Python itertools.chain() Examples

def process(parser, widget_dict):
  mutually_exclusive_groups = [
                  [mutex_action for mutex_action in group_actions._group_actions]
                  for group_actions in parser._mutually_exclusive_groups]

  group_options = list(chain(*mutually_exclusive_groups))

  base_actions = [action for action in parser._actions
                  if action not in group_options
                  and action.dest != 'help']

  required_actions = filter(is_required, base_actions)
  optional_actions = filter(is_optional, base_actions)

  return list(categorize(required_actions, widget_dict, required=True)) + \
         list(categorize(optional_actions, widget_dict)) + \
         map(build_radio_group, mutually_exclusive_groups) 

def sub_spf1(ni, subtree1, subtree2, op, calculate):
    """Implements spf1 single path function for the case when the
    other subtree is a single node

    Params:
      ni -- node indexer for the subtree that has more than one element
      subtree1 -- subtree that has a single element
      subtree2 -- subtree that has more than one element
      op -- cost of deleting/inserting node
      calculate -- function(node, other) that returns the cost of
        renaming nodes
    """
    # pylint: disable=invalid-name
    # pylint: disable=too-many-arguments
    cost = subtree2.sum_cost
    max_cost = cost + op
    min_ren_minus_op = min(chain([cost], [
        calculate(subtree1, info)
        for _, info in ni.preorder_ltr(subtree2)
    ]))
    return min(min_ren_minus_op + cost, max_cost) 

def point_entropy(self, unlabeled_sequence):
        """
        Returns the pointwise entropy over the possible states at each
        position in the chain, given the observation sequence.
        """
        unlabeled_sequence = self._transform(unlabeled_sequence)

        T = len(unlabeled_sequence)
        N = len(self._states)

        alpha = self._forward_probability(unlabeled_sequence)
        beta = self._backward_probability(unlabeled_sequence)
        normalisation = logsumexp2(alpha[T-1])

        entropies = np.zeros(T, np.float64)
        probs = np.zeros(N, np.float64)
        for t in range(T):
            for s in range(N):
                probs[s] = alpha[t, s] + beta[t, s] - normalisation

            for s in range(N):
                entropies[t] -= 2**(probs[s]) * probs[s]

        return entropies 

def CreateWeightLoss(self):
    """Returns L2 loss list of (almost) all variables used inside this block.

    When this method needs to be overridden, there are two choices.

    1. Override CreateWeightLoss() to change the weight loss of all variables
       that belong to this block, both directly and indirectly.
    2. Override _CreateWeightLoss() to change the weight loss of all
       variables that directly belong to this block but not to the sub-blocks.

    Returns:
      A Tensor object or None.
    """
    losses = list(itertools.chain(
        itertools.chain.from_iterable(
            t.CreateWeightLoss() for t in self._subblocks),
        self._CreateWeightLoss()))
    return losses 

def do_teardown_request(
        self, exc: Optional[BaseException], request_context: Optional[RequestContext] = None
    ) -> None:
        """Teardown the request, calling the teardown functions.

        Arguments:
            exc: Any exception not handled that has caused the request
                to teardown.
            request_context: The request context, optional as Flask
                omits this argument.
        """
        request_ = (request_context or _request_ctx_stack.top).request
        functions = self.teardown_request_funcs[None]
        blueprint = request_.blueprint
        if blueprint is not None:
            functions = chain(functions, self.teardown_request_funcs[blueprint])  # type: ignore

        for function in functions:
            await function(exc)
        await request_tearing_down.send(self, exc=exc) 

def do_teardown_websocket(
        self, exc: Optional[BaseException], websocket_context: Optional[WebsocketContext] = None
    ) -> None:
        """Teardown the websocket, calling the teardown functions.

        Arguments:
            exc: Any exception not handled that has caused the websocket
                to teardown.
            websocket_context: The websocket context, optional as Flask
                omits this argument.
        """
        websocket_ = (websocket_context or _websocket_ctx_stack.top).websocket
        functions = self.teardown_websocket_funcs[None]
        blueprint = websocket_.blueprint
        if blueprint is not None:
            functions = chain(functions, self.teardown_websocket_funcs[blueprint])  # type: ignore

        for function in functions:
            await function(exc)
        await websocket_tearing_down.send(self, exc=exc) 

def preprocess_request(
        self, request_context: Optional[RequestContext] = None
    ) -> Optional[ResponseReturnValue]:
        """Preprocess the request i.e. call before_request functions.

        Arguments:
            request_context: The request context, optional as Flask
                omits this argument.
        """
        request_ = (request_context or _request_ctx_stack.top).request
        blueprint = request_.blueprint
        processors = self.url_value_preprocessors[None]
        if blueprint is not None:
            processors = chain(processors, self.url_value_preprocessors[blueprint])  # type: ignore
        for processor in processors:
            processor(request.endpoint, request.view_args)

        functions = self.before_request_funcs[None]
        if blueprint is not None:
            functions = chain(functions, self.before_request_funcs[blueprint])  # type: ignore
        for function in functions:
            result = await function()
            if result is not None:
                return result
        return None 

def get_attribute_suggestions(type_str, attribute, frame):
    """Get the suggestions closest to the attribute name for a given type."""
    types = get_types_for_str(type_str, frame)
    attributes = set(a for t in types for a in dir(t))
    if type_str == 'module':
        # For module, we manage to get the corresponding 'module' type
        # but the type doesn't bring much information about its content.
        # A hacky way to do so is to assume that the exception was something
        # like 'module_name.attribute' so that we can actually find the module
        # based on the name. Eventually, we check that the found object is a
        # module indeed. This is not failproof but it brings a whole lot of
        # interesting suggestions and the (minimal) risk is to have invalid
        # suggestions.
        module_name = frame.f_code.co_names[0]
        objs = get_objects_in_frame(frame)
        mod = objs[module_name][0].obj
        if inspect.ismodule(mod):
            attributes = set(dir(mod))

    return itertools.chain(
        suggest_attribute_as_builtin(attribute, type_str, frame),
        suggest_attribute_alternative(attribute, type_str, attributes),
        suggest_attribute_as_typo(attribute, attributes),
        suggest_attribute_as_special_case(attribute)) 

def preprocess_websocket(
        self, websocket_context: Optional[WebsocketContext] = None
    ) -> Optional[ResponseReturnValue]:
        """Preprocess the websocket i.e. call before_websocket functions.

        Arguments:
            websocket_context: The websocket context, optional as Flask
                omits this argument.
        """
        websocket_ = (websocket_context or _websocket_ctx_stack.top).websocket
        blueprint = websocket_.blueprint
        processors = self.url_value_preprocessors[None]
        if blueprint is not None:
            processors = chain(processors, self.url_value_preprocessors[blueprint])  # type: ignore
        for processor in processors:
            processor(websocket_.endpoint, websocket_.view_args)

        functions = self.before_websocket_funcs[None]
        if blueprint is not None:
            functions = chain(functions, self.before_websocket_funcs[blueprint])  # type: ignore
        for function in functions:
            result = await function()
            if result is not None:
                return result
        return None 

def __init__(
        self, geom, debug=False, restart=None, maxsteps=100, logger=None, **params
    ):
        self._debug = debug
        self._maxsteps = maxsteps
        self._converged = False
        self._n = 0
        self._log = BernyAdapter(logger or log, {'step': self._n})
        s = self._state = Berny.State()
        if restart:
            vars(s).update(restart)
            return
        s.geom = geom
        s.params = dict(chain(defaults.items(), params.items()))
        s.trust = s.params['trust']
        s.coords = InternalCoords(
            s.geom, dihedral=s.params['dihedral'], superweakdih=s.params['superweakdih']
        )
        s.H = s.coords.hessian_guess(s.geom)
        s.weights = s.coords.weights(s.geom)
        s.future = Berny.Point(s.coords.eval_geom(s.geom), None, None)
        s.first = True
        for line in str(s.coords).split('\n'):
            self._log.info(line) 

def update_template_context(self, context: dict) -> None:
        """Update the provided template context.

        This adds additional context from the various template context
        processors.

        Arguments:
            context: The context to update (mutate).
        """
        processors = self.template_context_processors[None]
        if has_request_context():
            blueprint = _request_ctx_stack.top.request.blueprint
            if blueprint is not None and blueprint in self.template_context_processors:
                processors = chain(  # type: ignore
                    processors, self.template_context_processors[blueprint]
                )
        extra_context: dict = {}
        for processor in processors:
            extra_context.update(await processor())
        original = context.copy()
        context.update(extra_context)
        context.update(original) 

def forward(self, *inputs, **kwargs):
        if not self.device_ids:
            return self.module(*inputs, **kwargs)

        for t in chain(self.module.parameters(), self.module.buffers()):
            if t.device != self.src_device_obj:
                raise RuntimeError(
                    "module must have its parameters and buffers "
                    "on device {} (device_ids[0]) but found one of "
                    "them on device: {}".format(
                        self.src_device_obj, t.device))
        inputs, kwargs = self.scatter(inputs, kwargs, self.device_ids)
        if len(self.device_ids) == 1:
            return self.module(*inputs, **kwargs)
        replicas = self.replicate(self.module, self.device_ids[:len(inputs)])
        outputs = self.parallel_apply(replicas, inputs, kwargs)
        return outputs 

def setup_training(self):
    '''
    Setup Pyro SVI, optimizers.
    '''
    if not self.is_train:
      return

    self.pyro_optimizer = optim.Adam({'lr': self.lr_init})
    self.svis = {'elbo': SVI(self.model, self.guide, self.pyro_optimizer, loss=Trace_ELBO())}

    # Separate pose_model parameters and other networks' parameters
    params = []
    for name, net in self.nets.items():
      if name != 'pose_model':
        params.append(net.parameters())
    self.optimizer = torch.optim.Adam(\
                     [{'params': self.pose_model.parameters(), 'lr': self.lr_init},
                      {'params': itertools.chain(*params), 'lr': self.lr_init}
                     ], betas=(0.5, 0.999)) 

def _wait_for_volumes_deleted(self, context, volmaps, container,
                                  timeout=60, poll_interval=1):
        start_time = time.time()
        try:
            volmaps = itertools.chain(volmaps)
            volmap = next(volmaps)
            while time.time() - start_time < timeout:
                if not volmap.auto_remove:
                    volmap = next(volmaps)
                driver = self._get_driver(container)
                is_deleted, is_error = driver.is_volume_deleted(
                    context, volmap)
                if is_deleted:
                    volmap = next(volmaps)
                if is_error:
                    break
                time.sleep(poll_interval)
        except StopIteration:
            return
        msg = _("Volumes cannot be successfully deleted after "
                "%d seconds") % (timeout)
        self._fail_container(context, container, msg, unset_host=True)
        raise exception.Conflict(msg) 

def get_suggestions_for_exception(value, traceback):
    """Get suggestions for an exception."""
    frame = get_last_frame(traceback)
    return itertools.chain.from_iterable(
            func(value, frame)
            for error_type, functions in SUGGESTION_FUNCTIONS.items()
            if isinstance(value, error_type)
            for func in functions) 

def generate_chomsky(times=5, line_length=72):
    parts = []
    for part in (leadins, subjects, verbs, objects):
        phraselist = list(map(str.strip, part.splitlines()))
        random.shuffle(phraselist)
        parts.append(phraselist)
    output = chain(*islice(izip(*parts), 0, times))
    print(textwrap.fill(" ".join(output), line_length)) 

def build_command_line_string(self):
    optional_args = [arg.value for arg in self.optional_args]
    required_args = [c.value for c in self.required_args if c.commands]
    position_args = [c.value for c in self.required_args if not c.commands]
    if position_args:
      position_args.insert(0, "--")
    cmd_string = ' '.join(filter(None, chain(required_args, optional_args, position_args)))
    if self.layout_type == 'column':
      cmd_string = u'{} {}'.format(self.argument_groups[self.active_group].command, cmd_string)
    return u'{} --ignore-gooey {}'.format(self.build_spec['target'], cmd_string) 

def __iter__(self):
        return itertools.chain(reversed(self._queue_front),
                               iter(self._queue_back)) 

def group_arguments(self, widget_list):
    is_required = lambda widget: widget['required']
    not_checkbox = lambda widget: widget['type'] != 'CheckBox'

    required_args, optional_args  = self.partition(widget_list, is_required)
    if self.build_spec['group_by_type']:
      optional_args = chain(*self.partition(optional_args, not_checkbox))
    return map(self.to_object, required_args), map(self.to_object, optional_args) 

def build_vocab(sentences):
    """
    Builds a vocabulary mapping from word to index based on the sentences.
    Returns vocabulary mapping and inverse vocabulary mapping.
    """
    # Build vocabulary
    word_counts = Counter(itertools.chain(*sentences))
    # Mapping from index to word
    vocabulary_inv = [x[0] for x in word_counts.most_common()]
    # Mapping from word to index
    vocabulary = {x: i for i, x in enumerate(vocabulary_inv)}
    return [vocabulary, vocabulary_inv] 

def __iter__(self):
        # XXX does it break when underlying container state changed?
        return itertools.chain((self.default_section,), self._sections.keys()) 

def __hash__(self):
        h = 1
        for v in chain(self.keys(), self.values()):
            h = 31 * h + hash(v)
        return h 

def test(self):
        rows = list(chain(*self.rows))
        rows_by_key = {row['key'] : row for row in rows}

        self.sc \
            .parallelize(rows) \
            .saveToCassandra(self.keyspace, self.table)

        self.stream \
            .joinWithCassandraTable(self.keyspace, self.table, ['text'], ['key']) \
            .foreachRDD(self.checkRDD)

        self.ssc.start()
        self.ssc.awaitTermination((self.count + 1) * self.interval)
        self.ssc.stop(stopSparkContext=False, stopGraceFully=True)

        joined_rows = self.joined_rows.value
        self.assertEqual(len(joined_rows), len(rows))
        for row in joined_rows:
            self.assertEqual(len(row), 2)
            left, right = row

            self.assertEqual(type(left), type(right))
            self.assertEqual(rows_by_key[left['key']], left)
            self.assertEqual(left['text'], right['text'])
            self.assertEqual(len(right), 1) 

def _split_into_words(sentences):
  """Splits multiple sentences into words and flattens the result"""
  return list(itertools.chain(*[_.split(" ") for _ in sentences])) 

def generator(self) -> Generator[Tuple[str, Optional[Comment]], None, None]:
        """
        Line generator
        :return:
        """
        for line in chain(self.prefix(), self.dialogues(self.video.comments)):
            yield line 

def nlargest(n, iterable, key=None):
    """Find the n largest elements in a dataset.

    Equivalent to:  sorted(iterable, key=key, reverse=True)[:n]
    """

    # Short-cut for n==1 is to use max() when len(iterable)>0
    if n == 1:
        it = iter(iterable)
        head = list(islice(it, 1))
        if not head:
            return []
        if key is None:
            return [max(chain(head, it))]
        return [max(chain(head, it), key=key)]

    # When n>=size, it's faster to use sorted()
    try:
        size = len(iterable)
    except (TypeError, AttributeError):
        pass
    else:
        if n >= size:
            return sorted(iterable, key=key, reverse=True)[:n]

    # When key is none, use simpler decoration
    if key is None:
        it = zip(iterable, count(0,-1))                     # decorate
        result = _nlargest(n, it)
        return [r[0] for r in result]                       # undecorate

    # General case, slowest method
    in1, in2 = tee(iterable)
    it = zip(map(key, in1), count(0,-1), in2)               # decorate
    result = _nlargest(n, it)
    return [r[2] for r in result]                           # undecorate 

def nsmallest(n, iterable, key=None):
    """Find the n smallest elements in a dataset.

    Equivalent to:  sorted(iterable, key=key)[:n]
    """
    # Short-cut for n==1 is to use min() when len(iterable)>0
    if n == 1:
        it = iter(iterable)
        head = list(islice(it, 1))
        if not head:
            return []
        if key is None:
            return [min(chain(head, it))]
        return [min(chain(head, it), key=key)]

    # When n>=size, it's faster to use sorted()
    try:
        size = len(iterable)
    except (TypeError, AttributeError):
        pass
    else:
        if n >= size:
            return sorted(iterable, key=key)[:n]

    # When key is none, use simpler decoration
    if key is None:
        it = zip(iterable, count())                         # decorate
        result = _nsmallest(n, it)
        return [r[0] for r in result]                       # undecorate

    # General case, slowest method
    in1, in2 = tee(iterable)
    it = zip(map(key, in1), count(), in2)                   # decorate
    result = _nsmallest(n, it)
    return [r[2] for r in result]                           # undecorate 

def merge(*iterables):
    '''Merge multiple sorted inputs into a single sorted output.

    Similar to sorted(itertools.chain(*iterables)) but returns a generator,
    does not pull the data into memory all at once, and assumes that each of
    the input streams is already sorted (smallest to largest).

    >>> list(merge([1,3,5,7], [0,2,4,8], [5,10,15,20], [], [25]))
    [0, 1, 2, 3, 4, 5, 5, 7, 8, 10, 15, 20, 25]

    '''
    _heappop, _heapreplace, _StopIteration = heappop, heapreplace, StopIteration
    _len = len

    h = []
    h_append = h.append
    for itnum, it in enumerate(map(iter, iterables)):
        try:
            next = it.__next__
            h_append([next(), itnum, next])
        except _StopIteration:
            pass
    heapify(h)

    while _len(h) > 1:
        try:
            while True:
                v, itnum, next = s = h[0]
                yield v
                s[0] = next()               # raises StopIteration when exhausted
                _heapreplace(h, s)          # restore heap condition
        except _StopIteration:
            _heappop(h)                     # remove empty iterator
    if h:
        # fast case when only a single iterator remains
        v, itnum, next = h[0]
        yield v
        yield from next.__self__

# Extend the implementations of nsmallest and nlargest to use a key= argument 

def _join_multiline_values(self):
        defaults = self.default_section, self._defaults
        all_sections = itertools.chain((defaults,),
                                       self._sections.items())
        for section, options in all_sections:
            for name, val in options.items():
                if isinstance(val, list):
                    val = '\n'.join(val).rstrip()
                options[name] = self._interpolation.before_read(self,
                                                                section,
                                                                name, val) 

def all_core_instrucitons(core_type_lib):
    return set(chain(
        common.instructions(core_type_lib),
        io.instructions(core_type_lib),
        code.instructions(),
        numeric.instructions(),
        text.instructions(),
        logical.instructions(),
    ))