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stringlengths 12
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stringclasses 5
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TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) {
TF_LITE_ENSURE_EQ(context, NumInputs(node), 2);
TF_LITE_ENSURE_EQ(context, NumOutputs(node), 1);
// Reinterprete the opaque data provided by user.
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);
TF_LITE_ENSURE_TYPES_EQ(context, input1->type, input2->type);
const TfLiteType type = input1->type;
switch (type) {
case kTfLiteFloat32:
case kTfLiteInt32:
break;
default:
context->ReportError(context, "Type '%s' is not supported by floor_div.",
TfLiteTypeGetName(type));
return kTfLiteError;
}
output->type = 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);
}
return context->ResizeTensor(context, output, output_size);
} | Base | 1 |
TfLiteStatus LeakyReluEval(TfLiteContext* context, TfLiteNode* node) {
const TfLiteTensor* input = GetInput(context, node, 0);
TfLiteTensor* output = GetOutput(context, node, 0);
const auto* params =
reinterpret_cast<TfLiteLeakyReluParams*>(node->builtin_data);
const LeakyReluOpData* data =
reinterpret_cast<LeakyReluOpData*>(node->user_data);
LeakyReluParams op_params;
switch (input->type) {
case kTfLiteFloat32: {
op_params.alpha = params->alpha;
optimized_ops::LeakyRelu(
op_params, GetTensorShape(input), GetTensorData<float>(input),
GetTensorShape(output), GetTensorData<float>(output));
return kTfLiteOk;
} break;
case kTfLiteUInt8: {
QuantizeLeakyRelu<uint8_t>(input, output, data);
return kTfLiteOk;
} break;
case kTfLiteInt8: {
QuantizeLeakyRelu<int8_t>(input, output, data);
return kTfLiteOk;
} break;
case kTfLiteInt16: {
QuantizeLeakyRelu<int16_t>(input, output, data);
return kTfLiteOk;
} break;
default:
TF_LITE_KERNEL_LOG(
context,
"Only float32, int8, int16 and uint8 is supported currently, got %s.",
TfLiteTypeGetName(input->type));
return kTfLiteError;
}
} | Base | 1 |
TfLiteStatus LogicalImpl(TfLiteContext* context, TfLiteNode* node,
bool (*func)(bool, bool)) {
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 (data->requires_broadcast) {
reference_ops::BroadcastBinaryFunction4DSlow<bool, bool, bool>(
GetTensorShape(input1), GetTensorData<bool>(input1),
GetTensorShape(input2), GetTensorData<bool>(input2),
GetTensorShape(output), GetTensorData<bool>(output), func);
} else {
reference_ops::BinaryFunction<bool, bool, bool>(
GetTensorShape(input1), GetTensorData<bool>(input1),
GetTensorShape(input2), GetTensorData<bool>(input2),
GetTensorShape(output), GetTensorData<bool>(output), func);
}
return kTfLiteOk;
} | Base | 1 |
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;
} | Base | 1 |
CString CWebSock::GetSkinPath(const CString& sSkinName) {
CString sRet = CZNC::Get().GetZNCPath() + "/webskins/" + sSkinName;
if (!CFile::IsDir(sRet)) {
sRet = CZNC::Get().GetCurPath() + "/webskins/" + sSkinName;
if (!CFile::IsDir(sRet)) {
sRet = CString(_SKINDIR_) + "/" + sSkinName;
}
}
return sRet + "/";
} | Base | 1 |
boost::optional<SaplingNotePlaintext> SaplingNotePlaintext::decrypt(
const SaplingEncCiphertext &ciphertext,
const uint256 &epk,
const uint256 &esk,
const uint256 &pk_d,
const uint256 &cmu
)
{
auto pt = AttemptSaplingEncDecryption(ciphertext, epk, esk, pk_d);
if (!pt) {
return boost::none;
}
// Deserialize from the plaintext
CDataStream ss(SER_NETWORK, PROTOCOL_VERSION);
ss << pt.get();
SaplingNotePlaintext ret;
ss >> ret;
uint256 cmu_expected;
if (!librustzcash_sapling_compute_cm(
ret.d.data(),
pk_d.begin(),
ret.value(),
ret.rcm.begin(),
cmu_expected.begin()
))
{
return boost::none;
}
if (cmu_expected != cmu) {
return boost::none;
}
assert(ss.size() == 0);
return ret;
} | Class | 2 |
void PCRECache::dump(const std::string& filename) {
std::ofstream out(filename.c_str());
switch (m_kind) {
case CacheKind::Static:
for (auto& it : *m_staticCache) {
out << it.first->data() << "\n";
}
break;
case CacheKind::Lru:
case CacheKind::Scalable:
{
std::vector<LRUCacheKey> keys;
if (m_kind == CacheKind::Lru) {
m_lruCache->snapshotKeys(keys);
} else {
m_scalableCache->snapshotKeys(keys);
}
for (auto& key: keys) {
out << key.c_str() << "\n";
}
}
break;
}
out.close();
} | Base | 1 |
TEST_F(QuantizedConv2DTest, OddPaddingBatch) {
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 = 3;
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,
1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16,
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, //
348, 252, 274, 175, //
348, 252, 274, 175});
test::ExpectTensorEqual<qint32>(expected, *GetOutput(0));
} | Class | 2 |
TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) {
const TfLiteUnpackParams* data =
reinterpret_cast<TfLiteUnpackParams*>(node->builtin_data);
const TfLiteTensor* input = GetInput(context, node, kInputTensor);
switch (input->type) {
case kTfLiteFloat32: {
UnpackImpl<float>(context, node, input, data->num, data->axis);
break;
}
case kTfLiteInt32: {
UnpackImpl<int32_t>(context, node, input, data->num, data->axis);
break;
}
case kTfLiteUInt8: {
UnpackImpl<uint8_t>(context, node, input, data->num, data->axis);
break;
}
case kTfLiteInt8: {
UnpackImpl<int8_t>(context, node, input, data->num, data->axis);
break;
}
case kTfLiteBool: {
UnpackImpl<bool>(context, node, input, data->num, data->axis);
break;
}
case kTfLiteInt16: {
UnpackImpl<int16_t>(context, node, input, data->num, data->axis);
break;
}
default: {
context->ReportError(context, "Type '%s' is not supported by unpack.",
TfLiteTypeGetName(input->type));
return kTfLiteError;
}
}
return kTfLiteOk;
} | Base | 1 |
TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) {
auto* data =
reinterpret_cast<TfLiteAudioMicrofrontendParams*>(node->user_data);
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);
TF_LITE_ENSURE_EQ(context, NumDimensions(input), 1);
TF_LITE_ENSURE_EQ(context, input->type, kTfLiteInt16);
output->type = kTfLiteInt32;
if (data->out_float) {
output->type = kTfLiteFloat32;
}
TfLiteIntArray* output_size = TfLiteIntArrayCreate(2);
int num_frames = 0;
if (input->dims->data[0] >= data->state->window.size) {
num_frames = (input->dims->data[0] - data->state->window.size) /
data->state->window.step / data->frame_stride +
1;
}
output_size->data[0] = num_frames;
output_size->data[1] = data->state->filterbank.num_channels *
(1 + data->left_context + data->right_context);
return context->ResizeTensor(context, output, output_size);
} | Base | 1 |
ObfuscatedPasswd::ObfuscatedPasswd(const PlainPasswd& plainPwd) : CharArray(8), length(8) {
int l = strlen(plainPwd.buf), i;
for (i=0; i<8; i++)
buf[i] = i<l ? plainPwd.buf[i] : 0;
deskey(d3desObfuscationKey, EN0);
des((rdr::U8*)buf, (rdr::U8*)buf);
} | Base | 1 |
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;
} | Base | 1 |
R_API RBinJavaAttrInfo *r_bin_java_constant_value_attr_new(RBinJavaObj *bin, ut8 *buffer, ut64 sz, ut64 buf_offset) {
ut64 offset = 6;
RBinJavaAttrInfo *attr = r_bin_java_default_attr_new (bin, buffer, sz, buf_offset);
if (attr) {
attr->type = R_BIN_JAVA_ATTR_TYPE_CONST_VALUE_ATTR;
attr->info.constant_value_attr.constantvalue_idx = R_BIN_JAVA_USHORT (buffer, offset);
offset += 2;
attr->size = offset;
}
// IFDBG r_bin_java_print_constant_value_attr_summary(attr);
return attr;
} | Base | 1 |
v8::Local<v8::Object> CreateNativeEvent(
v8::Isolate* isolate,
v8::Local<v8::Object> sender,
content::RenderFrameHost* frame,
electron::mojom::ElectronBrowser::MessageSyncCallback callback) {
v8::Local<v8::Object> event;
if (frame && callback) {
gin::Handle<Event> native_event = Event::Create(isolate);
native_event->SetCallback(std::move(callback));
event = v8::Local<v8::Object>::Cast(native_event.ToV8());
} else {
// No need to create native event if we do not need to send reply.
event = CreateEvent(isolate);
}
Dictionary dict(isolate, event);
dict.Set("sender", sender);
// Should always set frameId even when callback is null.
if (frame)
dict.Set("frameId", frame->GetRoutingID());
return event;
} | Class | 2 |
char *uwsgi_expand_path(char *dir, int dir_len, char *ptr) {
char src[PATH_MAX + 1];
memcpy(src, dir, dir_len);
src[dir_len] = 0;
char *dst = ptr;
if (!dst)
dst = uwsgi_malloc(PATH_MAX + 1);
if (!realpath(src, dst)) {
uwsgi_error_realpath(src);
if (!ptr)
free(dst);
return NULL;
}
return dst;
} | Base | 1 |
TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) {
const TfLiteTensor* input = GetInput(context, node, 0);
switch (input->type) {
case kTfLiteFloat32:
return EvalImpl<kernel_type, kTfLiteFloat32>(context, node);
case kTfLiteUInt8:
return EvalImpl<kernel_type, kTfLiteUInt8>(context, node);
case kTfLiteInt8:
return EvalImpl<kernel_type, kTfLiteInt8>(context, node);
case kTfLiteInt16:
return EvalImpl<kernel_type, kTfLiteInt16>(context, node);
default:
TF_LITE_KERNEL_LOG(context, "Type %s not currently supported.",
TfLiteTypeGetName(input->type));
return kTfLiteError;
}
} | Base | 1 |
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;
} | Base | 1 |
jas_seq2d_t *jas_seq2d_copy(jas_seq2d_t *x)
{
jas_matrix_t *y;
int i;
int j;
y = jas_seq2d_create(jas_seq2d_xstart(x), jas_seq2d_ystart(x),
jas_seq2d_xend(x), jas_seq2d_yend(x));
assert(y);
for (i = 0; i < x->numrows_; ++i) {
for (j = 0; j < x->numcols_; ++j) {
*jas_matrix_getref(y, i, j) = jas_matrix_get(x, i, j);
}
}
return y;
} | Base | 1 |
void CalculateOutputIndexValueRowID(
OpKernelContext* context, 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);
}
OP_REQUIRES(context, result->size() == value_rowids.size(),
errors::InvalidArgument("Invalid row ids."));
} | Base | 1 |
bool ECDSA_Verification_Operation::verify(const uint8_t msg[], size_t msg_len,
const uint8_t sig[], size_t sig_len)
{
if(sig_len != m_group.get_order_bytes() * 2)
return false;
const BigInt e(msg, msg_len, m_group.get_order_bits());
const BigInt r(sig, sig_len / 2);
const BigInt s(sig + sig_len / 2, sig_len / 2);
if(r <= 0 || r >= m_group.get_order() || s <= 0 || s >= m_group.get_order())
return false;
const BigInt w = m_group.inverse_mod_order(s);
const BigInt u1 = m_group.multiply_mod_order(e, w);
const BigInt u2 = m_group.multiply_mod_order(r, w);
const PointGFp R = m_gy_mul.multi_exp(u1, u2);
if(R.is_zero())
return false;
const BigInt v = m_group.mod_order(R.get_affine_x());
return (v == r);
} | Class | 2 |
TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) {
const TfLiteTensor* input = GetInput(context, node, kInputTensor);
TfLiteTensor* output = GetOutput(context, node, kOutputTensor);
optimized_ops::Round(GetTensorShape(input), GetTensorData<float>(input),
GetTensorShape(output), GetTensorData<float>(output));
return kTfLiteOk;
} | Base | 1 |
int HexOutStream::length()
{
return offset + ptr - start;
} | Base | 1 |
TEST_P(SslSocketTest, Ipv6San) {
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: "::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);
} | Base | 1 |
std::string controller::bookmark(
const std::string& url,
const std::string& title,
const std::string& description,
const std::string& feed_title)
{
std::string bookmark_cmd = cfg.get_configvalue("bookmark-cmd");
bool is_interactive = cfg.get_configvalue_as_bool("bookmark-interactive");
if (bookmark_cmd.length() > 0) {
std::string cmdline = strprintf::fmt("%s '%s' %s %s %s",
bookmark_cmd,
utils::replace_all(url,"'", "%27"),
quote_empty(stfl::quote(title)),
quote_empty(stfl::quote(description)),
quote_empty(stfl::quote(feed_title)));
LOG(level::DEBUG, "controller::bookmark: cmd = %s", cmdline);
if (is_interactive) {
v->push_empty_formaction();
stfl::reset();
utils::run_interactively(cmdline, "controller::bookmark");
v->pop_current_formaction();
return "";
} else {
char * my_argv[4];
my_argv[0] = const_cast<char *>("/bin/sh");
my_argv[1] = const_cast<char *>("-c");
my_argv[2] = const_cast<char *>(cmdline.c_str());
my_argv[3] = nullptr;
return utils::run_program(my_argv, "");
}
} else {
return _("bookmarking support is not configured. Please set the configuration variable `bookmark-cmd' accordingly.");
}
} | Class | 2 |
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;
}
} | Base | 1 |
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;
} | Base | 1 |
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;
} | Base | 1 |
TfLiteStatus StoreAllDecodedSequences(
TfLiteContext* context,
const std::vector<std::vector<std::vector<int>>>& sequences,
TfLiteNode* node, int top_paths) {
const int32_t batch_size = sequences.size();
std::vector<int32_t> num_entries(top_paths, 0);
// Calculate num_entries per path
for (const auto& batch_s : sequences) {
TF_LITE_ENSURE_EQ(context, batch_s.size(), top_paths);
for (int p = 0; p < top_paths; ++p) {
num_entries[p] += batch_s[p].size();
}
}
for (int p = 0; p < top_paths; ++p) {
const int32_t p_num = num_entries[p];
// Resize the decoded outputs.
TfLiteTensor* indices = GetOutput(context, node, p);
TF_LITE_ENSURE_OK(context, Resize(context, {p_num, 2}, indices));
TfLiteTensor* values = GetOutput(context, node, p + top_paths);
TF_LITE_ENSURE_OK(context, Resize(context, {p_num}, values));
TfLiteTensor* decoded_shape = GetOutput(context, node, p + 2 * top_paths);
TF_LITE_ENSURE_OK(context, Resize(context, {2}, decoded_shape));
int32_t max_decoded = 0;
int32_t offset = 0;
int32_t* indices_data = GetTensorData<int32_t>(indices);
int32_t* values_data = GetTensorData<int32_t>(values);
int32_t* decoded_shape_data = GetTensorData<int32_t>(decoded_shape);
for (int b = 0; b < batch_size; ++b) {
auto& p_batch = sequences[b][p];
int32_t num_decoded = p_batch.size();
max_decoded = std::max(max_decoded, num_decoded);
std::copy_n(p_batch.begin(), num_decoded, values_data + offset);
for (int32_t t = 0; t < num_decoded; ++t, ++offset) {
indices_data[offset * 2] = b;
indices_data[offset * 2 + 1] = t;
}
}
decoded_shape_data[0] = batch_size;
decoded_shape_data[1] = max_decoded;
}
return kTfLiteOk;
} | Base | 1 |
TEST_F(ListenerManagerImplQuicOnlyTest, QuicListenerFactoryWithWrongCodec) {
const std::string yaml = TestEnvironment::substitute(R"EOF(
address:
socket_address:
address: 127.0.0.1
protocol: UDP
port_value: 1234
filter_chains:
- filter_chain_match:
transport_protocol: "quic"
filters: []
transport_socket:
name: envoy.transport_sockets.quic
typed_config:
"@type": type.googleapis.com/envoy.extensions.transport_sockets.quic.v3.QuicDownstreamTransport
downstream_tls_context:
common_tls_context:
tls_certificates:
- certificate_chain:
filename: "{{ test_rundir }}/test/extensions/transport_sockets/tls/test_data/san_uri_cert.pem"
private_key:
filename: "{{ test_rundir }}/test/extensions/transport_sockets/tls/test_data/san_uri_key.pem"
validation_context:
trusted_ca:
filename: "{{ test_rundir }}/test/extensions/transport_sockets/tls/test_data/ca_cert.pem"
match_subject_alt_names:
- exact: localhost
- exact: 127.0.0.1
udp_listener_config:
quic_options: {}
)EOF",
Network::Address::IpVersion::v4);
envoy::config::listener::v3::Listener listener_proto = parseListenerFromV3Yaml(yaml);
#if defined(ENVOY_ENABLE_QUIC)
EXPECT_THROW_WITH_REGEX(manager_->addOrUpdateListener(listener_proto, "", true), EnvoyException,
"error building network filter chain for quic listener: requires exactly "
"one http_connection_manager filter.");
#else
EXPECT_THROW_WITH_REGEX(manager_->addOrUpdateListener(listener_proto, "", true), EnvoyException,
"QUIC is configured but not enabled in the build.");
#endif
} | Base | 1 |
void Context::onDelete() {
if (wasm_->onDelete_) {
wasm_->onDelete_(this, id_);
}
} | Base | 1 |
TfLiteStatus ReluEval(TfLiteContext* context, TfLiteNode* node) {
const TfLiteTensor* input = GetInput(context, node, 0);
TfLiteTensor* output = GetOutput(context, node, 0);
const ReluOpData* data = reinterpret_cast<ReluOpData*>(node->user_data);
switch (input->type) {
case kTfLiteFloat32: {
optimized_ops::Relu(GetTensorShape(input), GetTensorData<float>(input),
GetTensorShape(output), GetTensorData<float>(output));
} break;
// TODO(renjieliu): We may revisit the quantization calculation logic,
// the unbounded upper limit is actually hard to quantize.
case kTfLiteUInt8: {
QuantizedReluX<uint8_t>(0.0f, std::numeric_limits<float>::infinity(),
input, output, data);
} break;
case kTfLiteInt8: {
QuantizedReluX<int8_t>(0.0f, std::numeric_limits<float>::infinity(),
input, output, data);
} break;
default:
TF_LITE_KERNEL_LOG(
context, "Only float32 & int8/uint8 is supported currently, got %s.",
TfLiteTypeGetName(input->type));
return kTfLiteError;
}
return kTfLiteOk;
} | Base | 1 |
TfLiteStatus Relu1Eval(TfLiteContext* context, TfLiteNode* node) {
const TfLiteTensor* input = GetInput(context, node, 0);
TfLiteTensor* output = GetOutput(context, node, 0);
const ReluOpData* data = reinterpret_cast<ReluOpData*>(node->user_data);
switch (input->type) {
case kTfLiteFloat32: {
optimized_ops::Relu1(GetTensorShape(input), GetTensorData<float>(input),
GetTensorShape(output),
GetTensorData<float>(output));
return kTfLiteOk;
} break;
case kTfLiteUInt8: {
QuantizedReluX<uint8_t>(-1.0f, 1.0f, input, output, data);
return kTfLiteOk;
} break;
case kTfLiteInt8: {
QuantizedReluX<int8_t>(-1, 1, input, output, data);
return kTfLiteOk;
} break;
default:
TF_LITE_KERNEL_LOG(context,
"Only float32, uint8, int8 supported "
"currently, got %s.",
TfLiteTypeGetName(input->type));
return kTfLiteError;
}
} | Base | 1 |
bool IsPadOpSupported(const TfLiteRegistration* registration,
const TfLiteNode* node, TfLiteContext* context) {
// padding is d x 2 tensor, where d is the dimension of input.
const TfLiteTensor* padding = GetInput(context, node, 1);
if (!IsConstantTensor(padding)) {
TF_LITE_KERNEL_LOG(context,
"%s: Only constant padding is supported for PAD.",
padding->name);
return false;
}
if (padding->dims->data[0] != 4 || padding->dims->data[1] != 2) {
TF_LITE_KERNEL_LOG(context, "%s: Only 4D inputs are supported for PAD.",
padding->name);
return false;
}
const int32_t* padding_data = GetTensorData<int32_t>(padding);
if (!(padding_data[0] == 0 && padding_data[1] == 0)) {
TF_LITE_KERNEL_LOG(
context, "%s: Padding for batch dimension is not supported in PAD.",
padding->name);
return false;
}
if (!(padding_data[6] == 0 && padding_data[7] == 0)) {
TF_LITE_KERNEL_LOG(
context, "%s: Padding for channel dimension is not supported in PAD.",
padding->name);
return false;
}
return true;
} | Base | 1 |
UnicodeString::doAppend(const UChar *srcChars, int32_t srcStart, int32_t srcLength) {
if(!isWritable() || srcLength == 0 || srcChars == NULL) {
return *this;
}
// Perform all remaining operations relative to srcChars + srcStart.
// From this point forward, do not use srcStart.
srcChars += srcStart;
if(srcLength < 0) {
// get the srcLength if necessary
if((srcLength = u_strlen(srcChars)) == 0) {
return *this;
}
}
int32_t oldLength = length();
int32_t newLength = oldLength + srcLength;
// Check for append onto ourself
const UChar* oldArray = getArrayStart();
if (isBufferWritable() &&
oldArray < srcChars + srcLength &&
srcChars < oldArray + oldLength) {
// Copy into a new UnicodeString and start over
UnicodeString copy(srcChars, srcLength);
if (copy.isBogus()) {
setToBogus();
return *this;
}
return doAppend(copy.getArrayStart(), 0, srcLength);
}
// optimize append() onto a large-enough, owned string
if((newLength <= getCapacity() && isBufferWritable()) ||
cloneArrayIfNeeded(newLength, getGrowCapacity(newLength))) {
UChar *newArray = getArrayStart();
// Do not copy characters when
// UChar *buffer=str.getAppendBuffer(...);
// is followed by
// str.append(buffer, length);
// or
// str.appendString(buffer, length)
// or similar.
if(srcChars != newArray + oldLength) {
us_arrayCopy(srcChars, 0, newArray, oldLength, srcLength);
}
setLength(newLength);
}
return *this;
} | Base | 1 |
TfLiteStatus EvalHashtableSize(TfLiteContext* context, TfLiteNode* node) {
const TfLiteTensor* input_resource_id_tensor =
GetInput(context, node, kInputResourceIdTensor);
int resource_id = input_resource_id_tensor->data.i32[0];
TfLiteTensor* output_tensor = GetOutput(context, node, kOutputTensor);
auto* output_data = GetTensorData<std::int64_t>(output_tensor);
Subgraph* subgraph = reinterpret_cast<Subgraph*>(context->impl_);
auto& resources = subgraph->resources();
auto* lookup = resource::GetHashtableResource(&resources, resource_id);
TF_LITE_ENSURE(context, lookup != nullptr);
output_data[0] = lookup->Size();
return kTfLiteOk;
} | Base | 1 |
bool read(ReadonlyBytes buffer)
{
auto fields_size = sizeof(LocalFileHeader) - (sizeof(u8*) * 3);
if (buffer.size() < fields_size)
return false;
if (memcmp(buffer.data(), local_file_header_signature, sizeof(local_file_header_signature)) != 0)
return false;
memcpy(reinterpret_cast<void*>(&minimum_version), buffer.data() + sizeof(local_file_header_signature), fields_size);
name = buffer.data() + sizeof(local_file_header_signature) + fields_size;
extra_data = name + name_length;
compressed_data = extra_data + extra_data_length;
return true;
} | Base | 1 |
int HttpFileImpl::save(const std::string &path) const
{
assert(!path.empty());
if (fileName_.empty())
return -1;
filesystem::path fsPath(utils::toNativePath(path));
if (!fsPath.is_absolute() &&
(!fsPath.has_parent_path() ||
(fsPath.begin()->string() != "." && fsPath.begin()->string() != "..")))
{
filesystem::path fsUploadPath(utils::toNativePath(
HttpAppFrameworkImpl::instance().getUploadPath()));
fsPath = fsUploadPath / fsPath;
}
filesystem::path fsFileName(utils::toNativePath(fileName_));
if (!filesystem::exists(fsPath))
{
LOG_TRACE << "create path:" << fsPath;
drogon::error_code err;
filesystem::create_directories(fsPath, err);
if (err)
{
LOG_SYSERR;
return -1;
}
}
return saveTo(fsPath / fsFileName);
} | Base | 1 |
static Array HHVM_METHOD(Memcache, getextendedstats,
const String& /*type*/ /* = null_string */,
int /*slabid*/ /* = 0 */, int /*limit*/ /* = 100 */) {
auto data = Native::data<MemcacheData>(this_);
memcached_return_t ret;
memcached_stat_st *stats;
stats = memcached_stat(&data->m_memcache, nullptr, &ret);
if (ret != MEMCACHED_SUCCESS) {
return Array();
}
int server_count = memcached_server_count(&data->m_memcache);
Array return_val;
for (int server_id = 0; server_id < server_count; server_id++) {
memcached_stat_st *stat;
char stats_key[30] = {0};
size_t key_len;
LMCD_SERVER_POSITION_INSTANCE_TYPE instance =
memcached_server_instance_by_position(&data->m_memcache, server_id);
const char *hostname = LMCD_SERVER_HOSTNAME(instance);
in_port_t port = LMCD_SERVER_PORT(instance);
stat = stats + server_id;
Array server_stats = memcache_build_stats(&data->m_memcache, stat, &ret);
if (ret != MEMCACHED_SUCCESS) {
continue;
}
key_len = snprintf(stats_key, sizeof(stats_key), "%s:%d", hostname, port);
return_val.set(String(stats_key, key_len, CopyString), server_stats);
}
free(stats);
return return_val;
} | Base | 1 |
bool handleBackslash(signed char& out) {
char ch = *p++;
switch (ch) {
case 0: return false;
case '"': out = ch; return true;
case '\\': out = ch; return true;
case '/': out = ch; return true;
case 'b': out = '\b'; return true;
case 'f': out = '\f'; return true;
case 'n': out = '\n'; return true;
case 'r': out = '\r'; return true;
case 't': out = '\t'; return true;
case 'u': {
if (UNLIKELY(is_tsimplejson)) {
auto const ch1 = *p++;
auto const ch2 = *p++;
auto const dch3 = dehexchar(*p++);
auto const dch4 = dehexchar(*p++);
if (UNLIKELY(ch1 != '0' || ch2 != '0' || dch3 < 0 || dch4 < 0)) {
return false;
}
out = (dch3 << 4) | dch4;
return true;
} else {
uint16_t u16cp = 0;
for (int i = 0; i < 4; i++) {
auto const hexv = dehexchar(*p++);
if (hexv < 0) return false; // includes check for end of string
u16cp <<= 4;
u16cp |= hexv;
}
if (u16cp > 0x7f) {
return false;
} else {
out = u16cp;
return true;
}
}
}
default: return false;
}
} | Base | 1 |
RestAuthHandler::RestAuthHandler(application_features::ApplicationServer& server,
GeneralRequest* request, GeneralResponse* response)
: RestVocbaseBaseHandler(server, request, response),
_validFor(60 * 60 * 24 * 30) {} | Base | 1 |
TEST(BasicFlatBufferModel, TestHandleMalformedModel) {
const auto model_paths = {
// These models use the same tensor as both input and ouput of a node
"tensorflow/lite/testdata/add_shared_tensors.bin",
};
for (const auto& model_path : model_paths) {
std::unique_ptr<tflite::FlatBufferModel> model =
FlatBufferModel::BuildFromFile(model_path);
ASSERT_NE(model, nullptr);
tflite::ops::builtin::BuiltinOpResolver resolver;
InterpreterBuilder builder(*model, resolver);
std::unique_ptr<Interpreter> interpreter;
ASSERT_EQ(builder(&interpreter), kTfLiteOk);
ASSERT_NE(interpreter, nullptr);
ASSERT_NE(interpreter->AllocateTensors(), kTfLiteOk);
}
} | Base | 1 |
R_API RBinJavaAttrInfo *r_bin_java_source_code_file_attr_new(RBinJavaObj *bin, ut8 *buffer, ut64 sz, ut64 buf_offset) {
if (!sz) {
return NULL;
}
ut64 offset = 0;
RBinJavaAttrInfo *attr = r_bin_java_default_attr_new (bin, buffer, sz, buf_offset);
offset += 6;
if (!attr) {
return NULL;
}
attr->type = R_BIN_JAVA_ATTR_TYPE_SOURCE_FILE_ATTR;
// if (buffer + offset > buffer + sz) return NULL;
attr->info.source_file_attr.sourcefile_idx = R_BIN_JAVA_USHORT (buffer, offset);
offset += 2;
attr->size = offset;
// IFDBG r_bin_java_print_source_code_file_attr_summary(attr);
return attr;
} | Base | 1 |
TfLiteStatus LogicalImpl(TfLiteContext* context, TfLiteNode* node,
bool (*func)(bool, bool)) {
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 (data->requires_broadcast) {
reference_ops::BroadcastBinaryFunction4DSlow<bool, bool, bool>(
GetTensorShape(input1), GetTensorData<bool>(input1),
GetTensorShape(input2), GetTensorData<bool>(input2),
GetTensorShape(output), GetTensorData<bool>(output), func);
} else {
reference_ops::BinaryFunction<bool, bool, bool>(
GetTensorShape(input1), GetTensorData<bool>(input1),
GetTensorShape(input2), GetTensorData<bool>(input2),
GetTensorShape(output), GetTensorData<bool>(output), func);
}
return kTfLiteOk;
} | Base | 1 |
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);
} | Base | 1 |
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);
output->type = kTfLiteInt32;
// 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 rank immediately, without waiting for Eval.
SetTensorToPersistentRo(output);
// Rank produces a 0-D int32 Tensor representing the rank of input.
TfLiteIntArray* output_size = TfLiteIntArrayCreate(0);
TF_LITE_ENSURE_STATUS(context->ResizeTensor(context, output, output_size));
TF_LITE_ENSURE_EQ(context, NumDimensions(output), 0);
// Immediately propagate the known rank to the output tensor. This allows
// downstream ops that rely on the value to use it during prepare.
if (output->type == kTfLiteInt32) {
int32_t* output_data = GetTensorData<int32_t>(output);
*output_data = NumDimensions(input);
} else {
return kTfLiteError;
}
return kTfLiteOk;
} | Base | 1 |
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);
TF_LITE_ENSURE_EQ(context, NumDimensions(input), 4);
TF_LITE_ENSURE_TYPES_EQ(context, output->type, kTfLiteFloat32);
TF_LITE_ENSURE_TYPES_EQ(context, input->type, output->type);
TfLiteIntArray* output_size = TfLiteIntArrayCreate(4);
output_size->data[0] = input->dims->data[0];
output_size->data[1] = input->dims->data[1];
output_size->data[2] = input->dims->data[2];
output_size->data[3] = input->dims->data[3];
return context->ResizeTensor(context, output, output_size);
} | Base | 1 |
TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) {
auto* params =
reinterpret_cast<TfLiteAudioSpectrogramParams*>(node->user_data);
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);
TF_LITE_ENSURE_EQ(context, NumDimensions(input), 2);
TF_LITE_ENSURE_TYPES_EQ(context, output->type, kTfLiteFloat32);
TF_LITE_ENSURE_TYPES_EQ(context, input->type, output->type);
TF_LITE_ENSURE(context, params->spectrogram->Initialize(params->window_size,
params->stride));
const int64_t sample_count = input->dims->data[0];
const int64_t length_minus_window = (sample_count - params->window_size);
if (length_minus_window < 0) {
params->output_height = 0;
} else {
params->output_height = 1 + (length_minus_window / params->stride);
}
TfLiteIntArray* output_size = TfLiteIntArrayCreate(3);
output_size->data[0] = input->dims->data[1];
output_size->data[1] = params->output_height;
output_size->data[2] = params->spectrogram->output_frequency_channels();
return context->ResizeTensor(context, output, output_size);
} | Base | 1 |
TypedValue HHVM_FUNCTION(substr_compare,
const String& main_str,
const String& str,
int offset,
int length /* = INT_MAX */,
bool case_insensitivity /* = false */) {
int s1_len = main_str.size();
int s2_len = str.size();
if (length <= 0) {
raise_warning("The length must be greater than zero");
return make_tv<KindOfBoolean>(false);
}
if (offset < 0) {
offset = s1_len + offset;
if (offset < 0) offset = 0;
}
if (offset >= s1_len) {
raise_warning("The start position cannot exceed initial string length");
return make_tv<KindOfBoolean>(false);
}
int cmp_len = s1_len - offset;
if (cmp_len < s2_len) cmp_len = s2_len;
if (cmp_len > length) cmp_len = length;
const char *s1 = main_str.data();
if (case_insensitivity) {
return tvReturn(bstrcasecmp(s1 + offset, cmp_len, str.data(), cmp_len));
}
return tvReturn(string_ncmp(s1 + offset, str.data(), cmp_len));
} | Base | 1 |
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;
} | Base | 1 |
TfLiteStatus GenericPrepare(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, 0);
TfLiteTensor* output = GetOutput(context, node, 0);
TF_LITE_ENSURE_TYPES_EQ(context, input->type, output->type);
if (!is_supported_type(input->type)) {
TF_LITE_UNSUPPORTED_TYPE(context, input->type, op_name);
}
return context->ResizeTensor(context, output,
TfLiteIntArrayCopy(input->dims));
} | Base | 1 |
CharString *Formattable::internalGetCharString(UErrorCode &status) {
if(fDecimalStr == NULL) {
if (fDecimalQuantity == NULL) {
// No decimal number for the formattable yet. Which means the value was
// set directly by the user as an int, int64 or double. If the value came
// from parsing, or from the user setting a decimal number, fDecimalNum
// would already be set.
//
LocalPointer<DecimalQuantity> dq(new DecimalQuantity(), status);
if (U_FAILURE(status)) { return nullptr; }
populateDecimalQuantity(*dq, status);
if (U_FAILURE(status)) { return nullptr; }
fDecimalQuantity = dq.orphan();
}
fDecimalStr = new CharString();
if (fDecimalStr == NULL) {
status = U_MEMORY_ALLOCATION_ERROR;
return NULL;
}
// Older ICUs called uprv_decNumberToString here, which is not exactly the same as
// DecimalQuantity::toScientificString(). The biggest difference is that uprv_decNumberToString does
// not print scientific notation for magnitudes greater than -5 and smaller than some amount (+5?).
if (fDecimalQuantity->isZero()) {
fDecimalStr->append("0", -1, status);
} else if (std::abs(fDecimalQuantity->getMagnitude()) < 5) {
fDecimalStr->appendInvariantChars(fDecimalQuantity->toPlainString(), status);
} else {
fDecimalStr->appendInvariantChars(fDecimalQuantity->toScientificString(), status);
}
}
return fDecimalStr;
} | Base | 1 |
void Context::onLog() {
if (wasm_->onLog_) {
wasm_->onLog_(this, id_);
}
} | Base | 1 |
mptctl_eventquery (unsigned long arg)
{
struct mpt_ioctl_eventquery __user *uarg = (void __user *) arg;
struct mpt_ioctl_eventquery karg;
MPT_ADAPTER *ioc;
int iocnum;
if (copy_from_user(&karg, uarg, sizeof(struct mpt_ioctl_eventquery))) {
printk(KERN_ERR MYNAM "%s@%d::mptctl_eventquery - "
"Unable to read in mpt_ioctl_eventquery struct @ %p\n",
__FILE__, __LINE__, uarg);
return -EFAULT;
}
if (((iocnum = mpt_verify_adapter(karg.hdr.iocnum, &ioc)) < 0) ||
(ioc == NULL)) {
printk(KERN_DEBUG MYNAM "%s::mptctl_eventquery() @%d - ioc%d not found!\n",
__FILE__, __LINE__, iocnum);
return -ENODEV;
}
dctlprintk(ioc, printk(MYIOC_s_DEBUG_FMT "mptctl_eventquery called.\n",
ioc->name));
karg.eventEntries = MPTCTL_EVENT_LOG_SIZE;
karg.eventTypes = ioc->eventTypes;
/* Copy the data from kernel memory to user memory
*/
if (copy_to_user((char __user *)arg, &karg, sizeof(struct mpt_ioctl_eventquery))) {
printk(MYIOC_s_ERR_FMT "%s@%d::mptctl_eventquery - "
"Unable to write out mpt_ioctl_eventquery struct @ %p\n",
ioc->name, __FILE__, __LINE__, uarg);
return -EFAULT;
}
return 0;
} | Class | 2 |
TEST_P(SslSocketTest, FailedClientAuthSanVerification) {
const std::string client_ctx_yaml = R"EOF(
common_tls_context:
tls_certificates:
certificate_chain:
filename: "{{ test_rundir }}/test/extensions/transport_sockets/tls/test_data/no_san_cert.pem"
private_key:
filename: "{{ test_rundir }}/test/extensions/transport_sockets/tls/test_data/no_san_key.pem"
)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_san"));
} | Base | 1 |
int bmp_validate(jas_stream_t *in)
{
int n;
int i;
uchar buf[2];
assert(JAS_STREAM_MAXPUTBACK >= 2);
/* Read the first two characters that constitute the signature. */
if ((n = jas_stream_read(in, (char *) buf, 2)) < 0) {
return -1;
}
/* Put the characters read back onto the stream. */
for (i = n - 1; i >= 0; --i) {
if (jas_stream_ungetc(in, buf[i]) == EOF) {
return -1;
}
}
/* Did we read enough characters? */
if (n < 2) {
return -1;
}
/* Is the signature correct for the BMP format? */
if (buf[0] == (BMP_MAGIC & 0xff) && buf[1] == (BMP_MAGIC >> 8)) {
return 0;
}
return -1;
} | Class | 2 |
TfLiteStatus ReluPrepare(TfLiteContext* context, TfLiteNode* node) {
ReluOpData* data = reinterpret_cast<ReluOpData*>(node->user_data);
TF_LITE_ENSURE_EQ(context, NumInputs(node), 1);
TF_LITE_ENSURE_EQ(context, NumOutputs(node), 1);
const TfLiteTensor* input = GetInput(context, node, 0);
TfLiteTensor* output = GetOutput(context, node, 0);
TF_LITE_ENSURE_TYPES_EQ(context, input->type, output->type);
if (input->type == kTfLiteInt8 || input->type == kTfLiteUInt8) {
double real_multiplier = input->params.scale / output->params.scale;
QuantizeMultiplier(real_multiplier, &data->output_multiplier,
&data->output_shift);
}
return context->ResizeTensor(context, output,
TfLiteIntArrayCopy(input->dims));
} | Base | 1 |
bool DecodeResourceHandleList(std::unique_ptr<port::StringListDecoder> d,
ResourceHandle* ps, int64_t n) {
std::vector<uint32> sizes(n);
if (!d->ReadSizes(&sizes)) return false;
ResourceHandleProto proto;
for (int i = 0; i < n; ++i) {
if (!proto.ParseFromArray(d->Data(sizes[i]), sizes[i])) {
return false;
}
ps[i].FromProto(proto);
}
return true;
} | Base | 1 |
QPDFObjectHandle::parse(PointerHolder<InputSource> input,
std::string const& object_description,
QPDFTokenizer& tokenizer, bool& empty,
StringDecrypter* decrypter, QPDF* context)
{
return parseInternal(input, object_description, tokenizer, empty,
decrypter, context, false, false, false);
} | Class | 2 |
Http::FilterMetadataStatus Context::onRequestMetadata() {
if (!wasm_->onRequestMetadata_) {
return Http::FilterMetadataStatus::Continue;
}
if (wasm_->onRequestMetadata_(this, id_).u64_ == 0) {
return Http::FilterMetadataStatus::Continue;
}
return Http::FilterMetadataStatus::Continue; // This is currently the only return code.
} | Base | 1 |
TfLiteStatus EvalImpl(TfLiteContext* context, TfLiteNode* node) {
auto* params =
reinterpret_cast<TfLiteDepthwiseConvParams*>(node->builtin_data);
OpData* data = reinterpret_cast<OpData*>(node->user_data);
TfLiteTensor* output = GetOutput(context, node, kOutputTensor);
const TfLiteTensor* input = GetInput(context, node, kInputTensor);
const TfLiteTensor* filter = GetInput(context, node, kFilterTensor);
const TfLiteTensor* bias =
(NumInputs(node) == 3) ? GetInput(context, node, kBiasTensor) : nullptr;
TFLITE_DCHECK_EQ(input_type, input->type);
switch (input_type) { // Already know in/out types are same.
case kTfLiteFloat32:
if (filter->type == kTfLiteFloat32) {
return EvalFloat<kernel_type>(context, node, params, data, input,
filter, bias, output);
} else if (filter->type == kTfLiteInt8) {
return EvalHybridPerChannel<kernel_type>(context, node, params, data,
input, filter, bias, output);
} else {
TF_LITE_KERNEL_LOG(
context, "Type %s with filter type %s not currently supported.",
TfLiteTypeGetName(input->type), TfLiteTypeGetName(filter->type));
return kTfLiteError;
}
break;
case kTfLiteUInt8:
return EvalQuantized<kernel_type>(context, node, params, data, input,
filter, bias, output);
break;
case kTfLiteInt8:
return EvalQuantizedPerChannel<kernel_type>(context, node, params, data,
input, filter, bias, output);
break;
case kTfLiteInt16:
return EvalQuantizedPerChannel16x8(params, data, input, filter, bias,
output);
break;
default:
context->ReportError(context, "Type %d not currently supported.",
input->type);
return kTfLiteError;
}
} | Base | 1 |
static void* OGRExpatRealloc( void *ptr, size_t size )
{
if( CanAlloc(size) )
return realloc(ptr, size);
free(ptr);
return nullptr;
} | Variant | 0 |
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;
} | Class | 2 |
R_API RBinJavaAnnotation *r_bin_java_annotation_new(ut8 *buffer, ut64 sz, ut64 buf_offset) {
ut32 i = 0;
RBinJavaAnnotation *annotation = NULL;
RBinJavaElementValuePair *evps = NULL;
ut64 offset = 0;
annotation = R_NEW0 (RBinJavaAnnotation);
if (!annotation) {
return NULL;
}
// (ut16) read and set annotation_value.type_idx;
annotation->type_idx = R_BIN_JAVA_USHORT (buffer, offset);
offset += 2;
// (ut16) read and set annotation_value.num_element_value_pairs;
annotation->num_element_value_pairs = R_BIN_JAVA_USHORT (buffer, offset);
offset += 2;
annotation->element_value_pairs = r_list_newf (r_bin_java_element_pair_free);
// read annotation_value.num_element_value_pairs, and append to annotation_value.element_value_pairs
for (i = 0; i < annotation->num_element_value_pairs; i++) {
if (offset > sz) {
break;
}
evps = r_bin_java_element_pair_new (buffer + offset, sz - offset, buf_offset + offset);
if (evps) {
offset += evps->size;
r_list_append (annotation->element_value_pairs, (void *) evps);
}
}
annotation->size = offset;
return annotation;
} | Base | 1 |
TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) {
TF_LITE_ENSURE_EQ(context, NumInputs(node), 2);
TF_LITE_ENSURE_EQ(context, NumOutputs(node), 1);
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);
TF_LITE_ENSURE_TYPES_EQ(context, input1->type, input2->type);
const TfLiteType type = input1->type;
if (type != kTfLiteInt32 && type != kTfLiteFloat32) {
TF_LITE_KERNEL_LOG(context, "Unsupported data type %s.",
TfLiteTypeGetName(type));
return kTfLiteError;
}
output->type = 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);
}
return context->ResizeTensor(context, output, output_size);
} | Base | 1 |
void ComputeAsync(OpKernelContext* c, DoneCallback done) override {
auto col_params = new CollectiveParams();
auto done_with_cleanup = [col_params, done = std::move(done)]() {
done();
col_params->Unref();
};
OP_REQUIRES_OK_ASYNC(c,
FillCollectiveParams(col_params, REDUCTION_COLLECTIVE,
/*group_size*/ c->input(1),
/*group_key*/ c->input(2),
/*instance_key*/ c->input(3)),
done);
col_params->instance.shape = c->input(0).shape();
col_params->merge_op = merge_op_.get();
col_params->final_op = final_op_.get();
VLOG(1) << "CollectiveReduceV2 group_size " << col_params->group.group_size
<< " group_key " << col_params->group.group_key << " instance_key "
<< col_params->instance.instance_key;
// Allocate the output tensor, trying to reuse the input.
Tensor* output = nullptr;
OP_REQUIRES_OK_ASYNC(c,
c->forward_input_or_allocate_output(
{0}, 0, col_params->instance.shape, &output),
done_with_cleanup);
Run(c, col_params, std::move(done_with_cleanup));
} | Variant | 0 |
int GetU8 (int nPos, bool *pbSuccess)
{
//*pbSuccess = true;
if ( nPos < 0 || nPos >= m_nLen )
{
*pbSuccess = false;
return 0;
}
return m_sFile[ nPos ];
} | Base | 1 |
void CIRCNetwork::SetEncoding(const CString& s) {
m_sEncoding = s;
if (GetIRCSock()) {
GetIRCSock()->SetEncoding(s);
}
} | Class | 2 |
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;
} | Base | 1 |
UnicodeString::doAppend(const UChar *srcChars, int32_t srcStart, int32_t srcLength) {
if(!isWritable() || srcLength == 0 || srcChars == NULL) {
return *this;
}
// Perform all remaining operations relative to srcChars + srcStart.
// From this point forward, do not use srcStart.
srcChars += srcStart;
if(srcLength < 0) {
// get the srcLength if necessary
if((srcLength = u_strlen(srcChars)) == 0) {
return *this;
}
}
int32_t oldLength = length();
int32_t newLength = oldLength + srcLength;
// Check for append onto ourself
const UChar* oldArray = getArrayStart();
if (isBufferWritable() &&
oldArray < srcChars + srcLength &&
srcChars < oldArray + oldLength) {
// Copy into a new UnicodeString and start over
UnicodeString copy(srcChars, srcLength);
if (copy.isBogus()) {
setToBogus();
return *this;
}
return doAppend(copy.getArrayStart(), 0, srcLength);
}
// optimize append() onto a large-enough, owned string
if((newLength <= getCapacity() && isBufferWritable()) ||
cloneArrayIfNeeded(newLength, getGrowCapacity(newLength))) {
UChar *newArray = getArrayStart();
// Do not copy characters when
// UChar *buffer=str.getAppendBuffer(...);
// is followed by
// str.append(buffer, length);
// or
// str.appendString(buffer, length)
// or similar.
if(srcChars != newArray + oldLength) {
us_arrayCopy(srcChars, 0, newArray, oldLength, srcLength);
}
setLength(newLength);
}
return *this;
} | Base | 1 |
bool MemoryManager::validate_user_read(const Process& process, VirtualAddress vaddr) const
{
auto* region = region_from_vaddr(process, vaddr);
return region && region->is_readable();
} | Class | 2 |
inline typename V::VariantType FBUnserializer<V>::unserializeThing() {
size_t code = nextCode();
switch (code) {
case FB_SERIALIZE_BYTE:
case FB_SERIALIZE_I16:
case FB_SERIALIZE_I32:
case FB_SERIALIZE_I64:
return V::fromInt64(unserializeInt64());
case FB_SERIALIZE_VARCHAR:
case FB_SERIALIZE_STRING:
return V::fromString(unserializeString());
case FB_SERIALIZE_STRUCT:
return V::fromMap(unserializeMap());
case FB_SERIALIZE_NULL:
++p_;
return V::createNull();
case FB_SERIALIZE_DOUBLE:
return V::fromDouble(unserializeDouble());
case FB_SERIALIZE_BOOLEAN:
return V::fromBool(unserializeBoolean());
case FB_SERIALIZE_VECTOR:
return V::fromVector(unserializeVector());
case FB_SERIALIZE_LIST:
return V::fromVector(unserializeList());
case FB_SERIALIZE_SET:
return V::fromSet(unserializeSet());
default:
throw UnserializeError("Invalid code: " + folly::to<std::string>(code)
+ " at location " + folly::to<std::string>(p_));
}
} | Class | 2 |
RectangleRequest &operator=(const struct RectangleRequest &req)
{
// Not nice, but this is really faster and simpler
memcpy(this,&req,sizeof(struct RectangleRequest));
// Not linked in any way if this is new.
rr_pNext = NULL;
//
return *this;
} | Class | 2 |
void * alloc_top(size_t size, size_t align)
{loop:
top -= size;
align_top(align);
if (top < bottom) {new_chunk(); goto loop;}
return top;
} | Base | 1 |
Network::FilterStatus Context::onUpstreamData(int data_length, bool end_of_stream) {
if (!wasm_->onUpstreamData_) {
return Network::FilterStatus::Continue;
}
auto result = wasm_->onUpstreamData_(this, id_, static_cast<uint32_t>(data_length),
static_cast<uint32_t>(end_of_stream));
// TODO(PiotrSikora): pull Proxy-WASM's FilterStatus values.
return result.u64_ == 0 ? Network::FilterStatus::Continue : Network::FilterStatus::StopIteration;
} | Base | 1 |
RequestHandler::RequestHandler(
std::shared_ptr<CheckWorkflow> check_workflow,
std::shared_ptr<context::ServiceContext> service_context,
std::unique_ptr<Request> request_data)
: context_(new context::RequestContext(service_context,
std::move(request_data))),
check_workflow_(check_workflow) {
// Remove x-endponts-api-userinfo from downstream client.
// It should be set by the last Endpoint proxy to prevent users spoofing.
std::string buffer;
if (context_->request()->FindHeader(
google::api_manager::auth::kEndpointApiUserInfo, &buffer)) {
context_->request()->AddHeaderToBackend(
google::api_manager::auth::kEndpointApiUserInfo, "");
}
} | Base | 1 |
friend bool operator==(const TensorKey& t1, const TensorKey& t2) {
if (t1.dtype() != t2.dtype() || t1.shape() != t2.shape()) {
return false;
}
if (DataTypeCanUseMemcpy(t1.dtype())) {
return t1.tensor_data() == t2.tensor_data();
}
if (t1.dtype() == DT_STRING) {
const auto s1 = t1.unaligned_flat<tstring>();
const auto s2 = t2.unaligned_flat<tstring>();
for (int64_t i = 0, n = t1.NumElements(); i < n; ++i) {
if (TF_PREDICT_FALSE(s1(i) != s2(i))) {
return false;
}
}
return true;
}
return false;
} | Variant | 0 |
void preprocessNodes(std::vector<Proxy> &nodes, extra_settings &ext)
{
std::for_each(nodes.begin(), nodes.end(), [&ext](Proxy &x)
{
if(ext.remove_emoji)
x.Remark = trim(removeEmoji(x.Remark));
nodeRename(x, ext.rename_array, ext);
if(ext.add_emoji)
x.Remark = addEmoji(x, ext.emoji_array, ext);
});
if(ext.sort_flag)
{
bool failed = true;
if(ext.sort_script.size())
{
std::string script = ext.sort_script;
if(startsWith(script, "path:"))
script = fileGet(script.substr(5), false);
script_safe_runner(ext.js_runtime, ext.js_context, [&](qjs::Context &ctx)
{
try
{
ctx.eval(script);
auto compare = (std::function<int(const Proxy&, const Proxy&)>) ctx.eval("compare");
auto comparer = [&](const Proxy &a, const Proxy &b)
{
if(a.Type == ProxyType::Unknow)
return 1;
if(b.Type == ProxyType::Unknow)
return 0;
return compare(a, b);
};
std::stable_sort(nodes.begin(), nodes.end(), comparer);
failed = false;
}
catch(qjs::exception)
{
script_print_stack(ctx);
}
}, global.scriptCleanContext);
}
if(failed) std::stable_sort(nodes.begin(), nodes.end(), [](const Proxy &a, const Proxy &b)
{
return a.Remark < b.Remark;
});
}
} | Base | 1 |
void readStructEnd() {
lastFieldId_ = nestedStructFieldIds_.back();
nestedStructFieldIds_.pop_back();
} | Class | 2 |
TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) {
const TfLiteTensor* input_tensor = GetInput(context, node, 0);
const TfLiteTensor* padding_matrix = GetInput(context, node, 1);
TfLiteTensor* output_tensor = GetOutput(context, node, 0);
TF_LITE_ENSURE_EQ(context, NumDimensions(padding_matrix), 2);
TF_LITE_ENSURE_EQ(context, SizeOfDimension(padding_matrix, 0),
NumDimensions(input_tensor));
if (!IsConstantTensor(padding_matrix)) {
SetTensorToDynamic(output_tensor);
return kTfLiteOk;
}
// We have constant padding, so we can infer output size.
auto output_size = GetPaddedOutputShape(input_tensor, padding_matrix);
if (output_size == nullptr) {
return kTfLiteError;
}
return context->ResizeTensor(context, output_tensor, output_size.release());
} | Base | 1 |
static inline bool isValid(const RemoteFsDevice::Details &d)
{
return d.isLocalFile() || RemoteFsDevice::constSshfsProtocol==d.url.scheme() ||
RemoteFsDevice::constSambaProtocol==d.url.scheme() || RemoteFsDevice::constSambaAvahiProtocol==d.url.scheme();
} | Class | 2 |
bool is_digit(char c) { return c >= '0' && c <= '9'; } | Class | 2 |
TfLiteStatus UseDynamicOutputTensors(TfLiteContext* context, TfLiteNode* node) {
for (int i = 0; i < NumOutputs(node); ++i) {
SetTensorToDynamic(GetOutput(context, node, i));
}
return kTfLiteOk;
} | Base | 1 |
StatusOr<FullTypeDef> SpecializeType(const AttrSlice& attrs,
const OpDef& op_def) {
FullTypeDef ft;
ft.set_type_id(TFT_PRODUCT);
for (int i = 0; i < op_def.output_arg_size(); i++) {
auto* t = ft.add_args();
*t = op_def.output_arg(i).experimental_full_type();
// Resolve dependent types. The convention for op registrations is to use
// attributes as type variables.
// See https://www.tensorflow.org/guide/create_op#type_polymorphism.
// Once the op signature can be defined entirely in FullType, this
// convention can be deprecated.
//
// Note: While this code performs some basic verifications, it generally
// assumes consistent op defs and attributes. If more complete
// verifications are needed, they should be done by separately, and in a
// way that can be reused for type inference.
for (int j = 0; j < t->args_size(); j++) {
auto* arg = t->mutable_args(i);
if (arg->type_id() == TFT_VAR) {
const auto* attr = attrs.Find(arg->s());
DCHECK(attr != nullptr);
if (attr->value_case() == AttrValue::kList) {
const auto& attr_list = attr->list();
arg->set_type_id(TFT_PRODUCT);
for (int i = 0; i < attr_list.type_size(); i++) {
map_dtype_to_tensor(attr_list.type(i), arg->add_args());
}
} else if (attr->value_case() == AttrValue::kType) {
map_dtype_to_tensor(attr->type(), arg);
} else {
return Status(error::UNIMPLEMENTED,
absl::StrCat("unknown attribute type",
attrs.DebugString(), " key=", arg->s()));
}
arg->clear_s();
}
}
}
return ft;
} | Base | 1 |
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;
} | Base | 1 |
int PackLinuxElf32::canUnpack()
{
if (super::canUnpack()) {
return true;
}
if (Elf32_Ehdr::ET_DYN==get_te16(&ehdri.e_type)) {
PackLinuxElf32help1(fi);
}
return false;
} | Base | 1 |
TEST(DefaultCertValidatorTest, TestMultiLevelMatch) {
// san_multiple_dns_cert matches *.example.com
bssl::UniquePtr<X509> cert = readCertFromFile(TestEnvironment::substitute(
"{{ test_rundir "
"}}/test/extensions/transport_sockets/tls/test_data/san_multiple_dns_cert.pem"));
envoy::type::matcher::v3::StringMatcher matcher;
matcher.set_exact("foo.api.example.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));
} | Base | 1 |
bool RequestParser::OnHeadersEnd() {
bool matched = view_matcher_(request_->method(), request_->url().path(),
&stream_);
if (!matched) {
LOG_WARN("No view matches the request: %s %s", request_->method().c_str(),
request_->url().path().c_str());
}
return matched;
} | Base | 1 |
TfLiteStatus NotEqualEval(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 kTfLiteBool:
Comparison<bool, reference_ops::NotEqualFn>(input1, input2, output,
requires_broadcast);
break;
case kTfLiteFloat32:
Comparison<float, reference_ops::NotEqualFn>(input1, input2, output,
requires_broadcast);
break;
case kTfLiteInt32:
Comparison<int32_t, reference_ops::NotEqualFn>(input1, input2, output,
requires_broadcast);
break;
case kTfLiteInt64:
Comparison<int64_t, reference_ops::NotEqualFn>(input1, input2, output,
requires_broadcast);
break;
case kTfLiteUInt8:
ComparisonQuantized<uint8_t, reference_ops::NotEqualFn>(
input1, input2, output, requires_broadcast);
break;
case kTfLiteInt8:
ComparisonQuantized<int8_t, reference_ops::NotEqualFn>(
input1, input2, output, requires_broadcast);
break;
case kTfLiteString:
ComparisonString(reference_ops::StringRefNotEqualFn, input1, input2,
output, requires_broadcast);
break;
default:
context->ReportError(
context,
"Does not support type %d, requires bool|float|int|uint8|string",
input1->type);
return kTfLiteError;
}
return kTfLiteOk;
} | Base | 1 |
TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) {
TF_LITE_ENSURE_EQ(context, NumInputs(node), 2);
TF_LITE_ENSURE_EQ(context, NumOutputs(node), 0);
OpData* op_data = reinterpret_cast<OpData*>(node->user_data);
OpContext op_context(context, node);
TF_LITE_ENSURE(context, op_context.input->type == kTfLiteUInt8 ||
op_context.input->type == kTfLiteInt8 ||
op_context.input->type == kTfLiteInt16 ||
op_context.input->type == kTfLiteFloat16);
TF_LITE_ENSURE(context, op_context.ref->type == kTfLiteFloat32);
op_data->max_diff = op_data->tolerance * op_context.input->params.scale;
switch (op_context.input->type) {
case kTfLiteUInt8:
case kTfLiteInt8:
op_data->max_diff *= (1 << 8);
break;
case kTfLiteInt16:
op_data->max_diff *= (1 << 16);
break;
default:
break;
}
// Allocate tensor to store the dequantized inputs.
if (op_data->cache_tensor_id == kTensorNotAllocated) {
TF_LITE_ENSURE_OK(
context, context->AddTensors(context, 1, &op_data->cache_tensor_id));
}
TfLiteIntArrayFree(node->temporaries);
node->temporaries = TfLiteIntArrayCreate(1);
node->temporaries->data[0] = op_data->cache_tensor_id;
TfLiteTensor* dequantized = GetTemporary(context, node, /*index=*/0);
dequantized->type = op_context.ref->type;
dequantized->allocation_type = kTfLiteDynamic;
TF_LITE_ENSURE_OK(context, context->ResizeTensor(
context, dequantized,
TfLiteIntArrayCopy(op_context.input->dims)));
return kTfLiteOk;
} | Base | 1 |
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;
} | Base | 1 |
TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) {
auto* params = reinterpret_cast<TfLiteMfccParams*>(node->user_data);
TF_LITE_ENSURE_EQ(context, NumInputs(node), 2);
TF_LITE_ENSURE_EQ(context, NumOutputs(node), 1);
const TfLiteTensor* input_wav = GetInput(context, node, kInputTensorWav);
const TfLiteTensor* input_rate = GetInput(context, node, kInputTensorRate);
TfLiteTensor* output = GetOutput(context, node, kOutputTensor);
TF_LITE_ENSURE_EQ(context, NumDimensions(input_wav), 3);
TF_LITE_ENSURE_EQ(context, NumElements(input_rate), 1);
TF_LITE_ENSURE_TYPES_EQ(context, output->type, kTfLiteFloat32);
TF_LITE_ENSURE_TYPES_EQ(context, input_wav->type, output->type);
TF_LITE_ENSURE_TYPES_EQ(context, input_rate->type, kTfLiteInt32);
TfLiteIntArray* output_size = TfLiteIntArrayCreate(3);
output_size->data[0] = input_wav->dims->data[0];
output_size->data[1] = input_wav->dims->data[1];
output_size->data[2] = params->dct_coefficient_count;
return context->ResizeTensor(context, output, output_size);
} | Base | 1 |
void ComputeAsync(OpKernelContext* c, DoneCallback done) override {
auto col_params = new CollectiveParams();
auto done_with_cleanup = [col_params, done = std::move(done)]() {
done();
col_params->Unref();
};
core::RefCountPtr<CollectiveGroupResource> resource;
OP_REQUIRES_OK_ASYNC(c, LookupResource(c, HandleFromInput(c, 1), &resource),
done);
Tensor group_assignment = c->input(2);
OP_REQUIRES_OK_ASYNC(
c,
FillCollectiveParams(col_params, group_assignment,
ALL_TO_ALL_COLLECTIVE, resource.get()),
done);
col_params->instance.shape = c->input(0).shape();
VLOG(1) << "CollectiveAllToAll group_size " << col_params->group.group_size
<< " group_key " << col_params->group.group_key << " instance_key "
<< col_params->instance.instance_key;
// Allocate the output tensor, trying to reuse the input.
Tensor* output = nullptr;
OP_REQUIRES_OK_ASYNC(c,
c->forward_input_or_allocate_output(
{0}, 0, col_params->instance.shape, &output),
done_with_cleanup);
Run(c, col_params, std::move(done_with_cleanup));
} | Variant | 0 |
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;
} | Base | 1 |
TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) {
const TfLitePackParams* data =
reinterpret_cast<TfLitePackParams*>(node->builtin_data);
TfLiteTensor* output = GetOutput(context, node, kOutputTensor);
switch (output->type) {
case kTfLiteFloat32: {
return PackImpl<float>(context, node, output, data->values_count,
data->axis);
}
case kTfLiteUInt8: {
return PackImpl<uint8_t>(context, node, output, data->values_count,
data->axis);
}
case kTfLiteInt8: {
return PackImpl<int8_t>(context, node, output, data->values_count,
data->axis);
}
case kTfLiteInt16: {
return PackImpl<int16_t>(context, node, output, data->values_count,
data->axis);
}
case kTfLiteInt32: {
return PackImpl<int32_t>(context, node, output, data->values_count,
data->axis);
}
case kTfLiteInt64: {
return PackImpl<int64_t>(context, node, output, data->values_count,
data->axis);
}
default: {
context->ReportError(context, "Type '%s' is not supported by pack.",
TfLiteTypeGetName(output->type));
return kTfLiteError;
}
}
return kTfLiteOk;
} | Base | 1 |
IntegrationStreamDecoderPtr HttpIntegrationTest::sendRequestAndWaitForResponse(
const Http::TestHeaderMapImpl& request_headers, uint32_t request_body_size,
const Http::TestHeaderMapImpl& response_headers, uint32_t response_size, int upstream_index) {
ASSERT(codec_client_ != nullptr);
// Send the request to Envoy.
IntegrationStreamDecoderPtr response;
if (request_body_size) {
response = codec_client_->makeRequestWithBody(request_headers, request_body_size);
} else {
response = codec_client_->makeHeaderOnlyRequest(request_headers);
}
waitForNextUpstreamRequest(upstream_index);
// Send response headers, and end_stream if there is no response body.
upstream_request_->encodeHeaders(response_headers, response_size == 0);
// Send any response data, with end_stream true.
if (response_size) {
upstream_request_->encodeData(response_size, true);
}
// Wait for the response to be read by the codec client.
response->waitForEndStream();
return response;
} | Class | 2 |
inline bool ShapeIsVector(TfLiteContext* context, TfLiteNode* node) {
const TfLiteTensor* shape = GetInput(context, node, kShapeTensor);
return (shape->dims->size == 1 && shape->type == kTfLiteInt32);
} | Base | 1 |
TEST_F(AllowMissingInAndOfOrListTest, GoodAndBadJwts) {
EXPECT_CALL(mock_cb_, onComplete(Status::Ok));
// Use the token with example.com issuer for x-other.
auto headers =
Http::TestRequestHeaderMapImpl{{kExampleHeader, GoodToken}, {kOtherHeader, GoodToken}};
context_ = Verifier::createContext(headers, parent_span_, &mock_cb_);
verifier_->verify(context_);
EXPECT_THAT(headers, JwtOutputSuccess(kExampleHeader));
EXPECT_THAT(headers, JwtOutputFailedOrIgnore(kOtherHeader));
} | Base | 1 |
static bool MR_primality_test(UnsignedBigInteger n, const Vector<UnsignedBigInteger, 256>& tests)
{
// Written using Wikipedia:
// https://en.wikipedia.org/wiki/Miller%E2%80%93Rabin_primality_test#Miller%E2%80%93Rabin_test
ASSERT(!(n < 4));
auto predecessor = n.minus({ 1 });
auto d = predecessor;
size_t r = 0;
{
auto div_result = d.divided_by(2);
while (div_result.remainder == 0) {
d = div_result.quotient;
div_result = d.divided_by(2);
++r;
}
}
if (r == 0) {
// n - 1 is odd, so n was even. But there is only one even prime:
return n == 2;
}
for (auto a : tests) {
// Technically: ASSERT(2 <= a && a <= n - 2)
ASSERT(a < n);
auto x = ModularPower(a, d, n);
if (x == 1 || x == predecessor)
continue;
bool skip_this_witness = false;
// r − 1 iterations.
for (size_t i = 0; i < r - 1; ++i) {
x = ModularPower(x, 2, n);
if (x == predecessor) {
skip_this_witness = true;
break;
}
}
if (skip_this_witness)
continue;
return false; // "composite"
}
return true; // "probably prime"
} | Base | 1 |
TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) {
const TfLiteTensor* input = GetInput(context, node, kInputTensor);
switch (input->type) { // Already know in/out types are same.
case kTfLiteFloat32:
return EvalImpl<kernel_type, kTfLiteFloat32>(context, node);
case kTfLiteUInt8:
return EvalImpl<kernel_type, kTfLiteUInt8>(context, node);
case kTfLiteInt8:
return EvalImpl<kernel_type, kTfLiteInt8>(context, node);
case kTfLiteInt16:
return EvalImpl<kernel_type, kTfLiteInt16>(context, node);
default:
context->ReportError(context, "Type %d not currently supported.",
input->type);
return kTfLiteError;
}
} | Base | 1 |
void jas_seq2d_bindsub(jas_matrix_t *s, jas_matrix_t *s1, int xstart,
int ystart, int xend, int yend)
{
jas_matrix_bindsub(s, s1, ystart - s1->ystart_, xstart - s1->xstart_,
yend - s1->ystart_ - 1, xend - s1->xstart_ - 1);
} | Class | 2 |
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