proposal adding deep_sort tracker model

have you tried to convert the dep sort reidentification model to tpu?
https://github.com/nwojke/deep_sort
mars-small128.pb
it would be nice to have

freeze_model.py
# vim: expandtab:ts=4:sw=4
import argparse
import tensorflow.compat.v1 as tf
# import tensorflow.contrib.slim as slim
import tf_slim as slim

def _batch_norm_fn(x, scope=None):
    if scope is None:
        scope = tf.get_variable_scope().name + "/bn"
    return slim.batch_norm(x, scope=scope)

def create_link(
        incoming, network_builder, scope, nonlinearity=tf.nn.elu,
        weights_initializer=tf.truncated_normal_initializer(stddev=1e-3),
        regularizer=None, is_first=False, summarize_activations=True):
    if is_first:
        network = incoming
    else:
        network = _batch_norm_fn(incoming, scope=scope + "/bn")
        network = nonlinearity(network)
        if summarize_activations:
            tf.summary.histogram(scope+"/activations", network)

    pre_block_network = network
    post_block_network = network_builder(pre_block_network, scope)

    incoming_dim = pre_block_network.get_shape().as_list()[-1]
    outgoing_dim = post_block_network.get_shape().as_list()[-1]
    if incoming_dim != outgoing_dim:
        assert outgoing_dim == 2 * incoming_dim, \
            "%d != %d" % (outgoing_dim, 2 * incoming)
        projection = slim.conv2d(
            incoming, outgoing_dim, 1, 2, padding="SAME", activation_fn=None,
            scope=scope+"/projection", weights_initializer=weights_initializer,
            biases_initializer=None, weights_regularizer=regularizer)
        network = projection + post_block_network
    else:
        network = incoming + post_block_network
    return network

def create_inner_block(
        incoming, scope, nonlinearity=tf.nn.elu,
        weights_initializer=tf.truncated_normal_initializer(1e-3),
        bias_initializer=tf.zeros_initializer(), regularizer=None,
        increase_dim=False, summarize_activations=True):
    n = incoming.get_shape().as_list()[-1]
    stride = 1
    if increase_dim:
        n *= 2
        stride = 2

    incoming = slim.conv2d(
        incoming, n, [3, 3], stride, activation_fn=nonlinearity, padding="SAME",
        normalizer_fn=_batch_norm_fn, weights_initializer=weights_initializer,
        biases_initializer=bias_initializer, weights_regularizer=regularizer,
        scope=scope + "/1")
    if summarize_activations:
        tf.summary.histogram(incoming.name + "/activations", incoming)

    incoming = slim.dropout(incoming, keep_prob=0.6)

    incoming = slim.conv2d(
        incoming, n, [3, 3], 1, activation_fn=None, padding="SAME",
        normalizer_fn=None, weights_initializer=weights_initializer,
        biases_initializer=bias_initializer, weights_regularizer=regularizer,
        scope=scope + "/2")
    return incoming

def residual_block(incoming, scope, nonlinearity=tf.nn.elu,
                   weights_initializer=tf.truncated_normal_initializer(1e3),
                   bias_initializer=tf.zeros_initializer(), regularizer=None,
                   increase_dim=False, is_first=False,
                   summarize_activations=True):

    def network_builder(x, s):
        return create_inner_block(
            x, s, nonlinearity, weights_initializer, bias_initializer,
            regularizer, increase_dim, summarize_activations)

    return create_link(
        incoming, network_builder, scope, nonlinearity, weights_initializer,
        regularizer, is_first, summarize_activations)

def _create_network(incoming, reuse=None, weight_decay=1e-8):
    nonlinearity = tf.nn.elu
    conv_weight_init = tf.truncated_normal_initializer(stddev=1e-3)
    conv_bias_init = tf.zeros_initializer()
    conv_regularizer = slim.l2_regularizer(weight_decay)
    fc_weight_init = tf.truncated_normal_initializer(stddev=1e-3)
    fc_bias_init = tf.zeros_initializer()
    fc_regularizer = slim.l2_regularizer(weight_decay)

    def batch_norm_fn(x):
        return slim.batch_norm(x, scope=tf.get_variable_scope().name + "/bn")

    network = incoming
    network = slim.conv2d(
        network, 32, [3, 3], stride=1, activation_fn=nonlinearity,
        padding="SAME", normalizer_fn=batch_norm_fn, scope="conv1_1",
        weights_initializer=conv_weight_init, biases_initializer=conv_bias_init,
        weights_regularizer=conv_regularizer)
    network = slim.conv2d(
        network, 32, [3, 3], stride=1, activation_fn=nonlinearity,
        padding="SAME", normalizer_fn=batch_norm_fn, scope="conv1_2",
        weights_initializer=conv_weight_init, biases_initializer=conv_bias_init,
        weights_regularizer=conv_regularizer)

    # NOTE(nwojke): This is missing a padding="SAME" to match the CNN
    # architecture in Table 1 of the paper. Information on how this affects
    # performance on MOT 16 training sequences can be found in
    # issue 10 https://github.com/nwojke/deep_sort/issues/10
    network = slim.max_pool2d(network, [3, 3], [2, 2], scope="pool1")

    network = residual_block(
        network, "conv2_1", nonlinearity, conv_weight_init, conv_bias_init,
        conv_regularizer, increase_dim=False, is_first=True)
    network = residual_block(
        network, "conv2_3", nonlinearity, conv_weight_init, conv_bias_init,
        conv_regularizer, increase_dim=False)

    network = residual_block(
        network, "conv3_1", nonlinearity, conv_weight_init, conv_bias_init,
        conv_regularizer, increase_dim=True)
    network = residual_block(
        network, "conv3_3", nonlinearity, conv_weight_init, conv_bias_init,
        conv_regularizer, increase_dim=False)

    network = residual_block(
        network, "conv4_1", nonlinearity, conv_weight_init, conv_bias_init,
        conv_regularizer, increase_dim=True)
    network = residual_block(
        network, "conv4_3", nonlinearity, conv_weight_init, conv_bias_init,
        conv_regularizer, increase_dim=False)

    feature_dim = network.get_shape().as_list()[-1]
    network = slim.flatten(network)

    network = slim.dropout(network, keep_prob=0.6)
    network = slim.fully_connected(
        network, feature_dim, activation_fn=nonlinearity,
        normalizer_fn=batch_norm_fn, weights_regularizer=fc_regularizer,
        scope="fc1", weights_initializer=fc_weight_init,
        biases_initializer=fc_bias_init)

    features = network

    # Features in rows, normalize axis 1.
    features = slim.batch_norm(features, scope="ball", reuse=reuse)
    feature_norm = tf.sqrt(
        tf.constant(1e-8, tf.float32) +
        tf.reduce_sum(tf.square(features), [1], keepdims=True))
    features = features / feature_norm
    return features, None

def _network_factory(weight_decay=1e-8):

    def factory_fn(image, reuse):
            with slim.arg_scope([slim.batch_norm, slim.dropout], is_training=False):
                with slim.arg_scope([slim.conv2d, slim.fully_connected,
                                     slim.batch_norm, slim.layer_norm],
                                    reuse=reuse):
                    features, logits = _create_network(
                        image, reuse=reuse, weight_decay=weight_decay)
                    return features, logits

    return factory_fn

def _preprocess(image):
    image = image[:, :, ::-1]  # BGR to RGB
    return image

def parse_args():
    """Parse command line arguments.
    """
    parser = argparse.ArgumentParser(description="Freeze old model")
    parser.add_argument(
        "--checkpoint_in",
        default="resources/networks/mars-small128.ckpt-68577",
        help="Path to checkpoint file")
    parser.add_argument(
        "--graphdef_out",
        default="resources/networks/mars-small128.pb")
    return parser.parse_args()

def main():
    args = parse_args()

    with tf.Session(graph=tf.Graph()) as session:
        input_var = tf.placeholder(tf.uint8, (1, 128, 64, 3), name="images")
        image_var = tf.map_fn(lambda x: _preprocess(x), tf.cast(input_var, tf.float32), back_prop=False)

        factory_fn = _network_factory()
        features, _ = factory_fn(image_var, reuse=None)
        features = tf.identity(features, name="features")

        saver = tf.train.Saver(slim.get_variables_to_restore())
        saver.restore(session, args.checkpoint_in)

        output_graph_def = tf.graph_util.convert_variables_to_constants(
            session, tf.get_default_graph().as_graph_def(),
            [features.name.split(":")[0]])
        with tf.gfile.GFile(args.graphdef_out, "wb") as file_handle:
            file_handle.write(output_graph_def.SerializeToString())

if __name__ == "__main__":
    main()

$ sudo pip3 install --upgrade tf_slim
$ mkdir -p resources/networks
$ cd resources/networks
$ gdown --id 1hF6Cehn1SNZvh-M7FItSjEFojf_MVUba
$ gdown --id 1FkpWjshRY1YZC3dtQT9DNUbVZLu97uqi
$ cd ../..

$ python3 tools/freeze_model.py

$ sudo pip3 install openvino2tensorflow --upgrade

$ pb_to_saved_model \
--pb_file_path resources/networks/mars-small128.pb \
--inputs images:0 \
--outputs features:0

$ saved_model_to_tflite \
--saved_model_dir_path saved_model_from_pb \
--output_no_quant_float32_tflite True \
--output_edgetpu True

Full Integer Quantization started ===================================================
loc(callsite("map/TensorArrayV2_1"("/usr/local/lib/python3.6/dist-packages/tensorflow/python/saved_model/load.py":909:0) at callsite("/usr/local/lib/python3.6/dist-packages/tensorflow/python/saved_model/load.py":859:0 at callsite("/usr/local/bin/saved_model_to_tflite":66:0 at callsite("/usr/local/bin/saved_model_to_tflite":429:0 at "/usr/local/bin/saved_model_to_tflite":433:0))))): error: requires element_shape to be 1D tensor during TF Lite transformation pass
loc(callsite("map/TensorArrayV2_1"("/usr/local/lib/python3.6/dist-packages/tensorflow/python/saved_model/load.py":909:0) at callsite("/usr/local/lib/python3.6/dist-packages/tensorflow/python/saved_model/load.py":859:0 at callsite("/usr/local/bin/saved_model_to_tflite":66:0 at callsite("/usr/local/bin/saved_model_to_tflite":429:0 at "/usr/local/bin/saved_model_to_tflite":433:0))))): error: failed to legalize operation 'tf.TensorListReserve' that was explicitly marked illegal
ERROR: /usr/local/lib/python3.6/dist-packages/tensorflow/python/saved_model/load.py:909:0: error: requires element_shape to be 1D tensor during TF Lite transformation pass
/usr/local/lib/python3.6/dist-packages/tensorflow/python/saved_model/load.py:859:0: note: called from
/usr/local/bin/saved_model_to_tflite:66:0: note: called from
/usr/local/bin/saved_model_to_tflite:429:0: note: called from
/usr/local/bin/saved_model_to_tflite:433:0: note: called from
/usr/local/lib/python3.6/dist-packages/tensorflow/python/saved_model/load.py:909:0: error: failed to legalize operation 'tf.TensorListReserve' that was explicitly marked illegal
/usr/local/lib/python3.6/dist-packages/tensorflow/python/saved_model/load.py:859:0: note: called from
/usr/local/bin/saved_model_to_tflite:66:0: note: called from
/usr/local/bin/saved_model_to_tflite:429:0: note: called from
/usr/local/bin/saved_model_to_tflite:433:0: note: called from

Traceback (most recent call last):
  File "/usr/local/lib/python3.6/dist-packages/tensorflow/lite/python/convert.py", line 213, in toco_convert_protos
    enable_mlir_converter)
  File "/usr/local/lib/python3.6/dist-packages/tensorflow/lite/python/wrap_toco.py", line 38, in wrapped_toco_convert
    enable_mlir_converter)
Exception: /usr/local/lib/python3.6/dist-packages/tensorflow/python/saved_model/load.py:909:0: error: requires element_shape to be 1D tensor during TF Lite transformation pass
/usr/local/lib/python3.6/dist-packages/tensorflow/python/saved_model/load.py:859:0: note: called from
/usr/local/bin/saved_model_to_tflite:66:0: note: called from
/usr/local/bin/saved_model_to_tflite:429:0: note: called from
/usr/local/bin/saved_model_to_tflite:433:0: note: called from
/usr/local/lib/python3.6/dist-packages/tensorflow/python/saved_model/load.py:909:0: error: failed to legalize operation 'tf.TensorListReserve' that was explicitly marked illegal
/usr/local/lib/python3.6/dist-packages/tensorflow/python/saved_model/load.py:859:0: note: called from
/usr/local/bin/saved_model_to_tflite:66:0: note: called from
/usr/local/bin/saved_model_to_tflite:429:0: note: called from
/usr/local/bin/saved_model_to_tflite:433:0: note: called from

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
  File "/usr/local/bin/saved_model_to_tflite", line 191, in convert
    tflite_model = converter.convert()
  File "/usr/local/lib/python3.6/dist-packages/tensorflow/lite/python/lite.py", line 1117, in convert
    return super(TFLiteConverterV2, self).convert()
  File "/usr/local/lib/python3.6/dist-packages/tensorflow/lite/python/lite.py", line 943, in convert
    self).convert(graph_def, input_tensors, output_tensors)
  File "/usr/local/lib/python3.6/dist-packages/tensorflow/lite/python/lite.py", line 628, in convert
    **converter_kwargs)
  File "/usr/local/lib/python3.6/dist-packages/tensorflow/lite/python/convert.py", line 613, in toco_convert_impl
    enable_mlir_converter=enable_mlir_converter)
  File "/usr/local/lib/python3.6/dist-packages/tensorflow/lite/python/convert.py", line 216, in toco_convert_protos
    raise ConverterError(str(e))
tensorflow.lite.python.convert.ConverterError: /usr/local/lib/python3.6/dist-packages/tensorflow/python/saved_model/load.py:909:0: error: requires element_shape to be 1D tensor during TF Lite transformation pass
/usr/local/lib/python3.6/dist-packages/tensorflow/python/saved_model/load.py:859:0: note: called from
/usr/local/bin/saved_model_to_tflite:66:0: note: called from
/usr/local/bin/saved_model_to_tflite:429:0: note: called from
/usr/local/bin/saved_model_to_tflite:433:0: note: called from
/usr/local/lib/python3.6/dist-packages/tensorflow/python/saved_model/load.py:909:0: error: failed to legalize operation 'tf.TensorListReserve' that was explicitly marked illegal
/usr/local/lib/python3.6/dist-packages/tensorflow/python/saved_model/load.py:859:0: note: called from
/usr/local/bin/saved_model_to_tflite:66:0: note: called from
/usr/local/bin/saved_model_to_tflite:429:0: note: called from
/usr/local/bin/saved_model_to_tflite:433:0: note: called from

EdgeTPU convertion started ==========================================================
ERROR:
Traceback (most recent call last):
  File "/usr/local/bin/saved_model_to_tflite", line 208, in convert
    stderr=subprocess.PIPE).decode('utf-8')
  File "/usr/lib/python3.6/subprocess.py", line 356, in check_output
    **kwargs).stdout
  File "/usr/lib/python3.6/subprocess.py", line 438, in run
    output=stdout, stderr=stderr)
subprocess.CalledProcessError: Command '['edgetpu_compiler', '-o', 'tflite_from_saved_model', '-s', 'tflite_from_saved_model/model_full_integer_quant.tflite']' returned non-zero exit status 1.
--------------------------------------------------------------------------------
Please install edgetpu_compiler according to the following website.
https://coral.ai/docs/edgetpu/compiler/#system-requirements

It cannot be converted because it contains layers that are not supported by INT8 quantized tflite. TensorArrayV2_1

saved_model and float32 tflite
https://drive.google.com/file/d/1QH7lfPv1uSXVPsULg1CbiOT1WkrAxOYS/view?usp=sharing

freeze_model.py
# vim: expandtab:ts=4:sw=4
import argparse
import tensorflow.compat.v1 as tf
# import tensorflow.contrib.slim as slim
import tf_slim as slim

def _batch_norm_fn(x, scope=None):
    if scope is None:
        scope = tf.get_variable_scope().name + "/bn"
    return slim.batch_norm(x, scope=scope)

def create_link(
        incoming, network_builder, scope, nonlinearity=tf.nn.elu,
        weights_initializer=tf.truncated_normal_initializer(stddev=1e-3),
        regularizer=None, is_first=False, summarize_activations=True):
    if is_first:
        network = incoming
    else:
        network = _batch_norm_fn(incoming, scope=scope + "/bn")
        network = nonlinearity(network)
        if summarize_activations:
            tf.summary.histogram(scope+"/activations", network)

    pre_block_network = network
    post_block_network = network_builder(pre_block_network, scope)

    incoming_dim = pre_block_network.get_shape().as_list()[-1]
    outgoing_dim = post_block_network.get_shape().as_list()[-1]
    if incoming_dim != outgoing_dim:
        assert outgoing_dim == 2 * incoming_dim, \
            "%d != %d" % (outgoing_dim, 2 * incoming)
        projection = slim.conv2d(
            incoming, outgoing_dim, 1, 2, padding="SAME", activation_fn=None,
            scope=scope+"/projection", weights_initializer=weights_initializer,
            biases_initializer=None, weights_regularizer=regularizer)
        network = projection + post_block_network
    else:
        network = incoming + post_block_network
    return network

def create_inner_block(
        incoming, scope, nonlinearity=tf.nn.elu,
        weights_initializer=tf.truncated_normal_initializer(1e-3),
        bias_initializer=tf.zeros_initializer(), regularizer=None,
        increase_dim=False, summarize_activations=True):
    n = incoming.get_shape().as_list()[-1]
    stride = 1
    if increase_dim:
        n *= 2
        stride = 2

    incoming = slim.conv2d(
        incoming, n, [3, 3], stride, activation_fn=nonlinearity, padding="SAME",
        normalizer_fn=_batch_norm_fn, weights_initializer=weights_initializer,
        biases_initializer=bias_initializer, weights_regularizer=regularizer,
        scope=scope + "/1")
    if summarize_activations:
        tf.summary.histogram(incoming.name + "/activations", incoming)

    incoming = slim.dropout(incoming, keep_prob=0.6)

    incoming = slim.conv2d(
        incoming, n, [3, 3], 1, activation_fn=None, padding="SAME",
        normalizer_fn=None, weights_initializer=weights_initializer,
        biases_initializer=bias_initializer, weights_regularizer=regularizer,
        scope=scope + "/2")
    return incoming

def residual_block(incoming, scope, nonlinearity=tf.nn.elu,
                   weights_initializer=tf.truncated_normal_initializer(1e3),
                   bias_initializer=tf.zeros_initializer(), regularizer=None,
                   increase_dim=False, is_first=False,
                   summarize_activations=True):

    def network_builder(x, s):
        return create_inner_block(
            x, s, nonlinearity, weights_initializer, bias_initializer,
            regularizer, increase_dim, summarize_activations)

    return create_link(
        incoming, network_builder, scope, nonlinearity, weights_initializer,
        regularizer, is_first, summarize_activations)

def _create_network(incoming, reuse=None, weight_decay=1e-8):
    nonlinearity = tf.nn.elu
    conv_weight_init = tf.truncated_normal_initializer(stddev=1e-3)
    conv_bias_init = tf.zeros_initializer()
    conv_regularizer = slim.l2_regularizer(weight_decay)
    fc_weight_init = tf.truncated_normal_initializer(stddev=1e-3)
    fc_bias_init = tf.zeros_initializer()
    fc_regularizer = slim.l2_regularizer(weight_decay)

    def batch_norm_fn(x):
        return slim.batch_norm(x, scope=tf.get_variable_scope().name + "/bn")

    network = incoming
    network = slim.conv2d(
        network, 32, [3, 3], stride=1, activation_fn=nonlinearity,
        padding="SAME", normalizer_fn=batch_norm_fn, scope="conv1_1",
        weights_initializer=conv_weight_init, biases_initializer=conv_bias_init,
        weights_regularizer=conv_regularizer)
    network = slim.conv2d(
        network, 32, [3, 3], stride=1, activation_fn=nonlinearity,
        padding="SAME", normalizer_fn=batch_norm_fn, scope="conv1_2",
        weights_initializer=conv_weight_init, biases_initializer=conv_bias_init,
        weights_regularizer=conv_regularizer)

    # NOTE(nwojke): This is missing a padding="SAME" to match the CNN
    # architecture in Table 1 of the paper. Information on how this affects
    # performance on MOT 16 training sequences can be found in
    # issue 10 https://github.com/nwojke/deep_sort/issues/10
    network = slim.max_pool2d(network, [3, 3], [2, 2], scope="pool1")

    network = residual_block(
        network, "conv2_1", nonlinearity, conv_weight_init, conv_bias_init,
        conv_regularizer, increase_dim=False, is_first=True)
    network = residual_block(
        network, "conv2_3", nonlinearity, conv_weight_init, conv_bias_init,
        conv_regularizer, increase_dim=False)

    network = residual_block(
        network, "conv3_1", nonlinearity, conv_weight_init, conv_bias_init,
        conv_regularizer, increase_dim=True)
    network = residual_block(
        network, "conv3_3", nonlinearity, conv_weight_init, conv_bias_init,
        conv_regularizer, increase_dim=False)

    network = residual_block(
        network, "conv4_1", nonlinearity, conv_weight_init, conv_bias_init,
        conv_regularizer, increase_dim=True)
    network = residual_block(
        network, "conv4_3", nonlinearity, conv_weight_init, conv_bias_init,
        conv_regularizer, increase_dim=False)

    feature_dim = network.get_shape().as_list()[-1]
    network = slim.flatten(network)

    network = slim.dropout(network, keep_prob=0.6)
    network = slim.fully_connected(
        network, feature_dim, activation_fn=nonlinearity,
        normalizer_fn=batch_norm_fn, weights_regularizer=fc_regularizer,
        scope="fc1", weights_initializer=fc_weight_init,
        biases_initializer=fc_bias_init)

    features = network

    # Features in rows, normalize axis 1.
    features = slim.batch_norm(features, scope="ball", reuse=reuse)
    feature_norm = tf.sqrt(
        tf.constant(1e-8, tf.float32) +
        tf.reduce_sum(tf.square(features), [1], keepdims=True))
    features = features / feature_norm
    return features, None

def _network_factory(weight_decay=1e-8):

    def factory_fn(image, reuse):
            with slim.arg_scope([slim.batch_norm, slim.dropout], is_training=False):
                with slim.arg_scope([slim.conv2d, slim.fully_connected,
                                     slim.batch_norm, slim.layer_norm],
                                    reuse=reuse):
                    features, logits = _create_network(
                        image, reuse=reuse, weight_decay=weight_decay)
                    return features, logits

    return factory_fn

def _preprocess(image):
    image = image[:, :, ::-1]  # BGR to RGB
    return image

def parse_args():
    """Parse command line arguments.
    """
    parser = argparse.ArgumentParser(description="Freeze old model")
    parser.add_argument(
        "--checkpoint_in",
        default="resources/networks/mars-small128.ckpt-68577",
        help="Path to checkpoint file")
    parser.add_argument(
        "--graphdef_out",
        default="resources/networks/mars-small128.pb")
    return parser.parse_args()

def main():
    args = parse_args()

    with tf.Session(graph=tf.Graph()) as session:
        input_var = tf.placeholder(tf.uint8, (1, 128, 64, 3), name="images")
        image_var = tf.map_fn(lambda x: _preprocess(x), tf.cast(input_var, tf.float32), back_prop=False)

        factory_fn = _network_factory()
        features, _ = factory_fn(image_var, reuse=None)
        features = tf.identity(features, name="features")

        saver = tf.train.Saver(slim.get_variables_to_restore())
        saver.restore(session, args.checkpoint_in)

        output_graph_def = tf.graph_util.convert_variables_to_constants(
            session, tf.get_default_graph().as_graph_def(),
            [features.name.split(":")[0]])
        with tf.gfile.GFile(args.graphdef_out, "wb") as file_handle:
            file_handle.write(output_graph_def.SerializeToString())

if __name__ == "__main__":
    main()

$ sudo pip3 install --upgrade tf_slim
$ mkdir -p resources/networks
$ cd resources/networks
$ gdown --id 1hF6Cehn1SNZvh-M7FItSjEFojf_MVUba
$ gdown --id 1FkpWjshRY1YZC3dtQT9DNUbVZLu97uqi
$ cd ../..

$ python3 tools/freeze_model.py

$ sudo pip3 install openvino2tensorflow --upgrade

$ pb_to_saved_model \
--pb_file_path resources/networks/mars-small128.pb \
--inputs images:0 \
--outputs features:0

$ saved_model_to_tflite \
--saved_model_dir_path saved_model_from_pb \
--output_no_quant_float32_tflite True \
--output_edgetpu True

Full Integer Quantization started ===================================================
loc(callsite("map/TensorArrayV2_1"("/usr/local/lib/python3.6/dist-packages/tensorflow/python/saved_model/load.py":909:0) at callsite("/usr/local/lib/python3.6/dist-packages/tensorflow/python/saved_model/load.py":859:0 at callsite("/usr/local/bin/saved_model_to_tflite":66:0 at callsite("/usr/local/bin/saved_model_to_tflite":429:0 at "/usr/local/bin/saved_model_to_tflite":433:0))))): error: requires element_shape to be 1D tensor during TF Lite transformation pass
loc(callsite("map/TensorArrayV2_1"("/usr/local/lib/python3.6/dist-packages/tensorflow/python/saved_model/load.py":909:0) at callsite("/usr/local/lib/python3.6/dist-packages/tensorflow/python/saved_model/load.py":859:0 at callsite("/usr/local/bin/saved_model_to_tflite":66:0 at callsite("/usr/local/bin/saved_model_to_tflite":429:0 at "/usr/local/bin/saved_model_to_tflite":433:0))))): error: failed to legalize operation 'tf.TensorListReserve' that was explicitly marked illegal
ERROR: /usr/local/lib/python3.6/dist-packages/tensorflow/python/saved_model/load.py:909:0: error: requires element_shape to be 1D tensor during TF Lite transformation pass
/usr/local/lib/python3.6/dist-packages/tensorflow/python/saved_model/load.py:859:0: note: called from
/usr/local/bin/saved_model_to_tflite:66:0: note: called from
/usr/local/bin/saved_model_to_tflite:429:0: note: called from
/usr/local/bin/saved_model_to_tflite:433:0: note: called from
/usr/local/lib/python3.6/dist-packages/tensorflow/python/saved_model/load.py:909:0: error: failed to legalize operation 'tf.TensorListReserve' that was explicitly marked illegal
/usr/local/lib/python3.6/dist-packages/tensorflow/python/saved_model/load.py:859:0: note: called from
/usr/local/bin/saved_model_to_tflite:66:0: note: called from
/usr/local/bin/saved_model_to_tflite:429:0: note: called from
/usr/local/bin/saved_model_to_tflite:433:0: note: called from

Traceback (most recent call last):
  File "/usr/local/lib/python3.6/dist-packages/tensorflow/lite/python/convert.py", line 213, in toco_convert_protos
    enable_mlir_converter)
  File "/usr/local/lib/python3.6/dist-packages/tensorflow/lite/python/wrap_toco.py", line 38, in wrapped_toco_convert
    enable_mlir_converter)
Exception: /usr/local/lib/python3.6/dist-packages/tensorflow/python/saved_model/load.py:909:0: error: requires element_shape to be 1D tensor during TF Lite transformation pass
/usr/local/lib/python3.6/dist-packages/tensorflow/python/saved_model/load.py:859:0: note: called from
/usr/local/bin/saved_model_to_tflite:66:0: note: called from
/usr/local/bin/saved_model_to_tflite:429:0: note: called from
/usr/local/bin/saved_model_to_tflite:433:0: note: called from
/usr/local/lib/python3.6/dist-packages/tensorflow/python/saved_model/load.py:909:0: error: failed to legalize operation 'tf.TensorListReserve' that was explicitly marked illegal
/usr/local/lib/python3.6/dist-packages/tensorflow/python/saved_model/load.py:859:0: note: called from
/usr/local/bin/saved_model_to_tflite:66:0: note: called from
/usr/local/bin/saved_model_to_tflite:429:0: note: called from
/usr/local/bin/saved_model_to_tflite:433:0: note: called from

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
  File "/usr/local/bin/saved_model_to_tflite", line 191, in convert
    tflite_model = converter.convert()
  File "/usr/local/lib/python3.6/dist-packages/tensorflow/lite/python/lite.py", line 1117, in convert
    return super(TFLiteConverterV2, self).convert()
  File "/usr/local/lib/python3.6/dist-packages/tensorflow/lite/python/lite.py", line 943, in convert
    self).convert(graph_def, input_tensors, output_tensors)
  File "/usr/local/lib/python3.6/dist-packages/tensorflow/lite/python/lite.py", line 628, in convert
    **converter_kwargs)
  File "/usr/local/lib/python3.6/dist-packages/tensorflow/lite/python/convert.py", line 613, in toco_convert_impl
    enable_mlir_converter=enable_mlir_converter)
  File "/usr/local/lib/python3.6/dist-packages/tensorflow/lite/python/convert.py", line 216, in toco_convert_protos
    raise ConverterError(str(e))
tensorflow.lite.python.convert.ConverterError: /usr/local/lib/python3.6/dist-packages/tensorflow/python/saved_model/load.py:909:0: error: requires element_shape to be 1D tensor during TF Lite transformation pass
/usr/local/lib/python3.6/dist-packages/tensorflow/python/saved_model/load.py:859:0: note: called from
/usr/local/bin/saved_model_to_tflite:66:0: note: called from
/usr/local/bin/saved_model_to_tflite:429:0: note: called from
/usr/local/bin/saved_model_to_tflite:433:0: note: called from
/usr/local/lib/python3.6/dist-packages/tensorflow/python/saved_model/load.py:909:0: error: failed to legalize operation 'tf.TensorListReserve' that was explicitly marked illegal
/usr/local/lib/python3.6/dist-packages/tensorflow/python/saved_model/load.py:859:0: note: called from
/usr/local/bin/saved_model_to_tflite:66:0: note: called from
/usr/local/bin/saved_model_to_tflite:429:0: note: called from
/usr/local/bin/saved_model_to_tflite:433:0: note: called from

EdgeTPU convertion started ==========================================================
ERROR:
Traceback (most recent call last):
  File "/usr/local/bin/saved_model_to_tflite", line 208, in convert
    stderr=subprocess.PIPE).decode('utf-8')
  File "/usr/lib/python3.6/subprocess.py", line 356, in check_output
    **kwargs).stdout
  File "/usr/lib/python3.6/subprocess.py", line 438, in run
    output=stdout, stderr=stderr)
subprocess.CalledProcessError: Command '['edgetpu_compiler', '-o', 'tflite_from_saved_model', '-s', 'tflite_from_saved_model/model_full_integer_quant.tflite']' returned non-zero exit status 1.
--------------------------------------------------------------------------------
Please install edgetpu_compiler according to the following website.
https://coral.ai/docs/edgetpu/compiler/#system-requirements

It cannot be converted because it contains layers that are not supported by INT8 quantized tflite. TensorArrayV2_1

saved_model and float32 tflite
https://drive.google.com/file/d/1QH7lfPv1uSXVPsULg1CbiOT1WkrAxOYS/view?usp=sharing

thank you, can you please point out which in the graph or in the code are TensorArrayV2_1 ? I am trying to understand, if it is possible to drop those layers and generate any vector from those convolutions in the model which could be used as a feature. thanks for pointing out, explaining

thank you, can you please point out which in the graph or in the code are TensorArrayV2_1 ? I am trying to understand, if it is possible to drop those layers and generate any vector from those convolutions in the model which could be used as a feature. thanks for pointing out, explaining

It took quite a while, but it seems that the INPUT of the While OP in the beginning of the model is TensorArrayV2. Also, it is not the TensorArrayV2 itself that is the problem, but the structure of INPUT:1 or INPUT:2 or INPUT:3 of TensorArrayV2 does not seem to support INT8 quantization.

It took quite a while, but it seems that the INPUT of the While OP in the beginning of the model is TensorArrayV2. Also, it is not the TensorArrayV2 itself that is the problem, but the structure of INPUT:1 or INPUT:2 or INPUT:3 of TensorArrayV2 does not seem to support INT8 quantization.


Keyboard shortcut to approve PR review

Description
Use the unused cmd+shift+enter keyboard shortcut to "approve" a PR review.

cmd+enter: comment
cmd+shift+enter: approve

Following up from https://github.com/refined-github/refined-github/issues/5010.
There are no userscripts for this.
Screenshot
No response
Example URLs
https://github.com/refined-github/refined-github/pull/6095/files

A shortcut for Request Changes might be nice too, although picking a shortcut for that case is probably tricky since you'd want to avoid accidentally pressing it (same for approving, I guess).
This isn't exactly helpful for the case where you're already writing a comment, but I've also  wanted a shortcut I can use without focusing the comment box at all, to quickly review a PR without adding additional commentary, for example:

v a – approve without comment
v r – request changes without comment (more rare, but maybe nice to have for symmetry)

PR welcome.

The shortcut should be added to the button itself via data-hotkey (or the variant that limits the shortcut to the containing form, so it's not a global shortcut)
The button should mention the shortcut in the tooltip, I think. I'm not a fan of shortcuts that nobody knows about (and that I myself don't remember)

Cmd+Enter already sends a comment, natively
Approval is best left to do without keyboard shortcuts


Localization

How to best apply localization to options and their descriptions?
With the annotation based approached, defining the description argument in an @Option requires to use a constant. So, defining the help text and description puts the string literal into the code base, rather than allowing to pull them from a ResourceBundle that allows for easy localization.
Is there any way to change that? For example, as an idea, introduce a descriptionProvider argument, expecting a class reference that will instantiate that referenced class and pass the name of the option along, so that can be used as a key in a ResourceBundle?
thanks,
Tobias W.
And I just noticed this is a duplicate of https://github.com/remkop/picocli/issues/415 and https://github.com/remkop/picocli/issues/22

Yes, I'm aware of this one, and it has been climbing in priority on the todo list. Hopefully for the 3.6 release.
If you really need this now, you need to use the programmatic API.

@remkop Thanks for the response. I will stick with the annotations for now and keep an eye on this for future releases. Thanks for this well designed library, I really appreciate the thought that went into it. Itś hard to find something this easy for some other languages.

Fixed in master.
Support for ResourceBundles will be available from the upcoming 3.6 release.


Release 1.4

Hello, the last release was on Aug 2012, almost 3 years ago, and since then a lot have been merge on master (~650 non-merge commits). It would be great if you can tag them as 1.4.
So, I'd like to ask for the current state of simplecv, and if there are plans for a new release.
Thank you for all the work you have put into this great project.

Wondering if this project is still active. The last issue closed is in somewhere Jul, 2014 and the simplecv.org/download page is redirecting to webarchive etc... with the forums (help.simplecv.org) having more than 650+ questions unanswered !!

@kscottz and  @xamox  are the main contributors SimpleCV, maybe they can help us.

+1. I would like to know if this software is still maintained as well.

I am new in programming. And simplecv is simple and powerful. Easy to understand. I Really hope they can update the library.

No commits since last April, no activity on any PRs or issues since then (?) either.

I wrote a message to Sight Machine. Those guys seems to be responsible for SimpleCV now as it's attached to their organization in GitHub.

No response from Sight Machine.


Excessive load in oohelperd incident - December 21, 2023

Impact:

Impact on performance
potentially some missing measurements from miniooni
further information: https://github.com/ooni/probe/issues/2649

Detection:

Alerts in ooni-bots channel on Slack started firing

Timeline:

[approx 8:00-10:00UTC] @hellais and @bassosimone started investigating, noticed an hourly pattern to the increase in load
[approx 15:40 UTC] a mitigation to respond with a 504 conditioned on the client name was deployed on 0.th and 1.th
[approx 17:18 UTC] mitigation was determined to be a success
[approx 19:00 UTC] @bassosimone starts to release ooni/probe 3.20.1 to ship the mitigation for all THs

What is still unclear:

who are what is causing this increased load
more clarity on what the impact to measurements has been

Next steps:

[x] merge, release, & deploy (today)
[x] monitor the TH dashboard
[ ] open issue about investigating if we can optimize the TLS handshake performance
[ ] see if we can understand who's running these probes
[ ] assess impact on measurements
[ ] have a proper post-mortem conversation, likely in January

Some notes from @FedericoCeratto:

we don't know where these probes are. They appear to come from at least 2-3 thousand ipaddrs and across various CCs.
we've been somewhat lucky that they all share the same software_name and we can throttle on it. If the same thing happened with ooniprobe-android-unattended it would be a problem
so far there's never been a similar event. In future there could be mitigation in place to do throttling based on combinations of CC / software_name / ASN - perhaps also using cryptographic Probe identity
for load-balancing workloads haproxy could be a better option than nginx because it can support more flexible mechanisms
it could be useful to log CC / ASN / software_name / software_version of probes hitting the TH and compare this stuff with what is in the fastpath and trigger alarms if there are unexpected differences
there is an ongoing effort for putting THs behind the ooni-bridges ipaddrs and using haproxy as a load balancer. With that it would be also possible to increase and decrease the number of THs to handle bigger workloads without having to modify probes

Some notes from @bassosimone:

it's unclear why but it seems these miniooni probes are not submitting measurements

Many measurements in China failed.
For example:
https://explorer.ooni.org/m/20231223030445.527152_CN_webconnectivity_bd81b5f4f92bba44

What we should consider doing in the future:

deploy the test helpers in hosts with more RAM and CPU capabilities (mostly CPU);
figure out whether there's a way to more easily scale to more hosts that requires less manual intervention;
evaluate whether restructuring oohelperd to cache measurements for individual endpoints for more time would possibly help us to reduce the load and would not cause more headaches than benefits;
query ooni/data to get information about recent measurements rather than performing measurements;
schedule measurements periodically rather than doing them on demand (which possibly reduces the load especially if we use a single table to keep test helpers information);
use special probes to collect test helper information rather than having test helpers;
think about whether we could reduce the CPU usage caused by the handshakes by making sure that we're using assembly instruction as much as possible (probably already the case, but it makes sense to double check);
see if it's possible to avoid one handshake by using an already-handshaked connection for the first HTTP request.

If other potential future activities come to mind, I will update this list.

Many measurements in China failed, does Web Connectivity v0.5 consume more resource than Web Connectivity v0.4? For example: https://explorer.ooni.org/m/20231223030445.527152_CN_webconnectivity_bd81b5f4f92bba44

@Lanius-collaris, Web Connectivity v0.5 consumes more resources on the probe side. Regarding the test helper side, I think it may be using a bit more resources if we discover more IP addresses with v0.5 than with v0.4, which may happen as a side effect of using three resolvers (system, DNS-over-UDP, and DNS-over-HTTPS every ~30 seconds to passively collect information about DNS-over-HTTPS functionality).
The reason why you see 503 errors is that we added extra code that refuses the service if there's already significant queue of clients in the test helper. The result is that measurements are marked as failed. However, we're rewriting the data processing pipeline to basically ignore the test helper and recompute whether there was blocking. When this process converges, we're going to have correct results in Explorer also for measurements that are failed. The key word for this data pipeline rewriting is ooni/data and the repository is https://github.com/ooni/data.
After switching to using ooni/data, we would still see measurements marked as failed locally by the probe if the test helper returns 503, therefore results on a mobile phone or in the CLI would be less useful than they could. To solve this problem, I think the correct backward compatible approach would be to use ooni/data information to populate the TH response when possible, thus reducing the need to measure several websites in the TH. Another approach would instead be that of adding more test helpers, where the bottleneck is that our deployment code is not designed for that, so we would need to put in some development work and some backend developer time for that.
On our end in terms of test helper metrics, as you can see below, the problem seems to still be present but it seems much less impactful than it was when we opened the issue:

Are you still affected?
Thank you!

Upon reflection, I think the next step should be this one: https://github.com/ooni/probe/issues/2672. Because it's relatively simpler it should be possible to do it soon. With more test helpers we would reduce the overall load and have less measurements being marked as faulty in the backend and the probe. In the meanwhile, we will buy us more time to implement smarter solutions to fix the problem itself.


Describe what differentiates Javalin from other micro web frameworks

Your website currently contains:

Javalin started as a fork of the Spark framework but quickly turned into a ground-up rewrite influenced by express.js.

As a Java developer looking for a simple web framework for a simple RESTful API, not knowing express.js, this doesn't help me a lot. I need to dive into code examples to find out. And what has been rewritten internally?
Add an abstract description or feature list or examples that show the differences.
Is it easier? Faster? Smaller memory-footprint?
Maybe add popular Spring Boot and Dropwizard to the comparison.

I will consider doing this at some point @philippgille, but the API is still very far from done.

Forgot about this. I've added a new section to the index since this issue was created: https://javalin.io/#fluent-and-readable-api
I have a comparison branch for the page, but I don't think I will merge it.

@steveperkins you might want to have a look at this project.

@tipsy Javalin looks interesting. On the comparison page (https://javalin.io/comparisons/sparkjava) there are a number of features called out that Spark Java has that Javalin does not. Are these intentional exclusions or is there an intention to add them in the future?

Most of them are intentional, is there any feature in particular you're curious about?

Mainly the ability to run on application servers other than embedded Jetty
and route mapping based on accepts and content-type headers.
On Sun, Aug 19, 2018 at 11:09 AM David<EMAIL_ADDRESS>wrote:

Most of them are intentional, is there any feature in particular you're
curious about?
—
You are receiving this because you were mentioned.
Reply to this email directly, view it on GitHub
https://github.com/tipsy/javalin/issues/17#issuecomment-414137832, or mute
the thread
https://github.com/notifications/unsubscribe-auth/ACn6rmBg11xVezHFda8TX3a8vFmnwHBdks5uSY3NgaJpZM4NwgQ9
.


CASSANDRA-18896 ClientRequestSize metrics should not treat CONTAINS restrictions as being equality-based

https://app.circleci.com/pipelines/github/maedhroz/cassandra/776/workflows/8686837d-d30e-4854-8b7d-8608b2417323

Circle run looks clean

https://github.com/apache/cassandra/commit/256e39fc62c0b0ce576fd29849672e6323eecea4


Remove dependency on Shouldly

It's probably better not to have an explicit dependency on Shouldly. Users should be able to pick their own assertion library.
Shouldly style assertion could be put into its own nuget package or just included in a sub-namespace.

This has been fixed.


Increase file level typedness at least typed: true

Issue
Part of https://github.com/Vonage/vonage-ruby-sdk/issues/178
Changes
As described in https://sorbet.org/docs/metrics, Sorbet recommends us to increase file-level typedness to measure adoption of Sorbet.
So I increased file-level typedness of files that have had low level typedness (typed: ignore or typed: false). With this change, all files get to have at least typed: true.

Codecov Report

Merging #183 into master will not change coverage.
The diff coverage is 100.00%.

@@           Coverage Diff           @@
##           master     #183   +/-   ##
=======================================
  Coverage   99.65%   99.65%
=======================================
  Files          54       54
  Lines         862      862
=======================================
  Hits          859      859
  Misses          3        3

Impacted Files
Coverage Δ

lib/vonage/abstract_authentication.rb
100.00% <ø> (ø)

lib/vonage/applications/list_response.rb
100.00% <ø> (ø)

lib/vonage/basic.rb
100.00% <ø> (ø)

lib/vonage/bearer_token.rb
100.00% <ø> (ø)

lib/vonage/form_data.rb
100.00% <ø> (ø)

lib/vonage/jwt.rb
100.00% <ø> (ø)

lib/vonage/numbers/list_response.rb
100.00% <ø> (ø)

lib/vonage/params.rb
100.00% <ø> (ø)

lib/vonage/secrets/list_response.rb
100.00% <ø> (ø)

lib/vonage/voice/list_response.rb
100.00% <ø> (ø)

... and 2 more

Continue to review full report at Codecov.

Legend - Click here to learn more
Δ = absolute <relative> (impact), ø = not affected, ? = missing data
Powered by Codecov. Last update dcf58c2...cdeb3ef. Read the comment docs.

Another good PR, @ohbarye. I'll take a look this week.

Thanks for your review!


Output Not Shown When CodeLenses Provided for Active Document

VSCode Version: 1.43.2 (user setup)
OS Version: Windows 10 1809

Steps to Reproduce:
Having a CodeLensProvider registered which returns a non-empty array of codelenses for the current document interrupts displaying output in the output window.
My extension (jamespearson.al-test-runner) displays test results in the output window. If the setting to enable CodeLens is also enabled - and the provider returns some code lenses for the current document - then the output is not shown properly. I have to switch to another pane (e.g. terminal) and back to the output screen to see the results.

Does this issue occur when all extensions are disabled?: Not applicable

Please share a repro sample


New BLUEPILL_F103C8 target problem

mbed compile --target BLUEPILL_F103C8 --toolchain GCC_ARM --profile debug
knows the BLUEPILL_F103C8 target and compiles OK while
mbed export -i vscode_gcc_arm --target BLUEPILL_F103C8 --profile debug
complains about unknown target. mbed-os is the latest 6.2.0

Thank you for raising this detailed GitHub issue. I am now notifying our internal issue triagers.
Internal Jira reference: https://jira.arm.com/browse/IOTBTOOL-889


404 on HTTP Request - unhandled exception

Hey there, cool project. I am using the example code and get the following error when I run into a device with a 404. I guess I could handle this exception slightly using an UnhandledException overload, but I was thinking there might be a fix in the lib for this?
Unhandled Exception:
System.UriFormatException: Invalid URI: The hostname could not be parsed.
at System.Uri.CreateThis (System.String uri, System.Boolean dontEscape, System.UriKind uriKind) [0x0007d] in /Users/builder/jenkins/workspace/build-package-osx-mono/2018-06/external/bockbuild/builds/mono-x64/mcs/class/referencesource/System/net/System/UriExt.cs:56
at System.Uri..ctor (System.String uriString, System.UriKind uriKind) [0x00014] in /Users/builder/jenkins/workspace/build-package-osx-mono/2018-06/external/bockbuild/builds/mono-x64/mcs/class/referencesource/System/net/System/URI.cs:450
at Rssdp.SsdpDevice.StringToUri (System.String value) [0x00008] in <5569f5ca71f84fafb50ec4dfa2ebd196>:0
at Rssdp.SsdpDevice.SetPropertyFromReader (System.Xml.XmlReader reader, Rssdp.SsdpDevice device) [0x00264] in <5569f5ca71f84fafb50ec4dfa2ebd196>:0
at Rssdp.SsdpDevice.LoadDeviceProperties (System.Xml.XmlReader reader, Rssdp.SsdpDevice device) [0x0002c] in <5569f5ca71f84fafb50ec4dfa2ebd196>:0
at Rssdp.SsdpDevice..ctor (System.String deviceDescriptionXml) [0x00044] in <5569f5ca71f84fafb50ec4dfa2ebd196>:0
at Rssdp.SsdpRootDevice..ctor (System.Uri location, System.TimeSpan cacheLifetime, System.String deviceDescriptionXml) [0x00000] in <5569f5ca71f84fafb50ec4dfa2ebd196>:0
at Rssdp.DiscoveredSsdpDevice.GetDeviceInfo (System.Net.Http.HttpClient downloadHttpClient) [0x0011a] in <5569f5ca71f84fafb50ec4dfa2ebd196>:0
at Rssdp.DiscoveredSsdpDevice.GetDeviceInfo () [0x00083] in <5569f5ca71f84fafb50ec4dfa2ebd196>:0
at wDisco.MainClass+Search.deviceLocator_DeviceAvailable (System.Object sender, Rssdp.DeviceAvailableEventArgs e) [0x00062] in /Users/michaelm/xmichael99@gmail.com/Projects/wDisco/wDisco/Program.cs:117
at System.Runtime.CompilerServices.AsyncMethodBuilderCore+<>c.b__6_1 (System.Object state) [0x00000] in /Users/builder/jenkins/workspace/build-package-osx-mono/2018-06/external/bockbuild/builds/mono-x64/mcs/class/referencesource/mscorlib/system/runtime/compilerservices/AsyncMethodBuilder.cs:1039
at System.Threading.QueueUserWorkItemCallback.WaitCallback_Context (System.Object state) [0x0000d] in /Users/builder/jenkins/workspace/build-package-osx-mono/2018-06/external/bockbuild/builds/mono-x64/mcs/class/referencesource/mscorlib/system/threading/threadpool.cs:1308
at System.Threading.ExecutionContext.RunInternal (System.Threading.ExecutionContext executionContext, System.Threading.ContextCallback callback, System.Object state, System.Boolean preserveSyncCtx) [0x00071] in /Users/builder/jenkins/workspace/build-package-osx-mono/2018-06/external/bockbuild/builds/mono-x64/mcs/class/referencesource/mscorlib/system/threading/executioncontext.cs:961
at System.Threading.ExecutionContext.Run (System.Threading.ExecutionContext executionContext, System.Threading.ContextCallback callback, System.Object state, System.Boolean preserveSyncCtx) [0x00000] in /Users/builder/jenkins/workspace/build-package-osx-mono/2018-06/external/bockbuild/builds/mono-x64/mcs/class/referencesource/mscorlib/system/threading/executioncontext.cs:908
at System.Threading.QueueUserWorkItemCallback.System.Threading.IThreadPoolWorkItem.ExecuteWorkItem () [0x00021] in /Users/builder/jenkins/workspace/build-package-osx-mono/2018-06/external/bockbuild/builds/mono-x64/mcs/class/referencesource/mscorlib/system/threading/threadpool.cs:1285
at System.Threading.ThreadPoolWorkQueue.Dispatch () [0x00074] in /Users/builder/jenkins/workspace/build-package-osx-mono/2018-06/external/bockbuild/builds/mono-x64/mcs/class/referencesource/mscorlib/system/threading/threadpool.cs:858
at System.Threading._ThreadPoolWaitCallback.PerformWaitCallback () [0x00000] in /Users/builder/jenkins/workspace/build-package-osx-mono/2018-06/external/bockbuild/builds/mono-x64/mcs/class/referencesource/mscorlib/system/threading/threadpool.cs:1213
Unhandled Exception:
System.Net.Http.HttpRequestException: 404 (Not Found)
at System.Net.Http.HttpResponseMessage.EnsureSuccessStatusCode () [0x0000a] in /Users/builder/jenkins/workspace/build-package-osx-mono/2018-06/external/bockbuild/builds/mono-x64/mcs/class/System.Net.Http/System.Net.Http/HttpResponseMessage.cs:122
at Rssdp.DiscoveredSsdpDevice.GetDeviceInfo (System.Net.Http.HttpClient downloadHttpClient) [0x00094] in <5569f5ca71f84fafb50ec4dfa2ebd196>:0
at Rssdp.DiscoveredSsdpDevice.GetDeviceInfo () [0x00083] in <5569f5ca71f84fafb50ec4dfa2ebd196>:0
at wDisco.MainClass+Search.deviceLocator_DeviceAvailable (System.Object sender, Rssdp.DeviceAvailableEventArgs e) [0x00062] in /Users/michaelm/xmichael99@gmail.com/Projects/wDisco/wDisco/Program.cs:117
at System.Runtime.CompilerServices.AsyncMethodBuilderCore+<>c.b__6_1 (System.Object state) [0x00000] in /Users/builder/jenkins/workspace/build-package-osx-mono/2018-06/external/bockbuild/builds/mono-x64/mcs/class/referencesource/mscorlib/system/runtime/compilerservices/AsyncMethodBuilder.cs:1039
at System.Threading.QueueUserWorkItemCallback.WaitCallback_Context (System.Object state) [0x0000d] in /Users/builder/jenkins/workspace/build-package-osx-mono/2018-06/external/bockbuild/builds/mono-x64/mcs/class/referencesource/mscorlib/system/threading/threadpool.cs:1308
at System.Threading.ExecutionContext.RunInternal (System.Threading.ExecutionContext executionContext, System.Threading.ContextCallback callback, System.Object state, System.Boolean preserveSyncCtx) [0x00071] in /Users/builder/jenkins/workspace/build-package-osx-mono/2018-06/external/bockbuild/builds/mono-x64/mcs/class/referencesource/mscorlib/system/threading/executioncontext.cs:961
at System.Threading.ExecutionContext.Run (System.Threading.ExecutionContext executionContext, System.Threading.ContextCallback callback, System.Object state, System.Boolean preserveSyncCtx) [0x00000] in /Users/builder/jenkins/workspace/build-package-osx-mono/2018-06/external/bockbuild/builds/mono-x64/mcs/class/referencesource/mscorlib/system/threading/executioncontext.cs:908
at System.Threading.QueueUserWorkItemCallback.System.Threading.IThreadPoolWorkItem.ExecuteWorkItem () [0x00021] in /Users/builder/jenkins/workspace/build-package-osx-mono/2018-06/external/bockbuild/builds/mono-x64/mcs/class/referencesource/mscorlib/system/threading/threadpool.cs:1285
at System.Threading.ThreadPoolWorkQueue.Dispatch () [0x00074] in /Users/builder/jenkins/workspace/build-package-osx-mono/2018-06/external/bockbuild/builds/mono-x64/mcs/class/referencesource/mscorlib/system/threading/threadpool.cs:858
at System.Threading._ThreadPoolWaitCallback.PerformWaitCallback () [0x00000] in /Users/builder/jenkins/workspace/build-package-osx-mono/2018-06/external/bockbuild/builds/mono-x64/mcs/class/referencesource/mscorlib/system/threading/threadpool.cs:1213
FreeBSD router
[ERROR] FATAL UNHANDLED EXCEPTION: System.UriFormatException: Invalid URI: The hostname could not be parsed.
at System.Uri.CreateThis (System.String uri, System.Boolean dontEscape, System.UriKind uriKind) [0x0007d] in /Users/builder/jenkins/workspace/build-package-osx-mono/2018-06/external/bockbuild/builds/mono-x64/mcs/class/referencesource/System/net/System/UriExt.cs:56
at System.Uri..ctor (System.String uriString, System.UriKind uriKind) [0x00014] in /Users/builder/jenkins/workspace/build-package-osx-mono/2018-06/external/bockbuild/builds/mono-x64/mcs/class/referencesource/System/net/System/URI.cs:450
at Rssdp.SsdpDevice.StringToUri (System.String value) [0x00008] in <5569f5ca71f84fafb50ec4dfa2ebd196>:0
at Rssdp.SsdpDevice.SetPropertyFromReader (System.Xml.XmlReader reader, Rssdp.SsdpDevice device) [0x00264] in <5569f5ca71f84fafb50ec4dfa2ebd196>:0
at Rssdp.SsdpDevice.LoadDeviceProperties (System.Xml.XmlReader reader, Rssdp.SsdpDevice device) [0x0002c] in <5569f5ca71f84fafb50ec4dfa2ebd196>:0
at Rssdp.SsdpDevice..ctor (System.String deviceDescriptionXml) [0x00044] in <5569f5ca71f84fafb50ec4dfa2ebd196>:0
at Rssdp.SsdpRootDevice..ctor (System.Uri location, System.TimeSpan cacheLifetime, System.String deviceDescriptionXml) [0x00000] in <5569f5ca71f84fafb50ec4dfa2ebd196>:0
at Rssdp.DiscoveredSsdpDevice.GetDeviceInfo (System.Net.Http.HttpClient downloadHttpClient) [0x0011a] in <5569f5ca71f84fafb50ec4dfa2ebd196>:0
at Rssdp.DiscoveredSsdpDevice.GetDeviceInfo () [0x00083] in <5569f5ca71f84fafb50ec4dfa2ebd196>:0
at wDisco.MainClass+Search.deviceLocator_DeviceAvailable (System.Object sender, Rssdp.DeviceAvailableEventArgs e) [0x00062] in /Users/michaelm/xmichael99@gmail.com/Projects/wDisco/wDisco/Program.cs:117
at System.Runtime.CompilerServices.AsyncMethodBuilderCore+<>c.b__6_1 (System.Object state) [0x00000] in /Users/builder/jenkins/workspace/build-package-osx-mono/2018-06/external/bockbuild/builds/mono-x64/mcs/class/referencesource/mscorlib/system/runtime/compilerservices/AsyncMethodBuilder.cs:1039
at System.Threading.QueueUserWorkItemCallback.WaitCallback_Context (System.Object state) [0x0000d] in /Users/builder/jenkins/workspace/build-package-osx-mono/2018-06/external/bockbuild/builds/mono-x64/mcs/class/referencesource/mscorlib/system/threading/threadpool.cs:1308
at System.Threading.ExecutionContext.RunInternal (System.Threading.ExecutionContext executionContext, System.Threading.ContextCallback callback, System.Object state, System.Boolean preserveSyncCtx) [0x00071] in /Users/builder/jenkins/workspace/build-package-osx-mono/2018-06/external/bockbuild/builds/mono-x64/mcs/class/referencesource/mscorlib/system/threading/executioncontext.cs:961
at System.Threading.ExecutionContext.Run (System.Threading.ExecutionContext executionContext, System.Threading.ContextCallback callback, System.Object state, System.Boolean preserveSyncCtx) [0x00000] in /Users/builder/jenkins/workspace/build-package-osx-mono/2018-06/external/bockbuild/builds/mono-x64/mcs/class/referencesource/mscorlib/system/threading/executioncontext.cs:908
at System.Threading.QueueUserWorkItemCallback.System.Threading.IThreadPoolWorkItem.ExecuteWorkItem () [0x00021] in /Users/builder/jenkins/workspace/build-package-osx-mono/2018-06/external/bockbuild/builds/mono-x64/mcs/class/referencesource/mscorlib/system/threading/threadpool.cs:1285
at System.Threading.ThreadPoolWorkQueue.Dispatch () [0x00074] in /Users/builder/jenkins/workspace/build-package-osx-mono/2018-06/external/bockbuild/builds/mono-x64/mcs/class/referencesource/mscorlib/system/threading/threadpool.cs:858
at System.Threading._ThreadPoolWaitCallback.PerformWaitCallback () [0x00000] in /Users/builder/jenkins/workspace/build-package-osx-mono/2018-06/external/bockbuild/builds/mono-x64/mcs/class/referencesource/mscorlib/system/threading/threadpool.cs:1213
[ERROR] FATAL UNHANDLED EXCEPTION: System.Net.Http.HttpRequestException: 404 (Not Found)
at System.Net.Http.HttpResponseMessage.EnsureSuccessStatusCode () [0x0000a] in /Users/builder/jenkins/workspace/build-package-osx-mono/2018-06/external/bockbuild/builds/mono-x64/mcs/class/System.Net.Http/System.Net.Http/HttpResponseMessage.cs:122
at Rssdp.DiscoveredSsdpDevice.GetDeviceInfo (System.Net.Http.HttpClient downloadHttpClient) [0x00094] in <5569f5ca71f84fafb50ec4dfa2ebd196>:0
at Rssdp.DiscoveredSsdpDevice.GetDeviceInfo () [0x00083] in <5569f5ca71f84fafb50ec4dfa2ebd196>:0
at wDisco.MainClass+Search.deviceLocator_DeviceAvailable (System.Object sender, Rssdp.DeviceAvailableEventArgs e) [0x00062] in /Users/michaelm/xmichael99@gmail.com/Projects/wDisco/wDisco/Program.cs:117
at System.Runtime.CompilerServices.AsyncMethodBuilderCore+<>c.b__6_1 (System.Object state) [0x00000] in /Users/builder/jenkins/workspace/build-package-osx-mono/2018-06/external/bockbuild/builds/mono-x64/mcs/class/referencesource/mscorlib/system/runtime/compilerservices/AsyncMethodBuilder.cs:1039
at System.Threading.QueueUserWorkItemCallback.WaitCallback_Context (System.Object state) [0x0000d] in /Users/builder/jenkins/workspace/build-package-osx-mono/2018-06/external/bockbuild/builds/mono-x64/mcs/class/referencesource/mscorlib/system/threading/threadpool.cs:1308
at System.Threading.ExecutionContext.RunInternal (System.Threading.ExecutionContext executionContext, System.Threading.ContextCallback callback, System.Object state, System.Boolean preserveSyncCtx) [0x00071] in /Users/builder/jenkins/workspace/build-package-osx-mono/2018-06/external/bockbuild/builds/mono-x64/mcs/class/referencesource/mscorlib/system/threading/executioncontext.cs:961
at System.Threading.ExecutionContext.Run (System.Threading.ExecutionContext executionContext, System.Threading.ContextCallback callback, System.Object state, System.Boolean preserveSyncCtx) [0x00000] in /Users/builder/jenkins/workspace/build-package-osx-mono/2018-06/external/bockbuild/builds/mono-x64/mcs/class/referencesource/mscorlib/system/threading/executioncontext.cs:908
at System.Threading.QueueUserWorkItemCallback.System.Threading.IThreadPoolWorkItem.ExecuteWorkItem () [0x00021] in /Users/builder/jenkins/workspace/build-package-osx-mono/2018-06/external/bockbuild/builds/mono-x64/mcs/class/referencesource/mscorlib/system/threading/threadpool.cs:1285
at System.Threading.ThreadPoolWorkQueue.Dispatch () [0x00074] in /Users/builder/jenkins/workspace/build-package-osx-mono/2018-06/external/bockbuild/builds/mono-x64/mcs/class/referencesource/mscorlib/system/threading/threadpool.cs:858
at System.Threading._ThreadPoolWaitCallback.PerformWaitCallback () [0x00000] in /Users/builder/jenkins/workspace/build-package-osx-mono/2018-06/external/bockbuild/builds/mono-x64/mcs/class/referencesource/mscorlib/system/threading/threadpool.cs:1213
Here is what I am running
public static void Main(string[] args)
{
Search searcher = new Search();
searcher.BeginSearch();
Thread.Sleep(10000);
}
public class Search
{
private SsdpDeviceLocator _DeviceLocator;
        public void BeginSearch()
        {
            _DeviceLocator = new SsdpDeviceLocator();

            // (Optional) Set the filter so we only see notifications for devices we care about
            // (can be any search target value i.e device type, uuid value etc - any value that appears in the
            // DiscoverdSsdpDevice.NotificationType property or that is used with the searchTarget parameter of the Search method).
            _DeviceLocator.NotificationFilter = "upnp:rootdevice";

            // Connect our event handler so we process devices as they are found
            _DeviceLocator.DeviceAvailable += deviceLocator_DeviceAvailable;

            // Enable listening for notifications (optional)
            _DeviceLocator.StartListeningForNotifications();

            // Perform a search so we don't have to wait for devices to broadcast notifications
            // again to get any results right away (notifications are broadcast periodically).
            _DeviceLocator.SearchAsync();

            Console.ReadLine();
        }

        // Process each found device in the event handler
        async static void deviceLocator_DeviceAvailable(object sender, DeviceAvailableEventArgs e)
        {
            //Device data returned only contains basic device details and location of full device description.
            Console.WriteLine("Found " + e.DiscoveredDevice.Usn + " at " + e.DiscoveredDevice.DescriptionLocation.ToString());

            //Can retrieve the full device description easily though.
            var fullDevice = await e.DiscoveredDevice.GetDeviceInfo();
            Console.WriteLine(fullDevice.FriendlyName);
            Console.WriteLine();
        }
    }

Thank you very much for taking the time to review this issue and making this project!

Hi,
Thanks for getting in touch, I'm glad you're finding RSSDP useful.
If I am reading the exception details correctly, this error is happening in the deviceLocator_DeviceAvailable event handler in the sample code/your app when the await e.DiscoveredDevice.GetDeviceInfo() block is executed.
This involved doing a network call to the server, and anything that could normally go wrong with a network call can happen at this point. That includes the network being down, a timeout, DNS failure, a 404/401/429/500 or other HTTP error etc.  The library cannot and should not handle this, if it can't get the data from the device then it cannot return the device information that is expected as the result of GetDeviceInfo. It could I guess catch any error and return null, but that would be bad as the calling application cannot then determine why it got null - was it a timeout or 429 where a retry might be appropriate? Is the network down, should it wait for a reconnect? etc. As such, there isn't a 'fix' available and I'm not sure what one would look like in terms of changing the library.
I wouldn't use an 'unhandled exception' event for a variety of reasons. I personally think the correct thing to do would be to add a try/catch block around that line of code (the call to GetDeviceInfo) in the application, and handle the error(s) appropriately in the catch. What that means depends on the app. Perhaps you just ignore devices that can't be contacted. Perhaps you add them to a separate list of problem devices which is displayed to the user at some point.
I hope that helps, if not or if I've misunderstood, please let me know.
Thanks again.

Thank for your very thoughtful and detailed response. Upon reading your response I sure do feel like a dummy, I honestly didn't even bother to read into the sample code instead I went looking through your code trying to see how the HTTP connection was made, thinking that there was a missing try catch for a 404 exception type, and that it should be handled up stream.
A simply try catch exception type seems to get it. I can't seem to catch a WebException, but that is probably ok (;
Thanks again for your time and help.

Hi,
No need to feel silly, it's a complicated stacktrace and being unfamiliar with the code doesn't help, I can easily see how it would be confusing :)
Good luck with your project!


When using styled-component, unit test becomes not fesible

Things like wrapper.find('.traveler-selection__input') with enzyme becomes no longer fesible because we don't use class name the regular way in styled-component.
Is there a solution on this?
Thanks

There are already a load of issues asking for different approaches. It’s not infeasible but part of separating presentational and structural components surfaces during testing when it’s not strictly executed
https://github.com/styled-components/styled-components/issues/896#issuecomment-324469861
https://github.com/styled-components/styled-components/issues/624
Apart from the necessity to search for issues first, please also adhere to the issue template. If you spend less time on the issue that’s more time we have to dedicate to it ;)

@philpl sorry! yes, I need to search for the issues first... duh... silly me since there are already 66 issues lol
Thanks for answering me with reference those I was reading those and found them very helpful!
Thanks


multisig wallet cannot sign, raise keyerror 'value'

Traceback (most recent call last):
File "/usr/local/bin/electron-cash", line 4, in
import('pkg_resources').run_script('Electron-Cash==2.9.3', 'electron-cash')
File "/usr/local/lib/python2.7/dist-packages/pkg_resources/init.py", line 748, in run_script
self.require(requires)[0].run_script(script_name, ns)
File "/usr/local/lib/python2.7/dist-packages/pkg_resources/init.py", line 1517, in run_script
exec(code, namespace, namespace)
File "/usr/local/lib/python2.7/dist-packages/Electron_Cash-2.9.3-py2.7.egg/EGG-INFO/scripts/electron-cash", line 423, in
result = run_offline_command(config, config_options)
File "/usr/local/lib/python2.7/dist-packages/Electron_Cash-2.9.3-py2.7.egg/EGG-INFO/scripts/electron-cash", line 286, in run_offline_command
result = func(*args)
File "/usr/local/lib/python2.7/dist-packages/Electron_Cash-2.9.3-py2.7.egg/electrum/commands.py", line 86, in func_wrapper
return func(*args, **kwargs)
File "/usr/local/lib/python2.7/dist-packages/Electron_Cash-2.9.3-py2.7.egg/electrum/commands.py", line 245, in signtransaction
self.wallet.sign_transaction(tx, password)
File "/usr/local/lib/python2.7/dist-packages/Electron_Cash-2.9.3-py2.7.egg/electrum/wallet.py", line 1104, in sign_transaction
k.sign_transaction(tx, password)
File "/usr/local/lib/python2.7/dist-packages/Electron_Cash-2.9.3-py2.7.egg/electrum/keystore.py", line 114, in sign_transaction
tx.sign(keypairs)
File "/usr/local/lib/python2.7/dist-packages/Electron_Cash-2.9.3-py2.7.egg/electrum/transaction.py", line 829, in sign
pre_hash = Hash(self.serialize_preimage(i).decode('hex'))
File "/usr/local/lib/python2.7/dist-packages/Electron_Cash-2.9.3-py2.7.egg/electrum/transaction.py", line 727, in serialize_preimage
amount = int_to_hex(txin['value'], 8)
KeyError: 'value'
in gui, file "gui/qt/main_window.py" line 2003 code "tx._inputs[i]['value'] = my_coins[my_index]['value']", added key 'value'. But in command line, there is no such code.
I add the code myself, maybe needing to be formated.

This is fixed by #196


automate portus release

This commit adds a "release.sh" script that will release portus in the
build service.
It assumes you will have already tagged and update the VERSION file, as
well as updated the CHANGELOG (see http://port.us.org/docs/how_we_release.html)
It includes a small fix in the make_spec.sh that checks for bundler
Signed-off-by: Jordi Massaguer Pla<EMAIL_ADDRESS>

LGTM :clap:


It can working web, desktop?

It can working web, desktop?

@jpoh281 yes it can work on any platform (including desktop),
except runtime js (web), because browsers does not supports plain UDP protocol

OMG...
I made this plugin https://pub.dev/packages/esptouch_smartconfig
but yours is more awesome than mine! !
Good.

Thank you.
If you found any bugs, issues or have request for library improvements, feel free to write me.
Best regards


Support transfer transaction encrypted messages

Add to nem2-cli transaction transfer the flag --encrypted.
The message in the body should be encrypted as shown in the following tutorial: https://nemtech.github.io/guides/transaction/sending-an-encrypted-message.html
Note: We might need to pass as well the public key of the account that can decrypt the message, as the address does not seem enough. To achieve so, we could create an additional property--recipientPublicKey that receives a public key instead of a regular address.

PR #125


Simple Setup - pair-setup.go:42: pairing is not allowed

I must be doing something dumb, but I keep getting this message. I'm certain this worked about a month ago.
Has there been a HomeKit Change that has stopped this package working ?
thank you

weird thing, deleted the through Visual Code studio for testing. Euro resolved it self with an rm -rf !

...


class file for com.google.android.gms.internal.zzbfm not found

Cant compile, getting this error:

BUILD FAILED in 49s
26 actionable tasks: 22 executed, 4 up-to-date
platforms/android/gradlew: Command failed with exit code 1 Error output:
platforms/android/app/src/main/java/com/marianhello/bgloc/data/BackgroundActivity.java:20: error: cannot access zzbfm
confidence = activity.getConfidence();
^
class file for com.google.android.gms.internal.zzbfm not found
Note: Some input files use or override a deprecated API.
Note: Recompile with -Xlint:deprecation for details.
Note: Some input files use unchecked or unsafe operations.
Note: Recompile with -Xlint:unchecked for details.

+1

Hello all, Do we have a fix for it ?  +1 as well.

Guys any solution for this ?

Good day. There are two solutions:

Install this plugin
Manually edit file platforms>android>project.properties like this

cordova.system.library.1=com.google.android.gms:play-services-location:11.0.4
cordova.system.library.2=com.android.support:support-v4:27.0.2
cordova.system.library.3=com.android.support:appcompat-v7:27.0.2
cordova.gradle.include.1=cordova-plugin-background-geolocation/quickrun-dependencies.gradle
cordova.system.library.4=com.android.support:support-v4:27.0.2
cordova.system.library.5=com.android.support:support-v4:27.0.2
cordova.system.library.6=com.google.android.gms:play-services-analytics:11.0.4
You need bold rows.

Same problem here with another issue:
src/main/java/com/marianhello/bgloc/data/BackgroundActivity.java:18: error: cannot find symbol
    public BackgroundActivity(Integer locationProvider, DetectedActivity activity) {
                                                        ^
  symbol:   class DetectedActivity
  location: class BackgroundActivity

Good day. There are two solutions:

Install this plugin
Manually edit file platforms>android>project.properties and specify versions for com.google.android.gms like this

hello, I try this but did not work. I installed the plugin but I do not have any project.properties file. I'm using ionic 6.
Any tips?
Thanks!


sql-kysely: Error not explicit

What version of Effect is running?
<EMAIL_ADDRESS>What steps can reproduce the bug?

use a setup like https://github.com/Effect-TS/effect/issues/3521
log error with @effect/Logger like so: Logger.withMinimumLogLevel(LogLevel.All),
then insert into a non existing table

What is the expected behavior?
I should get an error that I can understand
What do you see instead?
I see only SqlError. here is the complete error
timestamp=2024-08-30T23:19:16.638Z level=DEBUG fiber=#2 message="Fiber terminated with an unhandled error" cause="SqlError
    at cause (/my-project/node_modules/.pnpm/@effect+sql-kysely@0.5.0_@effect+sql@0.10.0_@effect+experimental@0.24.0_@effect+platform-node_boill4fvc32tejkn3w5hoksn3a/node_modules/@effect/sql-kysely/src/internal/patch.ts:70:23)
    at e (/my-project/node_modules/.pnpm/effect@3.1.1/node_modules/effect/src/internal/core-effect.ts:1670:23)
    at kysely.execute"

Additional information
No response

Message rendering could be improved, cc @tim-smart but this isn't a bug.

Have you got some example code? So I can see how the error is being logged.

I can't reproduce the issue anymore. I might have been on an old version of one of the packages. closing the issue


Fix API Explorer links

API Explorer expects links to end in a trailing slash
Fixes #1233

Codecov Report

Merging #1234 into master will not change coverage.
The diff coverage is n/a.

@@            Coverage Diff            @@
##             master    #1234   +/-   ##
=========================================
  Coverage     86.14%   86.14%
  Complexity     3771     3771
=========================================
  Files           380      380
  Lines         14965    14965
  Branches       1665     1665
=========================================
  Hits          12892    12892
  Misses         1623     1623
  Partials        450      450

Continue to review full report at Codecov.

Legend - Click here to learn more
Δ = absolute <relative> (impact), ø = not affected, ? = missing data
Powered by Codecov. Last update 21cac37...95e628f. Read the comment docs.


history issue. Forward button doesn't work

When I navigating from example.com/#/ to example.com/#/mailbox and then click browser back button everything works as expected. Url has changed to example.com/#/ and router noticed it. However from this point I should be able to click forward button and go to example.com/#/mailbox again. Unfortunately i'm not able to do that. Forward button is disabled.

Thanks for raising this and helping during the grinding teeth phase! :+1:

v0.2.6 released

At your service :+1: . Thanks for such fast answer.


Reproducing the results

Hi! Thanks for this amazing work!
I'm having hard time to reproduce the Table 6 in your paper with this code.
What I have done is-- I ran grab_acts.py and copied something similar to https://github.com/FasterDecoding/TEAL/blob/9f7e408d56134573ba4f97cbdb2ad4a20845e627/teal/ppl_test.py#L16-L40
in lm-evaluation-harness.
Could you provide more details how to reproduce it? (For example, on which corpus the histograms are collected, with which number of samples, etc)

Also, where can I find the greedy algorithm to generate the lookup folder?

Hi! I'm working on including the greedy algorithm, and will expect to have that very soon. You may be able to reproduce the results in Table 4 for the uniform case right now.
The histograms are collected from alpaca, 10 samples of sequence length 2048

Ah, Thanks!

I've pushed a serial version of the greedy algorithm for now (having trouble parallelizing over GPUs cleanly), and have also updated the models folder to include the associated lookup folders.
To load block-wise greedy sparsities, you can do the following:
        greedy_path = os.path.join(args.teal_path, "lookup")
        model.load_greedy_sparsities(greedy_path, args.sparsity)

Let me know if you still have any issues!


WIP: HOC options API

@alvaromb it's a work in progress, not to merge yet
if you have any opinion on this tell me please

hey @alvaromb , my code seems to work fine in my app
I am not able to merge my PR, not to push directly on master

@alvaromb can you give me the rights to merge and publish? Or approve the PR?

Sorry @slorber, please try again

thanks, this seems to be working now. I'll make a release with notes soon


fix(ci): L1 contracts directories

Please provide a paragraph or two giving a summary of the change, including relevant motivation and context.
Checklist:
Remove the checklist to signal you've completed it. Enable auto-merge if the PR is ready to merge.

[ ] If the pull request requires a cryptography review (e.g. cryptographic algorithm implementations) I have added the 'crypto' tag.
[ ] I have reviewed my diff in github, line by line and removed unexpected formatting changes, testing logs, or commented-out code.
[ ] Every change is related to the PR description.
[ ] I have linked this pull request to relevant issues (if any exist).

Benchmark results
Metrics with a significant change:

note_trial_decrypting_time_in_ms (8): 35.5 (+46%)

Detailed results
All benchmarks are run on txs on the Benchmarking contract on the repository. Each tx consists of a batch call  to create_note and increment_balance, which guarantees that each tx has a private call, a nested private call, a public call, and a nested public call, as well as an emitted private note, an unencrypted log, and public storage read and write.
This benchmark source data is available in JSON format on S3 here.
Values are compared against data from master at commit ebdeea3f and shown if the difference exceeds 1%.
L2 block published to L1
Each column represents the number of txs on an L2 block published to L1.

Metric
8 txs
32 txs
128 txs

l1_rollup_calldata_size_in_bytes
45,444
179,588
716,132

l1_rollup_calldata_gas
223,008
867,440
3,449,624

l1_rollup_execution_gas
842,095
3,594,548
22,204,993

l2_block_processing_time_in_ms
2,212
8,507 (-1%)
35,619

note_successful_decrypting_time_in_ms
326 (+3%)
929
3,421 (+2%)

note_trial_decrypting_time_in_ms
:warning: 35.5 (+46%)
56.1 (+3%)
202 (+1%)

l2_block_building_time_in_ms
20,548
81,143
326,073

l2_block_rollup_simulation_time_in_ms
16,996
67,151
269,908

l2_block_public_tx_process_time_in_ms
3,501
13,840
55,606

L2 chain processing
Each column represents the number of blocks on the L2 chain where each block has 16 txs.

Metric
5 blocks
10 blocks

node_history_sync_time_in_ms
24,473
47,151 (-1%)

note_history_successful_decrypting_time_in_ms
2,290 (+7%)
4,151

note_history_trial_decrypting_time_in_ms
163
226

node_database_size_in_bytes
2,789,259
5,022,427

pxe_database_size_in_bytes
29,748
59,307

Circuits stats
Stats on running time and I/O sizes collected for every circuit run across all benchmarks.

Circuit
circuit_simulation_time_in_ms
circuit_input_size_in_bytes
circuit_output_size_in_bytes

private-kernel-init
199 (-3%)
43,109
20,441

private-kernel-ordering
115 (-4%)
25,833
9,689

base-rollup
2,956 (-3%)
664,108
873

root-rollup
92.2 (+3%)
4,072
1,097

private-kernel-inner
260 (-1%)
64,516
20,441

public-kernel-private-input
171 (-1%)
25,203
20,441

public-kernel-non-first-iteration
168 (-2%)
25,245
20,441

merge-rollup
10.8 (-1%)
2,592
873

Miscellaneous
Transaction sizes based on how many contracts are deployed in the tx.

Metric
0 deployed contracts
1 deployed contracts

tx_size_in_bytes
10,323
25,938


master fails on insert/update/remove with "Unexpected server response"

Caused by
https://github.com/DevExpress/DevExtreme.AspNet.Data/blob/03fa77aa8eea6ad7a7106094fc41ab448fa7bbcc/js/dx.aspnet.data.js#L51
WIP: https://github.com/AlekseyMartynov/DevExtreme.AspNet.Data/tree/cud-tests
Blocked on https://github.com/DevExpress/DevExtreme/pull/2879
FYI @San4es

@NickMitrokhin
Please schedule a public fix build (17.2)

@AlekseyMartynov
<EMAIL_ADDRESS>with the https://github.com/DevExpress/DevExtreme/pull/2879 fix is published.


Backport PyPA link fix to v1.32.x

Backports #25647

The backport commit contained many unwanted changes like clang version, base image, Python version, also Twisted has deprecated 27 and created multiple small packages, so it's hard to find a right combination. The original backport is force merged, this PR is no longer needed, closing.


Prefect Deployment Override Failing for multi container ECS Fargate Task

Expectation / Proposal
Whenever we are trying to override the below mentioned sample multi-container ECS task block using the prefect deployment override we are facing this issue:

Current ECS Infra Block
{
  "cpu": 512,
  "memory": 1024,
  "containerDefinitions": [
    {
      "name": "log_router",
      "cpu": 256,
      "memory": 512,
      "image": "newrelic/logging-firelens-fluentbit",
    },
    {
      "name": "prefect",
      "cpu": 256,
      "memory": 512,
      "image": "prefect:latest"
    }]
}

Prefect Deployment Override

The Reason why we are seeing this issue is because the resulting ECS Task definition looks sth like this:
Resulting ECS Block
{
  "cpu": 1024,  #overrideen value
  "memory": 2048, "#overrideen value
  "containerDefinitions": [
    {
      "name": "log_router",
       "cpu": 256,
       "memory": 512,
      "image": "newrelic/logging-firelens-fluentbit",
    },
    {
      "name": "prefect",
      "cpu": 1024, #overrideen value
      "memory": 2048, #overrideen value
      "image": "prefect:latest"
    }]
}

Resulting Condition:  prefect container cpu,memory + log_router container cpu, memory > task level cpu, memory. As per the official docs, container resources should be less than or equal to task resources.
Traceback / Example
To Reproduce this issue:

create ECS infra block with at least two containers and specify cpu, memory size in all containers and task level.
Override the block with prefect deployment override.
Run the flow

Thanks for the issue @rbalman! How would you expect the CPU and memory overrides to affect the container-level CPU and memory? I'm thinking that we would assign any excess CPU/memory to the prefect container after subtracting the CPU/memory allocated to all other containers, but I want to make sure that's in line with your expectations.

I think by default would allocate that CPU/Memory to the Prefect container then add all of the other container CPU/Memory requests to compute the task level option? It depends a little bit on:

What you can update without registering a new task definition
The difficulty of rounding to a value that is supported by AWS

Thanks @zanieb  and @desertaxle for you response. The current behavior we are facing as mentioned in the Issue:

The prefect infra override is not just overriding the overall cpu/memory but it is also overriding the cpu/memory of prefect container but leaving other container cpu/memory as it is.

Expectation

I would expect prefect override to either override the prefect container cpu/memory only or just override the overall cpu/memory but not both overall(task level) and prefect container memory


Remove insecure mode

This removes insecure mode
Fixes: #410

LGTM.

This looks great :+1: lots of branches removed.
@ryansanford Can you confirm we don't need insecure & debug for anything anymore? I don't use it...

It was my understanding that we were going to remove insecure mode and debug links, but not debug mode, which prints stack traces, IIRC.
I strongly suggest that we keep this PR limited to the first two items and discuss the last one separately.
Just adding a couple of comments on what's mentioned above:

Given that we are open source, printing a stack trace in prod, doesn't reveal any secrets. Whether it's good form or not is debatable. It certainly makes debugging easier.
I vote that the API never ever returns anything but JSON.

@gsfr I agree leaving 500 errors JSON for the sake of consistency makes sense.  Right now we may have some 500's which are not json-encoded, #451 will address that in addition to logging the traceback.  Thus, I reverted my changes to that try-except block
I also restored debug mode, I know other frameworks have had security issues where debug mode has a remote code execution vulnerability and thus having a way to enable it in production carries some risk.  That's definitely not what this PR was about so I've reverted that change as well.

Per offline discussion, revert change in api/api.py and then we should be good to go.

@gsfr @nagem Can I get a once over and final sign-off from you two before I merge this?

Thanks, @coltonlw. LGTM, but untested.

I can test tomorrow but just looking it over it LGTM2.


Troubleshoot local shell plugin hanging

Hey, I'm after a bit of help please? I use a local dev CLI tool called fooctl, it simplifies my interactions with cloud resources. To use fooctl, I must provide a credentials file at ~/.fooctl/credentials. The credentials file expects a UUID and Token (I guess in the ini format, e.g. UUID = "4191234b-foo5-4bar-af3f-3d8266111d8b").
I followed the tutorial, but I'm getting stuck - literally.
My plugin validates & builds fine. It's also successfully looking for & importing the UUID and Token in 1Password when a credentials file already exists.
However, it hangs when using any fooctl commands with an arg.
I'm running this on macOS 14.4.1 (23E224) from ~/dev/shell-plugins.
This is what happens when I run footctl without any args, works as expected:
% fooctl
################################################################################
# WARNING: 'fooctl' is not from the official registry.                         #
# Only proceed if you are the developer of 'fooctl'.                           #
# Otherwise, delete the file at /Users/me/.config/op/plugins/local/fooctl.     #
################################################################################
Fooctl is a tool to ease working with applications, projects and infrastructure

Usage:
  fooctl [command]

... omitted help text

Now to use fooctl, I need to use the daemon command (which requires the credentials file):
% fooctl daemon
################################################################################
# WARNING: 'fooctl' is not from the official registry.                         #
# Only proceed if you are the developer of 'fooctl'.                           #
# Otherwise, delete the file at /Users/me/.config/op/plugins/local/fooctl.     #
################################################################################

... the command hangs
... ~/.fooctl/credentials is created but trying to open that also hangs

Interestingly, if I run fooctl configure (I thought I could avoid doing this as this is what the shell plugin creates), it gets a bit further. It automagically suggests Defaults using credentials that were imported into 1Password, creates the credentials file which I can open & see correct contents... but then hangs:
% fooctl configure
################################################################################
# WARNING: 'fooctl' is not from the official registry.                         #
# Only proceed if you are the developer of 'fooctl'.                           #
# Otherwise, delete the file at /Users/me/.config/op/plugins/local/fooctl.     #
################################################################################
To proceed with configuration you will need to either generate new credentials
or re-use an existing one that is still valid i.e. hasn't expired or hasn't been
revoked). To manage or view your credentials, visit:
https://localhost/fooctl-cli

Detected existing credentials, they are available as defaults.

What is your UUID?
Enter a value (Default is 5eae7028-1698-4045-9521-21580b4cddf1):

What is your Token?
Enter a value (Default is sL+****):

... the command hangs
... ~/.fooctl/credentials is created, can be opened & contains the correct credentials

Can you spot anything wrong in the code, or suggest ways to troubleshoot this?

This is a know limitation of the shell plugins API: the app you are trying to provision must only read once from the provisioning file. If it reads 2 times, or attempts to write to the file then the provisioning will hang forever.
Background:
We're using FIFO as a file abstraction to write secrets in order to provision to apps. FIFOs can only be read when they are open for write and when are only writing once to the FIFO in op . So when the app we provision reads a second time, or attempts to write to the FIFO then it will wait for the counterpart operation that comes from op , Just that  op  never responds.Ideally we could determine in advance what operations the app executes so op could respond appropriately for each. Unfortunately this is not possible unless we expose something in the Shell Plugins api to allow each app to define what the app reads and writes from the provisioning file.

Thanks for the heads up about this @AndyTitu, will take a look at what our app is doing!


Circular dependencies detection problem 2

Continuation of https://github.com/ExtendedXmlSerializer/home/issues/583
I have new case of same problem
Looks like this problem have more shades.
Problem found unexpectedly, previously we use .EnableReferences() but soon as we switched to .AllowMultipleReferences()
new problem is occured.
If I EnableReferences for same object it will serialize successfully, but will never use exs:reference="1"exs:identity="1"
I suppose this object should serialize with .AllowMultipleReferences only
Even Array.Empty will be detected as MultipleReferences
using ExtendedXmlSerializer;
using ExtendedXmlSerializer.Configuration;

var sameInnerObject = new InnerObject { Id = 100, Items = new string[] { } };
var testRootObject = new RootObject
{
    Item1 = sameInnerObject,
    Item2 = new InnerObject2 { Items = sameInnerObject.Items },
    List = new List<InnerObject>()
};

IExtendedXmlSerializer serializer = new ConfigurationContainer()
    .AllowMultipleReferences()
//.EnableReferences()
    .Create();
var serialized = serializer.Serialize(testRootObject);
Console.WriteLine("Success");

public class RootObject
{
    public InnerObject Item1 { get; set; }
    public InnerObject2 Item2 { get; set; }
    public List<InnerObject> List { get; set; } = new();
}

public class InnerObject
{
    public int Id { get; set; }
    public string[] Items { get; set; }
}

public class InnerObject2
{
    public string[] Items { get; set; }
}

Wow @UladimirLameyko I am a little shocked here.  It appears the reference is not detected at all.  Thank you for reporting this I will look into it for you. 👍

OK @UladimirLameyko I did look into this and we're running into a bit of a limitation here with ExtendedXmlSerializer.  Basically, it stems from the classic serializer and empty collections are not emitted.  This is what you are running into here with the above code.  In this case. if you are using references with collections you initialize with an empty instance, as such:
public class InnerObject
{
    public int Id { get; set; }
    public string[] Items { get; set; } = Array.Empty<string>();
}

public class InnerObject2
{
    public string[] Items { get; set; } = Array.Empty<string>();
}

I concede this is not perfect but poking around any further would introduce breaking changes and/or additional functionality I would not be comfortable introducing at this point with my limitations at the moment.
That stated, I am definitely open to further discussion around this if you have further ideas/considerations/suggestions and even a PR if we can land on one. :)

@Mike-E-angelo Problem not with empty arrays. Even if I put value into collection
var sameInnerObject = new InnerObject { Id = 100, Items = new string[] { "Some" } };
Exception still appear

Oh I see... are you saying this is throwing a CircularReferenceDetectedException instead of a MultipleReferencesDetectedException?  I can look into that if so. 👍

Great, sounds good @UladimirLameyko it has been deployed here:
https://www.nuget.org/packages/ExtendedXmlSerializer
I appreciate your continued feedback and cooperation towards improving ExtendedXmlSerializer 👍


Fix typo in README

Small typo in README file - "the the".

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finished the Search use case

We should now be able to see the Journey page updated, and an error message pops up if the page doesn't exist.
Some previous parts were refactored to be more extensible.


Dataset/Table/Column details missing on SQL editor

sqlalchemy_bigquery.txt
Hi,
I am using pybigquery with superset. The dataset/table/column details was missing in the sql editor dropdown list. Did some changes to pybigquery/sqlalchemy_bigquery.py and it seems to working now. The dataset, table and column definitions are getting listed.
I am not sure if the fix is complete. If possible could you review it. I have attached the edited file.
Versions of superset and pybigquery used
Name: superset
Version: 0.20.6
Name: pybigquery
Version: 0.2.3

Thanks! The code looks good. I'll merge it sometime next week and if it is all good — will push it in the next release.
I was on the fence about whether to treat datasets as schemas, but if superset relies on schemas in their interface — the library really should support it.

Ok 👍
Thank you @mxmzdlv

Pushed the fix in 0.2.5. Thanks for your contribution!

You are welcome.


Native Editor is marked as dirty when clicking a cell

Bug: Notebook Editor, Interactive Window, Python Editor cells

Steps to cause the bug to occur

Open VS Code
Open a folder with ipynb files
Open an existing ipynb in native editor (ensure it has at least 2 cells)
Click the editor in the first cell
Click the editor in the second cell

Bug: The editor is marked dirty, even when I didn't do anything
Actual & Expected behavior

Your Jupyter and/or Python environment
Please provide as much info as you readily know

Jupyter server running:                       Local
Extension version:                               N/A
VS Code version:                                Insiders
Setting python.jediEnabled:               N/A
Python and/or Anaconda version:     N/A
OS:                                                     Mac
Virtual environment:                          N/A

I don't repro this any more after the redux changes.


Unable to preventDefault inside passive event listener invocation.

Checks

[X] Not a duplicate.

Version
8.8.1
Description
原本是不会报错的，但是我用了这个方式，消除了另外一个js插件的控制台的警告
https://blog.csdn.net/qq_28378665/article/details/128757778
然后现在滚动的时候，控制台会报错，也许这是解决方案
https://blog.csdn.net/weixin_40716786/article/details/90675200
我已经在我的开源项目中使用smooth-scrollbar，在此非常感谢，还望尽快修复。

DeepL Translation:
Originally it would not have reported an error, but I used this to remove the warning from the console of another js plugin
https://blog.csdn.net/qq_28378665/article/details/128757778
Then now when scrolling, the console will report an error, maybe this is the solution
https://blog.csdn.net/weixin_40716786/article/details/90675200
I have used smooth-scrollbar in my open source project, thanks a lot here, also hope to fix it soon.

Steps to Reproduce
描述
Online Demo
No response

We have already turned off the passive events, see: https://github.com/idiotWu/smooth-scrollbar/blob/02a0d8179eb5902ded0a022047b1634939f1908d/src/utils/event-hub.ts#L35

Fixed in v8.8.2.