITE_ENSURE_STATUS(EvalImpl<int32_t>(context, input, node));
break;
case kTfLiteInt64:
TF_LITE_ENSURE_STATUS(EvalImpl<int64_t>(context, input, node));
break;
case kTfLiteFloat32:
TF_LITE_ENSURE_STATUS(EvalImpl<float>(context, input, node));
break;
case kTfLiteUInt8:
TF_LITE_ENSURE_STATUS(EvalImpl<uint8_t>(context, input, node));
break;
default:
context->ReportError(context, "Currently Unique doesn't support type: %s",
TfLiteTypeGetName(input->type));
return kTfLiteError;
}
return kTfLiteOk;
} | CWE-787 | 24 |
MemInStream(const void* data, int len, bool deleteWhenDone_=false)
: start((const U8*)data), deleteWhenDone(deleteWhenDone_)
{
ptr = start;
end = start + len;
} | CWE-787 | 24 |
inline int StringData::size() const { return m_len; } | CWE-125 | 47 |
int jas_memdump(FILE *out, void *data, size_t len)
{
size_t i;
size_t j;
uchar *dp;
dp = data;
for (i = 0; i < len; i += 16) {
fprintf(out, "%04zx:", i);
for (j = 0; j < 16; ++j) {
if (i + j < len) {
fprintf(out, " %02x", dp[i + j]);
}
}
fprintf(out, "\n");
}
return 0;
} | CWE-190 | 19 |
std::string encodeBase64(const std::string& input) {
using namespace boost::archive::iterators;
using b64it = base64_from_binary<transform_width<const char*, 6, 8>>;
auto data = input.data();
std::string encoded(b64it(data), b64it(data + (input.length())));
encoded.append((3 - (input.length() % 3)) % 3, '=');
return encoded;
} | CWE-787 | 24 |
QInt16() {} | CWE-908 | 48 |
vector <string> genECDSAKey() {
vector<char> errMsg(BUF_LEN, 0);
int errStatus = 0;
vector <uint8_t> encr_pr_key(BUF_LEN, 0);
vector<char> pub_key_x(BUF_LEN, 0);
vector<char> pub_key_y(BUF_LEN, 0);
uint32_t enc_len = 0;
sgx_status_t status = trustedGenerateEcdsaKeyAES(eid, &errStatus,
errMsg.data(), encr_pr_key.data(), &enc_len,
pub_key_x.data(), pub_key_y.data());
HANDLE_TRUSTED_FUNCTION_ERROR(status, errStatus,errMsg.data());
vector <string> keys(3);
vector<char> hexEncrKey(BUF_LEN * 2, 0);
carray2Hex(encr_pr_key.data(), enc_len, hexEncrKey.data(),
BUF_LEN * 2);
keys.at(0) = hexEncrKey.data();
keys.at(1) = string(pub_key_x.data()) + string(pub_key_y.data());
vector<unsigned char> randBuffer(32, 0);
fillRandomBuffer(randBuffer);
vector<char> rand_str(BUF_LEN, 0);
carray2Hex(randBuffer.data(), 32, rand_str.data(), BUF_LEN);
keys.at(2) = rand_str.data();
CHECK_STATE(keys.at(2).size() == 64);
return keys;
} | CWE-787 | 24 |
TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) {
auto* params =
reinterpret_cast<TfLiteLSHProjectionParams*>(node->builtin_data);
int32_t* out_buf = GetOutput(context, node, 0)->data.i32;
const TfLiteTensor* hash = GetInput(context, node, 0);
const TfLiteTensor* input = GetInput(context, node, 1);
const TfLiteTensor* weight =
NumInputs(node) == 2 ? nullptr : GetInput(context, node, 2);
switch (params->type) {
case kTfLiteLshProjectionDense:
DenseLshProjection(hash, input, weight, out_buf);
break;
case kTfLiteLshProjectionSparse:
SparseLshProjection(hash, input, weight, out_buf);
break;
default:
return kTfLiteError;
}
return kTfLiteOk;
} | CWE-787 | 24 |
MONGO_EXPORT int bson_append_element( bson *b, const char *name_or_null, const bson_iterator *elem ) {
bson_iterator next = *elem;
int size;
bson_iterator_next( &next );
size = next.cur - elem->cur;
if ( name_or_null == NULL ) {
if( bson_ensure_space( b, size ) == BSON_ERROR )
return BSON_ERROR;
bson_append( b, elem->cur, size );
}
else {
int data_size = size - 2 - strlen( bson_iterator_key( elem ) );
bson_append_estart( b, elem->cur[0], name_or_null, data_size );
bson_append( b, bson_iterator_value( elem ), data_size );
}
return BSON_OK;
} | CWE-190 | 19 |
void *jas_realloc(void *ptr, size_t size)
{
void *result;
JAS_DBGLOG(101, ("jas_realloc called with %x,%zu\n", ptr, size));
result = realloc(ptr, size);
JAS_DBGLOG(100, ("jas_realloc(%p, %zu) -> %p\n", ptr, size, result));
return result;
} | CWE-190 | 19 |
bool hex2carray(const char *_hex, uint64_t *_bin_len,
uint8_t *_bin, uint64_t _max_length) {
CHECK_STATE(_hex);
CHECK_STATE(_bin);
CHECK_STATE(_bin_len)
int len = strnlen(_hex, 2 * _max_length + 1);
CHECK_STATE(len != 2 * _max_length + 1);
CHECK_STATE(len <= 2 * _max_length );
if (len == 0 && len % 2 == 1)
return false;
*_bin_len = len / 2;
for (int i = 0; i < len / 2; i++) {
int high = char2int((char) _hex[i * 2]);
int low = char2int((char) _hex[i * 2 + 1]);
if (high < 0 || low < 0) {
return false;
}
_bin[i] = (unsigned char) (high * 16 + low);
}
return true;
} | CWE-787 | 24 |
const String& setSize(int len) {
assertx(m_str);
m_str->setSize(len);
return *this;
} | CWE-125 | 47 |
TfLiteStatus ReverseSequenceImpl(TfLiteContext* context, TfLiteNode* node) {
const TfLiteTensor* input = GetInput(context, node, kInputTensor);
const TfLiteTensor* seq_lengths_tensor =
GetInput(context, node, kSeqLengthsTensor);
const TS* seq_lengths = GetTensorData<TS>(seq_lengths_tensor);
auto* params =
reinterpret_cast<TfLiteReverseSequenceParams*>(node->builtin_data);
int seq_dim = params->seq_dim;
int batch_dim = params->batch_dim;
TF_LITE_ENSURE(context, seq_dim >= 0);
TF_LITE_ENSURE(context, batch_dim >= 0);
TF_LITE_ENSURE(context, seq_dim != batch_dim);
TF_LITE_ENSURE(context, seq_dim < NumDimensions(input));
TF_LITE_ENSURE(context, batch_dim < NumDimensions(input));
TF_LITE_ENSURE_EQ(context, SizeOfDimension(seq_lengths_tensor, 0),
SizeOfDimension(input, batch_dim));
for (int i = 0; i < NumDimensions(seq_lengths_tensor); ++i) {
TF_LITE_ENSURE(context, seq_lengths[i] <= SizeOfDimension(input, seq_dim));
}
TfLiteTensor* output = GetOutput(context, node, kOutputTensor);
reference_ops::ReverseSequence<T, TS>(
seq_lengths, seq_dim, batch_dim, GetTensorShape(input),
GetTensorData<T>(input), GetTensorShape(output),
GetTensorData<T>(output));
return kTfLiteOk;
} | CWE-787 | 24 |
TfLiteStatus EluPrepare(TfLiteContext* context, TfLiteNode* node) {
const TfLiteTensor* input = GetInput(context, node, 0);
TfLiteTensor* output = GetOutput(context, node, 0);
OpData* data = reinterpret_cast<OpData*>(node->user_data);
// Use LUT to handle quantized elu path.
if (input->type == kTfLiteInt8) {
PopulateLookupTable<int8_t>(data, input, output, [](float value) {
return value < 0.0 ? std::exp(value) - 1.0f : value;
});
}
return GenericPrepare(context, node);
} | CWE-787 | 24 |
TfLiteStatus LessEval(TfLiteContext* context, TfLiteNode* node) {
const TfLiteTensor* input1 = GetInput(context, node, kInputTensor1);
const TfLiteTensor* input2 = GetInput(context, node, kInputTensor2);
TfLiteTensor* output = GetOutput(context, node, kOutputTensor);
bool requires_broadcast = !HaveSameShapes(input1, input2);
switch (input1->type) {
case kTfLiteFloat32:
Comparison<float, reference_ops::LessFn>(input1, input2, output,
requires_broadcast);
break;
case kTfLiteInt32:
Comparison<int32_t, reference_ops::LessFn>(input1, input2, output,
requires_broadcast);
break;
case kTfLiteInt64:
Comparison<int64_t, reference_ops::LessFn>(input1, input2, output,
requires_broadcast);
break;
case kTfLiteUInt8:
ComparisonQuantized<uint8_t, reference_ops::LessFn>(
input1, input2, output, requires_broadcast);
break;
case kTfLiteInt8:
ComparisonQuantized<int8_t, reference_ops::LessFn>(input1, input2, output,
requires_broadcast);
break;
default:
context->ReportError(context,
"Does not support type %d, requires float|int|uint8",
input1->type);
return kTfLiteError;
}
return kTfLiteOk;
} | CWE-787 | 24 |
void BlockCodec::runPull()
{
AFframecount framesToRead = m_outChunk->frameCount;
AFframecount framesRead = 0;
assert(framesToRead % m_framesPerPacket == 0);
int blockCount = framesToRead / m_framesPerPacket;
// Read the compressed data.
ssize_t bytesRead = read(m_inChunk->buffer, m_bytesPerPacket * blockCount);
int blocksRead = bytesRead >= 0 ? bytesRead / m_bytesPerPacket : 0;
// Decompress into m_outChunk.
for (int i=0; i<blocksRead; i++)
{
decodeBlock(static_cast<const uint8_t *>(m_inChunk->buffer) + i * m_bytesPerPacket,
static_cast<int16_t *>(m_outChunk->buffer) + i * m_framesPerPacket * m_track->f.channelCount);
framesRead += m_framesPerPacket;
}
m_track->nextfframe += framesRead;
assert(tell() == m_track->fpos_next_frame);
if (framesRead < framesToRead)
reportReadError(framesRead, framesToRead);
m_outChunk->frameCount = framesRead;
} | CWE-190 | 19 |
void TileManager::crop( RawTile *ttt ){
int tw = image->getTileWidth();
int th = image->getTileHeight();
if( loglevel >= 5 ){
*logfile << "TileManager :: Edge tile: Base size: " << tw << "x" << th
<< ": This tile: " << ttt->width << "x" << ttt->height
<< endl;
}
// Create a new buffer, fill it with the old data, then copy
// back the cropped part into the RawTile buffer
int len = tw * th * ttt->channels * (ttt->bpc/8);
unsigned char* buffer = (unsigned char*) malloc( len );
unsigned char* src_ptr = (unsigned char*) memcpy( buffer, ttt->data, len );
unsigned char* dst_ptr = (unsigned char*) ttt->data;
// Copy one scanline at a time
len = ttt->width * ttt->channels * (ttt->bpc/8);
for( unsigned int i=0; i<ttt->height; i++ ){
memcpy( dst_ptr, src_ptr, len );
dst_ptr += len;
src_ptr += tw * ttt->channels * (ttt->bpc/8);
}
free( buffer );
// Reset the data length
len = ttt->width * ttt->height * ttt->channels * (ttt->bpc/8);
ttt->dataLength = len;
ttt->padded = false;
} | CWE-190 | 19 |
void onComplete(const Status& status, ContextImpl& context) const override {
auto& completion_state = context.getCompletionState(this);
if (completion_state.is_completed_) {
return;
}
// If any of children is OK, return OK
if (Status::Ok == status) {
completion_state.is_completed_ = true;
completeWithStatus(status, context);
return;
}
// Then wait for all children to be done.
if (++completion_state.number_completed_children_ == verifiers_.size()) {
// Aggregate all children status into a final status.
// JwtMissing should be treated differently than other failure status
// since it simply means there is not Jwt token for the required provider.
// If there is a failure status other than JwtMissing in the children,
// it should be used as the final status.
Status final_status = Status::JwtMissed;
for (const auto& it : verifiers_) {
// If a Jwt is extracted from a location not specified by the required provider,
// the authenticator returns JwtUnknownIssuer. It should be treated the same as
// JwtMissed.
Status child_status = context.getCompletionState(it.get()).status_;
if (child_status != Status::JwtMissed && child_status != Status::JwtUnknownIssuer) {
final_status = child_status;
}
}
if (is_allow_missing_or_failed_) {
final_status = Status::Ok;
} else if (is_allow_missing_ && final_status == Status::JwtMissed) {
final_status = Status::Ok;
}
completion_state.is_completed_ = true;
completeWithStatus(final_status, context);
}
} | CWE-303 | 89 |
TfLiteRegistration GetPassthroughOpRegistration() {
TfLiteRegistration reg = {nullptr, nullptr, nullptr, nullptr};
reg.init = [](TfLiteContext* context, const char*, size_t) -> void* {
auto* first_new_tensor = new int;
context->AddTensors(context, 2, first_new_tensor);
return first_new_tensor;
};
reg.free = [](TfLiteContext* context, void* buffer) {
delete static_cast<int*>(buffer);
};
reg.prepare = [](TfLiteContext* context, TfLiteNode* node) {
auto* first_new_tensor = static_cast<int*>(node->user_data);
const TfLiteTensor* tensor0 = GetInput(context, node, 0);
TfLiteTensor* tensor1 = GetOutput(context, node, 0);
TfLiteIntArray* newSize = TfLiteIntArrayCopy(tensor0->dims);
TF_LITE_ENSURE_STATUS(context->ResizeTensor(context, tensor1, newSize));
TfLiteIntArrayFree(node->temporaries);
node->temporaries = TfLiteIntArrayCreate(2);
for (int i = 0; i < 2; ++i) {
node->temporaries->data[i] = *(first_new_tensor) + i;
}
auto setup_temporary = [&](int id) {
TfLiteTensor* tmp = &context->tensors[id];
tmp->type = kTfLiteFloat32;
tmp->allocation_type = kTfLiteArenaRw;
return context->ResizeTensor(context, tmp,
TfLiteIntArrayCopy(tensor0->dims));
};
TF_LITE_ENSURE_STATUS(setup_temporary(node->temporaries->data[0]));
TF_LITE_ENSURE_STATUS(setup_temporary(node->temporaries->data[1]));
return kTfLiteOk;
};
reg.invoke = [](TfLiteContext* context, TfLiteNode* node) {
const TfLiteTensor* a0 = GetInput(context, node, 0);
auto populate = [&](int id) {
TfLiteTensor* t = &context->tensors[id];
int num = a0->dims->data[0];
for (int i = 0; i < num; i++) {
t->data.f[i] = a0->data.f[i];
}
};
populate(node->outputs->data[0]);
populate(node->temporaries->data[0]);
populate(node->temporaries->data[1]);
return kTfLiteOk;
};
return reg;
} | CWE-787 | 24 |
selaGetCombName(SELA *sela,
l_int32 size,
l_int32 direction)
{
char *selname;
char combname[L_BUF_SIZE];
l_int32 i, nsels, sx, sy, found;
SEL *sel;
PROCNAME("selaGetCombName");
if (!sela)
return (char *)ERROR_PTR("sela not defined", procName, NULL);
if (direction != L_HORIZ && direction != L_VERT)
return (char *)ERROR_PTR("invalid direction", procName, NULL);
/* Derive the comb name we're looking for */
if (direction == L_HORIZ)
snprintf(combname, L_BUF_SIZE, "sel_comb_%dh", size);
else /* direction == L_VERT */
snprintf(combname, L_BUF_SIZE, "sel_comb_%dv", size);
found = FALSE;
nsels = selaGetCount(sela);
for (i = 0; i < nsels; i++) {
sel = selaGetSel(sela, i);
selGetParameters(sel, &sy, &sx, NULL, NULL);
if (sy != 1 && sx != 1) /* 2-D; not a comb */
continue;
selname = selGetName(sel);
if (!strcmp(selname, combname)) {
found = TRUE;
break;
}
}
if (found)
return stringNew(selname);
else
return (char *)ERROR_PTR("sel not found", procName, NULL);
} | CWE-787 | 24 |
ResourceHandle::ResourceHandle(const ResourceHandleProto& proto) {
FromProto(proto);
} | CWE-617 | 51 |
TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) {
TF_LITE_ENSURE_EQ(context, NumInputs(node), 3);
TF_LITE_ENSURE_EQ(context, NumOutputs(node), 1);
const TfLiteTensor* start = GetInput(context, node, kStartTensor);
const TfLiteTensor* limit = GetInput(context, node, kLimitTensor);
const TfLiteTensor* delta = GetInput(context, node, kDeltaTensor);
// Make sure all the inputs are scalars.
TF_LITE_ENSURE_EQ(context, NumDimensions(start), 0);
TF_LITE_ENSURE_EQ(context, NumDimensions(limit), 0);
TF_LITE_ENSURE_EQ(context, NumDimensions(delta), 0);
// Currently only supports int32 and float.
// TODO(b/117912892): Support quantization as well.
const auto dtype = start->type;
if (dtype != kTfLiteFloat32 && dtype != kTfLiteInt32) {
context->ReportError(context, "Unknown index output data type: %s",
TfLiteTypeGetName(dtype));
return kTfLiteError;
}
TF_LITE_ENSURE_TYPES_EQ(context, limit->type, dtype);
TF_LITE_ENSURE_TYPES_EQ(context, delta->type, dtype);
TfLiteTensor* output = GetOutput(context, node, kOutputTensor);
output->type = dtype;
if (IsConstantTensor(start) && IsConstantTensor(limit) &&
IsConstantTensor(delta)) {
return ResizeOutput(context, start, limit, delta, output);
}
SetTensorToDynamic(output);
return kTfLiteOk;
} | CWE-125 | 47 |
PlainPasswd::PlainPasswd(int len) : CharArray(len) {
} | CWE-787 | 24 |
PackLinuxElf64::elf_find_dynamic(unsigned int key) const
{
Elf64_Dyn const *dynp= dynseg;
if (dynp)
for (; (unsigned)((char const *)dynp - (char const *)dynseg) < sz_dynseg
&& Elf64_Dyn::DT_NULL!=dynp->d_tag; ++dynp) if (get_te64(&dynp->d_tag)==key) {
upx_uint64_t const t= elf_get_offset_from_address(get_te64(&dynp->d_val));
if (t) {
return &((unsigned char const *)file_image)[(size_t)t];
}
break;
}
return 0;
} | CWE-190 | 19 |
optional<ARN> ARN::parse(const string& s, bool wildcards) {
static const char str_wild[] = "arn:([^:]*):([^:]*):([^:]*):([^:]*):([^:]*)";
static const regex rx_wild(str_wild,
sizeof(str_wild) - 1,
ECMAScript | optimize);
static const char str_no_wild[]
= "arn:([^:*]*):([^:*]*):([^:*]*):([^:*]*):([^:*]*)";
static const regex rx_no_wild(str_no_wild,
sizeof(str_no_wild) - 1,
ECMAScript | optimize);
smatch match;
if ((s == "*") && wildcards) {
return ARN(Partition::wildcard, Service::wildcard, "*", "*", "*");
} else if (regex_match(s, match, wildcards ? rx_wild : rx_no_wild)) {
ceph_assert(match.size() == 6);
ARN a;
{
auto p = to_partition(match[1], wildcards);
if (!p)
return none;
a.partition = *p;
}
{
auto s = to_service(match[2], wildcards);
if (!s) {
return none;
}
a.service = *s;
}
a.region = match[3];
a.account = match[4];
a.resource = match[5];
return a;
}
return none;
} | CWE-617 | 51 |
TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) {
TF_LITE_ENSURE_EQ(context, NumInputs(node), 2);
TF_LITE_ENSURE_EQ(context, NumOutputs(node), 1);
const TfLiteTensor* dims = GetInput(context, node, kDimsTensor);
const TfLiteTensor* value = GetInput(context, node, kValueTensor);
// Make sure the 1st input tensor is 1-D.
TF_LITE_ENSURE_EQ(context, NumDimensions(dims), 1);
// Make sure the 1st input tensor is int32 or int64.
const auto dtype = dims->type;
TF_LITE_ENSURE(context, dtype == kTfLiteInt32 || dtype == kTfLiteInt64);
// Make sure the 2nd input tensor is a scalar.
TF_LITE_ENSURE_EQ(context, NumDimensions(value), 0);
TfLiteTensor* output = GetOutput(context, node, kOutputTensor);
output->type = value->type;
if (IsConstantTensor(dims)) {
TF_LITE_ENSURE_OK(context, ResizeOutput(context, dims, output));
} else {
SetTensorToDynamic(output);
}
return kTfLiteOk;
} | CWE-125 | 47 |
const FieldID& activeUnionMemberId(const void* object, ptrdiff_t offset) {
return *reinterpret_cast<const FieldID*>(
offset + static_cast<const char*>(object));
} | CWE-763 | 61 |
Status DoCompute(OpKernelContext* ctx) {
tensorflow::ResourceTagger tag(kTFDataResourceTag,
ctx->op_kernel().type_string());
tstring filename;
TF_RETURN_IF_ERROR(
ParseScalarArgument<tstring>(ctx, "filename", &filename));
tstring compression_type;
TF_RETURN_IF_ERROR(ParseScalarArgument<tstring>(ctx, "compression_type",
&compression_type));
std::unique_ptr<WritableFile> file;
TF_RETURN_IF_ERROR(ctx->env()->NewWritableFile(filename, &file));
auto writer = absl::make_unique<io::RecordWriter>(
file.get(),
io::RecordWriterOptions::CreateRecordWriterOptions(compression_type));
DatasetBase* dataset;
TF_RETURN_IF_ERROR(GetDatasetFromVariantTensor(ctx->input(0), &dataset));
IteratorContext::Params params(ctx);
FunctionHandleCache function_handle_cache(params.flr);
params.function_handle_cache = &function_handle_cache;
ResourceMgr resource_mgr;
params.resource_mgr = &resource_mgr;
CancellationManager cancellation_manager(ctx->cancellation_manager());
params.cancellation_manager = &cancellation_manager;
IteratorContext iter_ctx(std::move(params));
DatasetBase* finalized_dataset;
TF_RETURN_IF_ERROR(FinalizeDataset(ctx, dataset, &finalized_dataset));
std::unique_ptr<IteratorBase> iterator;
TF_RETURN_IF_ERROR(finalized_dataset->MakeIterator(
&iter_ctx, /*parent=*/nullptr, "ToTFRecordOpIterator", &iterator));
std::vector<Tensor> components;
components.reserve(finalized_dataset->output_dtypes().size());
bool end_of_sequence;
do {
TF_RETURN_IF_ERROR(
iterator->GetNext(&iter_ctx, &components, &end_of_sequence));
if (!end_of_sequence) {
TF_RETURN_IF_ERROR(
writer->WriteRecord(components[0].scalar<tstring>()()));
}
components.clear();
} while (!end_of_sequence);
return Status::OK();
} | CWE-787 | 24 |
string gen_dkg_poly(int _t) {
vector<char> errMsg(BUF_LEN, 0);
int errStatus = 0;
uint32_t enc_len = 0;
vector <uint8_t> encrypted_dkg_secret(BUF_LEN, 0);
sgx_status_t status = trustedGenDkgSecretAES(eid, &errStatus, errMsg.data(), encrypted_dkg_secret.data(), &enc_len, _t);
HANDLE_TRUSTED_FUNCTION_ERROR(status, errStatus, errMsg.data());
uint64_t length = enc_len;;
vector<char> hexEncrPoly(BUF_LEN, 0);
CHECK_STATE(encrypted_dkg_secret.size() >= length);
carray2Hex(encrypted_dkg_secret.data(), length, hexEncrPoly.data(), BUF_LEN);
string result(hexEncrPoly.data());
return result;
} | CWE-787 | 24 |
TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) {
const TfLiteTensor* input = GetInput(context, node, kInputTensor);
TfLiteTensor* output = GetOutput(context, node, kOutputTensor);
const TfLiteTensor* multipliers = GetInput(context, node, kInputMultipliers);
if (IsDynamicTensor(output)) {
TF_LITE_ENSURE_OK(context, ResizeOutput(context, node));
}
switch (output->type) {
case kTfLiteFloat32:
Tile<float>(*(input->dims), input, multipliers, output);
break;
case kTfLiteUInt8:
Tile<uint8_t>(*(input->dims), input, multipliers, output);
break;
case kTfLiteInt32:
Tile<int32_t>(*(input->dims), input, multipliers, output);
break;
case kTfLiteInt64:
Tile<int64_t>(*(input->dims), input, multipliers, output);
break;
case kTfLiteString: {
DynamicBuffer buffer;
TileString(*(input->dims), input, multipliers, &buffer, output);
buffer.WriteToTensor(output, /*new_shape=*/nullptr);
break;
}
case kTfLiteBool:
Tile<bool>(*(input->dims), input, multipliers, output);
break;
default:
context->ReportError(context, "Type '%s' is not supported by tile.",
TfLiteTypeGetName(output->type));
return kTfLiteError;
}
return kTfLiteOk;
} | CWE-125 | 47 |
TfLiteStatus EvalImpl(TfLiteContext* context, const TfLiteTensor* input,
TfLiteNode* node) {
// Map from value, to index in the unique elements vector.
// Note that we prefer to use map than unordered_map as it showed less
// increase in the binary size.
std::map<T, int> unique_values;
TfLiteTensor* output_indexes = GetOutput(context, node, 1);
std::vector<T> output_values;
I* indexes = GetTensorData<I>(output_indexes);
const T* data = GetTensorData<T>(input);
const int num_elements = NumElements(input);
for (int i = 0; i < num_elements; ++i) {
const auto element_it = unique_values.find(data[i]);
if (element_it != unique_values.end()) {
indexes[i] = element_it->second;
} else {
const int unique_index = unique_values.size();
unique_values[data[i]] = unique_index;
indexes[i] = unique_index;
output_values.push_back(data[i]);
}
}
// Allocate output tensor.
TfLiteTensor* unique_output = GetOutput(context, node, 0);
std::unique_ptr<TfLiteIntArray, void (*)(TfLiteIntArray*)> shape(
TfLiteIntArrayCreate(NumDimensions(input)), TfLiteIntArrayFree);
shape->data[0] = unique_values.size();
TF_LITE_ENSURE_STATUS(
context->ResizeTensor(context, unique_output, shape.release()));
// Set the values in the output tensor.
T* output_unique_values = GetTensorData<T>(unique_output);
for (int i = 0; i < output_values.size(); ++i) {
output_unique_values[i] = output_values[i];
}
return kTfLiteOk;
} | CWE-787 | 24 |
TfLiteStatus UseDynamicOutputTensors(TfLiteContext* context, TfLiteNode* node) {
for (int i = 0; i < NumOutputs(node); ++i) {
SetTensorToDynamic(GetOutput(context, node, i));
}
return kTfLiteOk;
} | CWE-125 | 47 |
void* sspi_SecureHandleGetUpperPointer(SecHandle* handle)
{
void* pointer;
if (!handle)
return NULL;
pointer = (void*) ~((size_t) handle->dwUpper);
return pointer;
} | CWE-476 | 46 |
Http::FilterMetadataStatus Context::onResponseMetadata() {
if (!wasm_->onResponseMetadata_) {
return Http::FilterMetadataStatus::Continue;
}
if (wasm_->onResponseMetadata_(this, id_).u64_ == 0) {
return Http::FilterMetadataStatus::Continue;
}
return Http::FilterMetadataStatus::Continue; // This is currently the only return code.
} | CWE-476 | 46 |
void CalculateOutputIndexValueRowID(
const RowPartitionTensor& value_rowids,
const vector<INDEX_TYPE>& parent_output_index,
INDEX_TYPE output_index_multiplier, INDEX_TYPE output_size,
vector<INDEX_TYPE>* result) {
const INDEX_TYPE index_size = value_rowids.size();
result->reserve(index_size);
if (index_size == 0) {
return;
}
INDEX_TYPE current_output_column = 0;
INDEX_TYPE current_value_rowid = value_rowids(0);
DCHECK_LT(current_value_rowid, parent_output_index.size());
INDEX_TYPE current_output_index = parent_output_index[current_value_rowid];
result->push_back(current_output_index);
for (INDEX_TYPE i = 1; i < index_size; ++i) {
INDEX_TYPE next_value_rowid = value_rowids(i);
if (next_value_rowid == current_value_rowid) {
if (current_output_index >= 0) {
++current_output_column;
if (current_output_column < output_size) {
current_output_index += output_index_multiplier;
} else {
current_output_index = -1;
}
}
} else {
current_output_column = 0;
current_value_rowid = next_value_rowid;
DCHECK_LT(next_value_rowid, parent_output_index.size());
current_output_index = parent_output_index[next_value_rowid];
}
result->push_back(current_output_index);
}
DCHECK_EQ(result->size(), value_rowids.size());
} | CWE-131 | 88 |
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