static Jsi_RC jsi_BitfieldToValue(Jsi_Interp *interp, Jsi_OptionSpec* spec, Jsi_Value **outValue,
    Jsi_DString *outStr, void *record, Jsi_Wide flags)
{
    Jsi_csgset *bsget = spec->init.OPT_BITS;
    Jsi_Interp *d = interp;
    int idx = spec->idx;
    uchar *data = (uchar*)record;
    int64_t inum;
    Jsi_OptionSpec* enumSpec = (typeof(enumSpec))spec->data;

    if (!d || !bsget || idx<0)
        return Jsi_LogBug("invalid bitfield");
    Jsi_RC rc = (*bsget)(interp, data, &inum, spec, idx, 0);
    if (rc != JSI_OK)
        return JSI_ERROR;

    if (enumSpec) {
        struct numStruct { int64_t numVal; } nval = { inum };
        Jsi_OptionSpec eSpec[] = {
            JSI_OPT(CUSTOM, struct numStruct, numVal, .help=spec->help, .flags=JSI_OPT_ENUM_SPEC, .custom=Jsi_Opt_SwitchEnum,
            .data=(void*)enumSpec, .info=0, .tname=spec->tname, .value=0, .bits=0, .boffset=8*sizeof(int64_t) ), //TODO: extra
            JSI_OPT_END(struct numStruct)
        };
        if (JSI_OK != jsi_EnumToValue(interp, eSpec, outValue, outStr, (void*)&nval, flags))
            return JSI_ERROR;
    } else if (outStr) {
        char obuf[100];
        snprintf(obuf, sizeof(obuf), "%" PRId64, inum);
        Jsi_DSAppend(outStr, obuf, NULL);
    } else {
        Jsi_Number num = (Jsi_Number)inum;
        Jsi_ValueMakeNumber(interp, outValue, num);
    }
    return JSI_OK;
}


const TfLiteTensor* GetOptionalInputTensor(const TfLiteContext* context,
                                           const TfLiteNode* node, int index) {
  const bool use_tensor = index < node->inputs->size &&
                          node->inputs->data[index] != kTfLiteOptionalTensor;
  if (use_tensor) {
    return GetMutableInput(context, node, index);
  }
  return nullptr;
}


inline int StringData::size() const { return m_len; }


void FormatConverter<T>::Populate(const T* src_data, std::vector<int> indices,
                                  int level, int prev_idx, int* src_data_ptr,
                                  T* dest_data) {
  if (level == indices.size()) {
    int orig_rank = dense_shape_.size();
    std::vector<int> orig_idx;
    orig_idx.resize(orig_rank);
    int i = 0;
    for (; i < orig_idx.size(); i++) {
      int orig_dim = traversal_order_[i];
      orig_idx[orig_dim] = indices[i];
    }

    for (; i < indices.size(); i++) {
      const int block_idx = traversal_order_[i] - orig_rank;
      const int orig_dim = block_map_[block_idx];
      orig_idx[orig_dim] =
          orig_idx[orig_dim] * block_size_[block_idx] + indices[i];
    }

    dest_data[GetFlattenedIndex(orig_idx, dense_shape_)] =
        src_data[*src_data_ptr];

    *src_data_ptr = *src_data_ptr + 1;
    return;
  }

  const int metadata_idx = 2 * level;
  const int shape_of_level = dim_metadata_[metadata_idx][0];
  if (format_[level] == kTfLiteDimDense) {
    for (int i = 0; i < shape_of_level; i++) {
      indices[level] = i;
      Populate(src_data, indices, level + 1, prev_idx * shape_of_level + i,
               src_data_ptr, dest_data);
    }
  } else {
    const auto& array_segments = dim_metadata_[metadata_idx];
    const auto& array_indices = dim_metadata_[metadata_idx + 1];
    for (int i = array_segments[prev_idx]; i < array_segments[prev_idx + 1];
         i++) {
      indices[level] = array_indices[i];
      Populate(src_data, indices, level + 1, i, src_data_ptr, dest_data);
    }
  }
}


void carray2Hex(const unsigned char *d, uint64_t _len, char *_hexArray,
                uint64_t _hexArrayLen) {

    CHECK_STATE(d);
    CHECK_STATE(_hexArray);

    char hexval[16] = {'0', '1', '2', '3', '4', '5', '6', '7',
                       '8', '9', 'a', 'b', 'c', 'd', 'e', 'f'};

    CHECK_STATE(_hexArrayLen > 2 * _len);

    for (int j = 0; j < _len; j++) {
        _hexArray[j * 2] = hexval[((d[j] >> 4) & 0xF)];
        _hexArray[j * 2 + 1] = hexval[(d[j]) & 0x0F];
    }

    _hexArray[_len * 2] = 0;
}


String StringUtil::Implode(const Variant& items, const String& delim,
                           const bool checkIsContainer /* = true */) {
  if (checkIsContainer && !isContainer(items)) {
    throw_param_is_not_container();
  }
  int size = getContainerSize(items);
  if (size == 0) return empty_string();

  req::vector<String> sitems;
  sitems.reserve(size);
  int len = 0;
  int lenDelim = delim.size();
  for (ArrayIter iter(items); iter; ++iter) {
    sitems.emplace_back(iter.second().toString());
    len += sitems.back().size() + lenDelim;
  }
  len -= lenDelim; // always one delimiter less than count of items
  assert(sitems.size() == size);

  String s = String(len, ReserveString);
  char *buffer = s.mutableData();
  const char *sdelim = delim.data();
  char *p = buffer;
  String &init_str = sitems[0];
  int init_len = init_str.size();
  memcpy(p, init_str.data(), init_len);
  p += init_len;
  for (int i = 1; i < size; i++) {
    String &item = sitems[i];
    memcpy(p, sdelim, lenDelim);
    p += lenDelim;
    int lenItem = item.size();
    memcpy(p, item.data(), lenItem);
    p += lenItem;
  }
  assert(p - buffer == len);
  s.setSize(len);
  return s;
}


  const String& setSize(int len) {
    assertx(m_str);
    m_str->setSize(len);
    return *this;
  }


TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) {
  auto* params =
      reinterpret_cast<TfLiteLocalResponseNormParams*>(node->builtin_data);

  const TfLiteTensor* input = GetInput(context, node, kInputTensor);
  TfLiteTensor* output = GetOutput(context, node, kOutputTensor);

  if (output->type == kTfLiteFloat32) {
#define TF_LITE_LOCAL_RESPONSE_NORM(type)                            \
  tflite::LocalResponseNormalizationParams op_params;                \
  op_params.range = params->radius;                                  \
  op_params.bias = params->bias;                                     \
  op_params.alpha = params->alpha;                                   \
  op_params.beta = params->beta;                                     \
  type::LocalResponseNormalization(                                  \
      op_params, GetTensorShape(input), GetTensorData<float>(input), \
      GetTensorShape(output), GetTensorData<float>(output))
    if (kernel_type == kReference) {
      TF_LITE_LOCAL_RESPONSE_NORM(reference_ops);
    }
    if (kernel_type == kGenericOptimized) {
      TF_LITE_LOCAL_RESPONSE_NORM(optimized_ops);
    }
#undef TF_LITE_LOCAL_RESPONSE_NORM
  } else {
    context->ReportError(context, "Output type is %d, requires float.",
                         output->type);
    return kTfLiteError;
  }

  return kTfLiteOk;
}


  int size() const {
    return m_str ? m_str->size() : 0;
  }


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;
}


void RunOneAveragePoolTest(const PoolParams& params,
                           const RuntimeShape& input_shape,
                           const int8* input_data,
                           const RuntimeShape& output_shape) {
  const int buffer_size = output_shape.FlatSize();
  std::vector<int8> optimized_averagePool_output(buffer_size);
  std::vector<int8> reference_averagePool_output(buffer_size);

  reference_integer_ops::AveragePool(params, input_shape, input_data,
                                     output_shape,
                                     reference_averagePool_output.data());
  optimized_integer_ops::AveragePool(params, input_shape, input_data,
                                     output_shape,
                                     optimized_averagePool_output.data());

  for (int i = 0; i < buffer_size; i++) {
    EXPECT_TRUE(reference_averagePool_output[i] ==
                optimized_averagePool_output[i]);
  }
}


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;
}


TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) {
  OpData* data = static_cast<OpData*>(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);

  // TODO(b/128934713): Add support for fixed-point per-channel quantization.
  // Currently this only support affine per-layer quantization.
  TF_LITE_ENSURE_EQ(context, output->quantization.type,
                    kTfLiteAffineQuantization);
  const auto* affine_quantization =
      static_cast<TfLiteAffineQuantization*>(output->quantization.params);
  TF_LITE_ENSURE(context, affine_quantization);
  TF_LITE_ENSURE(context, affine_quantization->scale);
  TF_LITE_ENSURE(context, affine_quantization->scale->size == 1);

  if (input->type == kTfLiteFloat32) {
    // Quantize use case.
    TF_LITE_ENSURE(context, output->type == kTfLiteUInt8 ||
                                output->type == kTfLiteInt8 ||
                                output->type == kTfLiteInt16);
  } else {
    // Requantize use case.
    if (input->type == kTfLiteInt16) {
      TF_LITE_ENSURE(
          context, output->type == kTfLiteInt8 || output->type == kTfLiteInt16);
    } else {
      TF_LITE_ENSURE(context,
                     input->type == kTfLiteInt8 || input->type == kTfLiteUInt8);
      TF_LITE_ENSURE(
          context, output->type == kTfLiteUInt8 || output->type == kTfLiteInt8);
    }
    const double effective_output_scale =
        static_cast<double>(input->params.scale) /
        static_cast<double>(output->params.scale);
    QuantizeMultiplier(effective_output_scale, &data->output_multiplier,
                       &data->output_shift);
  }

  return context->ResizeTensor(context, output,
                               TfLiteIntArrayCopy(input->dims));
}


TEST(DefaultCertValidatorTest, TestMatchSubjectAltNameWildcardDNSMatched) {
  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("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_TRUE(DefaultCertValidator::matchSubjectAltName(cert.get(), subject_alt_name_matchers));
}


  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());
    }
  }


static int putint(jas_stream_t *out, int sgnd, int prec, long val)
{
	int n;
	int c;
	bool s;
	ulong tmp;
	assert((!sgnd && prec >= 1) || (sgnd && prec >= 2));
	if (sgnd) {
		val = encode_twos_comp(val, prec);
	}
	assert(val >= 0);
	val &= (1 << prec) - 1;
	n = (prec + 7) / 8;
	while (--n >= 0) {
		c = (val >> (n * 8)) & 0xff;
		if (jas_stream_putc(out, c) != c)
			return -1;
	}
	return 0;
}


  void Compute(OpKernelContext* context) override {
    // Get the input Tensors.
    OpInputList params_nested_splits_in;
    OP_REQUIRES_OK(context, context->input_list("params_nested_splits",
                                                &params_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));
  }


static int base64decode_block(unsigned char *target, const char *data, size_t data_size)
{
	int w1,w2,w3,w4;
	int i;
	size_t n;

	if (!data || (data_size <= 0)) {
		return 0;
	}

	n = 0;
	i = 0;
	while (n < data_size-3) {
		w1 = base64_table[(int)data[n]];
		w2 = base64_table[(int)data[n+1]];
		w3 = base64_table[(int)data[n+2]];
		w4 = base64_table[(int)data[n+3]];

		if (w2 >= 0) {
			target[i++] = (char)((w1*4 + (w2 >> 4)) & 255);
		}
		if (w3 >= 0) {
			target[i++] = (char)((w2*16 + (w3 >> 2)) & 255);
		}
		if (w4 >= 0) {
			target[i++] = (char)((w3*64 + w4) & 255);
		}
		n+=4;
	}
	return i;
}


  void setSanMatchers(std::vector<envoy::type::matcher::v3::StringMatcher> san_matchers) {
    san_matchers_ = san_matchers;
  };


static BOOL ntlm_av_pair_check(NTLM_AV_PAIR* pAvPair, size_t cbAvPair)
{
	if (!pAvPair || cbAvPair < sizeof(NTLM_AV_PAIR))
		return FALSE;
	return cbAvPair >= ntlm_av_pair_get_next_offset(pAvPair);
}


TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) {
  TfLiteTensor* output = GetOutput(context, node, kOutputTensor);
  const TfLiteTensor* input = GetInput(context, node, kInputTensor);
  FillDiagHelper(input, output);
  return kTfLiteOk;
}


TEST_P(SslSPIFFECertValidatorIntegrationTest, ServerRsaSPIFFEValidatorSANMatch) {
  auto typed_conf = new envoy::config::core::v3::TypedExtensionConfig();
  TestUtility::loadFromYaml(TestEnvironment::substitute(R"EOF(
name: envoy.tls.cert_validator.spiffe
typed_config:
  "@type": type.googleapis.com/envoy.extensions.transport_sockets.tls.v3.SPIFFECertValidatorConfig
  trust_domains:
    - name: lyft.com
      trust_bundle:
        filename: "{{ test_rundir }}/test/config/integration/certs/cacert.pem"
  )EOF"),
                            *typed_conf);
  custom_validator_config_ = typed_conf;

  envoy::type::matcher::v3::StringMatcher matcher;
  matcher.set_prefix("spiffe://lyft.com/");
  san_matchers_ = {matcher};

  ConnectionCreationFunction creator = [&]() -> Network::ClientConnectionPtr {
    return makeSslClientConnection({});
  };
  testRouterRequestAndResponseWithBody(1024, 512, false, false, &creator);
  checkVerifyErrorCouter(0);
}


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);
}


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;
}


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;
}


CbrDetectorRemote::Result CbrDetectorRemote::Decrypt(cricket::MediaType media_type,
						const std::vector<uint32_t>& csrcs,
						rtc::ArrayView<const uint8_t> additional_data,
						rtc::ArrayView<const uint8_t> encrypted_frame,
						rtc::ArrayView<uint8_t> frame)
{
	const uint8_t *src = encrypted_frame.data();
	uint8_t *dst = frame.data();
	uint32_t data_len = encrypted_frame.size();

	if (media_type == cricket::MEDIA_TYPE_AUDIO) {
		if (data_len == frame_size && frame_size >= 40) {
			frame_count++;
			if (frame_count > 200 && !detected) {
				info("CBR detector: remote cbr detected\n");
				detected = true;
			}
		}
		else {
			frame_count = 0;
			frame_size = data_len;
			if (detected) {
				info("CBR detector: remote cbr detected disabled\n");
				detected = false;
			}
		}

	}

	memcpy(dst, src, data_len);

out:
	return CbrDetectorRemote::Result(CbrDetectorRemote::Status::kOk, data_len);
}


void Context::onDelete() {
  if (wasm_->onDelete_) {
    wasm_->onDelete_(this, id_);
  }
}


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* seq_lengths = GetInput(context, node, kSeqLengthsTensor);
  TF_LITE_ENSURE_EQ(context, NumDimensions(seq_lengths), 1);

  if (input->type != kTfLiteInt32 && input->type != kTfLiteFloat32 &&
      input->type != kTfLiteUInt8 && input->type != kTfLiteInt16 &&
      input->type != kTfLiteInt64) {
    context->ReportError(context,
                         "Type '%s' is not supported by reverse_sequence.",
                         TfLiteTypeGetName(input->type));
    return kTfLiteError;
  }

  if (seq_lengths->type != kTfLiteInt32 && seq_lengths->type != kTfLiteInt64) {
    context->ReportError(
        context, "Seq_lengths type '%s' is not supported by reverse_sequence.",
        TfLiteTypeGetName(seq_lengths->type));
    return kTfLiteError;
  }

  TfLiteTensor* output = GetOutput(context, node, kOutputTensor);
  TfLiteIntArray* output_shape = TfLiteIntArrayCopy(input->dims);
  TF_LITE_ENSURE_TYPES_EQ(context, output->type, input->type);

  return context->ResizeTensor(context, output, output_shape);
}


TfLiteStatus AverageEval(TfLiteContext* context, TfLiteNode* node) {
  auto* params = reinterpret_cast<TfLitePoolParams*>(node->builtin_data);
  OpData* data = reinterpret_cast<OpData*>(node->user_data);

  TfLiteTensor* output;
  TF_LITE_ENSURE_OK(context, GetOutputSafe(context, node, 0, &output));
  const TfLiteTensor* input;
  TF_LITE_ENSURE_OK(context, GetInputSafe(context, node, 0, &input));
  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;
}


TfLiteStatus PrepareMeanOrSum(TfLiteContext* context, TfLiteNode* node) {
  TF_LITE_ENSURE_OK(context, PrepareSimple(context, node));
  OpData* data = reinterpret_cast<OpData*>(node->user_data);

  // reduce_mean requires a buffer to store intermediate sum result.
  OpContext op_context(context, node);
  if (op_context.input->type == kTfLiteInt8 ||
      op_context.input->type == kTfLiteUInt8 ||
      op_context.input->type == kTfLiteInt16) {
    const double real_multiplier =
        static_cast<double>(op_context.input->params.scale) /
        static_cast<double>(op_context.output->params.scale);
    int exponent;
    QuantizeMultiplier(real_multiplier, &data->multiplier, &exponent);
    data->shift = exponent;
  }
  TfLiteTensor* temp_sum = GetTemporary(context, node, /*index=*/2);
  if (!IsConstantTensor(op_context.axis)) {
    SetTensorToDynamic(temp_sum);
    return kTfLiteOk;
  }
  temp_sum->allocation_type = kTfLiteArenaRw;
  return ResizeTempSum(context, &op_context, temp_sum);
}


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));
  }
}


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));
}


TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) {
  const TfLiteTensor* input = GetInput(context, node, kInputTensor);
  const TfLiteTensor* axis_tensor = GetInput(context, node, kAxisTensor);
  int axis = GetTensorData<int32_t>(axis_tensor)[0];
  const int rank = NumDimensions(input);
  if (axis < 0) {
    axis += rank;
  }

  TF_LITE_ENSURE(context, axis >= 0 && axis < rank);
  TfLiteTensor* output = GetOutput(context, node, kOutputTensor);

  switch (output->type) {
    case kTfLiteFloat32: {
      reference_ops::Reverse<float>(
          axis, GetTensorShape(input), GetTensorData<float>(input),
          GetTensorShape(output), GetTensorData<float>(output));
      break;
    }
    case kTfLiteUInt8: {
      reference_ops::Reverse<uint8_t>(
          axis, GetTensorShape(input), GetTensorData<uint8_t>(input),
          GetTensorShape(output), GetTensorData<uint8_t>(output));
      break;
    }
    case kTfLiteInt16: {
      reference_ops::Reverse<int16_t>(
          axis, GetTensorShape(input), GetTensorData<int16_t>(input),
          GetTensorShape(output), GetTensorData<int16_t>(output));
      break;
    }
    case kTfLiteInt32: {
      reference_ops::Reverse<int32_t>(
          axis, GetTensorShape(input), GetTensorData<int32_t>(input),
          GetTensorShape(output), GetTensorData<int32_t>(output));
      break;
    }
    case kTfLiteInt64: {
      reference_ops::Reverse<int64_t>(
          axis, GetTensorShape(input), GetTensorData<int64_t>(input),
          GetTensorShape(output), GetTensorData<int64_t>(output));
      break;
    }
    case kTfLiteBool: {
      reference_ops::Reverse<bool>(
          axis, GetTensorShape(input), GetTensorData<bool>(input),
          GetTensorShape(output), GetTensorData<bool>(output));
      break;
    }
    default: {
      context->ReportError(context, "Type '%s' is not supported by reverse.",
                           TfLiteTypeGetName(output->type));
      return kTfLiteError;
    }
  }

  return kTfLiteOk;
}


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;
}


void pointZZ_pAdd(PointZZ_p * rop, const PointZZ_p * op1, const PointZZ_p * op2, const CurveZZ_p * curve) {
    mpz_t xdiff, ydiff, lambda;
    mpz_inits(xdiff, ydiff, lambda, NULL);

    // calculate lambda
    mpz_sub(ydiff, op2->y, op1->y);
    mpz_sub(xdiff, op2->x, op1->x);
    mpz_invert(xdiff, xdiff, curve->p);  // TODO check status
    mpz_mul(lambda, ydiff, xdiff);
    mpz_mod(lambda, lambda, curve->p);

    // calculate resulting x coord
    mpz_mul(rop->x, lambda, lambda);
    mpz_sub(rop->x, rop->x, op1->x);
    mpz_sub(rop->x, rop->x, op2->x);
    mpz_mod(rop->x, rop->x, curve->p);

    //calculate resulting y coord
    mpz_sub(rop->y, op1->x, rop->x);
    mpz_mul(rop->y, lambda, rop->y);
    mpz_sub(rop->y, rop->y, op1->y);
    mpz_mod(rop->y, rop->y, curve->p);

    mpz_clears(xdiff, ydiff, lambda, NULL);
}


TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) {
  auto* data =
      reinterpret_cast<TfLiteAudioMicrofrontendParams*>(node->user_data);
  FrontendReset(data->state);

  const TfLiteTensor* input = GetInput(context, node, kInputTensor);
  TfLiteTensor* output = GetOutput(context, node, kOutputTensor);

  if (data->out_float) {
    GenerateFeatures<float>(data, input, output);
  } else {
    GenerateFeatures<int32>(data, input, output);
  }

  return kTfLiteOk;
}


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;
        });
    }
}


  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());
    }
  }


int CommandData::IsProcessFile(FileHeader &FileHead,bool *ExactMatch,int MatchType,
                               wchar *MatchedArg,uint MatchedArgSize)
{
  if (MatchedArg!=NULL && MatchedArgSize>0)
    *MatchedArg=0;
  if (wcslen(FileHead.FileName)>=NM)
    return 0;
  bool Dir=FileHead.Dir;
  if (ExclCheck(FileHead.FileName,Dir,false,true))
    return 0;
#ifndef SFX_MODULE
  if (TimeCheck(FileHead.mtime))
    return 0;
  if ((FileHead.FileAttr & ExclFileAttr)!=0 || InclAttrSet && (FileHead.FileAttr & InclFileAttr)==0)
    return 0;
  if (!Dir && SizeCheck(FileHead.UnpSize))
    return 0;
#endif
  wchar *ArgName;
  FileArgs.Rewind();
  for (int StringCount=1;(ArgName=FileArgs.GetString())!=NULL;StringCount++)
    if (CmpName(ArgName,FileHead.FileName,MatchType))
    {
      if (ExactMatch!=NULL)
        *ExactMatch=wcsicompc(ArgName,FileHead.FileName)==0;
      if (MatchedArg!=NULL)
        wcsncpyz(MatchedArg,ArgName,MatchedArgSize);
      return StringCount;
    }
  return 0;
}


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;
}


TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) {
  TfLiteTensor* output = GetOutput(context, node, kOutputTensor);

  switch (output->type) {
    case kTfLiteFloat32: {
      return ReverseSequenceHelper<float>(context, node);
    }
    case kTfLiteUInt8: {
      return ReverseSequenceHelper<uint8_t>(context, node);
    }
    case kTfLiteInt16: {
      return ReverseSequenceHelper<int16_t>(context, node);
    }
    case kTfLiteInt32: {
      return ReverseSequenceHelper<int32_t>(context, node);
    }
    case kTfLiteInt64: {
      return ReverseSequenceHelper<int64_t>(context, node);
    }
    default: {
      context->ReportError(context,
                           "Type '%s' is not supported by reverse_sequence.",
                           TfLiteTypeGetName(output->type));
      return kTfLiteError;
    }
  }
  return kTfLiteOk;
}  // namespace


static inline int min(int a, int b) {return a<b ? a : b;}


TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) {
  auto* params =
      reinterpret_cast<TfLiteLocalResponseNormParams*>(node->builtin_data);

  const TfLiteTensor* input = GetInput(context, node, kInputTensor);
  TfLiteTensor* output = GetOutput(context, node, kOutputTensor);

  if (output->type == kTfLiteFloat32) {
#define TF_LITE_LOCAL_RESPONSE_NORM(type)                            \
  tflite::LocalResponseNormalizationParams op_params;                \
  op_params.range = params->radius;                                  \
  op_params.bias = params->bias;                                     \
  op_params.alpha = params->alpha;                                   \
  op_params.beta = params->beta;                                     \
  type::LocalResponseNormalization(                                  \
      op_params, GetTensorShape(input), GetTensorData<float>(input), \
      GetTensorShape(output), GetTensorData<float>(output))
    if (kernel_type == kReference) {
      TF_LITE_LOCAL_RESPONSE_NORM(reference_ops);
    }
    if (kernel_type == kGenericOptimized) {
      TF_LITE_LOCAL_RESPONSE_NORM(optimized_ops);
    }
#undef TF_LITE_LOCAL_RESPONSE_NORM
  } else {
    context->ReportError(context, "Output type is %d, requires float.",
                         output->type);
    return kTfLiteError;
  }

  return kTfLiteOk;
}


    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;
    }


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;
}


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;
}


  explicit ThreadPoolHandleOp(OpKernelConstruction* ctx) : OpKernel(ctx) {
    OP_REQUIRES_OK(ctx, ctx->GetAttr("display_name", &display_name_));
    OP_REQUIRES_OK(ctx, ctx->GetAttr("num_threads", &num_threads_));
    OP_REQUIRES_OK(ctx, ctx->GetAttr("max_intra_op_parallelism",
                                     &max_intra_op_parallelism_));
    OP_REQUIRES(
        ctx, num_threads_ > 0,
        errors::InvalidArgument("`num_threads` must be greater than zero."));
  }


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;
  TF_LITE_ENSURE_OK(context,
                    GetInputSafe(context, node, kInputTensor1, &input1));
  const TfLiteTensor* input2;
  TF_LITE_ENSURE_OK(context,
                    GetInputSafe(context, node, kInputTensor2, &input2));
  TfLiteTensor* output;
  TF_LITE_ENSURE_OK(context,
                    GetOutputSafe(context, node, kOutputTensor, &output));

  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;
}


TEST(DefaultCertValidatorTest, TestMatchSubjectAltNameURIMatched) {
  bssl::UniquePtr<X509> cert = readCertFromFile(TestEnvironment::substitute(
      "{{ test_rundir }}/test/extensions/transport_sockets/tls/test_data/san_uri_cert.pem"));
  envoy::type::matcher::v3::StringMatcher matcher;
  matcher.MergeFrom(TestUtility::createRegexMatcher("spiffe://lyft.com/.*-team"));
  std::vector<Matchers::StringMatcherImpl<envoy::type::matcher::v3::StringMatcher>>
      subject_alt_name_matchers;
  subject_alt_name_matchers.push_back(Matchers::StringMatcherImpl(matcher));
  EXPECT_TRUE(DefaultCertValidator::matchSubjectAltName(cert.get(), subject_alt_name_matchers));
}


  char * unescape(char * dest, const char * src)
  {
    while (*src) {
      if (*src == '\\') {
	++src;
	switch (*src) {
	case 'n': *dest = '\n'; break;
	case 'r': *dest = '\r'; break;
	case 't': *dest = '\t'; break;
	case 'f': *dest = '\f'; break;
	case 'v': *dest = '\v'; break;
	default: *dest = *src;
	}
      } else {
	*dest = *src;
      }
      ++src;
      ++dest;
    }
    *dest = '\0';
    return dest;
  }


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;
}


UrlQuery::UrlQuery(const std::string& encoded_str) {
  if (!encoded_str.empty()) {
    // Split into key value pairs separated by '&'.
    for (std::size_t i = 0; i != std::string::npos;) {
      std::size_t j = encoded_str.find_first_of('&', i);

      std::string kv;
      if (j == std::string::npos) {
        kv = encoded_str.substr(i);
        i = std::string::npos;
      } else {
        kv = encoded_str.substr(i, j - i);
        i = j + 1;
      }

      string_view key;
      string_view value;
      if (SplitKV(kv, '=', false, &key, &value)) {
        parameters_.push_back({ DecodeUnsafe(key), DecodeUnsafe(value) });
      }
    }
  }
}


PROCESS_THREAD(snmp_process, ev, data)
{
  PROCESS_BEGIN();

  /* new connection with remote host */
  snmp_udp_conn = udp_new(NULL, 0, NULL);
  udp_bind(snmp_udp_conn, SNMP_SERVER_PORT);
  LOG_DBG("Listening on port %u\n", uip_ntohs(snmp_udp_conn->lport));

  while(1) {
    PROCESS_YIELD();

    if(ev == tcpip_event) {
      if(uip_newdata()) {
        snmp_process_data();
      }
    }
  } /* while (1) */

  PROCESS_END();
}


void AveragePool(const float* input_data, const Dims<4>& input_dims,
                 int stride_width, int stride_height, int pad_width,
                 int pad_height, int kwidth, int kheight, float* output_data,
                 const Dims<4>& output_dims) {
  float output_activation_min, output_activation_max;
  GetActivationMinMax(Ac, &output_activation_min, &output_activation_max);

  AveragePool(input_data, input_dims, stride_width, stride_height, pad_width,
              pad_height, kwidth, kheight, output_activation_min,
              output_activation_max, output_data, output_dims);
}


int main(int argc, char * argv[])
{
    gr_face * face = 0;
    try
	{
		if (argc != 2)	throw std::length_error("not enough arguments: need a backing font");

		dummyFace = face_handle(argv[1]);
		testFeatTable<FeatTableTestA>(testDataA, "A\n");
		testFeatTable<FeatTableTestB>(testDataB, "B\n");
		testFeatTable<FeatTableTestB>(testDataBunsorted, "Bu\n");
		testFeatTable<FeatTableTestC>(testDataCunsorted, "C\n");
		testFeatTable<FeatTableTestD>(testDataDunsorted, "D\n");
		testFeatTable<FeatTableTestE>(testDataE, "E\n");

		// test a bad settings offset stradling the end of the table
		FeatureMap testFeatureMap;
		dummyFace.replace_table(TtfUtil::Tag::Feat, &testBadOffset, sizeof testBadOffset);
		face = gr_make_face_with_ops(&dummyFace, &face_handle::ops, gr_face_dumbRendering);
		bool readStatus = testFeatureMap.readFeats(*face);
		testAssert("fail gracefully on bad table", !readStatus);
	}
	catch (std::exception & e)
	{
		fprintf(stderr, "%s: %s\n", argv[0], e.what());
		gr_face_destroy(face);
		return 1;
	}

    gr_face_destroy(face);
    return 0;
}


TEST_CASE_METHOD(TestFixture, "DKG AES gen test", "[dkg-aes-gen]") {
    vector <uint8_t> encryptedDKGSecret(BUF_LEN, 0);
    vector<char> errMsg(BUF_LEN, 0);

    int errStatus = 0;
    uint32_t encLen = 0;

    PRINT_SRC_LINE
    auto status = trustedGenDkgSecretAES(eid, &errStatus, errMsg.data(), encryptedDKGSecret.data(), &encLen, 32);
    REQUIRE(status == SGX_SUCCESS);
    REQUIRE(errStatus == SGX_SUCCESS);

    vector<char> secret(BUF_LEN, 0);
    vector<char> errMsg1(BUF_LEN, 0);

    status = trustedDecryptDkgSecretAES(eid, &errStatus, errMsg1.data(), encryptedDKGSecret.data(),
                                        encLen, (uint8_t *) secret.data());

    REQUIRE(status == SGX_SUCCESS);
    REQUIRE(errStatus == SGX_SUCCESS);
}


        unsigned int GetU32BE (int nPos, bool *pbSuccess)
        {
            //*pbSuccess = true;

            if ( nPos < 0 || nPos + 3 >= m_nLen )
            {
                *pbSuccess = false;
                return 0;
            }
            unsigned int nRes = m_sFile[nPos];
            nRes = (nRes << 8) + m_sFile[nPos + 1];
            nRes = (nRes << 8) + m_sFile[nPos + 2];
            nRes = (nRes << 8) + m_sFile[nPos + 3];
            return nRes;
        }


static jas_image_cmpt_t *jas_image_cmpt_create(int_fast32_t tlx,
  int_fast32_t tly, int_fast32_t hstep, int_fast32_t vstep,
  int_fast32_t width, int_fast32_t height, uint_fast16_t depth, bool sgnd,
  uint_fast32_t inmem)
{
	jas_image_cmpt_t *cmpt;
	size_t size;

	cmpt = 0;
	if (width < 0 || height < 0 || hstep <= 0 || vstep <= 0) {
		goto error;
	}
	if (!jas_safe_intfast32_add(tlx, width, 0) ||
	  !jas_safe_intfast32_add(tly, height, 0)) {
		goto error;
	}

	if (!(cmpt = jas_malloc(sizeof(jas_image_cmpt_t)))) {
		goto error;
	}

	cmpt->type_ = JAS_IMAGE_CT_UNKNOWN;
	cmpt->tlx_ = tlx;
	cmpt->tly_ = tly;
	cmpt->hstep_ = hstep;
	cmpt->vstep_ = vstep;
	cmpt->width_ = width;
	cmpt->height_ = height;
	cmpt->prec_ = depth;
	cmpt->sgnd_ = sgnd;
	cmpt->stream_ = 0;
	cmpt->cps_ = (depth + 7) / 8;

	// Compute the number of samples in the image component, while protecting
	// against overflow.
	// size = cmpt->width_ * cmpt->height_ * cmpt->cps_;
	if (!jas_safe_size_mul(cmpt->width_, cmpt->height_, &size) ||
	  !jas_safe_size_mul(size, cmpt->cps_, &size)) {
		goto error;
	}
	cmpt->stream_ = (inmem) ? jas_stream_memopen(0, size) :
	  jas_stream_tmpfile();
	if (!cmpt->stream_) {
		goto error;
	}

	/* Zero the component data.  This isn't necessary, but it is
	convenient for debugging purposes. */
	/* Note: conversion of size - 1 to long can overflow */
	if (jas_stream_seek(cmpt->stream_, size - 1, SEEK_SET) < 0 ||
	  jas_stream_putc(cmpt->stream_, 0) == EOF ||
	  jas_stream_seek(cmpt->stream_, 0, SEEK_SET) < 0) {
		goto error;
	}

	return cmpt;

error:
	if (cmpt) {
		jas_image_cmpt_destroy(cmpt);
	}
	return 0;
}


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);
  TfLiteIntArray* input_dims = input->dims;
  int input_dims_size = input_dims->size;
  TF_LITE_ENSURE(context, input_dims_size >= 2);

  TfLiteTensor* output = GetOutput(context, node, kOutputTensor);

  TfLiteIntArray* output_shape = TfLiteIntArrayCreate(input_dims_size);
  for (int i = 0; i < input_dims_size; i++) {
    output_shape->data[i] = input_dims->data[i];
  }

  // Resize the output tensor to the same size as the input tensor.
  output->type = input->type;
  TF_LITE_ENSURE_OK(context,
                    context->ResizeTensor(context, output, output_shape));

  return kTfLiteOk;
}


TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) {
  Subgraph* subgraph = reinterpret_cast<Subgraph*>(context->impl_);

  const TfLiteTensor* input_resource_id_tensor =
      GetInput(context, node, kInputVariableId);
  const TfLiteTensor* input_value_tensor = GetInput(context, node, kInputValue);

  int resource_id = input_resource_id_tensor->data.i32[0];
  auto& resources = subgraph->resources();
  resource::CreateResourceVariableIfNotAvailable(&resources, resource_id);
  auto* variable = resource::GetResourceVariable(&resources, resource_id);
  TF_LITE_ENSURE(context, variable != nullptr);
  variable->AssignFrom(input_value_tensor);

  return kTfLiteOk;
}


void Context::onUpstreamConnectionClose(PeerType peer_type) {
  if (wasm_->onUpstreamConnectionClose_) {
    wasm_->onUpstreamConnectionClose_(this, id_, static_cast<uint32_t>(peer_type));
  }
}


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);
}


TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) {
  const TfLiteTensor* input = GetInput(context, node, kInputTensor);
  TfLiteTensor* output = GetOutput(context, node, kOutputTensor);
  const TfLiteTensor* multipliers = GetInput(context, node, kInputMultipliers);

  if (IsDynamicTensor(output)) {
    TF_LITE_ENSURE_OK(context, ResizeOutput(context, node));
  }

  switch (output->type) {
    case kTfLiteFloat32:
      Tile<float>(*(input->dims), input, multipliers, output);
      break;
    case kTfLiteUInt8:
      Tile<uint8_t>(*(input->dims), input, multipliers, output);
      break;
    case kTfLiteInt32:
      Tile<int32_t>(*(input->dims), input, multipliers, output);
      break;
    case kTfLiteInt64:
      Tile<int64_t>(*(input->dims), input, multipliers, output);
      break;
    case kTfLiteString: {
      DynamicBuffer buffer;
      TileString(*(input->dims), input, multipliers, &buffer, output);
      buffer.WriteToTensor(output, /*new_shape=*/nullptr);
      break;
    }
    case kTfLiteBool:
      Tile<bool>(*(input->dims), input, multipliers, output);
      break;
    default:
      context->ReportError(context, "Type '%s' is not supported by tile.",
                           TfLiteTypeGetName(output->type));
      return kTfLiteError;
  }
  return kTfLiteOk;
}


void jas_matrix_divpow2(jas_matrix_t *matrix, int n)
{
	int i;
	int j;
	jas_seqent_t *rowstart;
	int rowstep;
	jas_seqent_t *data;

	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 = (*data >= 0) ? ((*data) >> n) :
				  (-((-(*data)) >> n));
			}
		}
	}
}


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();
}


QString Helper::temporaryMountDevice(const QString &device, const QString &name, bool readonly)
{
    QString mount_point = mountPoint(device);

    if (!mount_point.isEmpty())
        return mount_point;

    mount_point = "%1/.%2/mount/%3";
    const QStringList &tmp_paths = QStandardPaths::standardLocations(QStandardPaths::TempLocation);

    mount_point = mount_point.arg(tmp_paths.isEmpty() ? "/tmp" : tmp_paths.first()).arg(qApp->applicationName()).arg(name);

    if (!QDir::current().mkpath(mount_point)) {
        dCError("mkpath \"%s\" failed", qPrintable(mount_point));

        return QString();
    }

    if (!mountDevice(device, mount_point, readonly)) {
        dCError("Mount the device \"%s\" to \"%s\" failed", qPrintable(device), qPrintable(mount_point));

        return QString();
    }

    return mount_point;
}


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;
}


bool Unpack::ProcessDecoded(UnpackThreadData &D)
{
  UnpackDecodedItem *Item=D.Decoded,*Border=D.Decoded+D.DecodedSize;
  while (Item<Border)
  {
    UnpPtr&=MaxWinMask;
    if (((WriteBorder-UnpPtr) & MaxWinMask)<MAX_LZ_MATCH+3 && WriteBorder!=UnpPtr)
    {
      UnpWriteBuf();
      if (WrittenFileSize>DestUnpSize)
        return false;
    }

    if (Item->Type==UNPDT_LITERAL)
    {
#if defined(LITTLE_ENDIAN) && defined(ALLOW_MISALIGNED)
      if (Item->Length==3 && UnpPtr<MaxWinSize-4)
      {
        *(uint32 *)(Window+UnpPtr)=*(uint32 *)Item->Literal;
        UnpPtr+=4;
      }
      else
#endif
        for (uint I=0;I<=Item->Length;I++)
          Window[UnpPtr++ & MaxWinMask]=Item->Literal[I];
    }
    else
      if (Item->Type==UNPDT_MATCH)
      {
        InsertOldDist(Item->Distance);
        LastLength=Item->Length;
        CopyString(Item->Length,Item->Distance);
      }
      else
        if (Item->Type==UNPDT_REP)
        {
          uint Distance=OldDist[Item->Distance];
          for (uint I=Item->Distance;I>0;I--)
            OldDist[I]=OldDist[I-1];
          OldDist[0]=Distance;
          LastLength=Item->Length;
          CopyString(Item->Length,Distance);
        }
        else
          if (Item->Type==UNPDT_FULLREP)
          {
            if (LastLength!=0)
              CopyString(LastLength,OldDist[0]);
          }
          else
            if (Item->Type==UNPDT_FILTER)
            {
              UnpackFilter Filter;

              Filter.Type=(byte)Item->Length;
              Filter.BlockStart=Item->Distance;

              Item++;

              Filter.Channels=(byte)Item->Length;
              Filter.BlockLength=Item->Distance;

              AddFilter(Filter);
            }
    Item++;
  }
  return true;
}


int MSADPCM::decodeBlock(const uint8_t *encoded, int16_t *decoded)
{
	ms_adpcm_state decoderState[2];
	ms_adpcm_state *state[2];

	int channelCount = m_track->f.channelCount;

	// Calculate the number of bytes needed for decoded data.
	int outputLength = m_framesPerPacket * sizeof (int16_t) * channelCount;

	state[0] = &decoderState[0];
	if (channelCount == 2)
		state[1] = &decoderState[1];
	else
		state[1] = &decoderState[0];

	// Initialize block predictor.
	for (int i=0; i<channelCount; i++)
	{
		state[i]->predictorIndex = *encoded++;
		assert(state[i]->predictorIndex < m_numCoefficients);
	}

	// Initialize delta.
	for (int i=0; i<channelCount; i++)
	{
		state[i]->delta = (encoded[1]<<8) | encoded[0];
		encoded += sizeof (uint16_t);
	}

	// Initialize first two samples.
	for (int i=0; i<channelCount; i++)
	{
		state[i]->sample1 = (encoded[1]<<8) | encoded[0];
		encoded += sizeof (uint16_t);
	}

	for (int i=0; i<channelCount; i++)
	{
		state[i]->sample2 = (encoded[1]<<8) | encoded[0];
		encoded += sizeof (uint16_t);
	}

	const int16_t *coefficient[2] =
	{
		m_coefficients[state[0]->predictorIndex],
		m_coefficients[state[1]->predictorIndex]
	};

	for (int i=0; i<channelCount; i++)
		*decoded++ = state[i]->sample2;

	for (int i=0; i<channelCount; i++)
		*decoded++ = state[i]->sample1;

	/*
		The first two samples have already been 'decoded' in
		the block header.
	*/
	int samplesRemaining = (m_framesPerPacket - 2) * m_track->f.channelCount;

	while (samplesRemaining > 0)
	{
		uint8_t code;
		int16_t newSample;

		code = *encoded >> 4;
		newSample = decodeSample(*state[0], code, coefficient[0]);
		*decoded++ = newSample;

		code = *encoded & 0x0f;
		newSample = decodeSample(*state[1], code, coefficient[1]);
		*decoded++ = newSample;

		encoded++;
		samplesRemaining -= 2;
	}

	return outputLength;
}


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;
}


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));
}


FdInStream::FdInStream(int fd_, FdInStreamBlockCallback* blockCallback_,
                       int bufSize_)
  : fd(fd_), timeoutms(0), blockCallback(blockCallback_),
    timing(false), timeWaitedIn100us(5), timedKbits(0),
    bufSize(bufSize_ ? bufSize_ : DEFAULT_BUF_SIZE), offset(0)
{
  ptr = end = start = new U8[bufSize];
}


  static bool TryParse(const char* inp, int length,
                       TypedValue* buf, Variant& out,
                       JSONContainerType container_type, bool is_tsimplejson) {
    SimpleParser parser(inp, length, buf, container_type, is_tsimplejson);
    bool ok = parser.parseValue();
    parser.skipSpace();
    if (!ok || parser.p != inp + length) {
      // Unsupported, malformed, or trailing garbage. Release entire stack.
      tvDecRefRange(buf, parser.top);
      return false;
    }
    out = Variant::attach(*--parser.top);
    return true;
  }


void XfccIntegrationTest::initialize() {
  config_helper_.addConfigModifier(
      [&](envoy::extensions::filters::network::http_connection_manager::v3::HttpConnectionManager&
              hcm) -> void {
        hcm.set_forward_client_cert_details(fcc_);
        hcm.mutable_set_current_client_cert_details()->CopyFrom(sccd_);
      });

  config_helper_.addConfigModifier([&](envoy::config::bootstrap::v3::Bootstrap& bootstrap) -> void {
    auto transport_socket =
        bootstrap.mutable_static_resources()->mutable_clusters(0)->mutable_transport_socket();
    envoy::extensions::transport_sockets::tls::v3::UpstreamTlsContext context;
    auto* validation_context = context.mutable_common_tls_context()->mutable_validation_context();
    validation_context->mutable_trusted_ca()->set_filename(
        TestEnvironment::runfilesPath("test/config/integration/certs/upstreamcacert.pem"));
    validation_context->add_match_subject_alt_names()->set_suffix("lyft.com");
    transport_socket->set_name("envoy.transport_sockets.tls");
    transport_socket->mutable_typed_config()->PackFrom(context);
  });

  if (tls_) {
    config_helper_.addSslConfig();
  }

  context_manager_ =
      std::make_unique<Extensions::TransportSockets::Tls::ContextManagerImpl>(timeSystem());
  client_tls_ssl_ctx_ = createClientSslContext(false);
  client_mtls_ssl_ctx_ = createClientSslContext(true);
  HttpIntegrationTest::initialize();
}


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;
}


int64_t BZ2File::readImpl(char * buf, int64_t length) {
  if (length == 0) {
    return 0;
  }
  assertx(m_bzFile);
  int len = BZ2_bzread(m_bzFile, buf, length);
  /* Sometimes libbz2 will return fewer bytes than requested, and set bzerror
   * to BZ_STREAM_END, but it's not actually EOF, and you can keep reading from
   * the file - so, only set EOF after a failed read. This matches PHP5.
   */
  if (len <= 0) {
    setEof(true);
    if (len < 0) {
      return -1;
    }
  }
  return len;
}


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;
}


TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) {
  // Just copy input to output.
  const TfLiteTensor* input = GetInput(context, node, kInput);
  TfLiteTensor* output = GetOutput(context, node, 0);
  const TfLiteTensor* axis = GetInput(context, node, kAxis);
  if (IsDynamicTensor(output)) {
    int axis_value;
    TF_LITE_ENSURE_OK(context,
                      GetAxisValueFromTensor(context, *axis, &axis_value));
    TF_LITE_ENSURE_OK(context,
                      ExpandTensorDim(context, *input, axis_value, output));
  }
  if (output->type == kTfLiteString) {
    TfLiteTensorRealloc(input->bytes, output);
  }
  memcpy(output->data.raw, input->data.raw, input->bytes);
  return kTfLiteOk;
}


void reposition(int pos) { ptr = start + pos; }


jas_image_t *jas_image_create0()
{
	jas_image_t *image;

	if (!(image = jas_malloc(sizeof(jas_image_t)))) {
		return 0;
	}

	image->tlx_ = 0;
	image->tly_ = 0;
	image->brx_ = 0;
	image->bry_ = 0;
	image->clrspc_ = JAS_CLRSPC_UNKNOWN;
	image->numcmpts_ = 0;
	image->maxcmpts_ = 0;
	image->cmpts_ = 0;
	image->inmem_ = true;
	image->cmprof_ = 0;

	return image;
}


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"));
}


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);
}


static x86newTokenType getToken(const char *str, size_t *begin, size_t *end) {
	// Skip whitespace
	while (begin && isspace ((ut8)str[*begin])) {
		++(*begin);
	}

	if (!str[*begin]) {                // null byte
		*end = *begin;
		return TT_EOF;
	} else if (isalpha ((ut8)str[*begin])) {   // word token
		*end = *begin;
		while (end && isalnum ((ut8)str[*end])) {
			++(*end);
		}
		return TT_WORD;
	} else if (isdigit ((ut8)str[*begin])) {   // number token
		*end = *begin;
		while (end && isalnum ((ut8)str[*end])) {     // accept alphanumeric characters, because hex.
			++(*end);
		}
		return TT_NUMBER;
	} else {                             // special character: [, ], +, *, ...
		*end = *begin + 1;
		return TT_SPECIAL;
	}
}


Status OpLevelCostEstimator::PredictFusedBatchNormGrad(
    const OpContext& op_context, NodeCosts* node_costs) const {
  bool found_unknown_shapes = false;
  const auto& op_info = op_context.op_info;
  // y_backprop: op_info.inputs(0)
  // x: op_info.inputs(1)
  // scale: op_info.inputs(2)
  // mean: op_info.inputs(3)
  // variance or inverse of variance: op_info.inputs(4)
  ConvolutionDimensions dims = OpDimensionsFromInputs(
      op_info.inputs(1).shape(), op_info, &found_unknown_shapes);

  int64_t ops = 0;
  const auto rsqrt_cost = Eigen::internal::functor_traits<
      Eigen::internal::scalar_rsqrt_op<float>>::Cost;
  ops = dims.iz * (dims.batch * dims.ix * dims.iy * 11 + 5 + rsqrt_cost);
  node_costs->num_compute_ops = ops;

  const int64_t size_nhwc =
      CalculateTensorSize(op_info.inputs(1), &found_unknown_shapes);
  const int64_t size_c =
      CalculateTensorSize(op_info.inputs(2), &found_unknown_shapes);
  // TODO(dyoon): fix missing memory cost for variance input (size_c) and
  // yet another read of y_backprop (size_nhwc) internally.
  node_costs->num_input_bytes_accessed = {size_nhwc, size_nhwc, size_c, size_c};
  node_costs->num_output_bytes_accessed = {size_nhwc, size_c, size_c};
  // FusedBatchNormGrad has to read y_backprop internally.
  node_costs->internal_read_bytes = size_nhwc;
  node_costs->max_memory = node_costs->num_total_output_bytes();

  if (found_unknown_shapes) {
    node_costs->inaccurate = true;
    node_costs->num_nodes_with_unknown_shapes = 1;
  }
  return Status::OK();
}


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);
}


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;
}


TfLiteStatus SimpleStatefulOp::Invoke(TfLiteContext* context,
                                      TfLiteNode* node) {
  OpData* data = reinterpret_cast<OpData*>(node->user_data);
  *data->invoke_count += 1;

  const TfLiteTensor* input = GetInput(context, node, kInputTensor);
  const uint8_t* input_data = GetTensorData<uint8_t>(input);
  int size = NumElements(input->dims);

  uint8_t* sorting_buffer = reinterpret_cast<uint8_t*>(
      context->GetScratchBuffer(context, data->sorting_buffer));
  // Copy inputs data to the sorting buffer. We don't want to mutate the input
  // tensor as it might be used by a another node.
  for (int i = 0; i < size; i++) {
    sorting_buffer[i] = input_data[i];
  }

  // In place insertion sort on `sorting_buffer`.
  for (int i = 1; i < size; i++) {
    for (int j = i; j > 0 && sorting_buffer[j] < sorting_buffer[j - 1]; j--) {
      std::swap(sorting_buffer[j], sorting_buffer[j - 1]);
    }
  }

  TfLiteTensor* median = GetOutput(context, node, kMedianTensor);
  uint8_t* median_data = GetTensorData<uint8_t>(median);
  TfLiteTensor* invoke_count = GetOutput(context, node, kInvokeCount);
  int32_t* invoke_count_data = GetTensorData<int32_t>(invoke_count);

  median_data[0] = sorting_buffer[size / 2];
  invoke_count_data[0] = *data->invoke_count;
  return kTfLiteOk;
}


std::string decodeBase64(
    const std::string& encoded) {

  if (encoded.size() == 0) {
    // special case, to prevent an integer overflow down below.
    return "";
  }

  using namespace boost::archive::iterators;
  using b64it =
    transform_width<binary_from_base64<std::string::const_iterator>, 8, 6>;

  std::string decoded = std::string(b64it(std::begin(encoded)),
                                    b64it(std::end(encoded)));

  uint32_t numPadding = std::count(encoded.begin(), encoded.end(), '=');
  decoded.erase(decoded.end() - numPadding, decoded.end());

  return decoded;
}


inline bool loadModule(const char* filename, IR::Module& outModule)
{
	// Read the specified file into an array.
	std::vector<U8> fileBytes;
	if(!loadFile(filename, fileBytes)) { return false; }

	// If the file starts with the WASM binary magic number, load it as a binary irModule.
	if(*(U32*)fileBytes.data() == 0x6d736100)
	{ return loadBinaryModule(fileBytes.data(), fileBytes.size(), outModule); }
	else
	{
		// Make sure the WAST file is null terminated.
		fileBytes.push_back(0);

		// Load it as a text irModule.
		std::vector<WAST::Error> parseErrors;
		if(!WAST::parseModule(
			   (const char*)fileBytes.data(), fileBytes.size(), outModule, parseErrors))
		{
			Log::printf(Log::error, "Error parsing WebAssembly text file:\n");
			reportParseErrors(filename, parseErrors);
			return false;
		}

		return true;
	}
}


inline TfLiteStatus EvalImpl(TfLiteContext* context, TfLiteNode* node,
                             std::function<T(T)> func,
                             TfLiteType expected_type) {
  const TfLiteTensor* input = GetInput(context, node, 0);
  TfLiteTensor* output = GetOutput(context, node, 0);
  TF_LITE_ENSURE_TYPES_EQ(context, input->type, expected_type);
  const int64_t num_elements = NumElements(input);
  const T* in_data = GetTensorData<T>(input);
  T* out_data = GetTensorData<T>(output);
  for (int64_t i = 0; i < num_elements; ++i) {
    out_data[i] = func(in_data[i]);
  }
  return kTfLiteOk;
}


TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) {
  const TfLiteTensor* input = GetInput(context, node, kInputTensor);
  TfLiteTensor* output = GetOutput(context, node, kOutputTensor);
  TF_LITE_ENSURE_EQ(context, NumInputs(node), 1);
  TF_LITE_ENSURE_EQ(context, NumOutputs(node), 1);
  TF_LITE_ENSURE_TYPES_EQ(context, input->type, kTfLiteFloat32);
  output->type = input->type;
  TfLiteIntArray* output_size = TfLiteIntArrayCopy(input->dims);
  return context->ResizeTensor(context, output, output_size);
}


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;
}


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);
}


static String HHVM_FUNCTION(bcsub, const String& left, const String& right,
                            int64_t scale /* = -1 */) {
  if (scale < 0) scale = BCG(bc_precision);
  bc_num first, second, result;
  bc_init_num(&first);
  bc_init_num(&second);
  bc_init_num(&result);
  php_str2num(&first, (char*)left.data());
  php_str2num(&second, (char*)right.data());
  bc_sub(first, second, &result, scale);
  if (result->n_scale > scale) {
    result->n_scale = scale;
  }
  String ret(bc_num2str(result), AttachString);
  bc_free_num(&first);
  bc_free_num(&second);
  bc_free_num(&result);
  return ret;
}


TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) {
  auto* params =
      reinterpret_cast<TfLiteResizeBilinearParams*>(node->builtin_data);

  const TfLiteTensor* input = GetInput(context, node, kInputTensor);
  TfLiteTensor* output = GetOutput(context, node, kOutputTensor);
  const TfLiteTensor* size = GetInput(context, node, kSizeTensor);

  if (IsDynamicTensor(output)) {
    TF_LITE_ENSURE_OK(context,
                      ResizeOutputTensor(context, input, size, output));
  }

  if (output->type == kTfLiteFloat32) {
#define TF_LITE_RESIZE_BILINEAR(type, datatype)                              \
  tflite::ResizeBilinearParams op_params;                                    \
  op_params.align_corners = params->align_corners;                           \
  op_params.half_pixel_centers = params->half_pixel_centers;                 \
  type::ResizeBilinear(op_params, GetTensorShape(input),                     \
                       GetTensorData<datatype>(input), GetTensorShape(size), \
                       GetTensorData<int32>(size), GetTensorShape(output),   \
                       GetTensorData<datatype>(output))

    if (kernel_type == kReference) {
      TF_LITE_RESIZE_BILINEAR(reference_ops, float);
    }
    if (kernel_type == kGenericOptimized || kernel_type == kNeonOptimized) {
      TF_LITE_RESIZE_BILINEAR(optimized_ops, float);
    }
  } else if (output->type == kTfLiteUInt8) {
    if (kernel_type == kReference) {
      TF_LITE_RESIZE_BILINEAR(reference_ops, uint8_t);
    }
    if (kernel_type == kGenericOptimized || kernel_type == kNeonOptimized) {
      TF_LITE_RESIZE_BILINEAR(optimized_ops, uint8_t);
    }
  } else if (output->type == kTfLiteInt8) {
    TF_LITE_RESIZE_BILINEAR(reference_ops, int8_t);
#undef TF_LITE_RESIZE_BILINEAR
  } else {
    context->ReportError(context, "Output type is %d, requires float.",
                         output->type);
    return kTfLiteError;
  }

  return kTfLiteOk;
}


inline void StringData::setSize(int len) {
  assertx(!isImmutable() && !hasMultipleRefs());
  assertx(len >= 0 && len <= capacity());
  mutableData()[len] = 0;
  m_lenAndHash = len;
  assertx(m_hash == 0);
  assertx(checkSane());
}


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"
}


TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) {
  auto* params = reinterpret_cast<TfLiteSubParams*>(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 ||
      output->type == kTfLiteInt64) {
    EvalSub<kernel_type>(context, node, params, data, input1, input2, output);
  } else if (output->type == kTfLiteUInt8 || output->type == kTfLiteInt8 ||
             output->type == kTfLiteInt16) {
    EvalQuantized<kernel_type>(context, node, params, data, input1, input2,
                               output);
  } else {
    context->ReportError(
        context,
        "output type %d is not supported, requires float|uint8|int32 types.",
        output->type);
    return kTfLiteError;
  }

  return kTfLiteOk;
}


inline int StringData::size() const { return m_len; }


const TfLiteTensor* GetOptionalInputTensor(const TfLiteContext* context,
                                           const TfLiteNode* node, int index) {
  const bool use_tensor = index < node->inputs->size &&
                          node->inputs->data[index] != kTfLiteOptionalTensor;
  if (use_tensor) {
    return GetMutableInput(context, node, index);
  }
  return nullptr;
}


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;
}


String StringUtil::Crypt(const String& input, const char *salt /* = "" */) {
  if (salt && salt[0] == '\0') {
    raise_notice("crypt(): No salt parameter was specified."
      " You must use a randomly generated salt and a strong"
      " hash function to produce a secure hash.");
  }
  return String(string_crypt(input.c_str(), salt), AttachString);
}


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;
  }
}


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);
}


void pcre_dump_cache(const std::string& filename) {
  s_pcreCache.dump(filename);
}


AP4_VisualSampleEntry::ReadFields(AP4_ByteStream& stream)
{
    // sample entry
    AP4_Result result = AP4_SampleEntry::ReadFields(stream);
    if (result < 0) return result;

    // read fields from this class
    stream.ReadUI16(m_Predefined1);
    stream.ReadUI16(m_Reserved2);
    stream.Read(m_Predefined2, sizeof(m_Predefined2));
    stream.ReadUI16(m_Width);
    stream.ReadUI16(m_Height);
    stream.ReadUI32(m_HorizResolution);
    stream.ReadUI32(m_VertResolution);
    stream.ReadUI32(m_Reserved3);
    stream.ReadUI16(m_FrameCount);

    char compressor_name[33];
    compressor_name[32] = 0;
    stream.Read(compressor_name, 32);
    int name_length = compressor_name[0];
    if (name_length < 32) {
        compressor_name[name_length+1] = 0; // force null termination
        m_CompressorName = &compressor_name[1];
    }

    stream.ReadUI16(m_Depth);
    stream.ReadUI16(m_Predefined3);

    return AP4_SUCCESS;
}


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;
}


    int overrun(int itemSize, int nItems) {
      int len = ptr - start + itemSize * nItems;
      if (len < (end - start) * 2)
        len = (end - start) * 2;

      U8* newStart = new U8[len];
      memcpy(newStart, start, ptr - start);
      ptr = newStart + (ptr - start);
      delete [] start;
      start = newStart;
      end = newStart + len;

      return nItems;
    }


int TLSOutStream::length()
{
  return offset + ptr - start;
}


static float *get_window(vorb *f, int len)
{
   len <<= 1;
   if (len == f->blocksize_0) return f->window[0];
   if (len == f->blocksize_1) return f->window[1];
   assert(0);
   return NULL;
}


TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) {
  const TfLiteTensor* input = GetInput(context, node, kInputTensor);
  TfLiteTensor* output = GetOutput(context, node, kOutputTensor);
  TF_LITE_ENSURE_EQ(context, NumInputs(node), 1);
  TF_LITE_ENSURE_EQ(context, NumOutputs(node), 1);
  TF_LITE_ENSURE_TYPES_EQ(context, input->type, kTfLiteFloat32);
  output->type = input->type;
  TfLiteIntArray* output_size = TfLiteIntArrayCopy(input->dims);
  return context->ResizeTensor(context, output, output_size);
}


R_API RBinJavaAttrInfo *r_bin_java_rti_annotations_attr_new(RBinJavaObj *bin, ut8 *buffer, ut64 sz, ut64 buf_offset) {
	ut32 i = 0;
	RBinJavaAttrInfo *attr = NULL;
	ut64 offset = 0;
	attr = r_bin_java_default_attr_new (bin, buffer, sz, buf_offset);
	offset += 6;
	if (attr) {
		attr->type = R_BIN_JAVA_ATTR_TYPE_RUNTIME_INVISIBLE_ANNOTATION_ATTR;
		attr->info.annotation_array.num_annotations = R_BIN_JAVA_USHORT (buffer, offset);
		offset += 2;
		attr->info.annotation_array.annotations = r_list_newf (r_bin_java_annotation_free);
		for (i = 0; i < attr->info.rtv_annotations_attr.num_annotations; i++) {
			if (offset >= sz) {
				break;
			}
			RBinJavaAnnotation *annotation = r_bin_java_annotation_new (buffer + offset, sz - offset, buf_offset + offset);
			if (annotation) {
				offset += annotation->size;
			}
			r_list_append (attr->info.annotation_array.annotations, (void *) annotation);
		}
		attr->size = offset;
	}
	return attr;
}


bool TemporaryFile::deleteTemporaryFile() const
{
    // Have a few attempts at deleting the file before giving up..
    for (int i = 5; --i >= 0;)
    {
        if (temporaryFile.deleteFile())
            return true;

        Thread::sleep (50);
    }

    return false;
}


Network::FilterStatus Context::onDownstreamData(int data_length, bool end_of_stream) {
  if (!wasm_->onDownstreamData_) {
    return Network::FilterStatus::Continue;
  }
  auto result = wasm_->onDownstreamData_(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;
}


otError Commissioner::GeneratePskc(const char *              aPassPhrase,
                                   const char *              aNetworkName,
                                   const Mac::ExtendedPanId &aExtPanId,
                                   Pskc &                    aPskc)
{
    otError     error      = OT_ERROR_NONE;
    const char *saltPrefix = "Thread";
    uint8_t     salt[OT_PBKDF2_SALT_MAX_LEN];
    uint16_t    saltLen = 0;

    VerifyOrExit((strlen(aPassPhrase) >= OT_COMMISSIONING_PASSPHRASE_MIN_SIZE) &&
                     (strlen(aPassPhrase) <= OT_COMMISSIONING_PASSPHRASE_MAX_SIZE) &&
                     (strlen(aNetworkName) <= OT_NETWORK_NAME_MAX_SIZE),
                 error = OT_ERROR_INVALID_ARGS);

    memset(salt, 0, sizeof(salt));
    memcpy(salt, saltPrefix, strlen(saltPrefix));
    saltLen += static_cast<uint16_t>(strlen(saltPrefix));

    memcpy(salt + saltLen, aExtPanId.m8, sizeof(aExtPanId));
    saltLen += OT_EXT_PAN_ID_SIZE;

    memcpy(salt + saltLen, aNetworkName, strlen(aNetworkName));
    saltLen += static_cast<uint16_t>(strlen(aNetworkName));

    otPbkdf2Cmac(reinterpret_cast<const uint8_t *>(aPassPhrase), static_cast<uint16_t>(strlen(aPassPhrase)),
                 reinterpret_cast<const uint8_t *>(salt), saltLen, 16384, OT_PSKC_MAX_SIZE, aPskc.m8);

exit:
    return error;
}


static INLINE UINT16 ntlm_av_pair_get_id(const NTLM_AV_PAIR* pAvPair)
{
	UINT16 AvId;

	Data_Read_UINT16(&pAvPair->AvId, AvId);

	return AvId;
}


int DummyOutStream::length()
{
  flush();
  return offset;
}


TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) {
  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);

  switch (input1->type) {
    case kTfLiteInt32: {
      return EvalImpl<int32_t>(context, data->requires_broadcast, input1,
                               input2, output);
    }
    case kTfLiteFloat32: {
      return EvalImpl<float>(context, data->requires_broadcast, input1, input2,
                             output);
    }
    default: {
      context->ReportError(context, "Type '%s' is not supported by floor_div.",
                           TfLiteTypeGetName(input1->type));
      return kTfLiteError;
    }
  }
}