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stringlengths 12
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stringclasses 5
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static inline ulong encode_twos_comp(long n, int prec)
{
ulong result;
assert(prec >= 2);
jas_eprintf("warning: support for signed data is untested\n");
// NOTE: Is this correct?
if (n < 0) {
result = -n;
result = (result ^ 0xffffffffUL) + 1;
result &= (1 << prec) - 1;
} else {
result = n;
}
return result;
} | Base | 1 |
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;
} | Base | 1 |
jas_matrix_t *jas_seq2d_input(FILE *in)
{
jas_matrix_t *matrix;
int i;
int j;
long x;
int numrows;
int numcols;
int xoff;
int yoff;
if (fscanf(in, "%d %d", &xoff, &yoff) != 2)
return 0;
if (fscanf(in, "%d %d", &numcols, &numrows) != 2)
return 0;
if (!(matrix = jas_seq2d_create(xoff, yoff, xoff + numcols, yoff + numrows)))
return 0;
if (jas_matrix_numrows(matrix) != numrows ||
jas_matrix_numcols(matrix) != numcols) {
abort();
}
/* Get matrix data. */
for (i = 0; i < jas_matrix_numrows(matrix); i++) {
for (j = 0; j < jas_matrix_numcols(matrix); j++) {
if (fscanf(in, "%ld", &x) != 1) {
jas_matrix_destroy(matrix);
return 0;
}
jas_matrix_set(matrix, i, j, JAS_CAST(jas_seqent_t, x));
}
}
return matrix;
} | Class | 2 |
bool AES_GCM_DecryptContext::Decrypt(
const void *pEncryptedDataAndTag, size_t cbEncryptedDataAndTag,
const void *pIV,
void *pPlaintextData, uint32 *pcbPlaintextData,
const void *pAdditionalAuthenticationData, size_t cbAuthenticationData
) {
unsigned long long pcbPlaintextData_longlong;
const int nDecryptResult = crypto_aead_aes256gcm_decrypt_afternm(
static_cast<unsigned char*>( pPlaintextData ), &pcbPlaintextData_longlong,
nullptr,
static_cast<const unsigned char*>( pEncryptedDataAndTag ), cbEncryptedDataAndTag,
static_cast<const unsigned char*>( pAdditionalAuthenticationData ), cbAuthenticationData,
static_cast<const unsigned char*>( pIV ), static_cast<const crypto_aead_aes256gcm_state*>( m_ctx )
);
*pcbPlaintextData = pcbPlaintextData_longlong;
return nDecryptResult == 0;
} | Base | 1 |
TfLiteStatus UseDynamicOutputTensors(TfLiteContext* context, TfLiteNode* node) {
for (int i = 0; i < NumOutputs(node); ++i) {
SetTensorToDynamic(GetOutput(context, node, i));
}
return kTfLiteOk;
} | Base | 1 |
ECDSA_Signature_Operation(const ECDSA_PrivateKey& ecdsa,
const std::string& emsa) :
PK_Ops::Signature_with_EMSA(emsa),
m_group(ecdsa.domain()),
m_x(ecdsa.private_value())
{
#if defined(BOTAN_HAS_RFC6979_GENERATOR)
m_rfc6979_hash = hash_for_emsa(emsa);
#endif
} | Class | 2 |
Status OpLevelCostEstimator::PredictAvgPoolGrad(const OpContext& op_context,
NodeCosts* node_costs) const {
bool found_unknown_shapes = false;
const auto& op_info = op_context.op_info;
// x's shape: op_info.inputs(0)
// y_grad: op_info.inputs(1)
// Extract x_shape from op_info.inputs(0).value() or op_info.outputs(0).
bool shape_found = false;
TensorShapeProto x_shape;
if (op_info.inputs_size() >= 1 && op_info.inputs(0).has_value()) {
const TensorProto& value = op_info.inputs(0).value();
shape_found = GetTensorShapeProtoFromTensorProto(value, &x_shape);
}
if (!shape_found && op_info.outputs_size() > 0) {
x_shape = op_info.outputs(0).shape();
shape_found = true;
}
if (!shape_found) {
// Set the minimum shape that's feasible.
x_shape.Clear();
for (int i = 0; i < 4; ++i) {
x_shape.add_dim()->set_size(1);
}
found_unknown_shapes = true;
}
ConvolutionDimensions dims =
OpDimensionsFromInputs(x_shape, op_info, &found_unknown_shapes);
int64_t ops = 0;
if (dims.kx <= dims.sx && dims.ky <= dims.sy) {
// Non-overlapping window.
ops = dims.batch * dims.iz * (dims.ix * dims.iy + dims.ox * dims.oy);
} else {
// Overlapping window.
ops = dims.batch * dims.iz *
(dims.ix * dims.iy + dims.ox * dims.oy * (dims.kx * dims.ky + 1));
}
auto s = PredictDefaultNodeCosts(ops, op_context, &found_unknown_shapes,
node_costs);
node_costs->max_memory = node_costs->num_total_output_bytes();
return s;
} | Base | 1 |
static unsigned HuffmanTree_makeFromFrequencies(HuffmanTree* tree, const unsigned* frequencies,
size_t mincodes, size_t numcodes, unsigned maxbitlen)
{
unsigned error = 0;
while(!frequencies[numcodes - 1] && numcodes > mincodes) numcodes--; /*trim zeroes*/
tree->maxbitlen = maxbitlen;
tree->numcodes = (unsigned)numcodes; /*number of symbols*/
tree->lengths = (unsigned*)realloc(tree->lengths, numcodes * sizeof(unsigned));
if(!tree->lengths) return 83; /*alloc fail*/
/*initialize all lengths to 0*/
memset(tree->lengths, 0, numcodes * sizeof(unsigned));
error = lodepng_huffman_code_lengths(tree->lengths, frequencies, numcodes, maxbitlen);
if(!error) error = HuffmanTree_makeFromLengths2(tree);
return error;
} | Variant | 0 |
TfLiteStatus ResizeOutputTensors(TfLiteContext* context, TfLiteNode* node,
const TfLiteTensor* axis,
const TfLiteTensor* input, int num_splits) {
int axis_value = GetTensorData<int>(axis)[0];
if (axis_value < 0) {
axis_value += NumDimensions(input);
}
TF_LITE_ENSURE(context, axis_value >= 0);
TF_LITE_ENSURE(context, axis_value < NumDimensions(input));
const int input_size = SizeOfDimension(input, axis_value);
TF_LITE_ENSURE_MSG(context, input_size % num_splits == 0,
"Not an even split");
const int slice_size = input_size / num_splits;
for (int i = 0; i < NumOutputs(node); ++i) {
TfLiteIntArray* output_dims = TfLiteIntArrayCopy(input->dims);
output_dims->data[axis_value] = slice_size;
TfLiteTensor* output = GetOutput(context, node, i);
TF_LITE_ENSURE_STATUS(context->ResizeTensor(context, output, output_dims));
}
return kTfLiteOk;
} | Base | 1 |
bool DependencyOptimizer::SafeToRemoveIdentity(const NodeDef& node) const {
if (!IsIdentity(node) && !IsIdentityN(node)) {
return true;
}
if (nodes_to_preserve_.find(node.name()) != nodes_to_preserve_.end()) {
return false;
}
if (!fetch_nodes_known_) {
// The output values of this node may be needed.
return false;
}
if (node.input_size() < 1) {
// Node lacks input, is invalid
return false;
}
const NodeDef* input = node_map_->GetNode(NodeName(node.input(0)));
CHECK(input != nullptr) << "node = " << node.name()
<< " input = " << node.input(0);
// Don't remove Identity nodes corresponding to Variable reads or following
// Recv.
if (IsVariable(*input) || IsRecv(*input)) {
return false;
}
for (const auto& consumer : node_map_->GetOutputs(node.name())) {
if (node.input_size() > 1 && (IsRetval(*consumer) || IsMerge(*consumer))) {
return false;
}
if (IsSwitch(*input)) {
for (const string& consumer_input : consumer->input()) {
if (consumer_input == AsControlDependency(node.name())) {
return false;
}
}
}
}
return true;
} | Base | 1 |
RawTile OpenJPEGImage::getRegion( int ha, int va, unsigned int res, int layers, int x, int y, unsigned int w, unsigned int h ){
// Scale up our output bit depth to the nearest factor of 8
unsigned int obpc = bpc;
if( bpc <= 16 && bpc > 8 ) obpc = 16;
else if( bpc <= 8 ) obpc = 8;
#ifdef DEBUG
Timer timer;
timer.start();
#endif
RawTile rawtile( 0, res, ha, va, w, h, channels, obpc );
if( obpc == 16 ) rawtile.data = new unsigned short[w * h * channels];
else if( obpc == 8 ) rawtile.data = new unsigned char[w * h * channels];
else throw file_error( "OpenJPEG :: Unsupported number of bits" );
rawtile.dataLength = w*h*channels*(obpc/8);
rawtile.filename = getImagePath();
rawtile.timestamp = timestamp;
process( res, layers, x, y, w, h, rawtile.data );
#ifdef DEBUG
logfile << "OpenJPEG :: getRegion() :: " << timer.getTime() << " microseconds" << endl;
#endif
return rawtile;
} | Base | 1 |
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;
} | Base | 1 |
void* sspi_SecureHandleGetUpperPointer(SecHandle* handle)
{
void* pointer;
if (!handle)
return NULL;
pointer = (void*) ~((size_t) handle->dwUpper);
return pointer;
} | Base | 1 |
req::ptr<XMLDocumentData> doc() const { return m_node->doc(); } | Base | 1 |
MONGO_EXPORT int bson_iterator_int( const bson_iterator *i ) {
switch ( bson_iterator_type( i ) ) {
case BSON_INT:
return bson_iterator_int_raw( i );
case BSON_LONG:
return bson_iterator_long_raw( i );
case BSON_DOUBLE:
return bson_iterator_double_raw( i );
default:
return 0;
}
} | Base | 1 |
jas_matrix_t *jas_matrix_copy(jas_matrix_t *x)
{
jas_matrix_t *y;
int i;
int j;
y = jas_matrix_create(x->numrows_, x->numcols_);
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 |
bool CSecurityTLS::processMsg(CConnection* cc)
{
rdr::InStream* is = cc->getInStream();
rdr::OutStream* os = cc->getOutStream();
client = cc;
initGlobal();
if (!session) {
if (!is->checkNoWait(1))
return false;
if (is->readU8() == 0) {
rdr::U32 result = is->readU32();
CharArray reason;
if (result == secResultFailed || result == secResultTooMany)
reason.buf = is->readString();
else
reason.buf = strDup("Authentication failure (protocol error)");
throw AuthFailureException(reason.buf);
}
if (gnutls_init(&session, GNUTLS_CLIENT) != GNUTLS_E_SUCCESS)
throw AuthFailureException("gnutls_init failed");
if (gnutls_set_default_priority(session) != GNUTLS_E_SUCCESS)
throw AuthFailureException("gnutls_set_default_priority failed");
setParam();
}
rdr::TLSInStream *tlsis = new rdr::TLSInStream(is, session);
rdr::TLSOutStream *tlsos = new rdr::TLSOutStream(os, session);
int err;
err = gnutls_handshake(session);
if (err != GNUTLS_E_SUCCESS) {
delete tlsis;
delete tlsos;
if (!gnutls_error_is_fatal(err))
return false;
vlog.error("TLS Handshake failed: %s\n", gnutls_strerror (err));
shutdown(false);
throw AuthFailureException("TLS Handshake failed");
}
checkSession();
cc->setStreams(fis = tlsis, fos = tlsos);
return true;
} | Class | 2 |
boost::int64_t lazy_entry::int_value() const
{
TORRENT_ASSERT(m_type == int_t);
boost::int64_t val = 0;
bool negative = false;
if (*m_data.start == '-') negative = true;
parse_int(negative?m_data.start+1:m_data.start, m_data.start + m_size, 'e', val);
if (negative) val = -val;
return val;
} | Class | 2 |
LiteralString(const std::string &s, bool ignore_case)
: lit_(s), ignore_case_(ignore_case),
is_word_(false) {} | Base | 1 |
TfLiteStatus LessEqualEval(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::LessEqualFn>(input1, input2, output,
requires_broadcast);
break;
case kTfLiteInt32:
Comparison<int32_t, reference_ops::LessEqualFn>(input1, input2, output,
requires_broadcast);
break;
case kTfLiteInt64:
Comparison<int64_t, reference_ops::LessEqualFn>(input1, input2, output,
requires_broadcast);
break;
case kTfLiteUInt8:
ComparisonQuantized<uint8_t, reference_ops::LessEqualFn>(
input1, input2, output, requires_broadcast);
break;
case kTfLiteInt8:
ComparisonQuantized<int8_t, reference_ops::LessEqualFn>(
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;
} | Base | 1 |
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;
} | Base | 1 |
TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) {
TF_LITE_ENSURE_EQ(context, NumInputs(node), 2);
TF_LITE_ENSURE_EQ(context, NumOutputs(node), 1);
const TfLiteTensor* input = GetInput(context, node, kInputTensor);
const TfLiteTensor* size = GetInput(context, node, kSizeTensor);
TfLiteTensor* output = GetOutput(context, node, kOutputTensor);
// TODO(ahentz): Our current implementations rely on the inputs being 4D.
TF_LITE_ENSURE_EQ(context, NumDimensions(input), 4);
TF_LITE_ENSURE_EQ(context, NumDimensions(size), 1);
TF_LITE_ENSURE_EQ(context, size->type, kTfLiteInt32);
// ResizeBilinear creates a float tensor even when the input is made of
// integers.
output->type = input->type;
if (!IsConstantTensor(size)) {
SetTensorToDynamic(output);
return kTfLiteOk;
}
// Ensure params are valid.
auto* params =
reinterpret_cast<TfLiteResizeBilinearParams*>(node->builtin_data);
if (params->half_pixel_centers && params->align_corners) {
context->ReportError(
context, "If half_pixel_centers is True, align_corners must be False.");
return kTfLiteError;
}
return ResizeOutputTensor(context, input, size, output);
} | 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);
return context->ResizeTensor(context, output,
TfLiteIntArrayCopy(input->dims));
} | Base | 1 |
TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) {
const TfLiteTensor* input = GetInput(context, node, kInputTensor);
TfLiteTensor* output = GetOutput(context, node, kOutputTensor);
// There are two ways in which the 'output' can be made dynamic: it could be
// a string tensor, or its shape cannot be calculated during Prepare(). In
// either case, we now have all the information to calculate its shape.
if (IsDynamicTensor(output)) {
TF_LITE_ENSURE_OK(context, ResizeOutput(context, node));
}
// Note that string tensors are always "dynamic" in the sense that their size
// is not known until we have all the content. This applies even when their
// shape is known ahead of time. As a result, a string tensor is never given
// any memory by ResizeOutput(), and we need to do it manually here. Since
// reshape doesn't change the data, the output tensor needs exactly as many
// bytes as the input tensor.
if (output->type == kTfLiteString) {
auto bytes_required = input->bytes;
TfLiteTensorRealloc(bytes_required, output);
output->bytes = bytes_required;
}
memcpy(output->data.raw, input->data.raw, input->bytes);
return kTfLiteOk;
} | Base | 1 |
Pl_ASCIIHexDecoder::flush()
{
if (this->pos == 0)
{
QTC::TC("libtests", "Pl_ASCIIHexDecoder no-op flush");
return;
}
int b[2];
for (int i = 0; i < 2; ++i)
{
if (this->inbuf[i] >= 'A')
{
b[i] = this->inbuf[i] - 'A' + 10;
}
else
{
b[i] = this->inbuf[i] - '0';
}
}
unsigned char ch = static_cast<unsigned char>((b[0] << 4) + b[1]);
QTC::TC("libtests", "Pl_ASCIIHexDecoder partial flush",
(this->pos == 2) ? 0 : 1);
getNext()->write(&ch, 1);
this->pos = 0;
this->inbuf[0] = '0';
this->inbuf[1] = '0';
this->inbuf[2] = '\0';
} | Base | 1 |
TEST_P(SslSocketTest, GetUriWithUriSan) {
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/san_uri_cert.pem"
private_key:
filename: "{{ test_rundir }}/test/extensions/transport_sockets/tls/test_data/san_uri_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: "spiffe://lyft.com/test-team"
)EOF";
TestUtilOptions test_options(client_ctx_yaml, server_ctx_yaml, true, GetParam());
testUtil(test_options.setExpectedClientCertUri("spiffe://lyft.com/test-team")
.setExpectedSerialNumber(TEST_SAN_URI_CERT_SERIAL));
} | Base | 1 |
static void jsiDumpInstr(Jsi_Interp *interp, jsi_Pstate *ps, Jsi_Value *_this,
jsi_TryList *trylist, jsi_OpCode *ip, Jsi_OpCodes *opcodes)
{
int i;
char buf[200];
jsi_code_decode(interp, ip, ip - opcodes->codes, buf, sizeof(buf));
Jsi_Printf(interp, jsi_Stderr, "%p: %-30.200s : THIS=%s, STACK=[", ip, buf, jsi_evalprint(_this));
for (i = 0; i < interp->framePtr->Sp; ++i) {
Jsi_Printf(interp, jsi_Stderr, "%s%s", (i>0?", ":""), jsi_evalprint(_jsi_STACKIDX(i)));
}
Jsi_Printf(interp, jsi_Stderr, "]");
if (ip->fname) {
const char *fn = ip->fname, *cp = Jsi_Strrchr(fn, '/');
if (cp) fn = cp+1;
Jsi_Printf(interp, jsi_Stderr, ", %s:%d", fn, ip->Line);
}
Jsi_Printf(interp, jsi_Stderr, "\n");
jsi_TryList *tlt = trylist;
for (i = 0; tlt; tlt = tlt->next) i++;
if (ps->last_exception)
Jsi_Printf(interp, jsi_Stderr, "TL: %d, excpt: %s\n", i, jsi_evalprint(ps->last_exception));
} | Base | 1 |
TfLiteStatus Resize(TfLiteContext* context, TfLiteNode* node) {
auto* params =
reinterpret_cast<TfLiteLSHProjectionParams*>(node->builtin_data);
TF_LITE_ENSURE(context, NumInputs(node) == 2 || NumInputs(node) == 3);
TF_LITE_ENSURE_EQ(context, NumOutputs(node), 1);
const TfLiteTensor* hash = GetInput(context, node, 0);
TF_LITE_ENSURE_EQ(context, NumDimensions(hash), 2);
// Support up to 32 bits.
TF_LITE_ENSURE(context, SizeOfDimension(hash, 1) <= 32);
const TfLiteTensor* input = GetInput(context, node, 1);
TF_LITE_ENSURE(context, NumDimensions(input) >= 1);
if (NumInputs(node) == 3) {
const TfLiteTensor* weight = GetInput(context, node, 2);
TF_LITE_ENSURE_EQ(context, NumDimensions(weight), 1);
TF_LITE_ENSURE_EQ(context, SizeOfDimension(weight, 0),
SizeOfDimension(input, 0));
}
TfLiteTensor* output = GetOutput(context, node, 0);
TfLiteIntArray* outputSize = TfLiteIntArrayCreate(1);
switch (params->type) {
case kTfLiteLshProjectionSparse:
outputSize->data[0] = SizeOfDimension(hash, 0);
break;
case kTfLiteLshProjectionDense:
outputSize->data[0] = SizeOfDimension(hash, 0) * SizeOfDimension(hash, 1);
break;
default:
return kTfLiteError;
}
return context->ResizeTensor(context, output, outputSize);
} | 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 |
void TestSocketLineReader::initTestCase()
{
m_server = new Server(this);
QVERIFY2(m_server->listen(QHostAddress::LocalHost, 8694), "Failed to create local tcp server");
m_timer.setInterval(4000);//For second is more enough to send some data via local socket
m_timer.setSingleShot(true);
connect(&m_timer, &QTimer::timeout, &m_loop, &QEventLoop::quit);
m_conn = new QSslSocket(this);
m_conn->connectToHost(QHostAddress::LocalHost, 8694);
connect(m_conn, &QAbstractSocket::connected, &m_loop, &QEventLoop::quit);
m_timer.start();
m_loop.exec();
QVERIFY2(m_conn->isOpen(), "Could not connect to local tcp server");
} | Class | 2 |
TfLiteStatus PrepareHashtableFind(TfLiteContext* context, TfLiteNode* node) {
TF_LITE_ENSURE_EQ(context, NumInputs(node), 3);
TF_LITE_ENSURE_EQ(context, NumOutputs(node), 1);
const TfLiteTensor* input_resource_id_tensor =
GetInput(context, node, kInputResourceIdTensor);
TF_LITE_ENSURE_EQ(context, input_resource_id_tensor->type, kTfLiteInt32);
TF_LITE_ENSURE_EQ(context, NumDimensions(input_resource_id_tensor), 1);
TF_LITE_ENSURE_EQ(context, SizeOfDimension(input_resource_id_tensor, 0), 1);
const TfLiteTensor* default_value_tensor =
GetInput(context, node, kDefaultValueTensor);
const TfLiteTensor* key_tensor = GetInput(context, node, kKeyTensor);
TfLiteTensor* output_tensor = GetOutput(context, node, kOutputTensor);
TF_LITE_ENSURE_EQ(context, default_value_tensor->type, output_tensor->type);
TF_LITE_ENSURE(context, (key_tensor->type == kTfLiteInt64 &&
output_tensor->type == kTfLiteString) ||
(key_tensor->type == kTfLiteString &&
output_tensor->type == kTfLiteInt64));
return context->ResizeTensor(context, output_tensor,
TfLiteIntArrayCopy(key_tensor->dims));
} | Base | 1 |
TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) {
const TfLiteTensor* output = GetOutput(context, node, kOutputTensor);
if (output->type == kTfLiteFloat32) {
EvalAddN<float>(context, node);
} else if (output->type == kTfLiteInt32) {
EvalAddN<int32_t>(context, node);
} else {
context->ReportError(context,
"AddN only supports FLOAT32|INT32 now, got %s.",
TfLiteTypeGetName(output->type));
return kTfLiteError;
}
return kTfLiteOk;
} | Base | 1 |
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;
} | Base | 1 |
void jas_matrix_asr(jas_matrix_t *matrix, int n)
{
int i;
int j;
jas_seqent_t *rowstart;
int rowstep;
jas_seqent_t *data;
assert(n >= 0);
if (jas_matrix_numrows(matrix) > 0 && jas_matrix_numcols(matrix) > 0) {
assert(matrix->rows_);
rowstep = jas_matrix_rowstep(matrix);
for (i = matrix->numrows_, rowstart = matrix->rows_[0]; i > 0; --i,
rowstart += rowstep) {
for (j = matrix->numcols_, data = rowstart; j > 0; --j,
++data) {
//*data >>= n;
*data = jas_seqent_asr(*data, n);
}
}
}
} | Base | 1 |
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;
}
} | Base | 1 |
static unsigned short get_ushort(const unsigned char *data)
{
unsigned short val = *(const unsigned short *)data;
#ifdef OPJ_BIG_ENDIAN
val = ((val & 0xffU) << 8) | (val >> 8);
#endif
return val;
} | Base | 1 |
compat_mptfwxfer_ioctl(struct file *filp, unsigned int cmd,
unsigned long arg)
{
struct mpt_fw_xfer32 kfw32;
struct mpt_fw_xfer kfw;
MPT_ADAPTER *iocp = NULL;
int iocnum, iocnumX;
int nonblock = (filp->f_flags & O_NONBLOCK);
int ret;
if (copy_from_user(&kfw32, (char __user *)arg, sizeof(kfw32)))
return -EFAULT;
/* Verify intended MPT adapter */
iocnumX = kfw32.iocnum & 0xFF;
if (((iocnum = mpt_verify_adapter(iocnumX, &iocp)) < 0) ||
(iocp == NULL)) {
printk(KERN_DEBUG MYNAM "::compat_mptfwxfer_ioctl @%d - ioc%d not found!\n",
__LINE__, iocnumX);
return -ENODEV;
}
if ((ret = mptctl_syscall_down(iocp, nonblock)) != 0)
return ret;
dctlprintk(iocp, printk(MYIOC_s_DEBUG_FMT "compat_mptfwxfer_ioctl() called\n",
iocp->name));
kfw.iocnum = iocnum;
kfw.fwlen = kfw32.fwlen;
kfw.bufp = compat_ptr(kfw32.bufp);
ret = mptctl_do_fw_download(kfw.iocnum, kfw.bufp, kfw.fwlen);
mutex_unlock(&iocp->ioctl_cmds.mutex);
return ret;
} | Class | 2 |
void ModifiablePixelBuffer::fillRect(const Rect& r, const void* pix)
{
int stride;
U8 *buf;
int w, h, b;
w = r.width();
h = r.height();
b = format.bpp/8;
if (h == 0)
return;
buf = getBufferRW(r, &stride);
if (b == 1) {
while (h--) {
memset(buf, *(const U8*)pix, w);
buf += stride * b;
}
} else {
U8 *start;
int w1;
start = buf;
w1 = w;
while (w1--) {
memcpy(buf, pix, b);
buf += b;
}
buf += (stride - w) * b;
h--;
while (h--) {
memcpy(buf, start, w * b);
buf += stride * b;
}
}
commitBufferRW(r);
} | 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* input = GetInput(context, node, kInputTensor);
const TfLiteTensor* axis = GetInput(context, node, kAxis);
// Make sure the axis is only 1 dimension.
TF_LITE_ENSURE_EQ(context, NumElements(axis), 1);
// Make sure the axis is only either int32 or int64.
TF_LITE_ENSURE(context,
axis->type == kTfLiteInt32 || axis->type == kTfLiteInt64);
TfLiteTensor* output = GetOutput(context, node, kOutputTensor);
auto* params = reinterpret_cast<TfLiteArgMaxParams*>(node->builtin_data);
switch (params->output_type) {
case kTfLiteInt32:
output->type = kTfLiteInt32;
break;
case kTfLiteInt64:
output->type = kTfLiteInt64;
break;
default:
context->ReportError(context, "Unknown index output data type: %d",
params->output_type);
return kTfLiteError;
}
// Check conditions for different types.
switch (input->type) {
case kTfLiteFloat32:
case kTfLiteUInt8:
case kTfLiteInt8:
case kTfLiteInt32:
break;
default:
context->ReportError(
context,
"Unknown input type: %d, only float32 and int types are supported",
input->type);
return kTfLiteError;
}
TF_LITE_ENSURE(context, NumDimensions(input) >= 1);
if (IsConstantTensor(axis)) {
TF_LITE_ENSURE_STATUS(ResizeOutput(context, input, axis, output));
} else {
SetTensorToDynamic(output);
}
return kTfLiteOk;
} | Base | 1 |
void Compute(OpKernelContext* context) override {
// Get the input Tensors.
OpInputList params_nested_splits_in;
OP_REQUIRES_OK(context, context->input_list("params_nested_splits",
¶ms_nested_splits_in));
const Tensor& params_dense_values_in =
context->input(params_nested_splits_in.size());
const Tensor& indices_in =
context->input(params_nested_splits_in.size() + 1);
DCHECK_GT(params_nested_splits_in.size(), 0); // Enforced by REGISTER_OP.
SPLITS_TYPE num_params = params_nested_splits_in[0].dim_size(0) - 1;
OP_REQUIRES_OK(context, ValidateIndices(indices_in, num_params));
OP_REQUIRES(context, params_dense_values_in.dims() > 0,
errors::InvalidArgument("params.rank must be nonzero"));
SPLITS_TYPE num_params_dense_values = params_dense_values_in.dim_size(0);
// Calculate the `splits`, and store the value slices that we need to
// copy in `value_slices`.
std::vector<std::pair<SPLITS_TYPE, SPLITS_TYPE>> value_slices;
SPLITS_TYPE num_values = 0;
std::vector<std::vector<SPLITS_TYPE>> out_splits;
OP_REQUIRES_OK(context, MakeSplits(indices_in, params_nested_splits_in,
num_params_dense_values, &out_splits,
&value_slices, &num_values));
// Write the output tensors.
OP_REQUIRES_OK(context, WriteSplits(out_splits, context));
OP_REQUIRES_OK(context,
WriteValues(params_dense_values_in, value_slices,
out_splits.size(), num_values, context));
} | Base | 1 |
TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) {
TF_LITE_ENSURE_EQ(context, NumInputs(node), 3);
TF_LITE_ENSURE_EQ(context, NumOutputs(node), 2);
const TfLiteTensor* lookup = GetInput(context, node, 0);
TF_LITE_ENSURE_EQ(context, NumDimensions(lookup), 1);
TF_LITE_ENSURE_EQ(context, lookup->type, kTfLiteInt32);
const TfLiteTensor* key = GetInput(context, node, 1);
TF_LITE_ENSURE_EQ(context, NumDimensions(key), 1);
TF_LITE_ENSURE_EQ(context, key->type, kTfLiteInt32);
const TfLiteTensor* value = GetInput(context, node, 2);
TF_LITE_ENSURE(context, NumDimensions(value) >= 1);
TF_LITE_ENSURE_EQ(context, SizeOfDimension(key, 0),
SizeOfDimension(value, 0));
if (value->type == kTfLiteString) {
TF_LITE_ENSURE_EQ(context, NumDimensions(value), 1);
}
TfLiteTensor* hits = GetOutput(context, node, 1);
TF_LITE_ENSURE_EQ(context, hits->type, kTfLiteUInt8);
TfLiteIntArray* hitSize = TfLiteIntArrayCreate(1);
hitSize->data[0] = SizeOfDimension(lookup, 0);
TfLiteTensor* output = GetOutput(context, node, 0);
TF_LITE_ENSURE_EQ(context, value->type, output->type);
TfLiteStatus status = kTfLiteOk;
if (output->type != kTfLiteString) {
TfLiteIntArray* outputSize = TfLiteIntArrayCreate(NumDimensions(value));
outputSize->data[0] = SizeOfDimension(lookup, 0);
for (int i = 1; i < NumDimensions(value); i++) {
outputSize->data[i] = SizeOfDimension(value, i);
}
status = context->ResizeTensor(context, output, outputSize);
}
if (context->ResizeTensor(context, hits, hitSize) != kTfLiteOk) {
status = kTfLiteError;
}
return status;
} | Base | 1 |
bool AES_GCM_EncryptContext::Encrypt(
const void *pPlaintextData, size_t cbPlaintextData,
const void *pIV,
void *pEncryptedDataAndTag, uint32 *pcbEncryptedDataAndTag,
const void *pAdditionalAuthenticationData, size_t cbAuthenticationData
) {
unsigned long long pcbEncryptedDataAndTag_longlong = *pcbEncryptedDataAndTag;
crypto_aead_aes256gcm_encrypt_afternm(
static_cast<unsigned char*>( pEncryptedDataAndTag ), &pcbEncryptedDataAndTag_longlong,
static_cast<const unsigned char*>( pPlaintextData ), cbPlaintextData,
static_cast<const unsigned char*>(pAdditionalAuthenticationData), cbAuthenticationData,
nullptr,
static_cast<const unsigned char*>( pIV ),
static_cast<const crypto_aead_aes256gcm_state*>( m_ctx )
);
*pcbEncryptedDataAndTag = pcbEncryptedDataAndTag_longlong;
return true;
} | Base | 1 |
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 |
QByteArray Cipher::blowfishECB(QByteArray cipherText, bool direction)
{
QCA::Initializer init;
QByteArray temp = cipherText;
//do padding ourselves
if (direction)
{
while ((temp.length() % 8) != 0) temp.append('\0');
}
else
{
temp = b64ToByte(temp);
while ((temp.length() % 8) != 0) temp.append('\0');
}
QCA::Direction dir = (direction) ? QCA::Encode : QCA::Decode;
QCA::Cipher cipher(m_type, QCA::Cipher::ECB, QCA::Cipher::NoPadding, dir, m_key);
QByteArray temp2 = cipher.update(QCA::MemoryRegion(temp)).toByteArray();
temp2 += cipher.final().toByteArray();
if (!cipher.ok())
return cipherText;
if (direction)
temp2 = byteToB64(temp2);
return temp2;
} | Base | 1 |
void Context::onUpstreamConnectionClose(PeerType peer_type) {
if (wasm_->onUpstreamConnectionClose_) {
wasm_->onUpstreamConnectionClose_(this, id_, static_cast<uint32_t>(peer_type));
}
} | 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);
TfLiteIntArray* input_dims = input->dims;
int input_dims_size = input_dims->size;
TF_LITE_ENSURE(context, input_dims_size >= 1);
TfLiteTensor* output = GetOutput(context, node, kOutputTensor);
// Resize the output tensor.
TfLiteIntArray* output_shape = TfLiteIntArrayCreate(input_dims_size + 1);
for (int i = 0; i < input_dims_size; i++) {
output_shape->data[i] = input_dims->data[i];
}
// Last dimension in the output is the same as the last dimension in the
// input.
output_shape->data[input_dims_size] = input_dims->data[input_dims_size - 1];
output->type = input->type;
TF_LITE_ENSURE_OK(context,
context->ResizeTensor(context, output, output_shape));
return kTfLiteOk;
} | Base | 1 |
FastCGIServer::FastCGIServer(const std::string &address,
int port,
int workers,
bool useFileSocket)
: Server(address, port),
m_worker(&m_eventBaseManager),
m_dispatcher(workers, workers,
RuntimeOption::ServerThreadDropCacheTimeoutSeconds,
RuntimeOption::ServerThreadDropStack,
this,
RuntimeOption::ServerThreadJobLIFOSwitchThreshold,
RuntimeOption::ServerThreadJobMaxQueuingMilliSeconds,
RequestPriority::k_numPriorities) {
folly::SocketAddress sock_addr;
if (useFileSocket) {
sock_addr.setFromPath(address);
} else if (address.empty()) {
sock_addr.setFromLocalPort(port);
} else {
sock_addr.setFromHostPort(address, port);
}
m_socketConfig.bindAddress = sock_addr;
m_socketConfig.acceptBacklog = RuntimeOption::ServerBacklog;
std::chrono::seconds timeout;
if (RuntimeOption::ConnectionTimeoutSeconds >= 0) {
timeout = std::chrono::seconds(RuntimeOption::ConnectionTimeoutSeconds);
} else {
// default to 2 minutes
timeout = std::chrono::seconds(120);
}
m_socketConfig.connectionIdleTimeout = timeout;
} | Class | 2 |
mptctl_eventenable (unsigned long arg)
{
struct mpt_ioctl_eventenable __user *uarg = (void __user *) arg;
struct mpt_ioctl_eventenable karg;
MPT_ADAPTER *ioc;
int iocnum;
if (copy_from_user(&karg, uarg, sizeof(struct mpt_ioctl_eventenable))) {
printk(KERN_ERR MYNAM "%s@%d::mptctl_eventenable - "
"Unable to read in mpt_ioctl_eventenable 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_eventenable() @%d - ioc%d not found!\n",
__FILE__, __LINE__, iocnum);
return -ENODEV;
}
dctlprintk(ioc, printk(MYIOC_s_DEBUG_FMT "mptctl_eventenable called.\n",
ioc->name));
if (ioc->events == NULL) {
/* Have not yet allocated memory - do so now.
*/
int sz = MPTCTL_EVENT_LOG_SIZE * sizeof(MPT_IOCTL_EVENTS);
ioc->events = kzalloc(sz, GFP_KERNEL);
if (!ioc->events) {
printk(MYIOC_s_ERR_FMT
": ERROR - Insufficient memory to add adapter!\n",
ioc->name);
return -ENOMEM;
}
ioc->alloc_total += sz;
ioc->eventContext = 0;
}
/* Update the IOC event logging flag.
*/
ioc->eventTypes = karg.eventTypes;
return 0;
} | Class | 2 |
static TfLiteRegistration DynamicCopyOpRegistration() {
TfLiteRegistration reg = {nullptr, nullptr, nullptr, nullptr};
reg.prepare = [](TfLiteContext* context, TfLiteNode* node) {
// Output 0 is dynamic
TfLiteTensor* output0 = GetOutput(context, node, 0);
SetTensorToDynamic(output0);
// Output 1 has the same shape as input.
const TfLiteTensor* input = GetInput(context, node, 0);
TfLiteTensor* output1 = GetOutput(context, node, 1);
TF_LITE_ENSURE_STATUS(context->ResizeTensor(
context, output1, TfLiteIntArrayCopy(input->dims)));
return kTfLiteOk;
};
reg.invoke = [](TfLiteContext* context, TfLiteNode* node) {
// Not implemented since this isn't required in testing.
return kTfLiteOk;
};
return reg;
} | Base | 1 |
void CreateNgrams(const tstring* data, tstring* output, int num_ngrams,
int ngram_width) const {
for (int ngram_index = 0; ngram_index < num_ngrams; ++ngram_index) {
int pad_width = get_pad_width(ngram_width);
int left_padding = std::max(0, pad_width - ngram_index);
int right_padding =
std::max(0, pad_width - (num_ngrams - (ngram_index + 1)));
int num_tokens = ngram_width - (left_padding + right_padding);
int data_start_index = left_padding > 0 ? 0 : ngram_index - pad_width;
// Calculate the total expected size of the ngram so we can reserve the
// correct amount of space in the string.
int ngram_size = 0;
// Size of the left padding.
ngram_size += left_padding * left_pad_.length();
// Size of the tokens.
for (int n = 0; n < num_tokens; ++n) {
ngram_size += data[data_start_index + n].length();
}
// Size of the right padding.
ngram_size += right_padding * right_pad_.length();
// Size of the separators.
int num_separators = left_padding + right_padding + num_tokens - 1;
ngram_size += num_separators * separator_.length();
// Build the ngram.
tstring* ngram = &output[ngram_index];
ngram->reserve(ngram_size);
for (int n = 0; n < left_padding; ++n) {
ngram->append(left_pad_);
ngram->append(separator_);
}
for (int n = 0; n < num_tokens - 1; ++n) {
ngram->append(data[data_start_index + n]);
ngram->append(separator_);
}
ngram->append(data[data_start_index + num_tokens - 1]);
for (int n = 0; n < right_padding; ++n) {
ngram->append(separator_);
ngram->append(right_pad_);
}
// In debug mode only: validate that we've reserved enough space for the
// ngram.
DCHECK_EQ(ngram_size, ngram->size());
}
} | Base | 1 |
TfLiteStatus AverageEval(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:
AverageEvalFloat<kernel_type>(context, node, params, data, input, output);
break;
case kTfLiteUInt8:
AverageEvalQuantizedUint8<kernel_type>(context, node, params, data, input,
output);
break;
case kTfLiteInt8:
AverageEvalQuantizedInt8<kernel_type>(context, node, params, data, input,
output);
break;
case kTfLiteInt16:
AverageEvalQuantizedInt16<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;
} | Base | 1 |
friend H AbslHashValue(H h, const TensorKey& k) {
const uint8* d = static_cast<uint8*>(k.data());
size_t s = k.AllocatedBytes();
std::vector<uint8> vec;
vec.reserve(s);
for (int i = 0; i < s; i++) {
vec.push_back(d[i]);
}
return H::combine(std::move(h), s);
} | 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 |
void pcre_dump_cache(const std::string& filename) {
s_pcreCache.dump(filename);
} | Base | 1 |
void writeStats(Array& /*ret*/) override {
fprintf(stderr, "writeStats start\n");
// RetSame: the return value is the same instance every time
// HasThis: call has a this argument
// AllSame: all returns were the same data even though args are different
// MemberCount: number of different arg sets (including this)
fprintf(stderr, "Count Function MinSerLen MaxSerLen RetSame HasThis "
"AllSame MemberCount\n");
for (auto& me : m_memos) {
if (me.second.m_ignore) continue;
if (me.second.m_count == 1) continue;
int min_ser_len = 999999999;
int max_ser_len = 0;
int count = 0;
int member_count = 0;
bool all_same = true;
if (me.second.m_has_this) {
bool any_multiple = false;
auto& fr = me.second.m_member_memos.begin()->second.m_return_value;
member_count = me.second.m_member_memos.size();
for (auto& mme : me.second.m_member_memos) {
if (mme.second.m_return_value != fr) all_same = false;
count += mme.second.m_count;
auto ser_len = mme.second.m_return_value.length();
min_ser_len = std::min(min_ser_len, ser_len);
max_ser_len = std::max(max_ser_len, ser_len);
if (mme.second.m_count > 1) any_multiple = true;
}
if (!any_multiple && !all_same) continue;
} else {
min_ser_len = max_ser_len = me.second.m_return_value.length();
count = me.second.m_count;
all_same = me.second.m_ret_tv_same;
}
fprintf(stderr, "%d %s %d %d %s %s %s %d\n",
count, me.first.data(),
min_ser_len, max_ser_len,
me.second.m_ret_tv_same ? " true" : "false",
me.second.m_has_this ? " true" : "false",
all_same ? " true" : "false",
member_count
);
}
fprintf(stderr, "writeStats end\n");
} | Base | 1 |
TcpOption *tcpGetOption(TcpHeader *segment, uint8_t kind)
{
size_t length;
uint_t i;
TcpOption *option;
//Make sure the TCP header is valid
if(segment->dataOffset < 5)
return NULL;
//Compute the length of the options field
length = segment->dataOffset * 4 - sizeof(TcpHeader);
//Point to the very first option
i = 0;
//Parse TCP options
while(i < length)
{
//Point to the current option
option = (TcpOption *) (segment->options + i);
//NOP option detected?
if(option->kind == TCP_OPTION_NOP)
{
i++;
continue;
}
//END option detected?
if(option->kind == TCP_OPTION_END)
break;
//Check option length
if((i + 1) >= length || (i + option->length) > length)
break;
//Current option kind match the specified one?
if(option->kind == kind)
return option;
//Jump to next the next option
i += option->length;
}
//Specified option code not found
return NULL;
} | Class | 2 |
friend H AbslHashValue(H h, const TensorKey& k) {
const uint8* d = static_cast<uint8*>(k.data());
size_t s = k.AllocatedBytes();
std::vector<uint8> vec;
vec.reserve(s);
for (int i = 0; i < s; i++) {
vec.push_back(d[i]);
}
return H::combine(std::move(h), s);
} | Variant | 0 |
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 |
void RequestContext::StartBackendSpanAndSetTraceContext() {
backend_span_.reset(CreateSpan(cloud_trace_.get(), "Backend"));
// TODO: A better logic would be to send for GRPC backends the grpc-trace-bin
// header, and for http/https backends the X-Cloud-Trace-Context header.
std::string trace_context_header = cloud_trace()->ToTraceContextHeader(
backend_span_->trace_span()->span_id());
// Set trace context header to backend.
Status status = request()->AddHeaderToBackend(
cloud_trace()->header_type() == HeaderType::CLOUD_TRACE_CONTEXT
? kCloudTraceContextHeader
: kGRpcTraceContextHeader,
trace_context_header);
if (!status.ok()) {
service_context()->env()->LogError(
"Failed to set trace context header to backend.");
}
} | Base | 1 |
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);
} | 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 |
bool ResourceHandle::ParseFromString(const string& s) {
ResourceHandleProto proto;
const bool status = proto.ParseFromString(s);
if (status) FromProto(proto);
return status;
} | Base | 1 |
AP4_AvccAtom::InspectFields(AP4_AtomInspector& inspector)
{
inspector.AddField("Configuration Version", m_ConfigurationVersion);
const char* profile_name = GetProfileName(m_Profile);
if (profile_name) {
inspector.AddField("Profile", profile_name);
} else {
inspector.AddField("Profile", m_Profile);
}
inspector.AddField("Profile Compatibility", m_ProfileCompatibility, AP4_AtomInspector::HINT_HEX);
inspector.AddField("Level", m_Level);
inspector.AddField("NALU Length Size", m_NaluLengthSize);
for (unsigned int i=0; i<m_SequenceParameters.ItemCount(); i++) {
inspector.AddField("Sequence Parameter", m_SequenceParameters[i].GetData(), m_SequenceParameters[i].GetDataSize());
}
for (unsigned int i=0; i<m_SequenceParameters.ItemCount(); i++) {
inspector.AddField("Picture Parameter", m_PictureParameters[i].GetData(), m_PictureParameters[i].GetDataSize());
}
return AP4_SUCCESS;
} | Base | 1 |
Pl_AES_PDF::flush(bool strip_padding)
{
assert(this->offset == this->buf_size);
if (first)
{
first = false;
bool return_after_init = false;
if (this->cbc_mode)
{
if (encrypt)
{
// Set cbc_block to the initialization vector, and if
// not zero, write it to the output stream.
initializeVector();
if (! (this->use_zero_iv || this->use_specified_iv))
{
getNext()->write(this->cbc_block, this->buf_size);
}
}
else if (this->use_zero_iv || this->use_specified_iv)
{
// Initialize vector with zeroes; zero vector was not
// written to the beginning of the input file.
initializeVector();
}
else
{
// Take the first block of input as the initialization
// vector. There's nothing to write at this time.
memcpy(this->cbc_block, this->inbuf, this->buf_size);
this->offset = 0;
return_after_init = true;
}
}
this->crypto->rijndael_init(
encrypt, this->key.get(), key_bytes,
this->cbc_mode, this->cbc_block);
if (return_after_init)
{
return;
}
}
if (this->encrypt)
{
this->crypto->rijndael_process(this->inbuf, this->outbuf);
}
else
{
this->crypto->rijndael_process(this->inbuf, this->outbuf);
}
unsigned int bytes = this->buf_size;
if (strip_padding)
{
unsigned char last = this->outbuf[this->buf_size - 1];
if (last <= this->buf_size)
{
bool strip = true;
for (unsigned int i = 1; i <= last; ++i)
{
if (this->outbuf[this->buf_size - i] != last)
{
strip = false;
break;
}
}
if (strip)
{
bytes -= last;
}
}
}
getNext()->write(this->outbuf, bytes);
this->offset = 0;
} | Base | 1 |
TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) {
auto* params =
reinterpret_cast<TfLiteSpaceToDepthParams*>(node->builtin_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), 4);
auto data_type = output->type;
TF_LITE_ENSURE(context,
data_type == kTfLiteFloat32 || data_type == kTfLiteUInt8 ||
data_type == kTfLiteInt8 || data_type == kTfLiteInt32 ||
data_type == kTfLiteInt64);
TF_LITE_ENSURE_TYPES_EQ(context, input->type, output->type);
const int block_size = params->block_size;
const int input_height = input->dims->data[1];
const int input_width = input->dims->data[2];
int output_height = input_height / block_size;
int output_width = input_width / block_size;
TF_LITE_ENSURE_EQ(context, input_height, output_height * block_size);
TF_LITE_ENSURE_EQ(context, input_width, output_width * block_size);
TfLiteIntArray* output_size = TfLiteIntArrayCreate(4);
output_size->data[0] = input->dims->data[0];
output_size->data[1] = output_height;
output_size->data[2] = output_width;
output_size->data[3] = input->dims->data[3] * block_size * block_size;
return context->ResizeTensor(context, output, output_size);
} | Base | 1 |
bool ParseAttrValue(StringPiece type, StringPiece text, AttrValue* out) {
// Parse type.
string field_name;
bool is_list = absl::ConsumePrefix(&type, "list(");
if (absl::ConsumePrefix(&type, "string")) {
field_name = "s";
} else if (absl::ConsumePrefix(&type, "int")) {
field_name = "i";
} else if (absl::ConsumePrefix(&type, "float")) {
field_name = "f";
} else if (absl::ConsumePrefix(&type, "bool")) {
field_name = "b";
} else if (absl::ConsumePrefix(&type, "type")) {
field_name = "type";
} else if (absl::ConsumePrefix(&type, "shape")) {
field_name = "shape";
} else if (absl::ConsumePrefix(&type, "tensor")) {
field_name = "tensor";
} else if (absl::ConsumePrefix(&type, "func")) {
field_name = "func";
} else if (absl::ConsumePrefix(&type, "placeholder")) {
field_name = "placeholder";
} else {
return false;
}
if (is_list && !absl::ConsumePrefix(&type, ")")) {
return false;
}
// Construct a valid text proto message to parse.
string to_parse;
if (is_list) {
// TextFormat parser considers "i: 7" to be the same as "i: [7]",
// but we only want to allow list values with [].
StringPiece cleaned = text;
str_util::RemoveLeadingWhitespace(&cleaned);
str_util::RemoveTrailingWhitespace(&cleaned);
if (cleaned.size() < 2 || cleaned[0] != '[' ||
cleaned[cleaned.size() - 1] != ']') {
return false;
}
cleaned.remove_prefix(1);
str_util::RemoveLeadingWhitespace(&cleaned);
if (cleaned.size() == 1) {
// User wrote "[]", so return empty list without invoking the TextFormat
// parse which returns an error for "i: []".
out->Clear();
out->mutable_list();
return true;
}
to_parse = strings::StrCat("list { ", field_name, ": ", text, " }");
} else {
to_parse = strings::StrCat(field_name, ": ", text);
}
return ProtoParseFromString(to_parse, out);
} | Class | 2 |
int size() const {
return m_str ? m_str->size() : 0;
} | Base | 1 |
void *UntrustedCacheMalloc::GetBuffer() {
void **buffers = nullptr;
void *buffer;
bool is_pool_empty;
{
LockGuard spin_lock(&lock_);
is_pool_empty = buffer_pool_.empty();
if (is_pool_empty) {
buffers =
primitives::AllocateUntrustedBuffers(kPoolIncrement, kPoolEntrySize);
for (int i = 0; i < kPoolIncrement; i++) {
if (!buffers[i] ||
!TrustedPrimitives::IsOutsideEnclave(buffers[i], kPoolEntrySize)) {
abort();
}
buffer_pool_.push(buffers[i]);
}
}
buffer = buffer_pool_.top();
buffer_pool_.pop();
busy_buffers_.insert(buffer);
}
if (is_pool_empty) {
// Free memory held by the array of buffer pointers returned by
// AllocateUntrustedBuffers.
Free(buffers);
}
return buffer;
} | Class | 2 |
TfLiteStatus EqualEval(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::EqualFn>(input1, input2, output,
requires_broadcast);
break;
case kTfLiteFloat32:
Comparison<float, reference_ops::EqualFn>(input1, input2, output,
requires_broadcast);
break;
case kTfLiteInt32:
Comparison<int32_t, reference_ops::EqualFn>(input1, input2, output,
requires_broadcast);
break;
case kTfLiteInt64:
Comparison<int64_t, reference_ops::EqualFn>(input1, input2, output,
requires_broadcast);
break;
case kTfLiteUInt8:
ComparisonQuantized<uint8_t, reference_ops::EqualFn>(
input1, input2, output, requires_broadcast);
break;
case kTfLiteInt8:
ComparisonQuantized<int8_t, reference_ops::EqualFn>(
input1, input2, output, requires_broadcast);
break;
case kTfLiteString:
ComparisonString(reference_ops::StringRefEqualFn, 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 |
HexOutStream::writeBuffer() {
U8* pos = start;
while (pos != ptr) {
out_stream.check(2);
U8* optr = out_stream.getptr();
U8* oend = out_stream.getend();
int length = min(ptr-pos, (oend-optr)/2);
for (int i=0; i<length; i++) {
optr[i*2] = intToHex((pos[i] >> 4) & 0xf);
optr[i*2+1] = intToHex(pos[i] & 0xf);
}
out_stream.setptr(optr + length*2);
pos += length;
}
offset += ptr - start;
ptr = start;
} | 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 |
int ecall_restore(const char *input, uint64_t input_len, char **output,
uint64_t *output_len) {
if (!asylo::primitives::TrustedPrimitives::IsOutsideEnclave(input,
input_len) ||
!asylo::primitives::TrustedPrimitives::IsOutsideEnclave(
output_len, sizeof(uint64_t))) {
asylo::primitives::TrustedPrimitives::BestEffortAbort(
"ecall_restore: input/output found to not be in untrusted memory.");
}
int result = 0;
size_t tmp_output_len;
try {
result = asylo::Restore(input, static_cast<size_t>(input_len), output,
&tmp_output_len);
} catch (...) {
LOG(FATAL) << "Uncaught exception in enclave";
}
if (output_len) {
*output_len = static_cast<uint64_t>(tmp_output_len);
}
return result;
} | Base | 1 |
bool IsReshapeOpSupported(const TfLiteRegistration* registration,
const TfLiteNode* node, TfLiteContext* context,
int coreml_version) {
if (coreml_version >= 3) {
return false;
}
if (node->inputs->size == 1) {
const auto* params =
reinterpret_cast<TfLiteReshapeParams*>(node->builtin_data);
return params->num_dimensions == 3 || params->num_dimensions == 4;
}
const int kShapeTensor = 1;
const auto* shape = GetInput(context, node, kShapeTensor);
if (shape->allocation_type != kTfLiteMmapRo) {
TF_LITE_KERNEL_LOG(context, "Reshape has non-const shape.");
return false;
}
const bool is_shape_tensor =
shape->dims->size == 1 && shape->type == kTfLiteInt32;
return is_shape_tensor &&
(shape->dims->data[0] == 3 || shape->dims->data[0] == 4);
} | Base | 1 |
TfLiteStatus L2Eval(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:
L2EvalFloat<kernel_type>(context, node, params, data, input, output);
break;
case kTfLiteUInt8:
// We don't have a quantized implementation, so just fall through to the
// 'default' case.
default:
context->ReportError(context, "Type %d not currently supported.",
input->type);
return kTfLiteError;
}
return kTfLiteOk;
} | Base | 1 |
int HexInStream::pos() {
return offset + ptr - start;
} | Base | 1 |
void FormatConverter<T>::InitSparseToDenseConverter(
std::vector<int> shape, std::vector<int> traversal_order,
std::vector<TfLiteDimensionType> format, std::vector<int> dense_size,
std::vector<std::vector<int>> segments,
std::vector<std::vector<int>> indices, std::vector<int> block_map) {
dense_shape_ = std::move(shape);
traversal_order_ = std::move(traversal_order);
block_map_ = std::move(block_map);
format_ = std::move(format);
dense_size_ = 1;
for (int i = 0; i < dense_shape_.size(); i++) {
dense_size_ *= dense_shape_[i];
}
dim_metadata_.resize(2 * format_.size());
for (int i = 0; i < format_.size(); i++) {
if (format_[i] == kTfLiteDimDense) {
dim_metadata_[2 * i] = {dense_size[i]};
} else {
dim_metadata_[2 * i] = std::move(segments[i]);
dim_metadata_[2 * i + 1] = std::move(indices[i]);
}
}
int original_rank = dense_shape_.size();
int block_dim = 0;
blocked_shape_.resize(original_rank);
block_size_.resize(block_map_.size());
for (int i = 0; i < original_rank; i++) {
if (block_dim < block_map_.size() && block_map_[block_dim] == i) {
int orig_dim = traversal_order_[original_rank + block_dim];
block_size_[block_dim] = dense_size[orig_dim];
blocked_shape_[i] = dense_shape_[i] / dense_size[orig_dim];
block_dim++;
} else {
blocked_shape_[i] = dense_shape_[i];
}
}
} | Base | 1 |
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;
} | Base | 1 |
void AddBatchOffsets(Tensor* indices, const Tensor& params) {
int64_t batch_size = 1; // The size of all batch dimensions.
for (int idx = 0; idx < batch_dims_; ++idx) {
batch_size *= params.dim_size(idx);
}
auto indices_flat = indices->flat<Index>();
int64_t const index_inner_size = indices->NumElements() / batch_size;
int64_t const batch_offset = params.dim_size(batch_dims_);
for (int64_t batch_idx = 0, dest_idx = 0; batch_idx < batch_size;
++batch_idx) {
for (int64_t idx = 0; idx < index_inner_size; ++idx) {
indices_flat(dest_idx++) += batch_offset * batch_idx;
}
}
} | Base | 1 |
int64_t MemFile::readImpl(char *buffer, int64_t length) {
assertx(m_len != -1);
assertx(length > 0);
int64_t remaining = m_len - m_cursor;
if (remaining < length) length = remaining;
if (length > 0) {
memcpy(buffer, (const void *)(m_data + m_cursor), length);
}
m_cursor += length;
return length;
} | Base | 1 |
Status TensorSliceReader::GetTensor(
const string& name, std::unique_ptr<tensorflow::Tensor>* out_tensor) const {
DataType type;
TensorShape shape;
TensorSlice slice;
{
mutex_lock l(mu_);
const TensorSliceSet* tss = gtl::FindPtrOrNull(tensors_, name);
if (tss == nullptr) {
return errors::NotFound(name, " not found in checkpoint file");
}
if (tss->Slices().size() > 1) {
// TODO(sherrym): Support multi-slice checkpoints.
return errors::Unimplemented("Sliced checkpoints are not supported");
}
type = tss->type();
shape = tss->shape();
slice = tss->Slices().begin()->second.slice;
}
std::unique_ptr<tensorflow::Tensor> t(new tensorflow::Tensor(type, shape));
bool success = false;
#define READER_COPY(dt) \
case dt: \
success = CopySliceData(name, slice, \
t->flat<EnumToDataType<dt>::Type>().data()); \
break;
switch (type) {
READER_COPY(DT_FLOAT);
READER_COPY(DT_DOUBLE);
READER_COPY(DT_INT32);
READER_COPY(DT_UINT8);
READER_COPY(DT_INT16);
READER_COPY(DT_INT8);
READER_COPY(DT_INT64);
READER_COPY(DT_STRING);
default:
return errors::Unimplemented("Data type not supported");
}
#undef READER_COPY
if (!success) {
return errors::NotFound(name, " not found in checkpoint file");
}
std::swap(*out_tensor, t);
return Status::OK();
} | Class | 2 |
TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) {
const TfLiteTensor* input = GetInput(context, node, kInputTensor);
TfLiteTensor* output = GetOutput(context, node, kOutputTensor);
const int num_elements = NumElements(input);
TF_LITE_ENSURE_EQ(context, num_elements, NumElements(output));
switch (input->type) {
case kTfLiteInt64:
return copyToTensor(context, input->data.i64, output, num_elements);
case kTfLiteInt32:
return copyToTensor(context, input->data.i32, output, num_elements);
case kTfLiteUInt8:
return copyToTensor(context, input->data.uint8, output, num_elements);
case kTfLiteFloat32:
return copyToTensor(context, GetTensorData<float>(input), output,
num_elements);
case kTfLiteBool:
return copyToTensor(context, input->data.b, output, num_elements);
case kTfLiteComplex64:
return copyToTensor(
context, reinterpret_cast<std::complex<float>*>(input->data.c64),
output, num_elements);
default:
// Unsupported type.
TF_LITE_UNSUPPORTED_TYPE(context, input->type, "Cast");
}
return kTfLiteOk;
} | Base | 1 |
void LibRaw::exp_bef(float shift, float smooth)
{
// params limits
if(shift>8) shift = 8;
if(shift<0.25) shift = 0.25;
if(smooth < 0.0) smooth = 0.0;
if(smooth > 1.0) smooth = 1.0;
unsigned short *lut = (ushort*)malloc((TBLN+1)*sizeof(unsigned short));
if(shift <=1.0)
{
for(int i=0;i<=TBLN;i++)
lut[i] = (unsigned short)((float)i*shift);
}
else
{
float x1,x2,y1,y2;
float cstops = log(shift)/log(2.0f);
float room = cstops*2;
float roomlin = powf(2.0f,room);
x2 = (float)TBLN;
x1 = (x2+1)/roomlin-1;
y1 = x1*shift;
y2 = x2*(1+(1-smooth)*(shift-1));
float sq3x=powf(x1*x1*x2,1.0f/3.0f);
float B = (y2-y1+shift*(3*x1-3.0f*sq3x)) / (x2+2.0f*x1-3.0f*sq3x);
float A = (shift - B)*3.0f*powf(x1*x1,1.0f/3.0f);
float CC = y2 - A*powf(x2,1.0f/3.0f)-B*x2;
for(int i=0;i<=TBLN;i++)
{
float X = (float)i;
float Y = A*powf(X,1.0f/3.0f)+B*X+CC;
if(i<x1)
lut[i] = (unsigned short)((float)i*shift);
else
lut[i] = Y<0?0:(Y>TBLN?TBLN:(unsigned short)(Y));
}
}
for(int i=0; i< S.height*S.width; i++)
{
imgdata.image[i][0] = lut[imgdata.image[i][0]];
imgdata.image[i][1] = lut[imgdata.image[i][1]];
imgdata.image[i][2] = lut[imgdata.image[i][2]];
imgdata.image[i][3] = lut[imgdata.image[i][3]];
}
C.data_maximum = lut[C.data_maximum];
C.maximum = lut[C.maximum];
// no need to adjust the minumum, black is already subtracted
free(lut);
} | Class | 2 |
inline TfLiteTensor* GetMutableInput(const TfLiteContext* context,
const TfLiteNode* node, int index) {
if (context->tensors != nullptr) {
return &context->tensors[node->inputs->data[index]];
} else {
return context->GetTensor(context, node->inputs->data[index]);
}
} | Base | 1 |
TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) {
int num_inputs = NumInputs(node);
TF_LITE_ENSURE(context, num_inputs >= 2);
TF_LITE_ENSURE_EQ(context, NumOutputs(node), 1);
const TfLiteTensor* input1 = GetInput(context, node, kInputTensor1);
TfLiteTensor* output = GetOutput(context, node, kOutputTensor);
output->type = input1->type;
// Check that all input tensors have the same shape and type.
for (int i = kInputTensor1 + 1; i < num_inputs; ++i) {
const TfLiteTensor* input = GetInput(context, node, i);
TF_LITE_ENSURE(context, HaveSameShapes(input1, input));
TF_LITE_ENSURE_TYPES_EQ(context, input1->type, input->type);
}
// Use the first input node's dimension to be the dimension of the output
// node.
TfLiteIntArray* input1_dims = input1->dims;
TfLiteIntArray* output_dims = TfLiteIntArrayCopy(input1_dims);
return context->ResizeTensor(context, output, output_dims);
} | Base | 1 |
AsyncSocket::WriteResult AsyncSSLSocket::interpretSSLError(int rc, int error) {
if (error == SSL_ERROR_WANT_READ) {
// Even though we are attempting to write data, SSL_write() may
// need to read data if renegotiation is being performed. We currently
// don't support this and just fail the write.
LOG(ERROR) << "AsyncSSLSocket(fd=" << fd_ << ", state=" << int(state_)
<< ", sslState=" << sslState_ << ", events=" << eventFlags_
<< "): "
<< "unsupported SSL renegotiation during write";
return WriteResult(
WRITE_ERROR,
std::make_unique<SSLException>(SSLError::INVALID_RENEGOTIATION));
} else {
if (zero_return(error, rc, errno)) {
return WriteResult(0);
}
auto errError = ERR_get_error();
VLOG(3) << "ERROR: AsyncSSLSocket(fd=" << fd_ << ", state=" << int(state_)
<< ", sslState=" << sslState_ << ", events=" << eventFlags_ << "): "
<< "SSL error: " << error << ", errno: " << errno
<< ", func: " << ERR_func_error_string(errError)
<< ", reason: " << ERR_reason_error_string(errError);
return WriteResult(
WRITE_ERROR,
std::make_unique<SSLException>(error, errError, rc, errno));
}
} | Base | 1 |
static BOOL ntlm_av_pair_add_copy(NTLM_AV_PAIR* pAvPairList, size_t cbAvPairList,
NTLM_AV_PAIR* pAvPair, size_t cbAvPair)
{
if (!ntlm_av_pair_check(pAvPair, cbAvPair))
return FALSE;
return ntlm_av_pair_add(pAvPairList, cbAvPairList, ntlm_av_pair_get_id(pAvPair),
ntlm_av_pair_get_value_pointer(pAvPair), ntlm_av_pair_get_len(pAvPair));
} | Base | 1 |
HeaderLookupTable_t::lookup (const char *buf, const std::size_t len) const {
const HeaderTableRecord *r = HttpHeaderHashTable::lookup(buf, len);
if (!r)
return BadHdr;
return *r;
} | Class | 2 |
void * alloc_top(size_t size) {
top -= size;
if (top < bottom) {new_chunk(); top -= size;}
return top;
} | Base | 1 |
TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) {
TF_LITE_ENSURE_EQ(context, NumInputs(node), 2);
TF_LITE_ENSURE_EQ(context, NumOutputs(node), 1);
const TfLiteTensor* input = GetInput(context, node, kInputTensor);
TfLiteTensor* output = GetOutput(context, node, kOutputTensor);
TF_LITE_ENSURE_TYPES_EQ(context, input->type, output->type);
const TfLiteTensor* multipliers = GetInput(context, node, kInputMultipliers);
// Only int32 and int64 multipliers type is supported.
if (multipliers->type != kTfLiteInt32 && multipliers->type != kTfLiteInt64) {
context->ReportError(context,
"Multipliers of type '%s' are not supported by tile.",
TfLiteTypeGetName(multipliers->type));
return kTfLiteError;
}
if (IsConstantTensor(multipliers)) {
TF_LITE_ENSURE_OK(context, ResizeOutput(context, node));
} else {
SetTensorToDynamic(output);
}
return kTfLiteOk;
} | Base | 1 |
TfLiteStatus EvalLogic(TfLiteContext* context, TfLiteNode* node,
OpContext* op_context, T init_value,
T reducer(const T current, const T in)) {
int64_t num_axis = NumElements(op_context->axis);
TfLiteTensor* temp_index = GetTemporary(context, node, /*index=*/0);
TfLiteTensor* resolved_axis = GetTemporary(context, node, /*index=*/1);
// Resize the output tensor if the output tensor is dynamic.
if (IsDynamicTensor(op_context->output)) {
TF_LITE_ENSURE_OK(context,
ResizeTempAxis(context, op_context, resolved_axis));
TF_LITE_ENSURE_OK(context, ResizeOutputTensor(context, op_context));
}
if (op_context->input->type == kTfLiteUInt8 ||
op_context->input->type == kTfLiteInt8) {
TF_LITE_ENSURE_EQ(context, op_context->input->params.scale,
op_context->output->params.scale);
TF_LITE_ENSURE_EQ(context, op_context->input->params.zero_point,
op_context->output->params.zero_point);
}
TF_LITE_ENSURE(
context,
reference_ops::ReduceGeneric<T>(
GetTensorData<T>(op_context->input), op_context->input->dims->data,
op_context->input->dims->size, GetTensorData<T>(op_context->output),
op_context->output->dims->data, op_context->output->dims->size,
GetTensorData<int>(op_context->axis), num_axis,
op_context->params->keep_dims, GetTensorData<int>(temp_index),
GetTensorData<int>(resolved_axis), init_value, reducer));
return kTfLiteOk;
} | Base | 1 |
static void nodeDestruct(struct SaveNode* node)
{
if (node->v == &node->sorted)
{
tr_free(node->sorted.val.l.vals);
}
} | Variant | 0 |
int CLASS parse_jpeg(int offset)
{
int len, save, hlen, mark;
fseek(ifp, offset, SEEK_SET);
if (fgetc(ifp) != 0xff || fgetc(ifp) != 0xd8)
return 0;
while (fgetc(ifp) == 0xff && (mark = fgetc(ifp)) != 0xda)
{
order = 0x4d4d;
len = get2() - 2;
save = ftell(ifp);
if (mark == 0xc0 || mark == 0xc3 || mark == 0xc9)
{
fgetc(ifp);
raw_height = get2();
raw_width = get2();
}
order = get2();
hlen = get4();
if (get4() == 0x48454150) /* "HEAP" */
{
#ifdef LIBRAW_LIBRARY_BUILD
imgdata.lens.makernotes.CameraMount = LIBRAW_MOUNT_FixedLens;
imgdata.lens.makernotes.LensMount = LIBRAW_MOUNT_FixedLens;
#endif
parse_ciff(save + hlen, len - hlen, 0);
}
if (parse_tiff(save + 6))
apply_tiff();
fseek(ifp, save + len, SEEK_SET);
}
return 1;
} | Class | 2 |
void CClient::EchoMessage(const CMessage& Message) {
CMessage EchoedMessage = Message;
for (CClient* pClient : GetClients()) {
if (pClient->HasEchoMessage() ||
(pClient != this && (m_pNetwork->IsChan(Message.GetParam(0)) ||
pClient->HasSelfMessage()))) {
EchoedMessage.SetNick(GetNickMask());
pClient->PutClient(EchoedMessage);
}
}
} | Base | 1 |
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);
} | Base | 1 |
static int em_jmp_far(struct x86_emulate_ctxt *ctxt)
{
int rc;
unsigned short sel, old_sel;
struct desc_struct old_desc, new_desc;
const struct x86_emulate_ops *ops = ctxt->ops;
u8 cpl = ctxt->ops->cpl(ctxt);
/* Assignment of RIP may only fail in 64-bit mode */
if (ctxt->mode == X86EMUL_MODE_PROT64)
ops->get_segment(ctxt, &old_sel, &old_desc, NULL,
VCPU_SREG_CS);
memcpy(&sel, ctxt->src.valptr + ctxt->op_bytes, 2);
rc = __load_segment_descriptor(ctxt, sel, VCPU_SREG_CS, cpl,
X86_TRANSFER_CALL_JMP,
&new_desc);
if (rc != X86EMUL_CONTINUE)
return rc;
rc = assign_eip_far(ctxt, ctxt->src.val, &new_desc);
if (rc != X86EMUL_CONTINUE) {
WARN_ON(ctxt->mode != X86EMUL_MODE_PROT64);
/* assigning eip failed; restore the old cs */
ops->set_segment(ctxt, old_sel, &old_desc, 0, VCPU_SREG_CS);
return rc;
}
return rc;
} | 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* input = GetInput(context, node, kInputTensor);
const TfLiteTensor* size = GetInput(context, node, kSizeTensor);
TfLiteTensor* output = GetOutput(context, node, kOutputTensor);
// TODO(ahentz): Our current implementations rely on the inputs being 4D.
TF_LITE_ENSURE_EQ(context, NumDimensions(input), 4);
TF_LITE_ENSURE_EQ(context, NumDimensions(size), 1);
TF_LITE_ENSURE_EQ(context, size->type, kTfLiteInt32);
// ResizeBilinear creates a float tensor even when the input is made of
// integers.
output->type = input->type;
if (!IsConstantTensor(size)) {
SetTensorToDynamic(output);
return kTfLiteOk;
}
// Ensure params are valid.
auto* params =
reinterpret_cast<TfLiteResizeBilinearParams*>(node->builtin_data);
if (params->half_pixel_centers && params->align_corners) {
context->ReportError(
context, "If half_pixel_centers is True, align_corners must be False.");
return kTfLiteError;
}
return ResizeOutputTensor(context, input, size, output);
} | Base | 1 |
void AveragePool(const uint8* input_data, const Dims<4>& input_dims, int stride,
int pad_width, int pad_height, int filter_width,
int filter_height, int32 output_activation_min,
int32 output_activation_max, uint8* output_data,
const Dims<4>& output_dims) {
AveragePool<Ac>(input_data, input_dims, stride, stride, pad_width, pad_height,
filter_width, filter_height, output_activation_min,
output_activation_max, output_data, output_dims);
} | Base | 1 |
GetRunner(
const ReferenceHandle& that,
Local<Value> key_handle,
MaybeLocal<Object> maybe_options,
bool inherit
) :
context{that.context},
reference{that.reference},
options{maybe_options, inherit ?
TransferOptions::Type::DeepReference : TransferOptions::Type::Reference},
inherit{inherit} {
that.CheckDisposed();
key = ExternalCopy::CopyIfPrimitive(key_handle);
if (!key) {
throw RuntimeTypeError("Invalid `key`");
}
} | Class | 2 |
void SocketLineReader::dataReceived()
{
while (m_socket->canReadLine()) {
const QByteArray line = m_socket->readLine();
if (line.length() > 1) { //we don't want a single \n
m_packets.enqueue(line);
}
}
//If we still have things to read from the socket, call dataReceived again
//We do this manually because we do not trust readyRead to be emitted again
//So we call this method again just in case.
if (m_socket->bytesAvailable() > 0) {
QMetaObject::invokeMethod(this, "dataReceived", Qt::QueuedConnection);
return;
}
//If we have any packets, tell it to the world.
if (!m_packets.isEmpty()) {
Q_EMIT readyRead();
}
} | Class | 2 |
bool IsConvolutionOpSupported(const TfLiteRegistration* registration,
const TfLiteNode* node, TfLiteContext* context) {
if (node->builtin_data == nullptr) return false;
TfLiteFusedActivation activation;
if (registration->builtin_code == kTfLiteBuiltinConv2d) {
const auto* conv_params =
reinterpret_cast<const TfLiteConvParams*>(node->builtin_data);
activation = conv_params->activation;
} else if (registration->builtin_code == kTfLiteBuiltinDepthwiseConv2d) {
const auto* depthwise_conv_params =
reinterpret_cast<const TfLiteDepthwiseConvParams*>(node->builtin_data);
activation = depthwise_conv_params->activation;
} else if (registration->builtin_code == kTfLiteBuiltinTransposeConv) {
activation = kTfLiteActNone;
} else {
TF_LITE_KERNEL_LOG(
context,
"Invalid op: op must be Conv2D, DepthwiseConv2D or TransposeConv.");
return false;
}
if (activation == kTfLiteActSignBit) {
return false;
}
const int kOutputShapeTensor = 0; // Only used for TransposeConv
const int kWeightTensor = 1;
const int kBiasTensor = 2; // Only used for non-TransposeConv
const TfLiteTensor* weights = GetInput(context, node, kWeightTensor);
const int max_kernel_size = 16384;
if (!IsConstantTensor(weights)) {
return false;
}
if (weights->dims->data[1] > max_kernel_size ||
weights->dims->data[2] > max_kernel_size) {
return false;
}
if (registration->builtin_code == kTfLiteBuiltinTransposeConv) {
if (!IsConstantTensor(GetInput(context, node, kOutputShapeTensor))) {
return false;
}
} else {
if (node->inputs->size >= kBiasTensor &&
!IsConstantTensor(GetInput(context, node, kBiasTensor))) {
return false;
}
}
return true;
} | Base | 1 |
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