def __init__(self, language_info, settings=None):
        dictionary = {}
        self._settings = settings
        self.info = language_info

        if 'skip' in language_info:
            skip = map(methodcaller('lower'), language_info['skip'])
            dictionary.update(zip_longest(skip, [], fillvalue=None))
        if 'pertain' in language_info:
            pertain = map(methodcaller('lower'), language_info['pertain'])
            dictionary.update(zip_longest(pertain, [], fillvalue=None))
        for word in ['monday', 'tuesday', 'wednesday', 'thursday', 'friday', 'saturday', 'sunday',
                     'january', 'february', 'march', 'april', 'may', 'june', 'july',
                     'august', 'september', 'october', 'november', 'december',
                     'year', 'month', 'week', 'day', 'hour', 'minute', 'second',
                     'ago']:
            translations = map(methodcaller('lower'), language_info[word])
            dictionary.update(zip_longest(translations, [], fillvalue=word))
        dictionary.update(zip_longest(ALWAYS_KEEP_TOKENS, ALWAYS_KEEP_TOKENS))
        dictionary.update(zip_longest(map(methodcaller('lower'),
                                          DATEUTIL_PARSERINFO_KNOWN_TOKENS),
                                      DATEUTIL_PARSERINFO_KNOWN_TOKENS))

        self._dictionary = dictionary
        self._no_word_spacing = language_info.get('no_word_spacing', False)

    def _load_data(self, languages=None, locales=None, region=None,
                   use_given_order=False, allow_conflicting_locales=False):
        locale_dict = OrderedDict()
        if locales:
            invalid_locales = []
            for locale in locales:
                lang_reg = LOCALE_SPLIT_PATTERN.split(locale)
                if len(lang_reg) == 1:
                    lang_reg.append('')
                locale_dict[locale] = tuple(lang_reg)
                if not _isvalidlocale(locale):
                    invalid_locales.append(locale)
            if invalid_locales:
                raise ValueError("Unknown locale(s): %s"
                                 % ', '.join(map(repr, invalid_locales)))

            if not allow_conflicting_locales:
                if len(set(locales)) > len(set([t[0] for t in locale_dict.values()])):
                    raise ValueError("Locales should not have same language and different region")

        else:
            if languages is None:
                languages = language_order
            unsupported_languages = set(languages) - set(language_order)
            if unsupported_languages:
                raise ValueError("Unknown language(s): %s"
                                 % ', '.join(map(repr, unsupported_languages)))
            if region is None:
                region = ''
            locales = _construct_locales(languages, region)
            locale_dict.update(zip_longest(locales,
                               tuple(zip_longest(languages, [], fillvalue=region))))

        if not use_given_order:
            locale_dict = OrderedDict(sorted(locale_dict.items(),
                                      key=lambda x: language_order.index(x[1][0])))

        for shortname, lang_reg in locale_dict.items():
            if shortname not in self._loaded_locales:
                lang, reg = lang_reg
                if lang in self._loaded_languages:
                    locale = Locale(shortname, language_info=deepcopy(self._loaded_languages[lang]))
                    self._loaded_locales[shortname] = locale
                else:
                    language_info = getattr(
                        import_module('dateparser.data.date_translation_data.' + lang), 'info')
                    language_info = convert_to_unicode(language_info)
                    locale = Locale(shortname, language_info=deepcopy(language_info))
                    self._loaded_languages[lang] = language_info
                    self._loaded_locales[shortname] = locale
            yield shortname, self._loaded_locales[shortname]

 def _add(s, A, B):
   # A + B
   if len(A) == 1 and len(A[0]) == 1:
     A = A[0][0]
     return s.element_class(s, [[A + B[0][0]] + list(B[0][1:])] + list(B[1:]))
   elif len(B) == 1 and len(B[0]) == 1:
     B = B[0][0]
     return s.element_class(s, [[A[0][0] + B] + list(A[0][1:])] + list(A[1:]))
   ret = []
   for x, y in zip_longest(A, B, fillvalue=[0]):
     t = []
     for xs, ys in zip_longest(x, y, fillvalue=0):
       t += [xs + ys]
     ret += [t]
   return s.element_class(s, ret)

    def reconstruct_coincidences(self, coincidences, station_numbers=None,
                                 offsets=None, progress=True, initials=None):
        """Reconstruct all coincidences

        :param coincidences: a list of coincidence events, each consisting
                             of three or more (station_number, event) tuples.
        :param station_numbers: list of station numbers, to only use
                                events from those stations.
        :param offsets: dictionary with detector offsets for each station.
                        These detector offsets should be relative to one
                        detector from a specific station.
        :param progress: if True show a progress bar while reconstructing.
        :param initials: list of dictionaries with already reconstructed shower
                         parameters.
        :return: list of theta, phi, and station numbers.

        """
        if offsets is None:
            offsets = {}
        if initials is None:
            initials = []
        coincidences = pbar(coincidences, show=progress)
        coin_init = zip_longest(coincidences, initials)
        angles = [self.reconstruct_coincidence(coincidence, station_numbers,
                                               offsets, initial)
                  for coincidence, initial in coin_init]
        if len(angles):
            theta, phi, nums = zip(*angles)
        else:
            theta, phi, nums = ((), (), ())
        return theta, phi, nums

    def test_logging(self):
        try:
            foo()
        except:
            logging.critical("Got exception", exc_info=True)
        expected = (
r'^WARNING$',
r'^warning here$',

r'^CRITICAL$',
r'^Got exception$',
r'^Traceback \(most recent call last\):$',
r'^  File ".+test_logging\.py", line [0-9]+, in test_logging$',
r'^    foo\(\)$',
r'^  File ".+test_logging\.py", line [0-9]+, in foo$',
r'^  \(in_foo=True\)$',
r'^  \(out_of_bar=True\)$',
r'^    baz\(\)$',
r'^  File ".+test_logging\.py", line [0-9]+, in baz$',
r'^  \(in_baz=True\)$',
r'^    raise ValueError\("ohno"\)$',
r'^ValueError: ohno$')
        actual = self.output.getvalue().split('\n')
        if actual and not actual[-1]:
            actual = actual[:-1]
        for a, e in zip_longest(actual, expected):
            self.assertIsNotNone(re.search(e, a), "%r != %r" % (a, e))

    def test_parse_stream(
        self, structure_and_messages1, structure_and_messages2, structure_and_messages3
    ):
        """
        L{Parser.parse_stream} returns an iterable of completed and then
        incompleted tasks.
        """
        _, messages1 = structure_and_messages1
        _, messages2 = structure_and_messages2
        _, messages3 = structure_and_messages3
        # Need at least one non-dropped message in partial tree:
        assume(len(messages3) > 1)
        # Need unique UUIDs per task:
        assume(
            len(set(m[0][TASK_UUID_FIELD] for m in (messages1, messages2, messages3)))
            == 3
        )

        # Two complete tasks, one incomplete task:
        all_messages = (messages1, messages2, messages3[:-1])

        all_tasks = list(
            Parser.parse_stream(
                [m for m in chain(*zip_longest(*all_messages)) if m is not None]
            )
        )
        assertCountEqual(
            self, all_tasks, [parse_to_task(msgs) for msgs in all_messages]
        )

def combine_slices(slice1, slice2):
    """Return two tuples of slices combined sequentially.

    These two should be equal:

        x[ combine_slices(s1, s2) ] == x[s1][s2]

    """
    out = []
    for exp1, exp2 in zip_longest(
            slice1, slice2, fillvalue=slice(None)):
        if isinstance(exp1, int):
            exp1 = slice(exp1, exp1+1)
        if isinstance(exp2, int):
            exp2 = slice(exp2, exp2+1)

        start = (exp1.start or 0) + (exp2.start or 0)
        step = (exp1.step or 1) * (exp2.step or 1)

        if exp1.stop is None and exp2.stop is None:
            stop = None
        elif exp1.stop is None:
            stop = (exp1.start or 0) + exp2.stop
        elif exp2.stop is None:
            stop = exp1.stop
        else:
            stop = min(exp1.stop, (exp1.start or 0) + exp2.stop)

        out.append(slice(start, stop, step))
    return tuple(out)

def _apply_funcs(return_vals, funcs):
    """
    将func函数作用在被装饰的函数的返回值上
    """
    # 检查iterable
    if not isinstance(return_vals, tuple):
        return_vals = (return_vals,)
    try:
        iter(funcs)
    except TypeError:
        funcs = (funcs,) if funcs else ()

    # 检查函数
    if not funcs:
        return return_vals

    # 函数和返回值不对齐
    if 1 < len(return_vals) < len(funcs):
        raise TypeError(
            "In _apply_funcs(), len(funcs) == {} more than len(return_vals) == {}".format(
                len(funcs), len(return_vals)
            )
        )
    # 函数多于返回值
    if 1 == len(return_vals) < len(funcs):
        raise TypeError(
            "In _apply_funcs(), only 1 return value with len(processors) == {}".format(len(funcs),
                                                                                       len(return_vals))
        )

    # 将函数作用在返回值上
    return tuple([f(v) if f else v for v, f in zip_longest(return_vals, funcs)])

    def __init__(self, rules=None, flags=0, branch_size=MAXGROUPS - 1):
        """Initialize index structures.

        :param rules: list of tuples (regular expression, data)
        :param flags: additional flags passed to SRE parser
        :param branch_size: number of groups in a branch (max. 99)
        """
        self._patterns = []
        self.flags = flags
        self.rules = rules or []
        self.branch_size = min(branch_size, len(self.rules))

        def make_pattern(rules, flags=0):
            """Compile a rules to single branch with groups."""
            return re.compile('|'.join('(?P<I{name}>{regex})'.format(
                name=name, regex=regex
            ) for regex, (name, creator) in rules), flags=flags)

        for rules in zip_longest(*[iter(self.rules)] * self.branch_size):
            self._patterns.append(make_pattern([
                rule for rule in rules if rule is not None
            ]))

        self._lookup = {
            name: (name, creator) for _, (name, creator) in self.rules
        }

def histogram(bidx, infile, bins, sampling, rgb):

    """
    Histogram of raster values.
    """

    with rio.open(infile) as src:

        if bidx is not None:
            indexes = [bidx]
        else:
            indexes = src.indexes

        if rgb and len(indexes) < 3:
            raise click.ClickException("Not enough bands to display a RGB histogram.")
        elif rgb and len(indexes) >= 3:
            colors = ('red', 'green', 'blue', 'violet')
        else:
            colors = ()

        for b, color in zip_longest(indexes, colors, fillvalue=None):

            data = np.zeros(
                (src.height // sampling, src.width // sampling),
                dtype=src.dtypes[src.indexes.index(b)])
            data = src.read(indexes=b, out=data, masked=False)

            plt.hist(data.flatten(), bins=bins, alpha=0.5, color=color)

        plt.show()

def cmp_like_py2(dict1, dict2):  # type: (Dict[Text, Any], Dict[Text, Any]) -> int
    """
    Comparision function to be used in sorting as python3 doesn't allow sorting
    of different types like str() and int().
    This function re-creates sorting nature in py2 of heterogeneous list of
    `int` and `str`
    """
    # extract lists from both dicts
    first, second = dict1["position"], dict2["position"]
    # iterate through both list till max of their size
    for i, j in zip_longest(first, second):
        if i == j:
            continue
        # in case 1st list is smaller
        # should come first in sorting
        if i is None:
            return -1
        # if 1st list is longer,
        # it should come later in sort
        elif j is None:
            return 1

        # if either of the list contains str element
        # at any index, both should be str before comparing
        if isinstance(i, str) or isinstance(j, str):
            return 1 if str(i) > str(j) else -1
        # int comparison otherwise
        return 1 if i > j else -1
    # if both lists are equal
    return 0

  def _ComparePrereleaseStrings(cls, s1, s2):
    """Compares the two given prerelease strings.

    Args:
      s1: str, The first prerelease string.
      s2: str, The second prerelease string.

    Returns:
      1 if s1 is greater than s2, -1 if s2 is greater than s1, and 0 if equal.
    """
    s1 = s1.split('.') if s1 else []
    s2 = s2.split('.') if s2 else []

    for (this, other) in zip_longest(s1, s2):
      # They can't both be None because empty parts of the string split will
      # come through as the empty string. None indicates it ran out of parts.
      if this is None:
        return 1
      elif other is None:
        return -1

      # Both parts have a value
      if this == other:
        # This part is the same, move on to the next.
        continue
      if this.isdigit() and other.isdigit():
        # Numerical comparison if they are both numbers.
        return SemVer._CmpHelper(int(this), int(other))
      # Lexical comparison if either is a string. Numbers will always sort
      # before strings.
      return SemVer._CmpHelper(this.lower(), other.lower())

    return 0

 def assertContainsSameWords(self, string1, string2):
     try:
         for w1, w2 in zip_longest(string1.split(), string2.split(), fillvalue=''):
             self.assertEqual(w1, w2)
     except AssertionError:
         raise AssertionError("%r does not contain the same words as %r" % (string1,
                                                                            string2))

def create_sounding_dataset(parent_group, dataset_name, in_data, shape_names=(), units=None):
    "Creates a new dataset but with the data reshaped so that the first dim is the sounding one"

    new_data = numpy.array(in_data)
    try:
        new_ds = parent_group.create_dataset(dataset_name, data=new_data.reshape(1, *new_data.shape))
    except RuntimeError as e:
        raise RuntimeError("Could not create dataset: %s with data: %s\n%s" % (dataset_name, new_data, e))

    # Adds optionally passed shape names as an attribute to the dataset
    # Missing dimensions will be called Dim%d
    if type(shape_names) is StringType:
        shape_names = [shape_names]

    all_shape_names = []
    for idx, s_name in zip_longest(list(range(len(new_ds.shape))), ["Retrieval"] + list(shape_names)):
        if s_name:
            all_shape_names.append(s_name)
        else:
            all_shape_names.append("Dim%d" % (idx+1))
    new_ds.attrs["Shape"] = [ numpy.array("_".join(all_shape_names) + "_Array") ]

    if units:
        new_ds.attrs["Units"] = [ numpy.array(units) ]

    return new_ds

def make_date_range_tuples(start, end, gap):
    """Make an iterable of date tuples for use in iterating forms

    For example, a form might allow start and end dates and you want to iterate
    it one week at a time starting on Jan 1 and ending on Feb 3:

    >>> make_date_range_tuples(date(2017, 1, 1), date(2017, 2, 3), 7)
    [(Jan 1, Jan 7), (Jan 8, Jan 14), (Jan 15, Jan 21), (Jan 22, Jan 28),
     (Jan 29, Feb 3)]

    :param start: date when the query should start.
    :param end: date when the query should end.
    :param gap: the number of days, inclusive, that a query should span at a
    time.

    :rtype list(tuple)
    :returns: list of start, end tuples
    """
    # We create a list of start dates and a list of end dates, then zip them
    # together. If end_dates is shorter than start_dates, fill the last value
    # with the original end date.
    start_dates = [d.date() for d in rrule(DAILY, interval=gap, dtstart=start,
                                           until=end)]
    end_start = start + datetime.timedelta(days=gap - 1)
    end_dates = [d.date() for d in rrule(DAILY, interval=gap, dtstart=end_start,
                                         until=end)]
    return list(zip_longest(start_dates, end_dates, fillvalue=end))

def merge_lists(base, mine, other):
    if mine == other:
        return mine
    if other == base:
        return mine
    if mine == base:
        return other
    result = []
    last_conflict = False
    for i, (m, o, b) in enumerate(zip_longest(mine, other, base,
                                              fillvalue=_BLANK)):
        if (m == o and _BLANK not in (m, o) or
                isinstance(m, dict) and isinstance(o, dict)):
            result.append(m)
        else:  # Conflict
            if last_conflict:
                c = result[-1]
                c.update(m, o, b)
            else:
                c = Conflict(m, o, b)
                result.append(c)
            last_conflict = True
            continue
        last_conflict = False
    offset = 0
    for i, r in enumerate(result[:]):
        if isinstance(r, Conflict):
            c = r.resolve_conflict()
            result = result[:i + offset] + c + result[i + offset + 1:]
            offset += len(c) - 1
    return result

  def _get_minibatch_feed_dict(self, target_q_values, 
                               non_terminal_minibatch, terminal_minibatch):
    """
    Helper to construct the feed_dict for train_op. Takes the non-terminal and 
    terminal minibatches as well as the max q-values computed from the target
    network for non-terminal states. Computes the expected q-values based on
    discounted future reward.

    @return: feed_dict to be used for train_op
    """
    assert len(target_q_values) == len(non_terminal_minibatch)

    states = []
    expected_q = []
    actions = []

    # Compute expected q-values to plug into the loss function
    minibatch = itertools.chain(non_terminal_minibatch, terminal_minibatch)
    for item, target_q in zip_longest(minibatch, target_q_values, fillvalue=0):
      state, action, reward, _, _ = item
      states.append(state)
      # target_q will be 0 for terminal states due to fillvalue in zip_longest
      expected_q.append(reward + self.config.reward_discount * target_q)
      actions.append(utils.one_hot(action, self.env.action_space.n))

    return {
      self.network.x_placeholder: states, 
      self.network.q_placeholder: expected_q,
      self.network.action_placeholder: actions,
    }

 def __bind_commands(self):
     if not self.parallel:
         for attr in ['complete_kill', 'do_kill', 'do_status']:
             delattr(FrameworkConsole, attr)
     for name, func in get_commands():
         longname = 'do_{}'.format(name)
         # set the behavior of the console command (multi-processed or not)
         # setattr(Console, longname, MethodType(FrameworkConsole.start_process_template(func) \
         #                                   if self.parallel and func.behavior.is_multiprocessed else func, self))
         setattr(Console, longname, MethodType(func, self))
         # retrieve parts of function's docstring to make console command's docstring
         parts = func.__doc__.split(':param ')
         description = parts[0].strip()
         arguments = [" ".join([l.strip() for l in x.split(":")[-1].split('\n')]) for x in parts[1:]]
         docstring = COMMAND_DOCSTRING["description"].format(description)
         if len(arguments) > 0:
             arg_descrs = [' - {}:\t{}'.format(n, d or "[no description]") \
                           for n, d in list(zip_longest(signature(func).parameters.keys(), arguments or []))]
             docstring += COMMAND_DOCSTRING["arguments"].format('\n'.join(arg_descrs))
         if hasattr(func, 'examples') and isinstance(func.examples, list):
             args_examples = [' >>> {} {}'.format(name, e) for e in func.examples]
             docstring += COMMAND_DOCSTRING["examples"].format('\n'.join(args_examples))
         setattr(getattr(getattr(Console, longname), '__func__'), '__doc__', docstring)
         # set the autocomplete list of values (can be lazy by using lambda) if relevant
         if hasattr(func, 'autocomplete'):
             setattr(Console, 'complete_{}'.format(name),
                     MethodType(FrameworkConsole.complete_template(func.autocomplete), self))
         if hasattr(func, 'reexec_on_emptyline') and func.reexec_on_emptyline:
             self.reexec.append(name)

def get_language_list_for_templates(default_language):
    # type: (Text) -> List[Dict[str, Dict[str, str]]]
    language_list = [l for l in get_language_list()
                     if 'percent_translated' not in l or
                        l['percent_translated'] >= 5.]

    formatted_list = []
    lang_len = len(language_list)
    firsts_end = (lang_len // 2) + operator.mod(lang_len, 2)
    firsts = list(range(0, firsts_end))
    seconds = list(range(firsts_end, lang_len))
    assert len(firsts) + len(seconds) == lang_len
    for row in zip_longest(firsts, seconds):
        item = {}
        for position, ind in zip(['first', 'second'], row):
            if ind is None:
                continue

            lang = language_list[ind]
            percent = name = lang['name']
            if 'percent_translated' in lang:
                percent = u"{} ({}%)".format(name, lang['percent_translated'])

            item[position] = {
                'name': name,
                'code': lang['code'],
                'percent': percent,
                'selected': True if default_language == lang['code'] else False
            }

        formatted_list.append(item)

    return formatted_list

def _check_bam_contigs(in_bam, ref_file, config):
    """Ensure a pre-aligned BAM file matches the expected reference genome.
    """
    # GATK allows chromosome M to be in multiple locations, skip checking it
    allowed_outoforder = ["chrM", "MT"]
    ref_contigs = [c.name for c in ref.file_contigs(ref_file, config)]
    with pysam.Samfile(in_bam, "rb") as bamfile:
        bam_contigs = [c["SN"] for c in bamfile.header["SQ"]]
    extra_bcs = [x for x in bam_contigs if x not in ref_contigs]
    extra_rcs = [x for x in ref_contigs if x not in bam_contigs]
    problems = []
    warnings = []
    for bc, rc in zip_longest([x for x in bam_contigs if (x not in extra_bcs and
                                                                     x not in allowed_outoforder)],
                                         [x for x in ref_contigs if (x not in extra_rcs and
                                                                     x not in allowed_outoforder)]):
        if bc != rc:
            if bc and rc:
                problems.append("Reference mismatch. BAM: %s Reference: %s" % (bc, rc))
            elif bc:
                warnings.append("Extra BAM chromosomes: %s" % bc)
            elif rc:
                warnings.append("Extra reference chromosomes: %s" % rc)
    for bc in extra_bcs:
        warnings.append("Extra BAM chromosomes: %s" % bc)
    for rc in extra_rcs:
        warnings.append("Extra reference chromosomes: %s" % rc)
    if problems:
        raise ValueError("Unexpected order, name or contig mismatches between input BAM and reference file:\n%s\n"
                         "Setting `bam_clean: remove_extracontigs` in the configuration can often fix this issue."
                         % "\n".join(problems))
    if warnings:
        print("*** Potential problems in input BAM compared to reference:\n%s\n" %
              "\n".join(warnings))