"functools" --- Funções e operações de ordem superior em objetos chamáveis
**************************************************************************

**Código-fonte:** Lib/functools.py

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

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

O módulo "functools" define as seguintes funções:

@functools.cache(user_function)

   Cache simples e leve de funções sem vínculo. Às vezes chamado de
   "memoizar".

   Retorna o mesmo que "lru_cache(maxsize=None)", criando um invólucro
   fino em torno de uma pesquisa de dicionário para os argumentos da
   função. Como nunca precisa remover valores antigos, isso é menor e
   mais rápido do que "lru_cache()" com um limite de tamanho.

   Por exemplo:

      @cache
      def factorial(n):
          return n * factorial(n-1) if n else 1

      >>> factorial(10)      # no previously cached result, makes 11 recursive calls
      3628800
      >>> factorial(5)       # just looks up cached value result
      120
      >>> factorial(12)      # makes two new recursive calls, the other 10 are cached
      479001600

   Novo na versão 3.9.

@functools.cached_property(func)

   Transforma um método de uma classe em uma propriedade cujo valor é
   calculado uma vez e, em seguida, armazenado em cache como um
   atributo normal para a vida útil da instância. Semelhante a
   "property()", com a adição de armazenamento em cache. Útil para
   propriedades computadas caras de instâncias que são efetivamente
   imutáveis.

   Exemplo:

      class DataSet:

          def __init__(self, sequence_of_numbers):
              self._data = tuple(sequence_of_numbers)

          @cached_property
          def stdev(self):
              return statistics.stdev(self._data)

   A mecânica de "cached_property()" difere um pouco da de
   "property()". Uma propriedade comum bloqueia a escrita de
   atributos, a menos que um método setter seja definido. Em
   contraste, uma *cached_property* permite escritas.

   O decorador *cached_property* só é executado em pesquisas e apenas
   quando um atributo com o mesmo nome não existe. Quando executado, o
   *cached_property* escreve no atributo com o mesmo nome. Leituras e
   escritas subsequentes do atributo têm precedência sobre o método
   *cached_property* e ele funciona como um atributo normal.

   O valor em cache pode ser apagado excluindo o atributo. Isso
   permite que o método *cached_property* seja executado novamente.

   Observe que este decorador interfere com a operação dos dicionários
   que compartilham chaves da **PEP 412**. Isso significa que os
   dicionários de instância podem ocupar mais espaço do que o normal.

   Also, this decorator requires that the "__dict__" attribute on each
   instance be a mutable mapping. This means it will not work with
   some types, such as metaclasses (since the "__dict__" attributes on
   type instances are read-only proxies for the class namespace), and
   those that specify "__slots__" without including "__dict__" as one
   of the defined slots (as such classes don't provide a "__dict__"
   attribute at all).

   If a mutable mapping is not available or if space-efficient key
   sharing is desired, an effect similar to "cached_property()" can be
   achieved by a stacking "property()" on top of "cache()":

      class DataSet:
          def __init__(self, sequence_of_numbers):
              self._data = sequence_of_numbers

          @property
          @cache
          def stdev(self):
              return statistics.stdev(self._data)

   Novo na versão 3.8.

functools.cmp_to_key(func)

   Transforma uma função de comparação de estilo antigo para um
   *função chave*. Usado com ferramentas que aceitam funções chave
   (como "sorted()", "min()", "max()", "heapq.nlargest()",
   "heapq.nsmallest()", "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.

   A comparison function is any callable that accepts two arguments,
   compares them, and returns a negative number for less-than, zero
   for equality, or a positive number for greater-than.  A key
   function is a callable that accepts one argument and returns
   another value to be used as the sort key.

   Exemplo:

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

   Para exemplos de classificação e um breve tutorial de
   classificação, veja HowTo - Ordenação.

   Novo na versão 3.2.

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

   Decorador para embrulhar uma função com um chamável memoizável que
   economiza até as chamadas mais recentes *maxsize*. Pode economizar
   tempo quando uma função cara ou E/S é periodicamente chamada com os
   mesmos argumentos.

   Since a dictionary is used to cache results, the positional and
   keyword arguments to the function must be *hashable*.

   Padrões de argumento distintos podem ser considerados chamadas
   distintas com entradas de cache separadas. Por exemplo, *f(a=1,
   b=2)* e *f(b=2, a=1)*  diferem em sua ordem de argumento nomeado e
   podem ter duas entradas de cache separadas.

   Se *user_function* especificado, deve ser um chamável. Isso permite
   que o decorador *lru_cache* seja aplicado diretamente a uma função
   do usuário, deixando *maxsize* em seu valor padrão de 128:

      @lru_cache
      def count_vowels(sentence):
          return sum(sentence.count(vowel) for vowel in 'AEIOUaeiou')

   Se *maxsize* for definido como "None", o recurso LRU é desabilitado
   e o cache pode crescer sem limites.

   If *typed* is set to true, function arguments of different types
   will be cached separately.  If *typed* is false, the implementation
   will usually regard them as equivalent calls and only cache a
   single result. (Some types such as *str* and *int* may be cached
   separately even when *typed* is false.)

   Note, type specificity applies only to the function's immediate
   arguments rather than their contents.  The scalar arguments,
   "Decimal(42)" and "Fraction(42)" are be treated as distinct calls
   with distinct results. In contrast, the tuple arguments "('answer',
   Decimal(42))" and "('answer', Fraction(42))" are treated as
   equivalent.

   A função envolta é instrumentada com uma função
   "cache_parameters()" que retorna um novo "dict" mostrando os
   valores para *maxsize* e *digitado*. Este é apenas para fins
   informativos. A alteração dos valores não tem efeito.

   To help measure the effectiveness of the cache and tune the
   *maxsize* parameter, the wrapped function is instrumented with a
   "cache_info()" function that returns a *named tuple* showing
   *hits*, *misses*, *maxsize* and *currsize*.

   O decorador também fornece uma função "cache_clear()" para limpar
   ou invalidar o cache.

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

   The cache keeps references to the arguments and return values until
   they age out of the cache or until the cache is cleared.

   Um cache LRU (menos usado recentemente) 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 limites em processos de longa
   execução, como servidores da 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 = 'https://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 usando um
   cache para implementar uma programação dinâmica 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: Adicionada a opção *typed*.

   Alterado na versão 3.8: Adicionada a opção *user_function*.

   Novo na versão 3.9: Adicionada a função "cache_parameters()"

@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.

   Nota:

     This decorator makes no attempt to override methods that have
     been declared in the class *or its superclasses*. Meaning that if
     a superclass defines a comparison operator, *total_ordering* will
     not implement it again, even if the original method is abstract.

   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, **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:
      ...     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
   *iterable*, from left to right, so as to reduce the iterable 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 *iterable*.  If the optional *initializer* is present, it
   is placed before the items of the iterable in the calculation, and
   serves as a default when the iterable is empty.  If *initializer*
   is not given and *iterable* 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

   See "itertools.accumulate()" for an iterator that yields all
   intermediate values.

@functools.singledispatch

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

   To define a generic function, decorate it with the
   "@singledispatch" decorator. When defining a function using
   "@singledispatch", note that the dispatch happens on the type of
   the first argument:

      >>> 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, which can be used
   as a decorator.  For functions annotated with types, the decorator
   will infer the type of the first argument automatically:

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

   For code which doesn't use type annotations, the appropriate type
   argument can be passed explicitly to the decorator itself:

      >>> @fun.register(complex)
      ... def _(arg, verbose=False):
      ...     if verbose:
      ...         print("Better than complicated.", end=" ")
      ...     print(arg.real, arg.imag)
      ...

   To enable registering *lambdas* and pre-existing functions, the
   "register()" attribute can also 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. This
   enables decorator stacking, "pickling", and the creation of 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.

   If an implementation is registered to an *abstract base class*,
   virtual subclasses of the base class will be dispatched to that
   implementation:

      >>> from collections.abc import Mapping
      >>> @fun.register
      ... def _(arg: Mapping, verbose=False):
      ...     if verbose:
      ...         print("Keys & Values")
      ...     for key, value in arg.items():
      ...         print(key, "=>", value)
      ...
      >>> fun({"a": "b"})
      a => b

   To check which implementation the generic function will 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.

   Alterado na versão 3.7: The "register()" attribute now supports
   using type annotations.

class functools.singledispatchmethod(func)

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

   To define a generic method, decorate it with the
   "@singledispatchmethod" decorator. When defining a function using
   "@singledispatchmethod", note that the dispatch happens on the type
   of the first non-*self* or non-*cls* argument:

      class Negator:
          @singledispatchmethod
          def neg(self, arg):
              raise NotImplementedError("Cannot negate a")

          @neg.register
          def _(self, arg: int):
              return -arg

          @neg.register
          def _(self, arg: bool):
              return not arg

   "@singledispatchmethod" supports nesting with other decorators such
   as "@classmethod". Note that to allow for "dispatcher.register",
   "singledispatchmethod" must be the *outer most* decorator. Here is
   the "Negator" class with the "neg" methods bound to the class,
   rather than an instance of the class:

      class Negator:
          @singledispatchmethod
          @classmethod
          def neg(cls, arg):
              raise NotImplementedError("Cannot negate a")

          @neg.register
          @classmethod
          def _(cls, arg: int):
              return -arg

          @neg.register
          @classmethod
          def _(cls, arg: bool):
              return not arg

   The same pattern can be used for other similar decorators:
   "@staticmethod", "@abstractmethod", and others.

   Novo na versão 3.8.

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.


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.
