Python collections.Sequence() Examples

def dict_gather(outputs, target_device, dim=0):
    """
    Gathers variables from different GPUs on a specified device
      (-1 means the CPU), with dictionary support.
    """
    def gather_map(outputs):
        out = outputs[0]
        if isinstance(out, Variable):
            # MJY(20180330) HACK:: force nr_dims > 0
            if out.dim() == 0:
                outputs = [o.unsqueeze(0) for o in outputs]
            return Gather.apply(target_device, dim, *outputs)
        elif out is None:
            return None
        elif isinstance(out, collections.Mapping):
            return {k: gather_map([o[k] for o in outputs]) for k in out}
        elif isinstance(out, collections.Sequence):
            return type(out)(map(gather_map, zip(*outputs)))
    return gather_map(outputs) 

def _sequence_like(instance, args):
  """Converts the sequence `args` to the same type as `instance`.

  Args:
    instance: an instance of `tuple`, `list`, or a `namedtuple` class.
    args: elements to be converted to a sequence.

  Returns:
    `args` with the type of `instance`.
  """
  if (isinstance(instance, tuple) and
      hasattr(instance, "_fields") and
      isinstance(instance._fields, collections.Sequence) and
      all(isinstance(f, six.string_types) for f in instance._fields)):
    # This is a namedtuple
    return type(instance)(*args)
  else:
    # Not a namedtuple
    return type(instance)(args) 

def _clone_node_with_lineno(node, parent, lineno):
    cls = node.__class__
    other_fields = node._other_fields
    _astroid_fields = node._astroid_fields
    init_params = {"lineno": lineno, "col_offset": node.col_offset, "parent": parent}
    postinit_params = {param: getattr(node, param) for param in _astroid_fields}
    if other_fields:
        init_params.update({param: getattr(node, param) for param in other_fields})
    new_node = cls(**init_params)
    if hasattr(node, "postinit") and _astroid_fields:
        for param, child in postinit_params.items():
            if child and not isinstance(child, collections.Sequence):
                cloned_child = _clone_node_with_lineno(
                    node=child, lineno=new_node.lineno, parent=new_node
                )
                postinit_params[param] = cloned_child
        new_node.postinit(**postinit_params)
    return new_node 

def aggregate_output(self):
        """Given a list of predictions from net, make a decision based on aggreagation rule"""
        if isinstance(self.predictions, collections.Sequence):
            logits = []
            for pred in self.predictions:
                logit = self.net.apply_argmax_softmax(pred).unsqueeze(0)
                logits.append(logit)

            logits = torch.cat(logits, 0)
            if self.aggregation == 'max':
                self.pred = logits.data.max(0)[0].max(1)
            elif self.aggregation == 'mean':
                self.pred = logits.data.mean(0).max(1)
            elif self.aggregation == 'weighted_mean':
                self.pred = (self.aggregation_weight.expand_as(logits) * logits).data.mean(0).max(1)
            elif self.aggregation == 'idx':
                self.pred = logits[self.aggregation_param].data.max(1)
        else:
            # Apply a softmax and return a segmentation map
            self.logits = self.net.apply_argmax_softmax(self.predictions)
            self.pred = self.logits.data.max(1) 

def __call__(self, image):
        if isinstance(self.sigma, collections.Sequence):
            sigma = random_num_generator(
                self.sigma, random_state=self.random_state)
        else:
            sigma = self.sigma
        if isinstance(self.mean, collections.Sequence):
            mean = random_num_generator(
                self.mean, random_state=self.random_state)
        else:
            mean = self.mean
        row, col, ch = image.shape
        gauss = self.random_state.normal(mean, sigma, (row, col, ch))
        gauss = gauss.reshape(row, col, ch)
        image += image * gauss
        return image 

def _clone_node_with_lineno(node, parent, lineno):
    cls = node.__class__
    other_fields = node._other_fields
    _astroid_fields = node._astroid_fields
    init_params = {"lineno": lineno, "col_offset": node.col_offset, "parent": parent}
    postinit_params = {param: getattr(node, param) for param in _astroid_fields}
    if other_fields:
        init_params.update({param: getattr(node, param) for param in other_fields})
    new_node = cls(**init_params)
    if hasattr(node, "postinit") and _astroid_fields:
        for param, child in postinit_params.items():
            if child and not isinstance(child, collections.Sequence):
                cloned_child = _clone_node_with_lineno(
                    node=child, lineno=new_node.lineno, parent=new_node
                )
                postinit_params[param] = cloned_child
        new_node.postinit(**postinit_params)
    return new_node 

def __init__(self, config, queue, events, loop=None):
        """
        Initialize instance of the NodeManager class

        :param config: config object
        :param queue: broadcast queue
        :type config: tattle.config.Configuration
        :type events: tattle.event.EventManager
        :type queue: tattle.queue.BroadcastQueue
        """
        self.config = config
        self._queue = queue
        self._events = events
        self._loop = loop or asyncio.get_event_loop()
        self._leaving = False
        self._nodes = list()
        self._nodes_map = dict()
        self._nodes_lock = asyncio.Lock()
        self._suspect_nodes = dict()
        self._local_node_name = None
        self._local_node_seq = sequence.Sequence() 

def _serialize_internal(cls, msg):
        # insert the name of the class
        data = [msg.__class__.__name__]

        # get list of fields
        fields = msg.__class__.get_fields()
        for field_name, field_type in fields:
            attr = getattr(msg, field_name)
            if field_type is not None and attr is not None:
                # if attr has a field type defined deserialize that field
                data.extend(cls._serialize_internal(attr))
            else:
                if isinstance(attr, str) or isinstance(attr, bytes):
                    data.append(attr)
                elif isinstance(attr, collections.Sequence):
                    data.append([cls._serialize_internal(i) for i in attr])
                elif isinstance(attr, collections.Mapping):
                    data.append({k: cls._serialize_internal(v) for k, v in attr.items()})
                else:
                    data.append(attr)
        return data 

def __call__(self, img):
        for t in self.transforms:
            if isinstance(t, collections.Sequence):
                assert isinstance(img, collections.Sequence) and len(img) == len(
                    t), "size of image group and transform group does not fit"
                tmp_ = []
                for i, im_ in enumerate(img):
                    if callable(t[i]):
                        tmp_.append(t[i](im_))
                    else:
                        tmp_.append(im_)
                img = tmp_
            elif callable(t):
                img = t(img)
            elif t is None:
                continue
            else:
                raise Exception('unexpected type')
        return img 

def to_tensor(data):
    """Convert objects of various python types to :obj:`torch.Tensor`.

    Supported types are: :class:`numpy.ndarray`, :class:`torch.Tensor`,
    :class:`Sequence`, :class:`int` and :class:`float`.
    """
    if isinstance(data, torch.Tensor):
        return data
    elif isinstance(data, np.ndarray):
        return torch.from_numpy(data)
    elif isinstance(data, Sequence) and not mmcv.is_str(data):
        return torch.tensor(data)
    elif isinstance(data, int):
        return torch.LongTensor([data])
    elif isinstance(data, float):
        return torch.FloatTensor([data])
    else:
        raise TypeError('type {} cannot be converted to tensor.'.format(
            type(data))) 

def normalize(tensor, mean, std):
    """Normalize a tensor image with mean and standard deviation.
    .. note::
        This transform acts in-place, i.e., it mutates the input tensor.
    See :class:`~torchvision.transforms.Normalize` for more details.
    Args:
        tensor (Tensor): Tensor image of size (C, H, W) to be normalized.
        mean (sequence): Sequence of means for each channel.
        std (sequence): Sequence of standard deviations for each channely.
    Returns:
        Tensor: Normalized Tensor image.
    """
    if not _is_tensor_image(tensor):
        raise TypeError('tensor is not a torch image.')

    # This is faster than using broadcasting, don't change without benchmarking
    for t, m, s in zip(tensor, mean, std):
        t.sub_(m).div_(s)
    return tensor 

def __init__( self, exprs, savelist = False ):
        super(ParseExpression,self).__init__(savelist)
        if isinstance( exprs, _generatorType ):
            exprs = list(exprs)

        if isinstance( exprs, basestring ):
            self.exprs = [ Literal( exprs ) ]
        elif isinstance( exprs, collections.Sequence ):
            # if sequence of strings provided, wrap with Literal
            if all(isinstance(expr, basestring) for expr in exprs):
                exprs = map(Literal, exprs)
            self.exprs = list(exprs)
        else:
            try:
                self.exprs = list( exprs )
            except TypeError:
                self.exprs = [ exprs ]
        self.callPreparse = False 

def flatten(x):
        """Flattens a cell state by concatenating a sequence of cell
        states along the last dimension. If the cell states are
        :tf_main:`LSTMStateTuple <contrib/rnn/LSTMStateTuple>`, only the
        hidden `LSTMStateTuple.h` is used.

        This process is used by default if :attr:`medium` is not provided
        to :meth:`_build`.
        """
        if isinstance(x, LSTMStateTuple):
            return x.h
        if isinstance(x, collections.Sequence):
            return tf.concat(
                [HierarchicalRNNEncoder.flatten(v) for v in x], -1)
        else:
            return x 

def collate_fn(self):
        def collate(batch):
            if len(self.fields) == 1:
                batch = [batch, ]
            else:
                batch = list(zip(*batch))

            tensors = []
            for field, data in zip(self.fields.values(), batch):
                tensor = field.process(data)
                if isinstance(tensor, collections.Sequence) and any(isinstance(t, torch.Tensor) for t in tensor):
                    tensors.extend(tensor)
                else:
                    tensors.append(tensor)

            if len(tensors) > 1:
                return tensors
            else:
                return tensors[0]

        return collate 

def __init__( self, exprs, savelist = False ):
        super(ParseExpression,self).__init__(savelist)
        if isinstance( exprs, _generatorType ):
            exprs = list(exprs)

        if isinstance( exprs, basestring ):
            self.exprs = [ Literal( exprs ) ]
        elif isinstance( exprs, collections.Sequence ):
            # if sequence of strings provided, wrap with Literal
            if all(isinstance(expr, basestring) for expr in exprs):
                exprs = map(Literal, exprs)
            self.exprs = list(exprs)
        else:
            try:
                self.exprs = list( exprs )
            except TypeError:
                self.exprs = [ exprs ]
        self.callPreparse = False 

def __init__( self, exprs, savelist = False ):
        super(ParseExpression,self).__init__(savelist)
        if isinstance( exprs, _generatorType ):
            exprs = list(exprs)

        if isinstance( exprs, basestring ):
            self.exprs = [ Literal( exprs ) ]
        elif isinstance( exprs, collections.Sequence ):
            # if sequence of strings provided, wrap with Literal
            if all(isinstance(expr, basestring) for expr in exprs):
                exprs = map(Literal, exprs)
            self.exprs = list(exprs)
        else:
            try:
                self.exprs = list( exprs )
            except TypeError:
                self.exprs = [ exprs ]
        self.callPreparse = False 

def normalize(tensor, mean, std):
    """Normalize a tensor image with mean and standard deviation.
    .. note::
        This transform acts in-place, i.e., it mutates the input tensor.
    See :class:`~torchvision.transforms.Normalize` for more details.
    Args:
        tensor (Tensor): Tensor image of size (C, H, W) to be normalized.
        mean (sequence): Sequence of means for each channel.
        std (sequence): Sequence of standard deviations for each channely.
    Returns:
        Tensor: Normalized Tensor image.
    """
    if not _is_tensor_image(tensor):
        raise TypeError('tensor is not a torch image.')

    # This is faster than using broadcasting, don't change without benchmarking
    for t, m, s in zip(tensor, mean, std):
        t.sub_(m).div_(s)
    return tensor 

def build(self, input_shape):
    if isinstance(input_shape, collections.Sequence):
      input_shape = input_shape[0]
    out_channels = input_shape[1]

    if self.weights_initializer is None:
      weights_initializer = tf.keras.initializers.VarianceScaling
    else:
      weights_initializer = self.weights_initializer

    self.dense_H = tf.keras.layers.Dense(
        out_channels,
        activation=self.activation_fn,
        bias_initializer=self.biases_initializer,
        kernel_initializer=weights_initializer)
    self.dense_T = tf.keras.layers.Dense(
        out_channels,
        activation=tf.nn.sigmoid,
        bias_initializer=tf.constant_initializer(-1),
        kernel_initializer=weights_initializer)
    self.built = True 

def _restore_sparse_tensors(stored_list, sparse_info_list):
  """Restore SparseTensors after dequeue in batch, batch_join, etc."""
  received_sequence = isinstance(stored_list, collections.Sequence)
  if not received_sequence:
    stored_list = (stored_list,)
  tensors = [
      _restore_sparse(sparse_map_op=info.map_op,
                      sparse_handles=array_ops.squeeze(s, [1]),
                      rank=(info.rank + 1).value)
      if info.sparse else s
      for (s, info) in zip(stored_list, sparse_info_list)]
  return tensors if received_sequence else tensors[0] 

def token_location(self):
        locations = current_app.config['JWT_TOKEN_LOCATION']
        if isinstance(locations, str):
            locations = (locations,)
        elif not isinstance(locations, (Sequence, Set)):
            raise RuntimeError('JWT_TOKEN_LOCATION must be a sequence or a set')
        elif not locations:
            raise RuntimeError('JWT_TOKEN_LOCATION must contain at least one '
                               'of "headers", "cookies", "query_string", or "json"')
        for location in locations:
            if location not in ('headers', 'cookies', 'query_string', 'json'):
                raise RuntimeError('JWT_TOKEN_LOCATION can only contain '
                                   '"headers", "cookies", "query_string", or "json"')
        return locations 

def is_sequence(seq):
  """Returns a true if its input is a collections.Sequence (except strings).

  Args:
    seq: an input sequence.

  Returns:
    True if the sequence is a not a string and is a collections.Sequence.
  """
  return (isinstance(seq, collections.Sequence)
          and not isinstance(seq, six.string_types)) 

def internal_convert_n_to_tensor_or_indexed_slices(values, dtype=None,
                                                   name=None, as_ref=False):
  """Converts `values` to a list of `Tensor` or `IndexedSlices` objects.

  Any `IndexedSlices` or `SparseTensor` objects in `values` are returned
  unmodified.

  Args:
    values: A list of `None`, `IndexedSlices`, `SparseTensor`, or objects that
      can be consumed by `convert_to_tensor()`.
    dtype: (Optional.) The required `DType` of the returned `Tensor`
      `IndexedSlices`.
    name: (Optional.) A name prefix to used when a new `Tensor` is
      created, in which case element `i` will be given the name `name
      + '_' + i`.
    as_ref: True if the caller wants the results as ref tensors.

  Returns:
    A list of `Tensor`, `IndexedSlices`, and/or `SparseTensor` objects.

  Raises:
    TypeError: If no conversion function is registered for an element in
      `values`.
    RuntimeError: If a registered conversion function returns an invalid
      value.
  """
  if not isinstance(values, collections.Sequence):
    raise TypeError("values must be a list.")
  ret = []
  for i, value in enumerate(values):
    if value is None:
      ret.append(value)
    else:
      n = None if name is None else "%s_%d" % (name, i)
      ret.append(
          internal_convert_to_tensor_or_indexed_slices(
              value, dtype=dtype, name=n, as_ref=as_ref))
  return ret 

def create(self):
        """Create a possibly more optimized representation of the mapping.

        In this worst case, this method returns an object which is
        essentially an immutable dictionary. In the best case, the
        space savings can be vast.

        Returns:
            A mapping, if a unique mapping from indexes to values is
            possible, otherwise None.

        """

        # This method examines the contents of the mapping using
        # various heuristics to come up with a better representation.

        # In-place sort by index
        self._catalog.sort(key=first)

        if contains_duplicates(index for index, value in self._catalog):
            return None

        if all(isinstance(index, Sequence) and (len(index) == 2)
               for index, value in self._catalog):
            return self._create_catalog_2()

        return self._create_catalog_1() 

def __instancecheck__(self, instance):
    return (isinstance(instance, collections.Sequence)
            and all(isinstance(x, self._type) for x in instance)) 

def call(self, inputs):
    if isinstance(inputs, collections.Sequence):
      parent = inputs[0]
    else:
      parent = inputs
    dense_H = self.dense_H(parent)
    dense_T = self.dense_T(parent)
    return tf.multiply(dense_H, dense_T) + tf.multiply(parent, 1 - dense_T) 

def default_collate(batch):
    "Puts each data field into a tensor with outer dimension batch size"
    if torch.is_tensor(batch[0]):
        out = None
        if _use_shared_memory:
            # If we're in a background process, concatenate directly into a
            # shared memory tensor to avoid an extra copy
            numel = sum([x.numel() for x in batch])
            storage = batch[0].storage()._new_shared(numel)
            out = batch[0].new(storage)
        return torch.stack(batch, 0, out=out)
    elif type(batch[0]).__module__ == 'numpy':
        elem = batch[0]
        if type(elem).__name__ == 'ndarray':
            return torch.stack([torch.from_numpy(b) for b in batch], 0)
        if elem.shape == ():  # scalars
            py_type = float if elem.dtype.name.startswith('float') else int
            return numpy_type_map[elem.dtype.name](list(map(py_type, batch)))
    elif isinstance(batch[0], int):
        return torch.LongTensor(batch)
    elif isinstance(batch[0], float):
        return torch.DoubleTensor(batch)
    elif isinstance(batch[0], string_classes):
        return batch
    elif isinstance(batch[0], collections.Mapping):
        return {key: default_collate([d[key] for d in batch]) for key in batch[0]}
    elif isinstance(batch[0], collections.Sequence):
        transposed = zip(*batch)
        return [default_collate(samples) for samples in transposed]

    raise TypeError(("batch must contain tensors, numbers, dicts or lists; found {}"
                     .format(type(batch[0])))) 

def call(self, inputs):
    if isinstance(inputs, collections.Sequence):
      if len(inputs) != 1:
        raise ValueError("NeighborList can only have one input")
      inputs = inputs[0]
    if len(inputs.get_shape()) != 2:
      # TODO(rbharath): Support batching
      raise ValueError("Parent tensor must be (num_atoms, ndum)")
    return self.compute_nbr_list(inputs) 

def __init__(self,
               state_shape,
               n_actions=None,
               state_dtype=None,
               action_shape=None):
    """Subclasses should call the superclass constructor in addition to doing their own initialization.

    A value should be provided for either n_actions (for discrete action spaces)
    or action_shape (for continuous action spaces), but not both.

    Parameters
    ----------
    state_shape: tuple or list of tuples
      the shape(s) of the array(s) making up the state
    n_actions: int
      the number of discrete actions that can be performed.  If the action space
      is continuous, this should be None.
    state_dtype: dtype or list of dtypes
      the type(s) of the array(s) making up the state.  If this is None, all
      arrays are assumed to be float32.
    action_shape: tuple
      the shape of the array describing an action.  If the action space
      is discrete, this should be none.
    """
    self._state_shape = state_shape
    self._n_actions = n_actions
    self._action_shape = action_shape
    self._state = None
    self._terminated = None
    if state_dtype is None:
      # Assume all arrays are float32.
      import numpy
      import collections
      if isinstance(state_shape[0], collections.Sequence):
        self._state_dtype = [numpy.float32] * len(state_shape)
      else:
        self._state_dtype = numpy.float32
    else:
      self._state_dtype = state_dtype 

def pin_memory_batch(batch):
    if torch.is_tensor(batch):
        return batch.pin_memory()
    elif isinstance(batch, string_classes):
        return batch
    elif isinstance(batch, collections.Mapping):
        return {k: pin_memory_batch(sample) for k, sample in batch.items()}
    elif isinstance(batch, collections.Sequence):
        return [pin_memory_batch(sample) for sample in batch]
    else:
        return batch 

def apply_projection(projection, value):
    """Apply projection."""
    if isinstance(value, Sequence):
        # Apply projection to each item in the list.
        return [apply_projection(projection, item) for item in value]
    elif not isinstance(value, Mapping):
        # Non-dictionary values are simply ignored.
        return value

    # Extract projection for current level.
    try:
        current_projection = [p[0] for p in projection]
    except IndexError:
        return value

    # Apply projection.
    for name in list(value.keys()):
        if name not in current_projection:
            value.pop(name)
        elif isinstance(value[name], dict):
            # Apply projection recursively.
            value[name] = apply_projection(
                [p[1:] for p in projection if p[0] == name], value[name]
            )

    return value