Metadata-Version: 2.1
Name: excitertools
Version: 2020.5.4
Summary: itertools with function chaining
Home-page: https://github.com/cjrh/excitertools
Author: Caleb Hattingh
Author-email: caleb.hattingh@gmail.com
License: UNKNOWN
Description: 
        
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        .. _more-itertools: https://more-itertools.readthedocs.io/en/stable/index.html
        
        .. _excitertools:
        
        excitertools
        ############
        
        ``itertools`` reimagined as a `fluent interface <https://en.wikipedia.org/wiki/Fluent_interface>`_.
        
            In software engineering, a fluent interface is an object-oriented API whose design
            relies extensively on method chaining. Its goal is to increase code legibility by
            creating a domain-specific language (DSL). The term was coined in 2005 by Eric
            Evans and Martin Fowler.
        
            `*Wikipedia - Fluent Interface* <https://en.wikipedia.org/wiki/Fluent_interface>`_
        
        Note that nearly all of the ``more-itertools`` extension library is included.
        
        Demo
        ****
        
        .. code-block:: python
        
            >>> range(10).map(lambda x: x*7).filter(lambda x: x % 3 == 0).collect()
            [0, 21, 42, 63]
            >>> range(10).map(lambda x: x*7).filter(lambda x: x > 0 and x % 3 == 0).collect()
            [21, 42, 63]
        
        When the lines get long, parens can be used to split up each instruction:
        
        .. code-block:: python
        
            >>> (
            ...     range(10)
            ...         .map(lambda x: x*7)
            ...         .filter(lambda x: x % 3 == 0)
            ...         .collect()
            ... )
            [0, 21, 42, 63]
        
        The operator module makes ``reduce`` quite useful for simple cases:
        
        .. code-block:: python
        
            >>> from operator import add, mul
            >>> range(10).map(lambda x: x*7).filter(lambda x: x > 0 and x % 3 == 0).reduce(add)
            126
            >>> range(10).map(lambda x: x*7).filter(lambda x: x > 0 and x % 3 == 0).reduce(mul)
            55566
        
        .. contents::
            :depth: 1
        
        
        .. |warning| unicode:: U+26A0
        .. |cool| unicode:: U+2728
        .. |flux| unicode:: U+1F6E0
        .. |source| unicode:: U+1F3A4
        .. |sink| unicode:: U+1F3A7
        .. |inf| unicode:: U+267E
        
        
        How to Understand the API Documentation
        #######################################
        
        Several symbols are used to indicate things about parts of the API:
        
        - |source| This function is a *source*, meaning that it produces data
          that will be processed in an iterator chain.
        - |sink| This function is a *sink*, meaning that it consumes data that
          was processed in an iterator chain.
        - |inf| This function returns an infinite iterable
        - |warning| Warning - pay attention
        - |flux| This API is still in flux, and might be changed or
          removed in the future
        - |cool| Noteworthy; could be especially useful in many situations.
        
        The API is arranged roughly with the module-level functions first, and
        thereafter the Iter_ class itself. It is the Iter_ class that does
        the work to allow these iterators to be chained together. However, the
        module-level functions are more likely to be used directly and that's
        why they're presented first.
        
        The API includes wrappers for the stdlib *itertools* module, including
        the "recipes" given in the *itertools* docs, as well as wrappers for
        the iterators from the more-itertools_ 3rd-party package.
        
        Module-level Replacements for Builtins
        ######################################
        
        The following module-level functions, like range_, zip_ and so on, are
        intended to be used as replacements for their homonymous builtins. The
        only difference between these and the builtin versions is that these
        return instances of the Iter_ class. Note that because Iter_ is itself
        iterable, it means that the functions here can be used as drop-in
        replacements.
        
        Once you have an Iter_ instance, all of its methods become available
        via function call chaining, so these toplevel functions are really only
        a convenience to "get started" using the chaining syntax with minimal
        upfront cost in your own code.
        
        .. contents::
            :local:
        
        
        
        .. _range:
        
        
        |source| ``range(*args) -> "Iter[int]"``
        ****************************************
        
        
        Replacement for the builtin ``range`` function.  This version returns
        an instance of Iter_ to allow further iterable chaining.
        
        All the same calling variations work because this function merely wraps
        the original function.
        
        .. code-block:: python
        
            >>> range(3).collect()
            [0, 1, 2]
            >>> range(1, 4).collect()
            [1, 2, 3]
            >>> range(1, 6, 2).collect()
            [1, 3, 5]
            >>> range(1, 101, 3).filter(lambda x: x % 7 == 0).collect()
            [7, 28, 49, 70, 91]
        
        This example multiples, element by element, the series [0:5] with the
        series [1:6]. Two things to note: Firstly, Iter.zip_ is used to emit
        the tuples from each series. Secondly, Iter.starmap_ is used to receive
        those tuples into separate arguments in the lambda.
        
        .. code-block:: python
        
            >>> range(5).zip(range(1, 6)).starmap(lambda x, y: x * y).collect()
            [0, 2, 6, 12, 20]
        
        When written in a single line as above, it can get difficult to follow
        the chain of logic if there are many processing steps. Parentheses in
        Python allow grouping such that expressions can be spread over multiple
        lines.
        
        This is the same example as the prior one, but formatted to be spread
        over several lines. This is much clearer:
        
        .. code-block:: python
        
            >>> # Written out differently
            >>> (
            ...     range(5)
            ...         .zip(range(1, 6))
            ...         .starmap(lambda x, y: x * y)
            ...         .collect()
            ... )
            [0, 2, 6, 12, 20]
        
        If you wanted the sum instead, it isn't necessary to do the collection
        at all:
        
        .. code-block:: python
        
            >>> (
            ...     range(5)
            ...         .zip(range(1, 6))
            ...         .starmap(lambda x, y: x * y)
            ...         .sum()
            ... )
            40
        
        
        
        .. _zip:
        
        
        ``zip(*iterables: Any) -> "Iter[Tuple[T, ...]]"``
        *************************************************
        Replacement for the builtin ``zip`` function.  This version returns
        an instance of Iter_ to allow further iterable chaining.
        
        .. _enumerate:
        
        
        ``enumerate(iterable) -> "Iter[Tuple[int, T]]"``
        ************************************************
        Replacement for the builtin ``enumerate`` function.  This version returns
        an instance of Iter_ to allow further iterable chaining.
        
        .. code-block:: python
        
            >>> enumerate(string.ascii_lowercase).take(3).collect()
            [(0, 'a'), (1, 'b'), (2, 'c')]
        
        
        
        
        .. _map:
        
        
        ``map(func: Union[Callable[..., C], str], iterable) -> "Iter[C]"``
        ******************************************************************
        Replacement for the builtin ``map`` function.  This version returns
        an instance of Iter_ to allow further iterable chaining.
        
        .. code-block:: python
        
            >>> result = map(lambda x: (x, ord(x)), 'caleb').dict()
            >>> assert result == {'a': 97, 'b': 98, 'c': 99, 'e': 101, 'l': 108}
        
            >>> result = map('x, ord(x)', 'caleb').dict()
            >>> assert result == {'a': 97, 'b': 98, 'c': 99, 'e': 101, 'l': 108}
        
        
        .. _filter:
        
        
        ``filter(function: "Callable[[Any], bool]", iterable: "Iterable[T]") -> "Iter[T]"``
        ***********************************************************************************
        Replacement for the builtin ``filter`` function.  This version returns
        an instance of Iter_ to allow further iterable chaining.
        
        .. code-block:: python
        
            >>> filter(lambda x: x % 3 == 0, range(10)).collect()
            [0, 3, 6, 9]
        
        
        
        
        .. _count:
        
        
        |source| ``count(start=0, step: int = 1) -> "Iter[int]"``
        *********************************************************
        
        
        Replacement for the itertools ``count`` function.  This version returns
        an instance of Iter_ to allow further iterable chaining.
        
        .. code-block:: python
        
            >>> count().take(5).collect()
            [0, 1, 2, 3, 4]
            >>> count(0).take(0).collect()
            []
            >>> count(10).take(0).collect()
            []
            >>> count(10).take(5).collect()
            [10, 11, 12, 13, 14]
            >>> count(1).filter(lambda x: x > 10).take(5).collect()
            [11, 12, 13, 14, 15]
        
        
        
        .. _cycle:
        
        
        ``cycle(iterable) -> "Iter[T]"``
        ********************************
        Replacement for the itertools ``count`` function.  This version returns
        an instance of Iter_ to allow further iterable chaining.
        
        .. code-block:: python
        
            >>> cycle(range(3)).take(6).collect()
            [0, 1, 2, 0, 1, 2]
            >>> cycle([]).take(6).collect()
            []
            >>> cycle(range(3)).take(0).collect()
            []
        
        
        
        .. _repeat:
        
        
        |source| ``repeat(object: C, times=None) -> "Iter[C]"``
        *******************************************************
        
        
        Replacement for the itertools ``count`` function.  This version returns
        an instance of Iter_ to allow further iterable chaining.
        
        .. code-block:: python
        
            >>> repeat('a').take(3).collect()
            ['a', 'a', 'a']
            >>> repeat([1, 2]).take(3).collect()
            [[1, 2], [1, 2], [1, 2]]
            >>> repeat([1, 2]).take(3).collapse().collect()
            [1, 2, 1, 2, 1, 2]
            >>> repeat([1, 2]).collapse().take(3).collect()
            [1, 2, 1]
            >>> repeat('a', times=3).collect()
            ['a', 'a', 'a']
        
        
        
        
        This next set of functions return iterators that terminate on the shortest 
        input sequence.
        
        
        
        .. _accumulate:
        
        
        ``accumulate(iterable, func=None, *, initial=None)``
        ****************************************************
        Replacement for the itertools ``accumulate`` function.  This version returns
        an instance of Iter_ to allow further iterable chaining.
        
        .. code-block:: python
        
            >>> accumulate([1, 2, 3, 4, 5]).collect()
            [1, 3, 6, 10, 15]
            >>> if sys.version_info >= (3, 8):
            ...     output = accumulate([1, 2, 3, 4, 5], initial=100).collect()
            ...     assert output == [100, 101, 103, 106, 110, 115]
            >>> accumulate([1, 2, 3, 4, 5], operator.mul).collect()
            [1, 2, 6, 24, 120]
            >>> accumulate([]).collect()
            []
            >>> accumulate('abc').collect()
            ['a', 'ab', 'abc']
            >>> accumulate(b'abc').collect()
            [97, 195, 294]
            >>> accumulate(bytearray(b'abc')).collect()
            [97, 195, 294]
        
        
        
        .. _chain:
        
        
        ``chain(*iterables: Iterable[T]) -> "Iter[T]"``
        ***********************************************
        Replacement for the itertools ``chain`` function.  This version returns
        an instance of Iter_ to allow further iterable chaining.
        
        .. code-block:: python
        
            >>> chain('ABC', 'DEF').collect()
            ['A', 'B', 'C', 'D', 'E', 'F']
            >>> chain().collect()
            []
        
        
        
        .. _chain_from_iterable:
        
        
        ``chain_from_iterable(iterable) -> "Iter[T]"``
        **********************************************
        Replacement for the itertools ``chain.from_iterable`` method.
        This version returns an instance of Iter_ to allow
        further iterable chaining.
        
        .. code-block:: python
        
            >>> chain_from_iterable(['ABC', 'DEF']).collect()
            ['A', 'B', 'C', 'D', 'E', 'F']
            >>> chain_from_iterable([]).collect()
            []
        
        
        
        .. _compress:
        
        
        ``compress(data, selectors)``
        *****************************
        Replacement for the itertools ``compress`` function.  This version returns
        an instance of Iter_ to allow further iterable chaining.
        
        .. code-block:: python
        
            >>> compress('ABCDEF', [1, 0, 1, 0, 1, 1]).collect()
            ['A', 'C', 'E', 'F']
        
        
        
        
        .. _dropwhile:
        
        
        ``dropwhile(pred, iterable)``
        *****************************
        Replacement for the itertools ``dropwhile`` function.  This version returns
        an instance of Iter_ to allow further iterable chaining.
        
        .. code-block:: python
        
            >>> dropwhile(lambda x: x < 4, range(6)).collect()
            [4, 5]
        
        
        
        .. _filterfalse:
        
        
        ``filterfalse(pred, iterable)``
        *******************************
        Replacement for the itertools ``filterfalse`` function.  This version returns
        an instance of Iter_ to allow further iterable chaining.
        
        .. code-block:: python
        
            >>> filterfalse(None, [2, 0, 3, None, 4, 0]).collect()
            [0, None, 0]
        
        
        
        .. _groupby:
        
        
        ``groupby(iterable, key=None)``
        *******************************
        Replacement for the itertools ``groupby`` function.  This version returns
        an instance of Iter_ to allow further iterable chaining.
        
        groupby_ returns an iterator of a key and "grouper" iterable. In the
        example below, we use Iter.starmap_ to collect each grouper iterable
        into a list, as this makes it neater for display here in the docstring.
        
        .. code-block:: python
        
            >>> (
            ...     groupby(['john', 'jill', 'anne', 'jack'], key=lambda x: x[0])
            ...         .starmap(lambda k, g: (k, list(g)))
            ...         .collect()
            ... )
            [('j', ['john', 'jill']), ('a', ['anne']), ('j', ['jack'])]
        
        
        
        
        .. _islice:
        
        
        ``islice(iterable, *args) -> "Iter"``
        *************************************
        Replacement for the itertools ``islice`` function.  This version returns
        an instance of Iter_ to allow further iterable chaining.
        
        .. code-block:: python
        
            >>> islice('ABCDEFG', 2).collect()
            ['A', 'B']
            >>> islice('ABCDEFG', 2, 4).collect()
            ['C', 'D']
            >>> islice('ABCDEFG', 2, None).collect()
            ['C', 'D', 'E', 'F', 'G']
            >>> islice('ABCDEFG', 0, None, 2).collect()
            ['A', 'C', 'E', 'G']
        
        
        
        .. _starmap:
        
        
        ``starmap(func, iterable)``
        ***************************
        Replacement for the itertools ``starmap`` function.  This version returns
        an instance of Iter_ to allow further iterable chaining.
        
        .. code-block:: python
        
            >>> starmap(pow, [(2, 5), (3, 2), (10, 3)]).collect()
            [32, 9, 1000]
        
        
        
        .. _takewhile:
        
        
        ``takewhile(pred, iterable)``
        *****************************
        Replacement for the itertools ``takewhile`` function.  This version returns
        an instance of Iter_ to allow further iterable chaining.
        
        .. code-block:: python
        
            >>> takewhile(lambda x: x < 5, [1, 4, 6, 4, 1]).collect()
            [1, 4]
        
        
        
        .. _tee:
        
        
        ``tee(iterable, n=2)``
        **********************
        Replacement for the itertools ``tee`` function.  This version returns
        an instance of Iter_ to allow further iterable chaining.
        
        .. code-block:: python
        
            >>> a, b = tee(range(5))
            >>> a.collect()
            [0, 1, 2, 3, 4]
            >>> b.sum()
            10
        
        It is also possible to operate on the returned iterators in the chain
        but it gets quite difficult to understand:
        
        .. code-block:: python
        
            >>> tee(range(5)).map(lambda it: it.sum()).collect()
            [10, 10]
        
        In the example above we passed in range_, but with excitertools_
        it's usually more natural to push data sources further left:
        
        .. code-block:: python
        
            >>> range(5).tee().map(lambda it: it.sum()).collect()
            [10, 10]
        
        Pay close attention to the above. The map_ is acting on each of the
        copied iterators.
        
        
        
        .. _zip_longest:
        
        
        ``zip_longest(*iterables, fillvalue=None)``
        *******************************************
        Replacement for the itertools ``zip_longest`` function.  This version returns
        an instance of Iter_ to allow further iterable chaining.
        
        .. code-block:: python
        
            >>> zip_longest('ABCD', 'xy', fillvalue='-').collect()
            [('A', 'x'), ('B', 'y'), ('C', '-'), ('D', '-')]
            >>> (
            ...     zip_longest('ABCD', 'xy', fillvalue='-')
            ...         .map(lambda tup: concat(tup, ''))
            ...         .collect()
            ... )
            ['Ax', 'By', 'C-', 'D-']
            >>> (
            ...     zip_longest('ABCD', 'xy', fillvalue='-')
            ...         .starmap(operator.add)
            ...         .collect()
            ... )
            ['Ax', 'By', 'C-', 'D-']
        
        
        
        .. _finditer_regex:
        
        
        ``finditer_regex(pat: "re.Pattern[AnyStr]", s: AnyStr, flags: Union[int, re.RegexFlag] = 0) -> "Iter[AnyStr]"``
        ***************************************************************************************************************
        
        Wrapper for ``re.finditer``. Returns an instance of Iter_ to allow
        chaining.
        
        >>> pat = r"\w+"
        >>> text = "Well hello there! How ya doin!"
        >>> finditer_regex(pat, text).map(str.lower).filter(lambda w: 'o' in w).collect()
        ['hello', 'how', 'doin']
        
        >>> finditer_regex(r"[A-Za-z']+", "A programmer's RegEx test.").collect()
        ['A', "programmer's", 'RegEx', 'test']
        >>> finditer_regex(r"[A-Za-z']+", "").collect()
        []
        >>> finditer_regex("", "").collect()
        ['']
        >>> finditer_regex("", "").filter(None).collect()
        []
        
        
        
        .. _splititer_regex:
        
        
        ``splititer_regex(pat: "re.Pattern[AnyStr]", s: AnyStr, flags: Union[int, re.RegexFlag] = 0) -> "Iter[AnyStr]"``
        ****************************************************************************************************************
        
        Lazy string splitting using regular expressions.
        
        Most of the time you want ``str.split``. Really! That will almost
        always be fastest. You might think that ``str.split`` is inefficient
        because it always has to build a list, but it can do this very, very
        quickly.
        
        The lazy splitting shown here is more about supporting a particular
        kind of programming model, rather than performance.
        
        See more discussion `here <https://stackoverflow.com/questions/3862010/is-there-a-generator-version-of-string-split-in-python>`_.
        
        >>> splititer_regex(r"\s", "A programmer's RegEx test.").collect()
        ['A', "programmer's", 'RegEx', 'test.']
        
        Note that splitting at a single whitespace character will return blanks
        for each found. This is different to how ``str.split()`` works.
        
        >>> splititer_regex(r"\s", "aaa     bbb  \n  ccc\nddd\teee").collect()
        ['aaa', '', '', '', '', 'bbb', '', '', '', '', 'ccc', 'ddd', 'eee']
        
        To match ``str.split()``, specify a sequence of whitespace as the
        regex pattern.
        
        >>> splititer_regex(r"\s+", "aaa     bbb  \n  ccc\nddd\teee").collect()
        ['aaa', 'bbb', 'ccc', 'ddd', 'eee']
        
        Counting the whitespace
        
        >>> splititer_regex(r"\s", "aaa     bbb  \n  ccc\nddd\teee").collect(Counter)
        Counter({'': 8, 'aaa': 1, 'bbb': 1, 'ccc': 1, 'ddd': 1, 'eee': 1})
        
        Lazy splitting at newlines
        
        >>> splititer_regex(r"\n", "aaa     bbb  \n  ccc\nddd\teee").collect()
        ['aaa     bbb  ', '  ccc', 'ddd\teee']
        
        >>> splititer_regex(r"", "aaa").collect()
        ['', 'a', 'a', 'a', '']
        >>> splititer_regex(r"", "").collect()
        ['', '']
        >>> splititer_regex(r"\s", "").collect()
        ['']
        >>> splititer_regex(r"a", "").collect()
        ['']
        >>> splititer_regex(r"\s", "aaa").collect()
        ['aaa']
        
        
        
        .. _concat:
        
        
        ``concat(iterable: Iterable[AnyStr], glue: AnyStr) -> "AnyStr"``
        ****************************************************************
        Concatenate strings, bytes and bytearrays. It is careful to avoid the
        problem with single bytes becoming integers, and it looks at the value
        of `glue` to know whether to handle bytes or strings.
        
        This function can raise ``ValueError`` if called with something
        other than ``bytes``, ``bytearray`` or ``str``.
        
        .. _from_queue:
        
        
        |cool| |source| ``from_queue(q: queue.Queue, timeout=None, sentinel=None) -> "Iter"``
        *************************************************************************************
        
        
        
        
        Wrap a queue with an iterator interface. This allows it to participate
        in chaining operations. The iterator will block while waiting for
        new values to appear on the queue. This is useful: it allows you
        to easily and safely pass data between threads or processes, and
        feed the incoming data into a pipeline.
        
        The sentinel value, default ``None``, will terminate the iterator.
        
        .. code-block:: python
        
            >>> q = queue.Queue()
            >>> # This line puts stuff onto a queue
            >>> range(10).chain([None]).map(q.put).consume()
            >>> from_queue(q).filter(lambda x: 2 < x < 9).collect()
            [3, 4, 5, 6, 7, 8]
        
        This can be used in the same way you would normally use a queue, in
        that it will block while waiting for future input. This makes it
        convenient to run in a thread and wait for work. Below is a rough
        sketch of how one might cobble together a thread pool using this
        feature. Note the use of Iter.side_effect_ to call ``task_done()``
        on the queue.
        
        .. code-block:: python
        
            import queue
            from threading import Thread
            import logging
            from excitertools import from_queue
        
            logger = logging.getLogger(__name__)
        
            def process_job(job):
                result = ...
                return result
        
            def worker(inputs: Queue, results: Queue):
                (
                    from_queue(inputs)
                    .side_effect(lambda job: logger.info(f"Received job {job}")
                    .map(process_job)
                    .side_effect(lambda result: logger.info(f"Got result {job}")
                    .into_queue(results)
                    # Note that we only mark the task as done after the result
                    # is added to the results queue.
                    .side_effect(lambda _: inputs.task_done()
                )
        
            def create_job_pool(n: int) -> Tuple[Queue, Queue, Callable]:
                """Returns two queues, and a pool shutdown method. The
                shutdown function can be called to shut down the pool and
                the ``inputs`` queue. Caller is responsible for draining
                the ``results`` queue."""
        
                # Independent control of the sizes of the input and output
                # queues is interesting: it lets you decide how to bias
                # backpressure depending on the details of your workload.
                inputs, results = Queue(maxsize=100), Queue(maxsize=3)
        
                kwargs = dict(target=worker, args=(inputs, results), daemon=True)
                threads = repeat(Thread).map(lambda T: T(**kwargs)).take(n).collect()
        
                def shutdown():
                    # Shut down each thread
                    repeat(None).map(inputs.put).take(n).consume()
                    inputs.join()
                    Iter(threads).map(lambda t: t.join()).consume()
        
                return inputs, results, shutdown
        
        Now the two queues ``inputs`` and ``results`` can be used in various
        other threads to supply and consume data.
        
        
        
        
        The ``Iter`` Class
        ##################
        
        .. contents::
            :backlinks: entry
            :local:
        
        
        
        .. _Iter:
        
        
        |cool| ``class Iter(Generic[T])``
        *********************************
        
        
        This class is what allows chaining. Many of the methods in this class
        return an instance of Iter_, which allows further chaining. There
        are two exceptions to this: *sources* and *sinks*.
        
        A "source" is usually a ``classmethod`` which can be used as an
        initializer to produce data via an iterable. For example, the Iter.range_
        classmethod can be used to get a sequence of numbers:
        
        .. code-block:: python
        
            >>> Iter.range(1_000_000).take(3).collect()
            [0, 1, 2]
        
        Even though our range was a million elements, the iterator chaining
        took only 3 of those elements before collecting.
        
        A "sink" is a method that is usually the last component of a processing
        chain and often (but not always!) consumes the entire iterator. In the
        example above, the call to Iter.collect_ was a sink. Note that we still
        call it a sink even though it did not consume the entire iterator.
        
        We're using the term "source" to refer to a classmethod of Iter_ that
        produces data; but, the most typical source is going to be data that
        you provide. Iter_ can be called with anything that is iterable, including
        sequences, iterators, mappings, sets, generators and so on.
        
        Examples:
        
        .. code-block:: python
        
            List
            >>> Iter([1, 2, 3]).map(lambda x: x * 2).sum()
            12
        
            Generator
            >>> Iter((1, 2, 3)).map(lambda x: x * 2).sum()
            12
            >>> def g():
            ...     for i in [1, 2, 3]:
            ...         yield i
            >>> Iter(g()).map(lambda x: x * 2).sum()
            12
        
            Iterator
            >>> Iter(iter([1, 2, 3])).map(lambda x: x * 2).sum()
            12
        
            Dict
            >>> Iter(dict(a=1, b=2)).map(lambda x: x.upper()).collect()
            ['A', 'B']
            >>> d = dict(a=1, b=2, c=3)
            >>> Iter(d.items()).starmap(lambda k, v: v).map(lambda x: x * 2).sum()
            12
        
        A common error with generators is forgetting to actually evaluate, i.e.,
        call a generator function. If you do this there's a friendly error
        pointing out the mistake:
        
        .. code-block:: python
        
            >>> def mygen(): yield 123
            >>> Iter(mygen).collect()
            Traceback (most recent call last):
                ...
            TypeError: It seems you passed a generator function, but you
            probably intended to pass a generator. Remember to evaluate the
            function to obtain a generator instance:
                       
            def mygen():
                yield 123
                       
            Iter(mygen)    # ERROR - a generator function object is not iterable
            Iter(mygen())  # CORRECT - a generator instance is iterable.
            >>> Iter(mygen()).collect()
            [123]
        
        Instance of Iter_ are resumable. Once an instance it created, it can
        be partially iterated in successive calls, like the following example
        shows:
        
        .. code-block:: python
        
            >>> it = Iter.range(1_000_000)
            >>> it.take(3).collect()
            [0, 1, 2]
            >>> it.take(4).collect()
            [3, 4, 5, 6]
            >>> # Consume most of the stream, collect the last few
            >>> it.consume(999_990).collect()
            [999997, 999998, 999999]
        
        This class implements the chaining. However, the module-level functions
        in excitertools_, such as range_, zip_ and so on, also return
        instances of Iter_, so they allow the chaining to continue. These are
        equivalent:
        
        .. code-block:: python
        
            >>> Iter.range(10).filter(lambda x: x > 7).collect()
            [8, 9]
            >>> range(10).filter(lambda x: x > 7).collect()
            [8, 9]
        
        It is intended that the module-level functions can act as drop-in
        replacements for the builtins they wrap:
        
        >>> import builtins
        >>> list(builtins.range(3))
        [0, 1, 2]
        >>> list(range(3))  # This is excitertools.range!
        [0, 1, 2]
        >>> list(Iter.range(3))
        [0, 1, 2]
        
        In your own code where you might like to use the excitertools_ version of
        range_ and the other functions, you can just import it and use it to access all the other
        cool stuff:
        
        .. code-block:: python
        
            # mymodule.py
            from excitertools import (
                range,
                map,
                filter,
                reduce,
                repeat,
                count,
                enumerate,
                zip,
                ...
            )
        
            def func(inputs):
                data = (
                    map(lambda x: x + 2, inputs)
                        .enumerate()
                        .filter(lambda x: x[1] > 10)
                        ...
                        .collect()
        
                )
        
        Alternatively, if you don't want to hide the builtins you can do just
        fine with importing this class only, or even importing the module only:
        
        .. code-block:: python
        
            # mymodule.py - same example as before
            import excitertools
        
            def func(inputs):
                data = (
                    excitertools.Iter(inputs)
                        .map(lambda x: x + 2, inputs)
                        .enumerate()
                        .filter(lambda x: x[1] > 10)
                        ...
                        .collect()
                )
        
                # Do something with data
        
        There are several valuable additions to the standard *itertools* and
        more-itertools_ functions. These usually involve sources and sinks,
        which are ways of getting data into an iterator pipeline, and then
        getting results out again. In the majority of documentation examples
        shown here, the Iter.collect_ method is used to collect all the
        remaining data on a stream into a list; but in practice this is not
        useful because large lists consume memory.
        
        In practice it is more useful to send iterator data to one of these
        common sinks:
        
        - files
        - sockets
        - queues
        - HTTP APIs
        - Cloud storage buckets
        - (Ideas for more to add here?)
        
        Iter_ has support for these use-cases, both for reading and for writing.
        
        
        
        .. _Iter.register:
        
        
        ``@classmethod Iter.register(cls, *func)``
        ==========================================
        
        Add a new method to Iter_. Sure, you could subclass Iter_ to get
        new chaining features, but it would be neat to let all existing
        Iter_ instance just immediately have the new registered function
        available.
        
        The new function must take ``iterable`` as the first parameter.
        
        .. code-block:: python
        
            >>> def up(iterable):
            ...     for v in iterable:
            ...         yield v.upper()
            >>> Iter.register(up)
            >>> Iter('abc').up().collect()
            ['A', 'B', 'C']
            >>> def poly(iterable, a, b, c):
            ...     # Polynomials a.x^2 + b.x + c
            ...     for x in iterable:
            ...         yield a*x**2 + b*x + c
            >>> Iter.register(poly)
            >>> Iter(range(-5, 5, 1)).poly(1, -5, 6).collect()
            [56, 42, 30, 20, 12, 6, 2, 0, 0, 2]
        
        Here's a math round-trip rollercoaster.
        
        .. code-block:: python
        
            >>> import math
            >>> def log(iterable):
            ...     for x in iterable:
            ...         yield math.log(x)
            >>> def exp(iterable):
            ...     for x in iterable:
            ...         yield math.exp(x)
            >>> def rnd(iterable):
            ...     for x in iterable:
            ...         yield round(x)
            >>> Iter.register(log, exp, rnd)
            >>> Iter(range(5)).exp().log().rnd().collect()
            [0, 1, 2, 3, 4]
        
        These are silly examples, but hopefully you get the idea.
        
        
        
        .. _Iter.collect:
        
        
        |sink| ``Iter.collect(self, container=list) -> "List[T]"``
        ==========================================================
        
        
        
        This is the most common way of "realizing" an interable chain
        into a concrete data structure. It should be the case that this
        is where most of the memory allocation occurs.
        
        The default container is a list and you'll see throughout this
        documentation that most examples produce lists. However,
        any container, and indeed any function, can be used as the sink.
        
        The basic example:
        
        .. code-block:: python
        
            >>> Iter(range(3)).collect()
            [0, 1, 2]
            >>> Iter(range(3)).collect(tuple)
            (0, 1, 2)
        
        You must pay attention to some things. For example, if your
        iterable is a string, the characters of the string are what
        get iterated over, and when you collect you'll get a collection
        of those atoms. You can however use ``str`` as your "container
        function" and that will give you back a string. It's like a join
        with blank joiner.
        
        .. code-block:: python
        
            >>> Iter('abc').collect()
            ['a', 'b', 'c']
            >>> Iter('abc').collect(str)
            'abc'
        
        With some types, things get a little more tricky. Take ``bytes``
        for example:
        
        .. code-block:: python
        
            >>> Iter(b'abc').collect()
            [97, 98, 99]
        
        You probably didn't expect to get the integers back right? Anyhow,
        you can use ``bytes`` as the "collection container", just like
        we did with strings and that will work:
        
        .. code-block:: python
        
            >>> Iter(b'abc').collect(bytes)
            b'abc'
            >>> Iter(b'abc').collect(bytearray)
            bytearray(b'abc')
        
        The other standard collections also work, here's a set for
        completeness.
        
        .. code-block:: python
        
            >>> Iter('abcaaaabbbbccc').collect(set) == {'a', 'b', 'c'}
            True
        
        
        
        .. _Iter.open:
        
        
        |cool| |source| ``@classmethod Iter.open(cls, file, mode="r", buffering=-1, encoding=None, errors=None, newline=None, closefd=True, opener=None, ) -> "Iter"``
        ==============================================================================================================================================================
        
        
        
        
        Wrap the ``open()`` builtin precisely, but return an ``Iter``
        instance to allow function chaining on the result.
        
        I know you're thinking that we should always use a context
        manager for files. Don't worry, there is one being used
        internally. When the iterator chain is terminated the underlying
        file will be closed.
        
        >>> import tempfile
        >>> with tempfile.TemporaryDirectory() as td:
        ...     # Put some random text into a temporary file
        ...     with open(td + 'text.txt', 'w') as f:
        ...         f.writelines(['abc\n', 'def\n', 'ghi\n'])
        ...
        ...     # Open the file, filter some lines, collect the result
        ...     Iter.open(td + 'text.txt').filter(lambda line: 'def' in line).collect()
        ['def\n']
        
        Note that this is a convenience method for *reading* from a file,
        not for writing. The function signature includes the ``mode``
        parameter for parity with the builtin ``open()`` function, but
        only reading is supported.
        
        
        
        .. _Iter.read_lines:
        
        
        |source| ``@classmethod Iter.read_lines(cls, stream: IO[str], rewind=True)``
        ============================================================================
        
        
        
        >>> import tempfile
        >>> with tempfile.TemporaryDirectory() as td:
        ...     # Put some random text into a temporary file
        ...     with open(td + 'text.txt', 'w') as f:
        ...         f.writelines(['abc\n', 'def\n', 'ghi\n'])
        ...
        ...     # Use read_lines to process the file data
        ...     with open(td + 'text.txt') as f:
        ...         Iter.read_lines(f).filter(lambda line: 'def' in line).collect()
        ['def\n']
        
        
        
        .. _Iter.read_bytes:
        
        
        |source| ``@classmethod Iter.read_bytes(cls, stream: IO[bytes], size: Union[Callable[[], int], int] = -1, rewind=True)``
        ========================================================================================================================
        
        
        
        The ``size`` parameter can be used to control how many bytes are
        read for each advancement of the iterator chain. Here we set ``size=1``
        which means we'll get back one byte at a time.
        
        >>> import tempfile
        >>> td = tempfile.TemporaryDirectory()
        >>> filename = td.name + 'bytes.bin'
        
        Put some random text into a temporary file
        >>> with open(filename, 'wb') as f:
        ...     x = f.write(b'\x00' * 100)
        ...
        >>> with open(filename, 'rb') as f:
        ...     data = Iter.read_bytes(f, size=1).collect()
        ...     len(data)
        100
        >>> with open(filename, 'rb') as f:
        ...     data = Iter.read_bytes(f).collect()
        ...     len(data)
        1
        
        A little more ambitious. Because ``size`` is a callable, we can use
        a ``deque`` and a ``side_effect`` to pass information back into
        the reader to control how many bytes are read in each chunk.
        
        >>> read_sizes = deque([1])
        >>> with open(filename, 'rb') as f:
        ...     data = (
        ...         Iter
        ...             .read_bytes(f, size=lambda: read_sizes.popleft())
        ...             .side_effect(lambda bytes: read_sizes.append(1))
        ...             .collect()
        ...     )
        ...     len(data)
        100
        >>> td.cleanup()
        
        
        .. _Iter.write_text_to_stream:
        
        
        |sink| ``Iter.write_text_to_stream(self, stream: IO[str], insert_newlines=True, flush=True)``
        =============================================================================================
        
        
        
        >>> import tempfile
        >>> td = tempfile.TemporaryDirectory()
        >>> filename = td.name + 'text.txt'
        
        >>> data = ['a', 'b', 'c']
        >>> with open(filename, 'w') as f:
        ...     Iter(data).map(str.upper).write_text_to_stream(f)
        ...     with open(filename) as f2:
        ...         Iter.read_lines(f2).concat()
        'A\nB\nC'
        
        If some prior step adds newlines, or more commonly, newlines
        originate with a data source and are simply carried through the
        processing chain unaltered, disable the insertion of newlines:
        
        >>> with open(filename, 'w') as f:
        ...     Iter(data).map(str.upper).write_text_to_stream(f, insert_newlines=False)
        ...     with open(filename) as f2:
        ...         Iter.read_lines(f2).concat()
        'ABC'
        
        
        
        .. _Iter.write_bytes_to_stream:
        
        
        |sink| ``Iter.write_bytes_to_stream(self, stream: IO[bytes], flush=True)``
        ==========================================================================
        
        
        
        >>> import tempfile
        >>> td = tempfile.TemporaryDirectory()
        >>> filename = td.name + 'bytes.bin'
        >>> data = [b'a', b'b', b'c']
        >>> with open(filename, 'wb') as f:
        ...     Iter(data).map(lambda x: x * 2 ).write_bytes_to_stream(f)
        ...     with open(filename, 'rb') as f2:
        ...         Iter.read_bytes(f2).collect()
        [b'aabbcc']
        >>> with open(filename, 'wb') as f:
        ...     Iter(data).map(lambda x: x * 2 ).write_bytes_to_stream(f)
        ...     with open(filename, 'rb') as f2:
        ...         Iter.read_bytes(f2).concat(b'')
        b'aabbcc'
        >>> with open(filename, 'wb') as f:
        ...     Iter(data).map(lambda x: x * 2 ).write_bytes_to_stream(f)
        ...     with open(filename, 'rb') as f2:
        ...         Iter.read_bytes(f2, size=1).collect()
        [b'a', b'a', b'b', b'b', b'c', b'c']
        >>> with open(filename, 'wb') as f:
        ...     Iter(data).map(lambda x: x * 2 ).write_bytes_to_stream(f)
        ...     with open(filename, 'rb') as f2:
        ...         Iter.read_bytes(f2, size=2).map(bytes.decode).collect()
        ['aa', 'bb', 'cc']
        
        
        
        .. _Iter.write_to_file:
        
        
        |cool| |sink| ``Iter.write_to_file(self, file, mode="w", buffering=-1, encoding=None, errors=None, newline=None, closefd=True, opener=None, )``
        ===============================================================================================================================================
        
        
        
        
        >>> import tempfile
        >>> with tempfile.TemporaryDirectory() as td:
        ...     # Put some random text into a temporary file
        ...     with open(td + 'text.txt', 'w') as f:
        ...         f.writelines(['abc\n', 'def\n', 'ghi\n'])
        ...
        ...     # Open the file, transform, write out to new file.
        ...     Iter.open(td + 'text.txt').map(str.upper).write_to_file(td + 'test2.txt')
        ...     # Read the new file, for the test
        ...     Iter.open(td + 'test2.txt').collect()
        ['ABC\n', 'DEF\n', 'GHI\n']
        
        
        
        .. _Iter.range:
        
        
        |source| ``@classmethod Iter.range(cls, *args) -> "Iter[int]"``
        ===============================================================
        
        
        
        The ``range`` function you all know and love.
        
        >>> Iter.range(3).collect()
        [0, 1, 2]
        >>> Iter.range(0).collect()
        []
        
        
        
        .. _Iter.zip:
        
        
        ``Iter.zip(self, *iterables: Any) -> "Iter[Tuple[T, ...]]"``
        ============================================================
        
        
        The ``zip`` function you all know and love. The only thing to
        note here is that the first iterable is really what the Iter_
        instance is wrapping. The Iter.zip_ invocation brings in the
        other iterables.
        
        Make an Iter_ instance, then call ``zip`` on that.
        
        >>> Iter('caleb').zip(range(10)).collect()
        [('c', 0), ('a', 1), ('l', 2), ('e', 3), ('b', 4)]
        
        Use a classmethod to get an infinite stream using Iter.count_
        and zip against that with more finite iterators.
        
        >>> Iter.count().zip(range(5), range(3, 100, 2)).collect()
        [(0, 0, 3), (1, 1, 5), (2, 2, 7), (3, 3, 9), (4, 4, 11)]
        
        It takes a few minutes to get used to that but feels comfortable
        pretty quickly.
        
        Iter.take_ can be used to stop infinite zip sequences:
        
        >>> Iter('caleb').cycle().enumerate().take(8).collect()
        [(0, 'c'), (1, 'a'), (2, 'l'), (3, 'e'), (4, 'b'), (5, 'c'), (6, 'a'), (7, 'l')]
        
        While we're here (assuming you worked through the previous
        example), note the difference if you switch the order of the
        Iter.cycle_ and Iter.enumerate_ calls:
        
        >>> Iter('caleb').enumerate().cycle().take(8).collect()
        [(0, 'c'), (1, 'a'), (2, 'l'), (3, 'e'), (4, 'b'), (0, 'c'), (1, 'a'), (2, 'l')]
        
        If you understand how this works, everything else in _excitertools_
        will be intuitive to use.
        
        
        
        .. _Iter.any:
        
        
        |sink| ``Iter.any(self) -> "bool"``
        ===================================
        
        
        
        >>> Iter([0, 0, 0]).any()
        False
        >>> Iter([0, 0, 1]).any()
        True
        >>> Iter([]).any()
        False
        
        
        
        .. _Iter.all:
        
        
        |sink| ``Iter.all(self) -> "bool"``
        ===================================
        
        
        
        
        >>> Iter([0, 0, 0]).all()
        False
        >>> Iter([0, 0, 1]).all()
        False
        >>> Iter([1, 1, 1]).all()
        True
        
        Now pay attention:
        
        >>> Iter([]).all()
        True
        
        This behaviour has some controversy around it, but that's how the
        ``all()`` builtin works so that's what we do too. The way to
        think about what ``all()`` does is this: it returns False if there
        is at least one element that is falsy.  Thus, if there are no elements
        it follows that there are no elements that are falsy and that's why
        ``all([]) == True``.
        
        
        
        .. _Iter.enumerate:
        
        
        ``Iter.enumerate(self) -> "Iter[Tuple[int, T]]"``
        =================================================
        
        Yup, *that* ``enumerate``.
        
        >>> Iter('abc').enumerate().collect()
        [(0, 'a'), (1, 'b'), (2, 'c')]
        >>> Iter([]).enumerate().collect()
        []
        
        
        
        .. _Iter.dict:
        
        
        ``Iter.dict(self) -> "Dict"``
        =============================
        
        In Python a dict can be constructed through an iterable of tuples:
        
        >>> dict([('a', 0), ('b', 1)])                  
        {'a': 0, 'b': 1}
        
        In *excitertools* we prefer chaining so this method is a shortcut
        for that:
        
        >>> d = Iter('abc').zip(count()).dict()
        >>> assert d == {'a': 0, 'b': 1, 'c': 2}
        
        
        
        .. _Iter.map:
        
        
        ``Iter.map(self, func: Union[Callable[..., C], str]) -> "Iter[C]"``
        ===================================================================
        
        The ``map`` function you all know and love.
        
        >>> Iter('abc').map(str.upper).collect()
        ['A', 'B', 'C']
        >>> Iter(['abc', 'def']).map(str.upper).collect()
        ['ABC', 'DEF']
        
        Using lambdas might seem convenient but in practice it turns
        out that they make code difficult to read:
        
        >>> result = Iter('caleb').map(lambda x: (x, ord(x))).dict()
        >>> assert result == {'a': 97, 'b': 98, 'c': 99, 'e': 101, 'l': 108}
        
        It's recommended that you make a separate function instead:
        
        >>> def f(x):
        ...     return x, ord(x)
        >>> result = Iter('caleb').map(f).dict()
        >>> assert result == {'a': 97, 'b': 98, 'c': 99, 'e': 101, 'l': 108}
        
        I know many people prefer anonymous functions (often on
        philosphical grouds) but in practice it's just easier to make
        a separate, named function.
        
        I've experimented with passing a string into the map, and using
        ``eval()`` to make a lambda internally. This simplifies the code
        very slightly, at the cost of using strings-as-code. I'm pretty
        sure this feature will be removed so don't use it.
        
        >>> result = Iter('caleb').map('x, ord(x)').dict()
        >>> assert result == {'a': 97, 'b': 98, 'c': 99, 'e': 101, 'l': 108}
        
        
        .. _Iter.filter:
        
        
        ``Iter.filter(self, function: "Optional[Callable[[T], bool]]" = None) -> "Iter[T]"``
        ====================================================================================
        
        The ``map`` function you all know and love.
        
        >>> Iter('caleb').filter(lambda x: x in 'aeiou').collect()
        ['a', 'e']
        
        There is a slight difference between this method signature and
        the builtin ``filter``:  how the identity function is handled.
        This is a consquence of chaining. In the function signature above
        it is possible for us to give the ``function`` parameter a
        default value of ``None`` because the parameter appears towards
        the end of the parameter list. Last, in fact.  In the
        `builtin filter signature <https://docs.python.org/3/library/functions.html#filter>`_
        it doesn't allow for this because the predicate parameter appears
        first.
        
        This is a long way of saying: if you just want to filter out
        falsy values, no parameter is needed:
        
        >>> Iter([0, 1, 0, 0, 0, 1, 1, 1, 0, 0]).filter().collect()
        [1, 1, 1, 1]
        
        Using the builtin, you'd have to do ``filter(None, iterable)``.
        
        You'll find that Iter.map_ and Iter.filter_
        (and Iter.reduce_, up next) work together very nicely:
        
        >>> def not_eve(x):
        ...    return x != 'eve'
        >>> Iter(['bob', 'eve', 'alice']).filter(not_eve).map(str.upper).collect()
        ['BOB', 'ALICE']
        
        The long chains get unwieldy so let's rewrite that:
        
        >>> (
        ...     Iter(['bob', 'eve', 'alice'])
        ...         .filter(not_eve)
        ...         .map(str.upper)
        ...         .collect()
        ... )
        ['BOB', 'ALICE']
        
        
        
        .. _Iter.starfilter:
        
        
        |cool| ``Iter.starfilter(self, function: "Optional[Callable[[T, ...], bool]]" = None) -> "Iter[T]"``
        ====================================================================================================
        
        
        Like Iter.filter_, but arg unpacking in lambdas will work.
        
        With the normal ``filter``, this fails:
        
        >>> Iter('caleb').enumerate().filter(lambda i, x: i > 2).collect()
        Traceback (most recent call last):
            ...
        TypeError: <lambda>() missing 1 required positional argument: 'x'
        
        This is a real buzzkill. ``starfilter`` is very to ``starmap`` in
        that tuples are unpacked when calling the function:
        
        >>> Iter('caleb').enumerate().starfilter(lambda i, x: i > 2).collect()
        [(3, 'e'), (4, 'b')]
        
        
        
        .. _Iter.reduce:
        
        
        |sink| ``Iter.reduce(self, func: Callable[..., T], *args) -> "T"``
        ==================================================================
        
        
        The ``reduce`` function you all know and...hang on, actually
        ``reduce is rather unloved. In the past I've found it very complex
        to reason about, when looking at a bunch of nested function calls
        in typical ``itertools`` code. Hopefully iterable chaining makes
        it easier to read code that uses ``reduce``?
        
        Let's check, does this make sense?
        
        >>> payments = [
        ...     ('bob', 100),
        ...     ('alice', 50),
        ...     ('eve', -100),
        ...     ('bob', 19.95),
        ...     ('bob', -5.50),
        ...     ('eve', 11.95),
        ...     ('eve', 200),
        ...     ('alice', -45),
        ...     ('alice', -67),
        ...     ('bob', 1.99),
        ...     ('alice', 89),
        ... ]
        >>> (
        ...     Iter(payments)
        ...         .filter(lambda entry: entry[0] == 'bob')
        ...         .map(lambda entry: entry[1])
        ...         .reduce(lambda total, value: total + value, 0)
        ... )
        116.44
        
        I intentionally omitted comments above so that you can try the
        "readability experiment", but in practice you would definitely
        want to add some comments on these chains:
        
        >>> (
        ...     # Iterate over all payments
        ...     Iter(payments)
        ...         # Only look at bob's payments
        ...         .filter(lambda entry: entry[0] == 'bob')
        ...         # Extract the value of the payment
        ...         .map(lambda entry: entry[1])
        ...         # Add all those payments together
        ...         .reduce(lambda total, value: total + value, 0)
        ... )
        116.44
        
        ``reduce`` is a quite crude low-level tool. In many cases you'll
        find that there are other functions and methods better suited
        to the situations you'll encounter most often. For example, it's
        much easier to use Iter.groupby_ for grouping than to try to
        make that work with Iter.reduce_. You *can* make it work but it'll
        be easier to use Iter.groupby_.
        
        
        
        .. _Iter.starreduce:
        
        
        |sink| ``Iter.starreduce(self, function: Callable[..., T], initializer=0) -> "T"``
        ==================================================================================
        
        
        Iter.starreduce_ is the same as Iter.reduce_ except that args are
        star-unpacked when passed into ``function``. This is frequently
        more convenient than the default behaviour.
        
        We can see this using the same example shown for Iter.reduce_.
        The star unpacking makes it easier to just do the filtering
        directly inside the reducer function.
        
        >>> payments = [
        ...     ('bob', 100),
        ...     ('alice', 50),
        ...     ('eve', -100),
        ...     ('bob', 19.95),
        ...     ('bob', -5.50),
        ...     ('eve', 11.95),
        ...     ('eve', 200),
        ...     ('alice', -45),
        ...     ('alice', -67),
        ...     ('bob', 1.99),
        ...     ('alice', 89),
        ... ]
        >>> (
        ...     Iter(payments)
        ...         .starreduce(
        ...             lambda tot, name, value: tot + value if name == 'bob' else tot,
        ...             0
        ...         )
        ... )
        116.44
        
        This is how that looks if you avoid a lambda:
        
        >>> def f(tot, name, value):
        ...     if name == 'bob':
        ...         return tot + value
        ...     else:
        ...         return tot
        >>> Iter(payments).starreduce(f)
        116.44
        
        
        
        .. _Iter.sum:
        
        
        |sink| ``Iter.sum(self)``
        =========================
        
        
        Exactly what you expect:
        
        >>> Iter(range(10)).sum()
        45
        
        
        
        .. _Iter.concat:
        
        
        |sink| ``Iter.concat(self, glue: AnyStr = '') -> "AnyStr"``
        ===========================================================
        
        
        
        Joining strings.
        
        >>> Iter(['hello', 'there']).concat()
        'hellothere'
        >>> Iter(['hello', 'there']).concat(' ')
        'hello there'
        >>> Iter(['hello', 'there']).concat(',')
        'hello,there'
        >>> Iter([b'hello', b'there']).concat(b',')
        b'hello,there'
        
        
        
        .. _Iter.insert:
        
        
        ``Iter.insert(self, glue: C) -> "Iter[Union[C, T]]"``
        =====================================================
        Docstring TBD
        
        
        .. _Iter.count:
        
        
        |source| ``@classmethod Iter.count(cls, *args) -> "Iter[int]"``
        ===============================================================
        
        
        
        >>> Iter.count().take(3).collect()
        [0, 1, 2]
        >>> Iter.count(100).take(3).collect()
        [100, 101, 102]
        >>> Iter.count(100, 2).take(3).collect()
        [100, 102, 104]
        
        
        
        .. _Iter.cycle:
        
        
        |inf| ``Iter.cycle(self) -> "Iter[T]"``
        =======================================
        
        
        
        .. code-block:: python
        
            >>> Iter('abc').cycle().take(8).collect()
            ['a', 'b', 'c', 'a', 'b', 'c', 'a', 'b']
            >>> Iter('abc').cycle().take(8).concat('')
            'abcabcab'
        
        
        
        .. _Iter.repeat:
        
        
        |source| |inf| ``@classmethod Iter.repeat(cls, elem: C, times=None) -> "Iter[C]"``
        ==================================================================================
        
        
        
        
        .. code-block:: python
        
            >>> Iter.repeat('c', times=3).collect()
            ['c', 'c', 'c']
        
        
        
        .. _Iter.accumulate:
        
        
        ``Iter.accumulate(self, func=None, *, initial=None)``
        =====================================================
        Docstring TBD
        
        .. code-block:: python
        
            >>> Iter([1, 2, 3, 4, 5]).accumulate().collect()
            [1, 3, 6, 10, 15]
            >>> if sys.version_info >= (3, 8):
            ...     out = Iter([1, 2, 3, 4, 5]).accumulate(initial=100).collect()
            ...     assert out == [100, 101, 103, 106, 110, 115]
            >>> Iter([1, 2, 3, 4, 5]).accumulate(operator.mul).collect()
            [1, 2, 6, 24, 120]
        
        
        
        .. _Iter.chain:
        
        
        ``Iter.chain(self, *iterables: Iterable[T]) -> "Iter[T]"``
        ==========================================================
        Docstring TBD
        
        .. code-block:: python
        
            >>> Iter('ABC').chain('DEF').collect()
            ['A', 'B', 'C', 'D', 'E', 'F']
            >>> Iter('ABC').chain().collect()
            ['A', 'B', 'C']
        
        
        
        .. _Iter.chain_from_iterable:
        
        
        ``Iter.chain_from_iterable(self) -> "Iter[T]"``
        ===============================================
        Docstring TBD
        
        .. code-block:: python
        
            >>> Iter(['ABC', 'DEF']).chain_from_iterable().collect()
            ['A', 'B', 'C', 'D', 'E', 'F']
        
        
        
        .. _Iter.compress:
        
        
        ``Iter.compress(self, selectors)``
        ==================================
        Replacement for the itertools ``compress`` function.  This version returns
        an instance of Iter_ to allow further iterable chaining.
        
        .. code-block:: python
        
            >>> Iter('ABCDEF').compress([1, 0, 1, 0, 1, 1]).collect()
            ['A', 'C', 'E', 'F']
        
        
        
        .. _Iter.dropwhile:
        
        
        ``Iter.dropwhile(self, pred)``
        ==============================
        Docstring TBD
        
        
        .. _Iter.filterfalse:
        
        
        ``Iter.filterfalse(self, pred)``
        ================================
        Docstring TBD
        
        
        .. _Iter.groupby:
        
        
        ``Iter.groupby(self, key=None)``
        ================================
        Docstring TBD
        
        
        .. _Iter.islice:
        
        
        ``Iter.islice(self, *args) -> "Iter"``
        ======================================
        Docstring TBD
        
        
        .. _Iter.starmap:
        
        
        ``Iter.starmap(self, func)``
        ============================
        Docstring TBD
        
        
        .. _Iter.takewhile:
        
        
        ``Iter.takewhile(self, pred)``
        ==============================
        Docstring TBD
        
        
        .. _Iter.tee:
        
        
        ``Iter.tee(self, n=2)``
        =======================
        Docstring TBD
        
        
        .. _Iter.zip_longest:
        
        
        ``Iter.zip_longest(self, *iterables, fillvalue=None)``
        ======================================================
        Docstring TBD
        
        
        .. _Iter.chunked:
        
        
        ``Iter.chunked(self, n: int) -> "Iter"``
        ========================================
        Docstring TBD
        
        
        .. _Iter.ichunked:
        
        
        ``Iter.ichunked(self, n: int) -> "Iter"``
        =========================================
        Docstring TBD
        
        
        .. _Iter.sliced:
        
        
        ``@classmethod Iter.sliced(cls, seq: Sequence, n: int) -> "Iter"``
        ==================================================================
        Docstring TBD
        
        
        .. _Iter.distribute:
        
        
        ``Iter.distribute(self, n: int) -> "Iter"``
        ===========================================
        Docstring TBD
        
        
        .. _Iter.divide:
        
        
        ``Iter.divide(self, n: int) -> "Iter"``
        =======================================
        Docstring TBD
        
        
        .. _Iter.split_at:
        
        
        ``Iter.split_at(self, pred)``
        =============================
        Docstring TBD
        
        
        .. _Iter.split_before:
        
        
        ``Iter.split_before(self, pred)``
        =================================
        Docstring TBD
        
        
        .. _Iter.split_after:
        
        
        ``Iter.split_after(self, pred)``
        ================================
        Docstring TBD
        
        
        .. _Iter.split_into:
        
        
        ``Iter.split_into(self, sizes)``
        ================================
        Docstring TBD
        
        
        .. _Iter.split_when:
        
        
        ``Iter.split_when(self, pred)``
        ===============================
        Docstring TBD
        
        
        .. _Iter.bucket:
        
        
        ``Iter.bucket(self, key, validator=None)``
        ==========================================
        Docstring TBD
        
        
        .. _Iter.unzip:
        
        
        ``Iter.unzip(self)``
        ====================
        Docstring TBD
        
        
        .. _Iter.grouper:
        
        
        ``Iter.grouper(self, n: int, fillvalue=None) -> "Iter"``
        ========================================================
        Docstring TBD
        
        
        .. _Iter.partition:
        
        
        ``Iter.partition(self, pred) -> "Iter"``
        ========================================
        Docstring TBD
        
        
        .. _Iter.spy:
        
        
        ``Iter.spy(self, n=1) -> "Tuple[Iter, Iter]"``
        ==============================================
        Docstring TBD
        
        
        .. _Iter.peekable:
        
        
        ``Iter.peekable(self) -> "more_itertools.peekable"``
        ====================================================
        
        Docstring TBD
        
        >>> p = Iter(['a', 'b']).peekable()
        >>> p.peek()
        'a'
        >>> next(p)
        'a'
        
        The peekable can be used to inspect what will be coming up.
        But if you then want to resume iterator chaining, pass the
        peekable back into an Iter_ instance.
        
        >>> p = Iter(range(10)).peekable()
        >>> p.peek()
        0
        >>> Iter(p).take(3).collect()
        [0, 1, 2]
        
        A peekable is not an Iter_ instance so it doesn't provide
        the iterator chaining methods. But if you want to get into
        chaining, use the ``iter()`` method.
        
        >>> p = Iter(range(5)).peekable()
        >>> p.peek()
        0
        >>> p[1]
        1
        >>> p.iter().take(3).collect()
        [0, 1, 2]
        
        Peekables can be prepended. But then you usually want to go
        right back to iterator chaining. Thus, the ``prepend`` method
        (on the returned ``peekable`` instance) returns an Iter_ instance.
        
        >>> p = Iter(range(3)).peekable()
        >>> p.peek()
        0
        >>> p.prepend('a', 'b').take(4).collect()
        ['a', 'b', 0, 1]
        
        
        
        .. _Iter.seekable:
        
        
        ``Iter.seekable(self) -> "more_itertools.seekable"``
        ====================================================
        Docstring TBD
        
        
        .. _Iter.windowed:
        
        
        ``Iter.windowed(self, n, fillvalue=None, step=1) -> "Iter"``
        ============================================================
        Docstring TBD
        
        
        .. _Iter.substrings:
        
        
        ``Iter.substrings(self)``
        =========================
        Docstring TBD
        
        
        .. _Iter.substrings_indexes:
        
        
        ``Iter.substrings_indexes(self, reverse=False)``
        ================================================
        Docstring TBD
        
        
        .. _Iter.stagger:
        
        
        ``Iter.stagger(self, offsets=(-1, 0, 1), longest=False, fillvalue=None)``
        =========================================================================
        
        .. code-block:: python
        
            >>> Iter([0, 1, 2, 3]).stagger().collect()
            [(None, 0, 1), (0, 1, 2), (1, 2, 3)]
            >>> Iter(range(8)).stagger(offsets=(0, 2, 4)).collect()
            [(0, 2, 4), (1, 3, 5), (2, 4, 6), (3, 5, 7)]
            >>> Iter([0, 1, 2, 3]).stagger(longest=True).collect()
            [(None, 0, 1), (0, 1, 2), (1, 2, 3), (2, 3, None), (3, None, None)]
        
        
        
        .. _Iter.pairwise:
        
        
        ``Iter.pairwise(self)``
        =======================
        
        Reference `more_itertools.pairwise <https://more-itertools.readthedocs.io/en/stable/api.html#more_itertools.pairwise>`_
        
        .. code-block:: python
        
            >>> Iter.count().pairwise().take(4).collect()
            [(0, 1), (1, 2), (2, 3), (3, 4)]
        
        
        .. _Iter.count_cycle:
        
        
        ``Iter.count_cycle(self, n=None) -> "Iter"``
        ============================================
        
        
        Reference: `more_itertools.count_cycle <https://more-itertools.readthedocs.io/en/stable/api.html#more_itertools.count_cycle>`_
        
        .. code-block:: python
        
            >>> Iter('AB').count_cycle(3).collect()
            [(0, 'A'), (0, 'B'), (1, 'A'), (1, 'B'), (2, 'A'), (2, 'B')]
        
        
        
        .. _Iter.intersperse:
        
        
        ``Iter.intersperse(self, e, n=1) -> "Iter"``
        ============================================
        
        Reference: `more_itertools.intersperse <https://more-itertools.readthedocs.io/en/stable/api.html#more_itertools.intersperse>`_
        
        .. code-block:: python
        
            >>> Iter([1, 2, 3, 4, 5]).intersperse('!').collect()
            [1, '!', 2, '!', 3, '!', 4, '!', 5]
        
            >>> Iter([1, 2, 3, 4, 5]).intersperse(None, n=2).collect()
            [1, 2, None, 3, 4, None, 5]
        
        
        
        .. _Iter.padded:
        
        
        ``Iter.padded(self, fillvalue: Optional[C] = None, n: Optional[int] = None, next_multiple: bool = False, ) -> "Iter[Union[T, C]]"``
        ===================================================================================================================================
        
        Reference: `more_itertools.padded <https://more-itertools.readthedocs.io/en/stable/api.html#more_itertools.padded>`_
        
        .. code-block:: python
        
            >>> Iter([1, 2, 3]).padded('?', 5).collect()
            [1, 2, 3, '?', '?']
        
            >>> Iter([1, 2, 3, 4]).padded(n=3, next_multiple=True).collect()
            [1, 2, 3, 4, None, None]
        
        
        
        .. _Iter.repeat_last:
        
        
        ``Iter.repeat_last(self, default=None) -> "Iter[T]"``
        =====================================================
        
        Reference: `more_itertools.repeat_last <https://more-itertools.readthedocs.io/en/stable/api.html#more_itertools.repeat_last>`_
        
        .. code-block:: python
        
            >>> Iter(range(3)).repeat_last().islice(5).collect()
            [0, 1, 2, 2, 2]
        
            >>> Iter(range(0)).repeat_last(42).islice(5).collect()
            [42, 42, 42, 42, 42]
        
        
        
        .. _Iter.adjacent:
        
        
        ``Iter.adjacent(self, pred, distance=1) -> "Iter[Tuple[bool, T]]"``
        ===================================================================
        
        Reference: `more_itertools.adjacent <https://more-itertools.readthedocs.io/en/stable/api.html#more_itertools.adjacent>`_
        
        .. code-block:: python
        
            >>> Iter(range(6)).adjacent(lambda x: x == 3).collect()
            [(False, 0), (False, 1), (True, 2), (True, 3), (True, 4), (False, 5)]
        
            >>> Iter(range(6)).adjacent(lambda x: x == 3, distance=2).collect()
            [(False, 0), (True, 1), (True, 2), (True, 3), (True, 4), (True, 5)]
        
        
        
        
        .. _Iter.groupby_transform:
        
        
        ``Iter.groupby_transform(self, keyfunc: Optional[Callable[..., K]] = None, valuefunc: Optional[Callable[..., V]] = None, ) -> "Iter[Tuple[K, Iterable[V]]]"``
        =============================================================================================================================================================
        
        Reference: `more_itertools.groupby_transform <https://more-itertools.readthedocs.io/en/stable/api.html#more_itertools.groupby_transform>`_
        
        This example has been modified somewhat from the original. We're using
        ``starmap`` here to "unzip" the tuples produced by the group
        transform.
        
        .. code-block:: python
        
            >>> iterable = 'AaaABbBCcA'
            >>> keyfunc = lambda x: x.upper()
            >>> valuefunc = lambda x: x.lower()
            >>> (
            ...    Iter(iterable)
            ...        .groupby_transform(keyfunc, valuefunc)
            ...        .starmap(lambda k, g: (k, ''.join(g)))
            ...        .collect()
            ... )
            [('A', 'aaaa'), ('B', 'bbb'), ('C', 'cc'), ('A', 'a')]
        
            >>> from operator import itemgetter
            >>> keys = [0, 0, 1, 1, 1, 2, 2, 2, 3]
            >>> values = 'abcdefghi'
            >>> iterable = zip(keys, values)
            >>> (
            ...     Iter(iterable)
            ...        .groupby_transform(itemgetter(0), itemgetter(1))
            ...        .starmap(lambda k, g: (k, ''.join(g)))
            ...        .collect()
            ... )
            [(0, 'ab'), (1, 'cde'), (2, 'fgh'), (3, 'i')]
        
        
        
        .. _Iter.padnone:
        
        
        ``Iter.padnone(self) -> "Iter[Union[T, None]]"``
        ================================================
        
        Reference: `more_itertools.padnone <https://more-itertools.readthedocs.io/en/stable/api.html#more_itertools.padnone>`_
        
        .. code-block:: python
        
            >>> Iter(range(3)).padnone().take(5).collect()
            [0, 1, 2, None, None]
        
        
        
        .. _Iter.ncycles:
        
        
        ``Iter.ncycles(self, n) -> "Iter[T]"``
        ======================================
        
        Reference: `more_itertools.ncycles <https://more-itertools.readthedocs.io/en/stable/api.html#more_itertools.ncycles>`_
        
        .. code-block:: python
        
            >>> Iter(['a', 'b']).ncycles(3).collect()
            ['a', 'b', 'a', 'b', 'a', 'b']
        
        
        
        .. _Iter.collapse:
        
        
        ``Iter.collapse(self, base_type=None, levels=None) -> "Iter"``
        ==============================================================
        
        Reference: `more_itertools.collapse <https://more-itertools.readthedocs.io/en/stable/api.html#more_itertools.collapse>`_
        
        .. code-block:: python
        
            >>> iterable = [(1, 2), ([3, 4], [[5], [6]])]
            >>> Iter(iterable).collapse().collect()
            [1, 2, 3, 4, 5, 6]
        
            >>> iterable = ['ab', ('cd', 'ef'), ['gh', 'ij']]
            >>> Iter(iterable).collapse(base_type=tuple).collect()
            ['ab', ('cd', 'ef'), 'gh', 'ij']
        
            >>> iterable = [('a', ['b']), ('c', ['d'])]
            >>> Iter(iterable).collapse().collect() # Fully flattened
            ['a', 'b', 'c', 'd']
            >>> Iter(iterable).collapse(levels=1).collect() # Only one level flattened
            ['a', ['b'], 'c', ['d']]
        
        
        
        .. _Iter.sort_together:
        
        
        ``@class_or_instancemethod Iter.sort_together(self_or_cls, iterables, key_list=(0,), reverse=False)``
        =====================================================================================================
        
        Reference: `more_itertools.sort_together <https://more-itertools.readthedocs.io/en/stable/api.html#more_itertools.sort_together>`_
        
        This can be called either as an instance method or a class method.
        The classmethod form is more convenient if all the iterables are
        already available. The instancemethod form is more convenient if
        one of the iterables already goes through some transformation.
        
        Here are examples from the classmethod form, which mirror the
        examples in the more-itertools_ documentation:
        
        .. code-block:: python
        
            >>> iterables = [(4, 3, 2, 1), ('a', 'b', 'c', 'd')]
            >>> Iter.sort_together(iterables).collect()
            [(1, 2, 3, 4), ('d', 'c', 'b', 'a')]
        
            >>> iterables = [(3, 1, 2), (0, 1, 0), ('c', 'b', 'a')]
            >>> Iter.sort_together(iterables, key_list=(1, 2)).collect()
            [(2, 3, 1), (0, 0, 1), ('a', 'c', 'b')]
        
            >>> Iter.sort_together([(1, 2, 3), ('c', 'b', 'a')], reverse=True).collect()
            [(3, 2, 1), ('a', 'b', 'c')]
        
        Here is an examples using the instancemethod form:
        
            >>> iterables = [('a', 'b', 'c', 'd')]
            >>> Iter([4, 3, 2, 1]).sort_together(iterables).collect()
            [(1, 2, 3, 4), ('d', 'c', 'b', 'a')]
        
        
        
        .. _Iter.interleave:
        
        
        ``@class_or_instancemethod Iter.interleave(self_or_cls, *iterables) -> "Iter"``
        ===============================================================================
        
        Reference: `more_itertools.interleave <https://more-itertools.readthedocs.io/en/stable/api.html#more_itertools.interleave>`_
        
        Classmethod form:
        
        .. code-block:: python
        
            >>> Iter.interleave([1, 2, 3], [4, 5], [6, 7, 8]).collect()
            [1, 4, 6, 2, 5, 7]
        
        Instancemethod form:
        
        .. code-block:: python
        
            >>> Iter([1, 2, 3]).interleave([4, 5], [6, 7, 8]).collect()
            [1, 4, 6, 2, 5, 7]
        
        
        
        .. _Iter.interleave_longest:
        
        
        ``@class_or_instancemethod Iter.interleave_longest(self_or_cls, *iterables) -> "Iter"``
        =======================================================================================
        
        Reference: `more_itertools.interleave_longest <https://more-itertools.readthedocs.io/en/stable/api.html#more_itertools.interleave_longest>`_
        
        Classmethod form:
        
        .. code-block:: python
        
            >>> Iter.interleave_longest([1, 2, 3], [4, 5], [6, 7, 8]).collect()
            [1, 4, 6, 2, 5, 7, 3, 8]
        
        Instancemethod form:
        
        .. code-block:: python
        
            >>> Iter([1, 2, 3]).interleave_longest([4, 5], [6, 7, 8]).collect()
            [1, 4, 6, 2, 5, 7, 3, 8]
        
        
        
        .. _Iter.zip_offset:
        
        
        ``@classmethod Iter.zip_offset(cls, *iterables, offsets, longest=False, fillvalue=None) -> "Iter"``
        ===================================================================================================
        
        Reference: `more_itertools.zip_offset <https://more-itertools.readthedocs.io/en/stable/api.html#more_itertools.zip_offset>`_
        
        .. code-block:: python
        
            >>> Iter.zip_offset('0123', 'abcdef', offsets=(0, 1)).collect()
            [('0', 'b'), ('1', 'c'), ('2', 'd'), ('3', 'e')]
        
            >>> Iter.zip_offset('0123', 'abcdef', offsets=(0, 1), longest=True).collect()
            [('0', 'b'), ('1', 'c'), ('2', 'd'), ('3', 'e'), (None, 'f')]
        
        
        .. _Iter.dotproduct:
        
        
        ``Iter.dotproduct(self, vec2: Iterable)``
        =========================================
        
        Reference: `more_itertools.dotproduct <https://more-itertools.readthedocs.io/en/stable/api.html#more_itertools.dotproduct>`_
        
        .. code-block:: python
        
            >>> Iter([10, 10]).dotproduct([20, 20])
            400
        
        
        .. _Iter.flatten:
        
        
        ``Iter.flatten(self) -> "Iter[T]"``
        ===================================
        
        Reference: `more_itertools.flatten <https://more-itertools.readthedocs.io/en/stable/api.html#more_itertools.flatten>`_
        
        .. code-block:: python
        
            >>> Iter([[0, 1], [2, 3]]).flatten().collect()
            [0, 1, 2, 3]
        
        
        
        .. _Iter.roundrobin:
        
        
        ``@class_or_instancemethod Iter.roundrobin(self_or_cls: Union[Type[T], T], *iterables: C) -> "Iter[Union[T, C]]"``
        ==================================================================================================================
        
        Reference: `more_itertools.roundrobin <https://more-itertools.readthedocs.io/en/stable/api.html#more_itertools.roundrobin>`_
        
        Classmethod form:
        
        .. code-block:: python
        
            >>> Iter.roundrobin('ABC', 'D', 'EF').collect()
            ['A', 'D', 'E', 'B', 'F', 'C']
        
        Instancemethod form:
        
        .. code-block:: python
        
            >>> Iter('ABC').roundrobin('D', 'EF').collect()
            ['A', 'D', 'E', 'B', 'F', 'C']
        
        
        
        .. _Iter.prepend:
        
        
        ``Iter.prepend(self, value: C) -> "Iter[Union[T, C]]"``
        =======================================================
        
        Reference: `more_itertools.prepend <https://more-itertools.readthedocs.io/en/stable/api.html#more_itertools.prepend>`_
        
        .. code-block:: python
        
            >>> value = '0'
            >>> iterator = ['1', '2', '3']
            >>> Iter(iterator).prepend(value).collect()
            ['0', '1', '2', '3']
        
        
        
        .. _Iter.ilen:
        
        
        |sink| ``Iter.ilen(self) -> "int"``
        ===================================
        
        
        
        Reference: `more_itertools.ilen <https://more-itertools.readthedocs.io/en/stable/api.html#more_itertools.ilen>`_
        
        .. code-block:: python
        
            >>> Iter(x for x in range(1000000) if x % 3 == 0).ilen()
            333334
        
        
        
        .. _Iter.unique_to_each:
        
        
        ``Iter.unique_to_each(self) -> "Iter[T]"``
        ==========================================
        
        Reference: `more_itertools.unique_to_each <https://more-itertools.readthedocs.io/en/stable/api.html#more_itertools.unique_to_each>`_
        
        .. code-block:: python
        
            >>> Iter([{'A', 'B'}, {'B', 'C'}, {'B', 'D'}]).unique_to_each().collect()
            [['A'], ['C'], ['D']]
        
            >>> Iter(["mississippi", "missouri"]).unique_to_each().collect()
            [['p', 'p'], ['o', 'u', 'r']]
        
        
        .. _Iter.sample:
        
        
        ``Iter.sample(self, k=1, weights=None) -> "Iter"``
        ==================================================
        
        Reference: `more_itertools.sample <https://more-itertools.readthedocs.io/en/stable/api.html#more_itertools.sample>`_
        
        .. code-block:: python
        
            >>> iterable = range(100)
            >>> Iter(iterable).sample(5).collect()                  
            [81, 60, 96, 16, 4]
        
            >>> iterable = range(100)
            >>> weights = (i * i + 1 for i in range(100))
            >>> Iter(iterable).sample(5, weights=weights)                  
            [79, 67, 74, 66, 78]
        
            >>> data = "abcdefgh"
            >>> weights = range(1, len(data) + 1)
            >>> Iter(data).sample(k=len(data), weights=weights)                  
            ['c', 'a', 'b', 'e', 'g', 'd', 'h', 'f']
        
        
            >>> # This one just to let the doctest run
            >>> iterable = range(100)
            >>> Iter(iterable).sample(5).map(lambda x: 0 <= x < 100).all()
            True
        
        
        
        .. _Iter.consecutive_groups:
        
        
        ``Iter.consecutive_groups(self, ordering=lambda x: x)``
        =======================================================
        
        Reference: `more_itertools.consecutive_groups <https://more-itertools.readthedocs.io/en/stable/api.html#more_itertools.consecutive_groups>`_
        
        .. code-block:: python
        
            >>> iterable = [1, 10, 11, 12, 20, 30, 31, 32, 33, 40]
            >>> Iter(iterable).consecutive_groups().map(lambda g: list(g)).print('{v}').consume()
            [1]
            [10, 11, 12]
            [20]
            [30, 31, 32, 33]
            [40]
        
        
        
        .. _Iter.run_length_encode:
        
        
        ``Iter.run_length_encode(self) -> "Iter[Tuple[T, int]]"``
        =========================================================
        
        Reference: `more_itertools.run_length <https://more-itertools.readthedocs.io/en/stable/api.html#more_itertools.run_length>`_
        
        .. code-block:: python
        
            >>> uncompressed = 'abbcccdddd'
            >>> Iter(uncompressed).run_length_encode().collect()
            [('a', 1), ('b', 2), ('c', 3), ('d', 4)]
        
        
        
        .. _Iter.run_length_decode:
        
        
        ``Iter.run_length_decode(self) -> "Iter"``
        ==========================================
        
        Reference: `more_itertools.run_length <https://more-itertools.readthedocs.io/en/stable/api.html#more_itertools.run_length>`_
        
        .. code-block:: python
        
            >>> compressed = [('a', 1), ('b', 2), ('c', 3), ('d', 4)]
            >>> Iter(compressed).run_length_decode().collect()
            ['a', 'b', 'b', 'c', 'c', 'c', 'd', 'd', 'd', 'd']
        
        
        
        .. _Iter.map_reduce:
        
        
        ``Iter.map_reduce(self, keyfunc, valuefunc=None, reducefunc=None) -> "Dict"``
        =============================================================================
        
        Reference: `more_itertools.map_reduce <https://more-itertools.readthedocs.io/en/stable/api.html#more_itertools.map_reduce>`_
        
        This interface mirrors what more-itertools_ does in that it returns
        a dict. See ``map_reduce_it()`` for a slightly-modified interface
        that returns the dict items as another iterator.
        
        .. code-block:: python
        
            >>> keyfunc = lambda x: x.upper()
            >>> d = Iter('abbccc').map_reduce(keyfunc)
            >>> sorted(d.items())
            [('A', ['a']), ('B', ['b', 'b']), ('C', ['c', 'c', 'c'])]
        
            >>> keyfunc = lambda x: x.upper()
            >>> valuefunc = lambda x: 1
            >>> d = Iter('abbccc').map_reduce(keyfunc, valuefunc)
            >>> sorted(d.items())
            [('A', [1]), ('B', [1, 1]), ('C', [1, 1, 1])]
        
            >>> keyfunc = lambda x: x.upper()
            >>> valuefunc = lambda x: 1
            >>> reducefunc = sum
            >>> d = Iter('abbccc').map_reduce(keyfunc, valuefunc, reducefunc)
            >>> sorted(d.items())
            [('A', 1), ('B', 2), ('C', 3)]
        
        Note the warning given in the more-itertools_ docs about how
        lists are created before the reduce step. This means you always want
        to filter *before* applying map_reduce, not after.
        
        .. code-block:: python
        
            >>> all_items = _range(30)
            >>> keyfunc = lambda x: x % 2  # Evens map to 0; odds to 1
            >>> categories = Iter(all_items).filter(lambda x: 10<=x<=20).map_reduce(keyfunc=keyfunc)
            >>> sorted(categories.items())
            [(0, [10, 12, 14, 16, 18, 20]), (1, [11, 13, 15, 17, 19])]
            >>> summaries = Iter(all_items).filter(lambda x: 10<=x<=20).map_reduce(keyfunc=keyfunc, reducefunc=sum)
            >>> sorted(summaries.items())
            [(0, 90), (1, 75)]
        
        
        
        .. _Iter.map_reduce_it:
        
        
        ``Iter.map_reduce_it(self, keyfunc: Callable[..., K], valuefunc: Optional[Callable[..., V]] = None, reducefunc: Optional[Callable[..., R]] = None, ) -> "Iter[Tuple[K, R]]"``
        =============================================================================================================================================================================
        
        Reference: `more_itertools.map_reduce <https://more-itertools.readthedocs.io/en/stable/api.html#more_itertools.map_reduce>`_
        
        .. code-block:: python
        
            >>> keyfunc = lambda x: x.upper()
            >>> Iter('abbccc').map_reduce_it(keyfunc).collect()
            [('A', ['a']), ('B', ['b', 'b']), ('C', ['c', 'c', 'c'])]
        
            >>> keyfunc = lambda x: x.upper()
            >>> valuefunc = lambda x: 1
            >>> Iter('abbccc').map_reduce_it(keyfunc, valuefunc).collect()
            [('A', [1]), ('B', [1, 1]), ('C', [1, 1, 1])]
        
            >>> keyfunc = lambda x: x.upper()
            >>> valuefunc = lambda x: 1
            >>> reducefunc = sum
            >>> Iter('abbccc').map_reduce_it(keyfunc, valuefunc, reducefunc).collect()
            [('A', 1), ('B', 2), ('C', 3)]
        
        
        
        .. _Iter.exactly_n:
        
        
        |sink| ``Iter.exactly_n(self, n, predicate=bool) -> "bool"``
        ============================================================
        
        
        
        Docstring TBD
        
        .. code-block:: python
        
            >>> Iter([True, True, False]).exactly_n(2)
            True
        
        
        
        .. _Iter.all_equal:
        
        
        ``Iter.all_equal(self)``
        ========================
        
        .. _Iter.first_true:
        
        
        ``Iter.first_true(self)``
        =========================
        
        .. _Iter.quantify:
        
        
        ``Iter.quantify(self)``
        =======================
        
        .. _Iter.islice_extended:
        
        
        ``Iter.islice_extended(self, *args)``
        =====================================
        
        Reference: `more_itertools.islice_extended <https://more-itertools.readthedocs.io/en/stable/api.html#more_itertools.islice_extended>`_
        
        .. code-block:: python
        
            >>> Iter('abcdefgh').islice_extended(-4, -1).collect()
            ['e', 'f', 'g']
        
        .. code-block:: python
        
            >>> Iter.count().islice_extended( 110, 99, -2).collect()
            [110, 108, 106, 104, 102, 100]
        
        
        
        .. _Iter.first:
        
        
        ``Iter.first(self)``
        ====================
        
        Reference: `more_itertools.first <https://more-itertools.readthedocs.io/en/stable/api.html#more_itertools.first>`_
        
        
        .. _Iter.last:
        
        
        ``Iter.last(self)``
        ===================
        
        Reference: `more_itertools.last <https://more-itertools.readthedocs.io/en/stable/api.html#more_itertools.last>`_
        
        
        .. _Iter.one:
        
        
        ``Iter.one(self)``
        ==================
        
        Reference: `more_itertools.one <https://more-itertools.readthedocs.io/en/stable/api.html#more_itertools.one>`_
        
        
        
        .. _Iter.only:
        
        
        ``Iter.only(self, default=None, too_long=ValueError) -> "T"``
        =============================================================
        
        Reference: `more_itertools.one <https://more-itertools.readthedocs.io/en/stable/api.html#more_itertools.one>`_
        
        .. code-block:: python
        
            >>> Iter([]).only(default='missing')
            'missing'
            >>> Iter([42]).only(default='missing')
            42
            >>> Iter([1, 2]).only()
            Traceback (most recent call last):
                ...
            ValueError: ...
        
        
        
        .. _Iter.strip:
        
        
        ``Iter.strip(self, pred) -> "Iter[T]"``
        =======================================
        
        Reference: `more_itertools.strip <https://more-itertools.readthedocs.io/en/stable/api.html#more_itertools.strip>`_
        
        .. code-block:: python
        
            >>> iterable = (None, False, None, 1, 2, None, 3, False, None)
            >>> pred = lambda x: x in {None, False, ''}
            >>> Iter(iterable).strip(pred).collect()
            [1, 2, None, 3]
        
        
        
        .. _Iter.lstrip:
        
        
        ``Iter.lstrip(self, pred) -> "Iter[T]"``
        ========================================
        
        Reference: `more_itertools.lstrip <https://more-itertools.readthedocs.io/en/stable/api.html#more_itertools.lstrip>`_
        
        .. code-block:: python
        
            >>> iterable = (None, False, None, 1, 2, None, 3, False, None)
            >>> pred = lambda x: x in {None, False, ''}
            >>> Iter(iterable).lstrip(pred).collect()
            [1, 2, None, 3, False, None]
        
        
        
        .. _Iter.rstrip:
        
        
        ``Iter.rstrip(self, pred) -> "Iter[T]"``
        ========================================
        
        Reference: `more_itertools.rstrip <https://more-itertools.readthedocs.io/en/stable/api.html#more_itertools.rstrip>`_
        
        .. code-block:: python
        
            >>> iterable = (None, False, None, 1, 2, None, 3, False, None)
            >>> pred = lambda x: x in {None, False, ''}
            >>> Iter(iterable).rstrip(pred).collect()
            [None, False, None, 1, 2, None, 3]
        
        
        
        .. _Iter.filter_except:
        
        
        ``Iter.filter_except(self, validator, *exceptions) -> "Iter[T]"``
        =================================================================
        
        Reference: `more_itertools.filter_except <https://more-itertools.readthedocs.io/en/stable/api.html#more_itertools.filter_except>`_
        
        .. code-block:: python
        
            >>> iterable = ['1', '2', 'three', '4', None]
            >>> Iter(iterable).filter_except(int, ValueError, TypeError).collect()
            ['1', '2', '4']
        
        
        
        .. _Iter.map_except:
        
        
        ``Iter.map_except(self, function, *exceptions) -> "Iter"``
        ==========================================================
        
        Reference: `more_itertools.map_except <https://more-itertools.readthedocs.io/en/stable/api.html#more_itertools.map_except>`_
        
        .. code-block:: python
        
            >>> iterable = ['1', '2', 'three', '4', None]
            >>> Iter(iterable).map_except(int, ValueError, TypeError).collect()
            [1, 2, 4]
        
        
        
        .. _Iter.nth_or_last:
        
        
        ``Iter.nth_or_last(self, n, default=_marker) -> "T"``
        =====================================================
        
        Reference: `more_itertools.nth_or_last <https://more-itertools.readthedocs.io/en/stable/api.html#more_itertools.nth_or_last>`_
        
        .. code-block:: python
        
            >>> Iter([0, 1, 2, 3]).nth_or_last(2)
            2
            >>> Iter([0, 1]).nth_or_last(2)
            1
            >>> Iter([]).nth_or_last(0, 'some default')
            'some default'
        
        
        
        .. _Iter.nth:
        
        
        ``Iter.nth(self, n, default=None)``
        ===================================
        
        Reference: `more_itertools.nth <https://more-itertools.readthedocs.io/en/stable/api.html#more_itertools.nth>`_
        
        
        .. _Iter.take:
        
        
        ``Iter.take(self, n: int) -> "Iter"``
        =====================================
        
        Reference: `more_itertools.take <https://more-itertools.readthedocs.io/en/stable/api.html#more_itertools.take>`_
        
        
        .. _Iter.tail:
        
        
        ``Iter.tail(self, n) -> "Iter[T]"``
        ===================================
        
        Reference: `more_itertools.tail <https://more-itertools.readthedocs.io/en/stable/api.html#more_itertools.tail>`_
        
        >>> Iter('ABCDEFG').tail(3).collect()
        ['E', 'F', 'G']
        
        
        
        .. _Iter.unique_everseen:
        
        
        ``Iter.unique_everseen(self, key=None) -> "Iter[T]"``
        =====================================================
        
        Reference: `more_itertools.unique_everseen <https://more-itertools.readthedocs.io/en/stable/api.html#more_itertools.unique_everseen>`_
        
        >>> Iter('AAAABBBCCDAABBB').unique_everseen().collect()
        ['A', 'B', 'C', 'D']
        >>> Iter('ABBCcAD').unique_everseen(key=str.lower).collect()
        ['A', 'B', 'C', 'D']
        
        Be sure to read the *more-itertools* docs whne using unhashable
        items.
        
        >>> iterable = ([1, 2], [2, 3], [1, 2])
        >>> Iter(iterable).unique_everseen().collect()  # Slow
        [[1, 2], [2, 3]]
        >>> Iter(iterable).unique_everseen(key=tuple).collect()  # Faster
        [[1, 2], [2, 3]]
        
        
        
        .. _Iter.unique_justseen:
        
        
        ``Iter.unique_justseen(self, key=None) -> "Iter[T]"``
        =====================================================
        
        Reference: `more_itertools.unique_justseen <https://more-itertools.readthedocs.io/en/stable/api.html#more_itertools.unique_justseen>`_
        
        >>> Iter('AAAABBBCCDAABBB').unique_justseen().collect()
        ['A', 'B', 'C', 'D', 'A', 'B']
        >>> Iter('ABBCcAD').unique_justseen(key=str.lower).collect()
        ['A', 'B', 'C', 'A', 'D']
        
        
        
        .. _Iter.distinct_permutations:
        
        
        ``Iter.distinct_permutations(self)``
        ====================================
        
        Reference: `more_itertools.distinct_permutations <https://more-itertools.readthedocs.io/en/stable/api.html#more_itertools.distinct_permutations>`_
        
        >>> Iter([1, 0, 1]).distinct_permutations().sorted().collect()
        [(0, 1, 1), (1, 0, 1), (1, 1, 0)]
        
        
        
        .. _Iter.distinct_combinations:
        
        
        ``Iter.distinct_combinations(self, r) -> "Iter[T]"``
        ====================================================
        
        Reference: `more_itertools.distinct_combinations <https://more-itertools.readthedocs.io/en/stable/api.html#more_itertools.distinct_combinations>`_
        
        >>> Iter([0, 0, 1]).distinct_combinations(2).collect()
        [(0, 0), (0, 1)]
        
        
        
        .. _Iter.circular_shifts:
        
        
        ``Iter.circular_shifts(self) -> "Iter[T]"``
        ===========================================
        
        Reference: `more_itertools.circular_shifts <https://more-itertools.readthedocs.io/en/stable/api.html#more_itertools.circular_shifts>`_
        
        >>> Iter(range(4)).circular_shifts().collect()
        [(0, 1, 2, 3), (1, 2, 3, 0), (2, 3, 0, 1), (3, 0, 1, 2)]
        
        
        
        .. _Iter.partitions:
        
        
        ``Iter.partitions(self) -> "Iter[T]"``
        ======================================
        
        Reference: `more_itertools.partitions <https://more-itertools.readthedocs.io/en/stable/api.html#more_itertools.partitions>`_
        
        >>> Iter('abc').partitions().collect()
        [[['a', 'b', 'c']], [['a'], ['b', 'c']], [['a', 'b'], ['c']], [['a'], ['b'], ['c']]]
        >>> Iter('abc').partitions().print('{v}').consume()
        [['a', 'b', 'c']]
        [['a'], ['b', 'c']]
        [['a', 'b'], ['c']]
        [['a'], ['b'], ['c']]
        >>> Iter('abc').partitions().map(lambda v: [''.join(p) for p in v]).print('{v}').consume()
        ['abc']
        ['a', 'bc']
        ['ab', 'c']
        ['a', 'b', 'c']
        
        
        
        .. _Iter.set_partitions:
        
        
        ``Iter.set_partitions(self, k=None) -> "Iter[T]"``
        ==================================================
        
        Reference: `more_itertools.set_partitions <https://more-itertools.readthedocs.io/en/stable/api.html#more_itertools.set_partitions>`_
        
        >>> Iter('abc').set_partitions(2).collect()
        [[['a'], ['b', 'c']], [['a', 'b'], ['c']], [['b'], ['a', 'c']]]
        
        
        
        .. _Iter.powerset:
        
        
        ``Iter.powerset(self)``
        =======================
        
        Reference: `more_itertools.powerset <https://more-itertools.readthedocs.io/en/stable/api.html#more_itertools.powerset>`_
        
        >>> Iter([1, 2, 3]).powerset().collect()
        [(), (1,), (2,), (3,), (1, 2), (1, 3), (2, 3), (1, 2, 3)]
        
        
        
        .. _Iter.random_product:
        
        
        ``@class_or_instancemethod Iter.random_product(self_or_cls, *args, repeat=1)``
        ==============================================================================
        
        Reference: `more_itertools.random_product <https://more-itertools.readthedocs.io/en/stable/api.html#more_itertools.random_product>`_
        
        >>> Iter('abc').random_product(range(4), 'XYZ').collect()                  
        ['c', 3, 'X']
        >>> Iter.random_product('abc', range(4), 'XYZ').collect()                  
        ['c', 0, 'Z']
        >>> Iter('abc').random_product(range(0)).collect()
        Traceback (most recent call last):
            ...
        IndexError: Cannot choose from an empty sequence
        >>> Iter.random_product(range(0)).collect()
        Traceback (most recent call last):
            ...
        IndexError: Cannot choose from an empty sequence
        
        
        
        .. _Iter.random_permutation:
        
        
        ``Iter.random_permutation(self, r=None)``
        =========================================
        
        Reference: `more_itertools.random_permutation <https://more-itertools.readthedocs.io/en/stable/api.html#more_itertools.random_permutation>`_
        
        >>> Iter(range(5)).random_permutation().collect()                  
        [2, 0, 4, 3, 1]
        >>> Iter(range(0)).random_permutation().collect()
        []
        
        
        
        .. _Iter.random_combination:
        
        
        ``Iter.random_combination(self, r)``
        ====================================
        
        Reference: `more_itertools.random_combination <https://more-itertools.readthedocs.io/en/stable/api.html#more_itertools.random_combination>`_
        
        >>> Iter(range(5)).random_combination(3).collect()                  
        [0, 1, 4]
        >>> Iter(range(5)).random_combination(0).collect()
        []
        
        
        
        .. _Iter.random_combination_with_replacement:
        
        
        ``Iter.random_combination_with_replacement(self, r)``
        =====================================================
        
        Reference: `more_itertools.random_combination_with_replacement <https://more-itertools.readthedocs.io/en/stable/api.html#more_itertools.random_combination_with_replacement>`_
        
        >>> Iter(range(3)).random_combination_with_replacement(5).collect()                  
        [0, 0, 1, 2, 2]
        >>> Iter(range(3)).random_combination_with_replacement(0).collect()
        []
        
        
        
        .. _Iter.nth_combination:
        
        
        ``Iter.nth_combination(self, r, index)``
        ========================================
        
        Reference: `more_itertools.nth_combination <https://more-itertools.readthedocs.io/en/stable/api.html#more_itertools.nth_combination>`_
        
        >>> Iter(range(9)).nth_combination(3, 1).collect()
        [0, 1, 3]
        >>> Iter(range(9)).nth_combination(3, 2).collect()
        [0, 1, 4]
        >>> Iter(range(9)).nth_combination(3, 3).collect()
        [0, 1, 5]
        >>> Iter(range(9)).nth_combination(4, 3).collect()
        [0, 1, 2, 6]
        >>> Iter(range(9)).nth_combination(3, 7).collect()
        [0, 2, 3]
        
        
        
        .. _Iter.always_iterable:
        
        
        ``@classmethod Iter.always_iterable(cls, obj, base_type=(str, bytes)) -> 'Iter'``
        =================================================================================
        
        Reference: `more_itertools.always_iterable <https://more-itertools.readthedocs.io/en/stable/api.html#more_itertools.always_iterable>`_
        
        .. code-block: python
        
        >>> Iter.always_iterable([1, 2, 3]).collect()
        [1, 2, 3]
        >>> Iter.always_iterable(1).collect()
        [1]
        >>> Iter.always_iterable(None).collect()
        []
        >>> Iter.always_iterable('foo').collect()
        ['foo']
        >>> Iter.always_iterable(dict(a=1), base_type=dict).collect()
        [{'a': 1}]
        
        
        
        .. _Iter.always_reversible:
        
        
        ``Iter.always_reversible(self)``
        ================================
        
        Reference: `more_itertools.always_reversible <https://more-itertools.readthedocs.io/en/stable/api.html#more_itertools.always_reversible>`_
        
        This is like ``reversed()`` but it also operates on things that
        wouldn't normally be reversible, like generators. It does this with
        internal caching, so be careful with memory use.
        
        .. code-block: python
        
        >>> Iter('abc').always_reversible().collect()
        ['c', 'b', 'a']
        >>> Iter(x for x in 'abc').always_reversible().collect()
        ['c', 'b', 'a']
        
        
        
        .. _Iter.with_iter:
        
        
        ``@classmethod Iter.with_iter(cls, context_manager)``
        =====================================================
        
        Reference: `more_itertools.with_iter <https://more-itertools.readthedocs.io/en/stable/api.html?highlight=numeric_range#more_itertools.with_iter>`_
        
        Note: Any context manager which returns an iterable is a candidate for
        Iter.with_iter_.
        
        .. code-block:: python
        
            >>> import tempfile
            >>> with tempfile.TemporaryDirectory() as td:
            ...     with open(td + 'text.txt', 'w') as f:
            ...         f.writelines(['abc\n', 'def\n', 'ghi\n'])
            ...     Iter.with_iter(open(td + 'text.txt')).map(lambda x: x.upper()).collect()
            ['ABC\n', 'DEF\n', 'GHI\n']
        
        See also: Iter.open_
        
        |flux| TODO: perhaps we should get rid of Iter.open_ and just use this?
        
        
        
        .. _Iter.iter_except:
        
        
        ``@classmethod Iter.iter_except(cls, func, exception, first=None) -> "Iter"``
        =============================================================================
        
        Reference: `more_itertools.iter_except <https://more-itertools.readthedocs.io/en/stable/api.html?highlight=numeric_range#more_itertools.iter_except>`_
        
        .. code-block:: python
        
            >>> l = [0, 1, 2]
            >>> Iter.iter_except(l.pop, IndexError).collect()
            [2, 1, 0]
        
        
        
        .. _Iter.locate:
        
        
        ``Iter.locate(self, pred=bool, window_size=None) -> "Iter"``
        ============================================================
        
        Reference: `more_itertools.locate <https://more-itertools.readthedocs.io/en/stable/api.html?highlight=numeric_range#more_itertools.locate>`_
        
        .. code-block:: python
        
            >>> Iter([0, 1, 1, 0, 1, 0, 0]).locate().collect()
            [1, 2, 4]
        
        .. code-block:: python
        
            >>> Iter(['a', 'b', 'c', 'b']).locate(lambda x: x == 'b').collect()
            [1, 3]
        
        .. code-block:: python
        
            >>> iterable = [0, 1, 2, 3, 0, 1, 2, 3, 0, 1, 2, 3]
            >>> pred = lambda *args: args == (1, 2, 3)
            >>> Iter(iterable).locate(pred=pred, window_size=3).collect()
            [1, 5, 9]
        
        .. code-block:: python
        
            >>> from itertools import count
            >>> from more_itertools import seekable
            >>> source = (3 * n + 1 if (n % 2) else n // 2 for n in count())
            >>> it = Iter(source).seekable()
            >>> pred = lambda x: x > 100
            >>> # TODO: can we avoid making two instances?
            >>> indexes = Iter(it).locate(pred=pred)
            >>> i = next(indexes)
            >>> it.seek(i)
            >>> next(it)
            106
        
        
        
        .. _Iter.rlocate:
        
        
        ``Iter.rlocate(self, pred=bool, window_size=None) -> "Iter"``
        =============================================================
        
        Reference: `more_itertools.rlocate <https://more-itertools.readthedocs.io/en/stable/api.html?highlight=numeric_range#more_itertools.rlocate>`_
        
        .. code-block:: python
        
            >>> Iter([0, 1, 1, 0, 1, 0, 0]).rlocate().collect()  # Truthy at 1, 2, and 4
            [4, 2, 1]
        
        .. code-block:: python
        
            >>> pred = lambda x: x == 'b'
            >>> Iter('abcb').rlocate(pred).collect()
            [3, 1]
        
        .. code-block:: python
        
            >>> iterable = [0, 1, 2, 3, 0, 1, 2, 3, 0, 1, 2, 3]
            >>> pred = lambda *args: args == (1, 2, 3)
            >>> Iter(iterable).rlocate(pred=pred, window_size=3).collect()
            [9, 5, 1]
        
        
        
        .. _Iter.replace:
        
        
        ``Iter.replace(self, pred, substitutes, count=None, window_size=1) -> "Iter"``
        ==============================================================================
        
        Reference: `more_itertools.replace <https://more-itertools.readthedocs.io/en/stable/api.html?highlight=numeric_range#more_itertools.replace>`_
        
        .. code-block:: python
        
            >>> iterable = [1, 1, 0, 1, 1, 0, 1, 1]
            >>> pred = lambda x: x == 0
            >>> substitutes = (2, 3)
            >>> Iter(iterable).replace(pred, substitutes).collect()
            [1, 1, 2, 3, 1, 1, 2, 3, 1, 1]
        
        .. code-block:: python
        
            >>> iterable = [1, 1, 0, 1, 1, 0, 1, 1, 0]
            >>> pred = lambda x: x == 0
            >>> substitutes = [None]
            >>> Iter(iterable).replace(pred, substitutes, count=2).collect()
            [1, 1, None, 1, 1, None, 1, 1, 0]
        
        .. code-block:: python
        
            >>> iterable = [0, 1, 2, 5, 0, 1, 2, 5]
            >>> window_size = 3
            >>> pred = lambda *args: args == (0, 1, 2)  # 3 items passed to pred
            >>> substitutes = [3, 4] # Splice in these items
            >>> Iter(iterable).replace(
            ...     pred, substitutes, window_size=window_size
            ... ).collect()
            [3, 4, 5, 3, 4, 5]
        
        
        
        .. _Iter.numeric_range:
        
        
        ``@classmethod Iter.numeric_range(cls, *args) -> "Iter"``
        =========================================================
        
        Reference: `more_itertools.numeric_range <https://more-itertools.readthedocs.io/en/stable/api.html?highlight=numeric_range#more_itertools.numeric_range>`_
        
        .. code-block:: python
        
            >>> Iter.numeric_range(3.5).collect()
            [0.0, 1.0, 2.0, 3.0]
        
        .. code-block:: python
        
            >>> from decimal import Decimal
            >>> start = Decimal('2.1')
            >>> stop = Decimal('5.1')
            >>> Iter.numeric_range(start, stop).collect()
            [Decimal('2.1'), Decimal('3.1'), Decimal('4.1')]
        
        .. code-block:: python
        
            >>> from fractions import Fraction
            >>> start = Fraction(1, 2)  # Start at 1/2
            >>> stop = Fraction(5, 2)  # End at 5/2
            >>> step = Fraction(1, 2)  # Count by 1/2
            >>> Iter.numeric_range(start, stop, step).collect()
            [Fraction(1, 2), Fraction(1, 1), Fraction(3, 2), Fraction(2, 1)]
        
        .. code-block:: python
        
            >>> Iter.numeric_range(3, -1, -1.0).collect()
            [3.0, 2.0, 1.0, 0.0]
        
        
        
        .. _Iter.side_effect:
        
        
        ``Iter.side_effect(self, func, *args, chunk_size=None, before=None, after=None)``
        =================================================================================
        
        Reference: `more_itertools.side_effect <https://more-itertools.readthedocs.io/en/stable/api.html?highlight=numeric_range#more_itertools.side_effect>`_
        
        .. code-block:: python
        
            >>> def f(item):
            ...     if item == 3:
            ...         raise Exception('got 3')
            >>> Iter.range(5).side_effect(f).consume()
            Traceback (most recent call last):
                ...
            Exception: got 3
        
        .. code-block:: python
        
            >>> func = lambda item: print('Received {}'.format(item))
            >>> Iter.range(2).side_effect(func).consume()
            Received 0
            Received 1
        
        This version of ``side_effect`` also allows extra args:
        
        .. code-block:: python
        
            >>> func = lambda item, format_str='Received {}': print(format_str.format(item))
            >>> Iter.range(2).side_effect(func).consume()
            Received 0
            Received 1
            >>> func = lambda item, format_str='Received {}': print(format_str.format(item))
            >>> Iter.range(2).side_effect(func, 'Got {}').consume()
            Got 0
            Got 1
        
        
        
        
        .. _Iter.iterate:
        
        
        ``Iter.iterate(self)``
        ======================
        
        .. _Iter.difference:
        
        
        ``Iter.difference(self, func=operator.sub, *, initial=None)``
        =============================================================
        
        Reference: `more_itertools.difference <https://more-itertools.readthedocs.io/en/stable/api.html?highlight=difference#more_itertools.difference>`_
        
        .. code-block:: python
        
            >>> iterable = [0, 1, 3, 6, 10]
            >>> Iter(iterable).difference().collect()
            [0, 1, 2, 3, 4]
        
        .. code-block:: python
        
            >>> iterable = [1, 2, 6, 24, 120]  # Factorial sequence
            >>> func = lambda x, y: x // y
            >>> Iter(iterable).difference(func).collect()
            [1, 2, 3, 4, 5]
        
        
        
        .. _Iter.make_decorator:
        
        
        ``Iter.make_decorator(self)``
        =============================
        
        .. _Iter.SequenceView:
        
        
        ``Iter.SequenceView(self)``
        ===========================
        
        .. _Iter.time_limited:
        
        
        ``Iter.time_limited(self, limit_seconds) -> "Iter"``
        ====================================================
        
        Reference: `more_itertools.time_limited <https://more-itertools.readthedocs.io/en/stable/api.html?highlight=time_limited#more_itertools.time_limited>`_
        
        .. code-block:: python
        
            >>> from time import sleep
            >>> def generator():
            ...     yield 1
            ...     yield 2
            ...     sleep(0.2)
            ...     yield 3
            >>> Iter(generator()).time_limited(0.1).collect()
            [1, 2]
        
        
        
        .. _Iter.consume:
        
        
        |sink| ``Iter.consume(self, n: Optional[int] = None) -> "Optional[Iter[T]]"``
        =============================================================================
        
        
        If n is not provided, the entire iterator is consumed and
        ``None`` is returned. Otherwise, an iterator will *always* be
        returned, even if n is greater than the number of items left in
        the iterator.
        
        In this example, the source has more elements than what we consume,
        so there will still be data available on the chain:
        
        .. code-block:: python
        
            >>> range(10).consume(5).collect()
            [5, 6, 7, 8, 9]
        
        We can bump up the count of how many items can be consumed. Note that
        even though ``n`` is greater than the number of items in the source,
        it is still required to call Iter.collect_ to consume the remaining
        items.
        
        .. code-block:: python
        
            >>> range(10).consume(50).collect()
            []
        
        Finally, if ``n`` is not provided, the entire stream is consumed.
        In this scenario, Iter.collect_ would fail since nothing is being
        returned from the consume call.
        
        .. code-block:: python
        
            >>> assert range(10).consume() is None
        
        
        
        .. _Iter.tabulate:
        
        
        ``Iter.tabulate(self)``
        =======================
        
        .. _Iter.repeatfunc:
        
        
        |source| ``@classmethod Iter.repeatfunc(cls, func, *args, times=None)``
        =======================================================================
        
        
        Docstring TBD
        
        .. code-block:: python
        
            >>> Iter.repeatfunc(operator.add, 3, 5, times=4).collect()
            [8, 8, 8, 8]
        
        
        
        .. _Iter.wrap:
        
        
        ``Iter.wrap(self, ends: "Sequence[T, T]" = "()")``
        ==================================================
        Other examples for ends: '"' * 2, or '`' * 2, or '[]' etc.
        
        
        .. _Iter.print:
        
        
        ``Iter.print(self, template="{i}: {v}") -> "Iter[T]"``
        ======================================================
        
        Printing during the execution of an iterator. Mostly useful
        for debugging. Returns another iterator instance through which
        the original data is passed unchanged. This means you can include
        a `print()` step as necessary to observe data during iteration.
        
        .. code-block:: python
        
            >>> Iter('abc').print().collect()
            0: a
            1: b
            2: c
            ['a', 'b', 'c']
        
            >>> (
            ...    Iter(range(5))
            ...        .print('before filter {i}: {v}')
            ...        .filter(lambda x: x > 2)
            ...        .print('after filter {i}: {v}')
            ...        .collect()
            ... )
            before filter 0: 0
            before filter 1: 1
            before filter 2: 2
            before filter 3: 3
            after filter 0: 3
            before filter 4: 4
            after filter 1: 4
            [3, 4]
        
        
        
        .. _Iter.from_queue:
        
        
        |source| ``@classmethod Iter.from_queue(cls, q: queue.Queue, timeout=None, sentinel=None)``
        ===========================================================================================
        
        
        Wrap a queue with an iterator interface. This allows it to participate
        in chaining operations. The iterator will block while waiting for
        new values to appear on the queue. This is useful: it allows you
        to easily and safely pass data between threads or processes, and
        feed the incoming data into a pipeline.
        
        The sentinel value, default ``None``, will terminate the iterator.
        
        .. code-block:: python
        
            >>> q = queue.Queue()
            >>> # This line puts stuff onto a queue
            >>> range(10).chain([None]).map(q.put).consume()
            >>> # This is where we consume data from the queue:
            >>> Iter.from_queue(q).filter(lambda x: 2 < x < 9).collect()
            [3, 4, 5, 6, 7, 8]
        
        If ``None`` had not been chained onto the data, the iterator would
        have waited in Iter.collect_ forever.
        
        
        
        .. _Iter.into_queue:
        
        
        ``Iter.into_queue(self, q: queue.Queue) -> "Iter[T]"``
        ======================================================
        
        This is a sink, like Iter.collect_, that consumes data from
        an iterator chain and puts the data into the given queue.
        
        .. code-block:: python
        
            >>> q = queue.Queue()
            >>> # This demonstrates the queue sink
            >>> range(5).into_queue(q).consume()
            >>> # Code below is only for verification
            >>> out = []
            >>> finished = False
            >>> while not finished:
            ...     try:
            ...         out.append(q.get_nowait())
            ...     except queue.Empty:
            ...         finished = True
            >>> out
            [0, 1, 2, 3, 4]
        
        
        
        .. _Iter.send:
        
        
        |sink| ``Iter.send(self, collector: Generator, close_collector_when_done=False) -> "None"``
        ===========================================================================================
        
        
        See also: `more_itertools.consumer <https://more-itertools.readthedocs.io/en/stable/api.html?highlight=numeric_range#more_itertools.consumer>`_
        
        Send data into a generator. You do not have to first call ``next()``
        on the generator. Iter.send_ will do this for you.
        
        |warning| Look carefully at the examples below; you'll see that the
        ``yield`` keyword is wrapped in a second set of parens, e.g.
        ``output.append((yield))``. This is required!
        
        Simple case:
        
        .. code-block:: python
        
            >>> output = []
            >>> def collector():
            ...     while True:
            ...         output.append((yield))
            >>> Iter.range(3).send(collector())
            >>> output
            [0, 1, 2]
        
        Note that the generator is **not** closed by default after the iterable is
        exhausted. But this can be changed. If you choose to close the
        generator, use the parameter:
        
        .. code-block:: python
        
            >>> output = []
            >>> def collector():
            ...     while True:
            ...         output.append((yield))
            >>> g = collector()
            >>> Iter.range(3).send(g, close_collector_when_done=True)
            >>> Iter.range(3).send(g)
            Traceback (most recent call last):
                ...
            StopIteration
        
        The default behaviour is that the generator is left open which means you
        can keep using it for other iterators:
        
        .. code-block:: python
        
            >>> output = []
            >>> def collector():
            ...     while True:
            ...         output.append((yield))
            >>> g = collector()
            >>> Iter.range(3).send(g)
            >>> Iter.range(10, 13).send(g)
            >>> Iter.range(100, 103).send(g)
            >>> output
            [0, 1, 2, 10, 11, 12, 100, 101, 102]
        
        
        If the generator is closed before the iteration is complete,
        you'll get a ``StopIteration`` exception:
        
        .. code-block:: python
        
            >>> output = []
            >>> def collector():
            ...   for i in range(3):
            ...       output.append((yield))
            >>> Iter.range(5).send(collector())
            Traceback (most recent call last):
                ...
            StopIteration
        
        Note that Iter.send_ is a sink, so no further chaining is allowed.
        
        
        
        .. _Iter.send_also:
        
        
        ``Iter.send_also(self, collector: Generator) -> "Iter"``
        ========================================================
        
        Reference: `more_itertools.consumer <https://more-itertools.readthedocs.io/en/stable/api.html?highlight=numeric_range#more_itertools.consumer>`_
        
        Some ideas around a reverse iterator as a sink. The requirement to
        first "next" a just-started generator before you can send values
        into it is irritating, but not insurmountable. This method will
        automatically detect the "just-started generator" situation, do the
        ``next()``, and then send in the first value as necessary.
        
        Simple case:
        
        .. code-block:: python
        
            >>> output = []
            >>> def collector():
            ...     while True:
            ...         output.append((yield))
            >>> Iter.range(3).send_also(collector()).collect()
            [0, 1, 2]
            >>> output
            [0, 1, 2]
        
        If the generator is closed before the iteration is complete,
        you'll get an exception (Python 3.7+):
        
        .. code-block:: python
        
            >>> output = []
            >>> def collector():
            ...   for i in builtins.range(3):
            ...       output.append((yield))
            >>> Iter.range(50).send_also(collector()).collect()                  
            Traceback (most recent call last):
                ...
            RuntimeError
        
        Note that the above doesn't happen in Python < 3.7 (which includes
        pypy 7.3.1 that matches Python 3.6.9 compatibility). Instead, you
        collect out the items up to until the point that the collector
        returns; in this case, you'd get [0, 1, 2]. This change was made
        as part of `PEP 479 <https://www.python.org/dev/peps/pep-0479/>`_.
        
        Regardless, for any Python it's recommended that your generator
        live at least as long as the iterator feeding it.
        
        
        
        .. _Iter.sorted:
        
        
        |sink| |warning| ``Iter.sorted(self, key=None, reverse=False) -> "Iter[T]"``
        ============================================================================
        
        
        
        Simple wrapper for the ``sorted`` builtin.
        
        
        Calling this will read the entire stream before producing
        results.
        
        >>> Iter('bac').sorted().collect()
        ['a', 'b', 'c']
        >>> Iter('bac').sorted(reverse=True).collect()
        ['c', 'b', 'a']
        >>> Iter('bac').zip([2, 1, 0]).sorted(key=lambda tup: tup[1]).collect()
        [('c', 0), ('a', 1), ('b', 2)]
        
        
        
        .. _Iter.reversed:
        
        
        |sink| |warning| ``Iter.reversed(self) -> "Iter[T]"``
        =====================================================
        
        
        
        Simple wrapper for the ``reversed`` builtin.
        
        
        Calling this will read the entire stream before producing
        results.
        
        >>> Iter(range(4)).reversed().collect()
        [3, 2, 1, 0]
        
        
        
        
        Experiments and Provisional Ideas
        #################################
        
        
        
        .. _IterDict:
        
        
        |flux| ``class IterDict(UserDict)``
        ***********************************
        
        
        
        The idea here was to make a custom dict where several of
        the standard dict methods return ``Iter`` instances, which can then
        be chained. I'm not sure if this will be kept yet.
        
        
        .. _IterDict.keys:
        
        
        ``IterDict.keys(self) -> "Iter"``
        =================================
        
        .. _IterDict.values:
        
        
        ``IterDict.values(self) -> "Iter"``
        ===================================
        
        .. _IterDict.items:
        
        
        ``IterDict.items(self) -> "Iter"``
        ==================================
        
        .. _IterDict.update:
        
        
        ``IterDict.update(self, *args, **kwargs) -> "IterDict"``
        ========================================================
        
        .. _insert_separator:
        
        
        ``insert_separator(iterable: Iterable[Any], glue: Any) -> "Iterable[Any]"``
        ***************************************************************************
        Similar functionality can be obtained with, e.g.,
        interleave, as in
        
        >>> result = Iter('caleb').interleave(Iter.repeat('x')).collect()
        >>> result == list('cxaxlxexbx')
        True
        
        But you'll see a trailing "x" there, which join avoids. join
        makes sure to only add the glue separator if another element
        has arrived.
        
        It can handle strings without any special considerations, but it doesn't
        do any special handling for bytes and bytearrays. For that, rather
        look at `concat()`.
        
        
        
        Related projects
        ################
        
        It turns out the idea of chaining iterators is not new. There are many
        libraries that offer similar features:
        
        * My fork of a now-missing library: `chained-iterable <https://github.com/cjrh/chained-iterable>`_.
        
        * `https://github.com/olirice/flupy <https://github.com/olirice/flupy>`_
        
        * `https://github.com/ddstte/chiter <https://github.com/ddstte/chiter>`_
        
        * `https://github.com/neverendingqs/pyiterable <https://github.com/neverendingqs/pyiterable>`_
        
        * `https://github.com/alliefitter/iterable_collections <https://github.com/alliefitter/iterable_collections>`_
        
        * `https://github.com/halprin/iterator-chain <https://github.com/halprin/iterator-chain>`_
        
        * `https://github.com/jagill/python-chainz <https://github.com/jagill/python-chainz>`_
        
        * `https://github.com/ZianVW/IterPipe <https://github.com/ZianVW/IterPipe>`_
        
        * `https://github.com/Evelyn-H/iterchain <https://github.com/Evelyn-H/iterchain>`_
        
        * `https://github.com/EntilZha/PyFunctional <https://github.com/EntilZha/PyFunctional>`_
        
        Somewhat related:
        
        * `https://github.com/jreese/aioitertools <https://github.com/jreese/aioitertools>`_
        
        Dev Instructions
        ################
        
        Setup
        *****
        
        .. code-block:: shell
        
            $ python -m venv venv
            $ source venv/bin/activate
            (venv) $ pip install -e .[dev,test]
        
        Testing
        *******
        
        .. code-block:: shell
        
            (venv) $ pytest --cov
        
        Documentation
        *************
        
        To regenerate the documentation, file ``README.rst``:
        
        .. code-block:: shell
        
            (venv) $ python regenerate_readme.py -m excitertools.py > README.rst
        
        Releasing
        *********
        
        To do a release, we're using `bumpymcbumpface <https://pypi.org/project/bumpymcbumpface/>`_.
        Make sure that is set up correctly according to its own documentation. I 
        like to use `pipx <https://github.com/pipxproject/pipx>`_ to install and 
        manage these kinds of tools.
        
        .. code-block:: shell
        
            $ bumpymcbumpface --push-git --push-pypi
        
        |
        |
        
        -----
        
        |
        |
        
            Work is a necessary evil to be avoided. 
            *Mark Twain*
        
        
        
        
Keywords: itertools
Platform: UNKNOWN
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: Implementation :: CPython
Classifier: Programming Language :: Python :: Implementation :: PyPy
Description-Content-Type: text/x-rst
Provides-Extra: dev
Provides-Extra: test
