10.2. "functools" --- Funções e operações de ordem superior em objetos chamáveis
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** Código fonte: >>**<<: source:*Lib/functools.py*

======================================================================

O módulo: mod: *functools* é para funções de ordem superior: funções
que atuam ou retornam outras funções. Em geral, qualquer objeto
invocável pode ser tratado como uma função para os propósitos deste
módulo.

O módulo: mod:*functools* define as seguintes funções:

functools.cmp_to_key(func)

   Transforma uma função de comparação de estilo antigo para um:
   termo: *função de chave '. Usado com ferramentas que aceitam
   funções-chave (como: func: `sorted*,: func:` min`,: func: *max*,:
   func:` heapq.nlargest`,: func: *heapq.nsmallest*,: Func:
   *itertools.groupby*). Esta função é usada principalmente como uma
   ferramenta de transição para programas que estão sendo convertidos
   a partir do Python 2, que suportou o uso de funções de comparação.

   Uma função de comparação é qualquer chamada que aceita dois
   argumentos, os compara e retorna um número negativo por menos de
   zero, igual a igualdade ou um número positivo por maior que. Uma
   função de chave é escalável que aceita um argumento e retorna outro
   valor para ser usado como a chave de classificação.

   Exemplo:

      sorted(iterable, key=cmp_to_key(locale.strcoll))  # locale-aware sort order

   Para selecionar exemplos e um breve tutorial de classificação,
   veja: ref: *sortinghowto*.

   Novo na versão 3.2.

@functools.lru_cache(maxsize=128, typed=False)

   Decorador para embrulhar uma função com memorizável é chamada e que
   economiza até as chamadas mais recentes * maxsize >>*<<. Pode
   economizar tempo quando uma função cara ou I/O é periodicamente
   chamada com os mesmos argumentos.

   dicionárioUma vez que um dicionário é usado para armazenar
   resultados em cache, os argumentos posicionais e de palavras-chave
   para a função devem ser hashable.

   Se * maxsize * estiver configurado para `` None``, o recurso LRU
   está desabilitado e o cache pode crescer sem ligação. O recurso LRU
   funciona melhor quando * maxsize * é um poder de dois.

   Se *tipo* for definido como verdadeiro, os argumentos de função de
   diferentes tipos serão armazenados em cache separadamente. Por
   exemplo, `` f (3) `` e `` f (3.0) `` serão tratados como chamadas
   distintas com resultados distintos.

   Para ajudar a medir a eficácia do cache e ajustar o parâmetro
   *maxsize*, a função envolvida é instrumentada com uma função: func:
   *cache_info* que retorna um: termo:` nomeado tuple` mostrando *
   hits >>*<<, * misses >>*<<, * Maxsize * e * currsize >>*<<. Em um
   ambiente multi-threaded, os hits e erros são aproximados.

   O decorador também fornece uma função: func: *cache_clear* para
   limpar ou invalidar o cache.

   A função subjacente original é acessível através do atributo: attr:
   *__wrapped__*. Isso é útil para introspecção, para ignorar o cache,
   ou para reinstalar a função com um cache diferente.

   Um cache *LRU (least recently used - em português - menos usado
   recentemente)
   <https://en.wikipedia.org/wiki/Cache_algorithms#Examples>* _
   funciona melhor quando as chamadas mais recentes são os melhores
   preditores de chamadas futuras (por exemplo, os artigos mais
   populares em um servidor de notícias tendem a mudar a cada dia). O
   limite de tamanho do cache garante que o cache não cresça sem está
   ligado a processos de longa duração, como servidores web.

   Em geral, o cache LRU deve ser usado somente quando você deseja
   reutilizar valores calculados anteriormente. Da mesma forma, não
   faz sentido armazenar em cache funções com efeitos colaterais,
   funções que precisam criar objetos mutáveis distintos em cada
   chamada ou funções impuras, como time() ou random().

   Exemplo de um cache LRU para conteúdo web estático

      @lru_cache(maxsize=32)
      def get_pep(num):
          'Retrieve text of a Python Enhancement Proposal'
          resource = 'http://www.python.org/dev/peps/pep-%04d/' % num
          try:
              with urllib.request.urlopen(resource) as s:
                  return s.read()
          except urllib.error.HTTPError:
              return 'Not Found'

      >>> for n in 8, 290, 308, 320, 8, 218, 320, 279, 289, 320, 9991:
      ...     pep = get_pep(n)
      ...     print(n, len(pep))

      >>> get_pep.cache_info()
      CacheInfo(hits=3, misses=8, maxsize=32, currsize=8)

   Exemplo de computação eficiente dos números Fibonacci
   <https://en.wikipedia.org/wiki/Fibonacci_number> *_ usando um cache
   para implementar uma" programação dinâmica
   "<https://en.wikipedia.org/wiki/Dynamic_programming>* _ técnica:

      @lru_cache(maxsize=None)
      def fib(n):
          if n < 2:
              return n
          return fib(n-1) + fib(n-2)

      >>> [fib(n) for n in range(16)]
      [0, 1, 1, 2, 3, 5, 8, 13, 21, 34, 55, 89, 144, 233, 377, 610]

      >>> fib.cache_info()
      CacheInfo(hits=28, misses=16, maxsize=None, currsize=16)

   Novo na versão 3.2.

   Alterado na versão 3.3: Adicionado a opção * digitado >>*<<.

@functools.total_ordering

   Given a class defining one or more rich comparison ordering
   methods, this class decorator supplies the rest.  This simplifies
   the effort involved in specifying all of the possible rich
   comparison operations:

   The class must define one of "__lt__()", "__le__()", "__gt__()", or
   "__ge__()". In addition, the class should supply an "__eq__()"
   method.

   Por exemplo:

      @total_ordering
      class Student:
          def _is_valid_operand(self, other):
              return (hasattr(other, "lastname") and
                      hasattr(other, "firstname"))
          def __eq__(self, other):
              if not self._is_valid_operand(other):
                  return NotImplemented
              return ((self.lastname.lower(), self.firstname.lower()) ==
                      (other.lastname.lower(), other.firstname.lower()))
          def __lt__(self, other):
              if not self._is_valid_operand(other):
                  return NotImplemented
              return ((self.lastname.lower(), self.firstname.lower()) <
                      (other.lastname.lower(), other.firstname.lower()))

   Nota:

     While this decorator makes it easy to create well behaved totally
     ordered types, it *does* come at the cost of slower execution and
     more complex stack traces for the derived comparison methods. If
     performance benchmarking indicates this is a bottleneck for a
     given application, implementing all six rich comparison methods
     instead is likely to provide an easy speed boost.

   Novo na versão 3.2.

   Alterado na versão 3.4: Returning NotImplemented from the
   underlying comparison function for unrecognised types is now
   supported.

functools.partial(func, *args, **keywords)

   Return a new partial object which when called will behave like
   *func* called with the positional arguments *args* and keyword
   arguments *keywords*. If more arguments are supplied to the call,
   they are appended to *args*. If additional keyword arguments are
   supplied, they extend and override *keywords*. Roughly equivalent
   to:

      def partial(func, *args, **keywords):
          def newfunc(*fargs, **fkeywords):
              newkeywords = keywords.copy()
              newkeywords.update(fkeywords)
              return func(*args, *fargs, **newkeywords)
          newfunc.func = func
          newfunc.args = args
          newfunc.keywords = keywords
          return newfunc

   The "partial()" is used for partial function application which
   "freezes" some portion of a function's arguments and/or keywords
   resulting in a new object with a simplified signature.  For
   example, "partial()" can be used to create a callable that behaves
   like the "int()" function where the *base* argument defaults to
   two:

   >>> from functools import partial
   >>> basetwo = partial(int, base=2)
   >>> basetwo.__doc__ = 'Convert base 2 string to an int.'
   >>> basetwo('10010')
   18

class functools.partialmethod(func, *args, **keywords)

   Return a new "partialmethod" descriptor which behaves like
   "partial" except that it is designed to be used as a method
   definition rather than being directly callable.

   *func* must be a *descriptor* or a callable (objects which are
   both, like normal functions, are handled as descriptors).

   When *func* is a descriptor (such as a normal Python function,
   "classmethod()", "staticmethod()", "abstractmethod()" or another
   instance of "partialmethod"), calls to "__get__" are delegated to
   the underlying descriptor, and an appropriate partial object
   returned as the result.

   When *func* is a non-descriptor callable, an appropriate bound
   method is created dynamically. This behaves like a normal Python
   function when used as a method: the *self* argument will be
   inserted as the first positional argument, even before the *args*
   and *keywords* supplied to the "partialmethod" constructor.

   Exemplo:

      >>> class Cell(object):
      ...     def __init__(self):
      ...         self._alive = False
      ...     @property
      ...     def alive(self):
      ...         return self._alive
      ...     def set_state(self, state):
      ...         self._alive = bool(state)
      ...     set_alive = partialmethod(set_state, True)
      ...     set_dead = partialmethod(set_state, False)
      ...
      >>> c = Cell()
      >>> c.alive
      False
      >>> c.set_alive()
      >>> c.alive
      True

   Novo na versão 3.4.

functools.reduce(function, iterable[, initializer])

   Apply *function* of two arguments cumulatively to the items of
   *sequence*, from left to right, so as to reduce the sequence to a
   single value.  For example, "reduce(lambda x, y: x+y, [1, 2, 3, 4,
   5])" calculates "((((1+2)+3)+4)+5)". The left argument, *x*, is the
   accumulated value and the right argument, *y*, is the update value
   from the *sequence*.  If the optional *initializer* is present, it
   is placed before the items of the sequence in the calculation, and
   serves as a default when the sequence is empty.  If *initializer*
   is not given and *sequence* contains only one item, the first item
   is returned.

   Aproximadamente equivalente a:

      def reduce(function, iterable, initializer=None):
          it = iter(iterable)
          if initializer is None:
              value = next(it)
          else:
              value = initializer
          for element in it:
              value = function(value, element)
          return value

@functools.singledispatch

   Transform a function into a *single-dispatch* *generic function*.

   To define a generic function, decorate it with the
   "@singledispatch" decorator. Note that the dispatch happens on the
   type of the first argument, create your function accordingly:

      >>> from functools import singledispatch
      >>> @singledispatch
      ... def fun(arg, verbose=False):
      ...     if verbose:
      ...         print("Let me just say,", end=" ")
      ...     print(arg)

   To add overloaded implementations to the function, use the
   "register()" attribute of the generic function.  It is a decorator,
   taking a type parameter and decorating a function implementing the
   operation for that type:

      >>> @fun.register(int)
      ... def _(arg, verbose=False):
      ...     if verbose:
      ...         print("Strength in numbers, eh?", end=" ")
      ...     print(arg)
      ...
      >>> @fun.register(list)
      ... def _(arg, verbose=False):
      ...     if verbose:
      ...         print("Enumerate this:")
      ...     for i, elem in enumerate(arg):
      ...         print(i, elem)

   To enable registering lambdas and pre-existing functions, the
   "register()" attribute can be used in a functional form:

      >>> def nothing(arg, verbose=False):
      ...     print("Nothing.")
      ...
      >>> fun.register(type(None), nothing)

   The "register()" attribute returns the undecorated function which
   enables decorator stacking, pickling, as well as creating unit
   tests for each variant independently:

      >>> @fun.register(float)
      ... @fun.register(Decimal)
      ... def fun_num(arg, verbose=False):
      ...     if verbose:
      ...         print("Half of your number:", end=" ")
      ...     print(arg / 2)
      ...
      >>> fun_num is fun
      False

   When called, the generic function dispatches on the type of the
   first argument:

      >>> fun("Hello, world.")
      Hello, world.
      >>> fun("test.", verbose=True)
      Let me just say, test.
      >>> fun(42, verbose=True)
      Strength in numbers, eh? 42
      >>> fun(['spam', 'spam', 'eggs', 'spam'], verbose=True)
      Enumerate this:
      0 spam
      1 spam
      2 eggs
      3 spam
      >>> fun(None)
      Nothing.
      >>> fun(1.23)
      0.615

   Where there is no registered implementation for a specific type,
   its method resolution order is used to find a more generic
   implementation. The original function decorated with
   "@singledispatch" is registered for the base "object" type, which
   means it is used if no better implementation is found.

   To check which implementation will the generic function choose for
   a given type, use the "dispatch()" attribute:

      >>> fun.dispatch(float)
      <function fun_num at 0x1035a2840>
      >>> fun.dispatch(dict)    # note: default implementation
      <function fun at 0x103fe0000>

   To access all registered implementations, use the read-only
   "registry" attribute:

      >>> fun.registry.keys()
      dict_keys([<class 'NoneType'>, <class 'int'>, <class 'object'>,
                <class 'decimal.Decimal'>, <class 'list'>,
                <class 'float'>])
      >>> fun.registry[float]
      <function fun_num at 0x1035a2840>
      >>> fun.registry[object]
      <function fun at 0x103fe0000>

   Novo na versão 3.4.

functools.update_wrapper(wrapper, wrapped, assigned=WRAPPER_ASSIGNMENTS, updated=WRAPPER_UPDATES)

   Update a *wrapper* function to look like the *wrapped* function.
   The optional arguments are tuples to specify which attributes of
   the original function are assigned directly to the matching
   attributes on the wrapper function and which attributes of the
   wrapper function are updated with the corresponding attributes from
   the original function. The default values for these arguments are
   the module level constants "WRAPPER_ASSIGNMENTS" (which assigns to
   the wrapper function's "__module__", "__name__", "__qualname__",
   "__annotations__" and "__doc__", the documentation string) and
   "WRAPPER_UPDATES" (which updates the wrapper function's "__dict__",
   i.e. the instance dictionary).

   To allow access to the original function for introspection and
   other purposes (e.g. bypassing a caching decorator such as
   "lru_cache()"), this function automatically adds a "__wrapped__"
   attribute to the wrapper that refers to the function being wrapped.

   The main intended use for this function is in *decorator* functions
   which wrap the decorated function and return the wrapper. If the
   wrapper function is not updated, the metadata of the returned
   function will reflect the wrapper definition rather than the
   original function definition, which is typically less than helpful.

   "update_wrapper()" may be used with callables other than functions.
   Any attributes named in *assigned* or *updated* that are missing
   from the object being wrapped are ignored (i.e. this function will
   not attempt to set them on the wrapper function). "AttributeError"
   is still raised if the wrapper function itself is missing any
   attributes named in *updated*.

   Novo na versão 3.2: Automatic addition of the "__wrapped__"
   attribute.

   Novo na versão 3.2: Copying of the "__annotations__" attribute by
   default.

   Alterado na versão 3.2: Missing attributes no longer trigger an
   "AttributeError".

   Alterado na versão 3.4: The "__wrapped__" attribute now always
   refers to the wrapped function, even if that function defined a
   "__wrapped__" attribute. (see bpo-17482)

@functools.wraps(wrapped, assigned=WRAPPER_ASSIGNMENTS, updated=WRAPPER_UPDATES)

   This is a convenience function for invoking "update_wrapper()" as a
   function decorator when defining a wrapper function.  It is
   equivalent to "partial(update_wrapper, wrapped=wrapped,
   assigned=assigned, updated=updated)". For example:

      >>> from functools import wraps
      >>> def my_decorator(f):
      ...     @wraps(f)
      ...     def wrapper(*args, **kwds):
      ...         print('Calling decorated function')
      ...         return f(*args, **kwds)
      ...     return wrapper
      ...
      >>> @my_decorator
      ... def example():
      ...     """Docstring"""
      ...     print('Called example function')
      ...
      >>> example()
      Calling decorated function
      Called example function
      >>> example.__name__
      'example'
      >>> example.__doc__
      'Docstring'

   Without the use of this decorator factory, the name of the example
   function would have been "'wrapper'", and the docstring of the
   original "example()" would have been lost.


10.2.1. Objetos "partial"
=========================

"partial" objects are callable objects created by "partial()". They
have three read-only attributes:

partial.func

   A callable object or function.  Calls to the "partial" object will
   be forwarded to "func" with new arguments and keywords.

partial.args

   The leftmost positional arguments that will be prepended to the
   positional arguments provided to a "partial" object call.

partial.keywords

   The keyword arguments that will be supplied when the "partial"
   object is called.

"partial" objects are like "function" objects in that they are
callable, weak referencable, and can have attributes.  There are some
important differences.  For instance, the "__name__" and "__doc__"
attributes are not created automatically.  Also, "partial" objects
defined in classes behave like static methods and do not transform
into bound methods during instance attribute look-up.
