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# Unique Representation

Abstract classes for cached and unique representation behavior.

See also

`sage.structure.factory.UniqueFactory`

AUTHORS:

- Nicolas M. Thiery (2008): Original version.
- Simon A. King (2013-02): Separate cached and unique representation.
- Simon A. King (2013-08): Extended documentation.

## What is a cached representation?

Instances of a class have a cached representation behavior when several instances constructed with the same arguments share the same memory representation. For example, calling twice:

    sage: G = SymmetricGroup(6)
    sage: H = SymmetricGroup(6)

to create the symmetric group on six elements gives back the same object:

    sage: G is H
    True

This is a standard design pattern. Besides saving memory, it allows for sharing cached data (say representation theoretical information about a group). And of course a look-up in the cache is faster than the creation of a new object.

### Implementing a cached representation

Sage provides two standard ways to create a cached representation: `CachedRepresentation` and `UniqueFactory`. Note that, in spite of its name, `UniqueFactory` does not ensure unique representation behaviour, which will be explained below.

#### Using `CachedRepresentation`

It is often very easy to use `CachedRepresentation`: One simply writes a Python class and adds `CachedRepresentation` to the list of base classes. If one does so, then the arguments used to create an instance of this class will by default also be used as keys for the cache:

    sage: from sage.structure.unique_representation import CachedRepresentation
    sage: class C(CachedRepresentation):
    ....:     def __init__(self, a, b=0):
    ....:         self.a = a
    ....:         self.b = b
    ....:     def __repr__(self):
    ....:         return "C(%s, %s)"%(self.a, self.b)
    ....:
    sage: a = C(1)
    sage: a is C(1)
    True

In addition, pickling just works, provided that Python is able to look up the class. Hence, in the following two lines, we explicitly put the class into the `__main__` module. This is needed in doctests, but not in an interactive session:

    sage: import __main__
    sage: __main__.C = C
    sage: loads(dumps(a)) is a
    True

Often, this very easy approach is sufficient for applications. However, there are some pitfalls. Since the arguments are used for caching, all arguments must be hashable, i.e., must be valid as dictionary keys:

    sage: C((1,2))
    C((1, 2), 0)
    sage: C([1,2])
    Traceback (most recent call last):
    ...
    TypeError: unhashable type: 'list'

In addition, equivalent ways of providing the arguments are not automatically normalised when forming the cache key, and hence different but equivalent arguments may yield distinct instances:

    sage: C(1) is C(1,0)
    False
    sage: C(1) is C(a=1)
    False
    sage: repr(C(1)) == repr(C(a=1))
    True

It should also be noted that the arguments are compared by equality, not by identity. This is often desired, but can imply subtle problems. For example, since `C(1)` already is in the cache, and since the unit elements in different finite fields are all equal to the integer one, we find:

    sage: GF(5)(1) == 1 == GF(3)(1)
    True
    sage: C(1) is C(GF(3)(1)) is C(GF(5)(1))
    True

But `C(2)` is not in the cache, and the number two is not equal in different finite fields (i. e., `GF(5)(2) == GF(3)(2)` returns as `False`), even though it is equal to the number two in the ring of integers ( `GF(5)(2) == 2 == GF(3)(2)` returns as `True`; equality is not transitive when comparing elements of distinct algebraic structures!!). Hence, we have:

    sage: GF(5)(2) == GF(3)(2)
    False
    sage: C(GF(3)(2)) is C(GF(5)(2))
    False

##### Normalising the arguments

`CachedRepresentation` uses the metaclass `ClasscallMetaclass`. Its `__classcall__` method is a `WeakCachedFunction`. This function creates an instance of the given class using the given arguments, unless it finds the result in the cache. This has the following implications:

- The arguments must be valid dictionary keys (i.e., they must be hashable; see above).
- It is a weak cache, hence, if the user does not keep a reference to the resulting instance, then it may be removed from the cache during garbage collection.
- It is possible to preprocess the input arguments by implementing a `__classcall__` or a `__classcall_private__` method, but in order to benefit from caching, `CachedRepresentation.__classcall__()` should at some point be called.

Note

For technical reasons, it is needed that `__classcall__` respectively `__classcall_private__` are “static methods”, i.e., they are callable objects that do not bind to an instance or class. For example, a `cached_function` can be used here, because it is callable, but does not bind to an instance or class, because it has no `__get__()` method. A usual Python function, however, has a `__get__()` method and would thus under normal circumstances bind to an instance or class, and thus the instance or class would be passed to the function as the first argument. To prevent a callable object from being bound to the instance or class, one can prepend the `@staticmethod` decorator to the definition; see `staticmethod`.

For more on Python’s `__get__()` method, see: http://docs.python.org/2/howto/descriptor.html

Warning

If there is preprocessing, then the preprocessed arguments passed to passed to `CachedRepresentation.__classcall__()` must be invariant under the preprocessing. That is to say, preprocessing the input arguments twice must have the same effect as preprocessing the input arguments only once. That is to say, the preprocessing must be idempotent.

The reason for this warning lies in the way pickling is implemented. If the preprocessed arguments are passed to `CachedRepresentation.__classcall__()`, then the resulting instance will store the preprocessed arguments in some attribute, and will use them for pickling. If the pickle is unpickled, then preprocessing is applied to the preprocessed arguments—and this second round of preprocessing must not change the arguments further, since otherwise a different instance would be created.

We illustrate the warning by an example. Imagine that one has instances that are created with an integer-valued argument, but only depend on the square of the argument. It would be a mistake to square the given argument during preprocessing:

    sage: class WrongUsage(CachedRepresentation):
    ....:     @staticmethod
    ....:     def __classcall__(cls, n):
    ....:         return super(WrongUsage,cls).__classcall__(cls, n^2)
    ....:     def __init__(self, n):
    ....:         self.n = n
    ....:     def __repr__(self):
    ....:         return "Something(%d)"%self.n
    ....:
    sage: import __main__
    sage: __main__.WrongUsage = WrongUsage # This is only needed in doctests
    sage: w = WrongUsage(3); w
    Something(9)
    sage: w._reduction
    (<class '__main__.WrongUsage'>, (9,), {})

Indeed, the reduction data are obtained from the preprocessed argument. By consequence, if the resulting instance is pickled and unpickled, the argument gets squared again:

    sage: loads(dumps(w))
    Something(81)

Instead, the preprocessing should only take the absolute value of the given argument, while the squaring should happen inside of the `__init__` method, where it won’t mess with the cache:

    sage: class BetterUsage(CachedRepresentation):
    ....:     @staticmethod
    ....:     def __classcall__(cls, n):
    ....:         return super(BetterUsage, cls).__classcall__(cls, abs(n))
    ....:     def __init__(self, n):
    ....:         self.n = n^2
    ....:     def __repr__(self):
    ....:         return "SomethingElse(%d)"%self.n
    ....:
    sage: __main__.BetterUsage = BetterUsage # This is only needed in doctests
    sage: b = BetterUsage(3); b
    SomethingElse(9)
    sage: loads(dumps(b)) is b
    True
    sage: b is BetterUsage(-3)
    True

In our next example, we create a cached representation class `C` that returns an instance of a sub-class `C1` or `C2` depending on the given arguments. This is implemented in a static `__classcall_private__` method of `C`, letting it choose the sub-class according to the given arguments. Since a `__classcall_private__` method will be ignored on sub-classes, the caching of `CachedRepresentation` is available to both `C1` and `C2`. But for illustration, we overload the static `__classcall__` method on `C2`, doing some argument preprocessing. We also create a sub-class `C2b` of `C2`, demonstrating that the `__classcall__` method is used on the sub-class (in contrast to a `__classcall_private__` method!).

    sage: class C(CachedRepresentation):
    ....:     @staticmethod
    ....:     def __classcall_private__(cls, n, implementation=0):
    ....:         if not implementation:
    ....:             return C.__classcall__(cls, n)
    ....:         if implementation==1:
    ....:             return C1(n)
    ....:         if implementation>1:
    ....:             return C2(n,implementation)
    ....:     def __init__(self, n):
    ....:         self.n = n
    ....:     def __repr__(self):
    ....:         return "C(%d, 0)"%self.n
    ....:
    sage: class C1(C):
    ....:     def __repr__(self):
    ....:         return "C1(%d)"%self.n
    ....:
    sage: class C2(C):
    ....:     @staticmethod
    ....:     def __classcall__(cls, n, implementation=0):
    ....:         if implementation:
    ....:             return super(C2, cls).__classcall__(cls, (n,)*implementation)
    ....:         return super(C2, cls).__classcall__(cls, n)
    ....:     def __init__(self, t):
    ....:         self.t = t
    ....:     def __repr__(self):
    ....:         return "C2(%s)"%repr(self.t)
    ....:
    sage: class C2b(C2):
    ....:     def __repr__(self):
    ....:         return "C2b(%s)"%repr(self.t)
    ....:
    sage: __main__.C2 = C2      # not needed in an interactive session
    sage: __main__.C2b = C2b

In the above example, `C` drops the argument `implementation` if it evaluates to `False`, and since the cached `__classcall__` is called in this case, we have:

    sage: C(1)
    C(1, 0)
    sage: C(1) is C(1,0)
    True
    sage: C(1) is C(1,0) is C(1,None) is C(1,[])
    True

(Note that we were able to bypass the issue of arguments having to be hashable by catching the empty list `[]` during preprocessing in the `__classcall_private__` method. Similarly, unhashable arguments can be made hashable – e. g., lists normalized to tuples – in the `__classcall_private__` method before they are further delegated to `__classcall__`. See `TCrystal` for an example.)

If we call `C1` directly or if we provide `implementation=1` to `C`, we obtain an instance of `C1`. Since it uses the `__classcall__` method inherited from `CachedRepresentation`, the resulting instances are cached:

    sage: C1(2)
    C1(2)
    sage: C(2, implementation=1)
    C1(2)
    sage: C(2, implementation=1) is C1(2)
    True

The class `C2` preprocesses the input arguments. Instances can, again, be obtained directly or by calling `C`:

    sage: C(1, implementation=3)
    C2((1, 1, 1))
    sage: C(1, implementation=3) is C2(1,3)
    True

The argument preprocessing of `C2` is inherited by `C2b`, since `__classcall__` and not `__classcall_private__` is used. Pickling works, since the preprocessing of arguments is idempotent:

    sage: c2b = C2b(2,3); c2b
    C2b((2, 2, 2))
    sage: loads(dumps(c2b)) is c2b
    True

#### Using `UniqueFactory`

For creating a cached representation using a factory, one has to

- create a class separately from the factory. This class must inherit from `object`. Its instances must allow attribute assignment.
- write a method `create_key` (or `create_key_and_extra_args`) that creates the cache key from the given arguments.
- write a method `create_object` that creates an instance of the class from a given cache key.
- create an instance of the factory with a name that allows to conclude where it is defined.

An example:

    sage: class C(object):
    ....:     def __init__(self, t):
    ....:         self.t = t
    ....:     def __repr__(self):
    ....:         return "C%s"%repr(self.t)
    ....:
    sage: from sage.structure.factory import UniqueFactory
    sage: class MyFactory(UniqueFactory):
    ....:     def create_key(self, n, m=None):
    ....:         if isinstance(n, (tuple,list)) and m is None:
    ....:             return tuple(n)
    ....:         return (n,)*m
    ....:     def create_object(self, version, key, **extra_args):
    ....:         # We ignore version and extra_args
    ....:         return C(key)
    ....:

Now, we define an instance of the factory, stating that it can be found under the name `"F"` in the `__main__` module. By consequence, pickling works:

    sage: F = MyFactory("__main__.F")
    sage: __main__.F = F                # not needed in an interactive session
    sage: loads(dumps(F)) is F
    True

We can now create cached instances of `C` by calling the factory. The cache only takes into account the key computed with the method `create_key` that we provided. Hence, different given arguments may result in the same instance. Note that, again, the cache is weak, hence, the instance might be removed from the cache during garbage collection, unless an external reference is preserved.

    sage: a = F(1, 2); a
    C(1, 1)
    sage: a is F((1,1))
    True

If the class of the returned instances is a sub-class of `object`, and if the resulting instance allows attribute assignment, then pickling of the resulting instances is automatically provided for, and respects the cache.

    sage: loads(dumps(a)) is a
    True

This is because an attribute is stored that explains how the instance was created:

    sage: a._factory_data
    (<class '__main__.MyFactory'>, (...), (1, 1), {})

Note

If a class is used that does not inherit from `object` then unique pickling is not provided.

Caching is only available if the factory is called. If an instance of the class is directly created, then the cache is not used:

    sage: C((1,1))
    C(1, 1)
    sage: C((1,1)) is a
    False

### Comparing the two ways of implementing a cached representation

In this sub-section, we discuss advantages and disadvantages of the two ways of implementing a cached representation, depending on the type of application.

#### Simplicity and transparency

In many cases, turning a class into a cached representation requires nothing more than adding `CachedRepresentation` to the list of base classes of this class. This is, of course, a very easy and convenient way. Writing a factory would involve a lot more work.

If preprocessing of the arguments is needed, then we have seen how to do this by a `__classcall_private__` or `__classcall__` method. But these are double underscore methods and hence, for example, invisible in the automatically created reference manual. Moreover, preprocessing and caching are implemented in the same method, which might be confusing. In a unique factory, these two tasks are cleanly implemented in two separate methods. With a factory, it is possible to create the resulting instance by arguments that are different from the key used for caching. This is significantly restricted with CachedRepresentation due to the requirement that argument preprocessing be idempotent.

Hence, if advanced preprocessing is needed, then `UniqueFactory` might be easier and more transparent to use than `CachedRepresentation`.

#### Class inheritance

Using `CachedRepresentation` has the advantage that one has a class and creates cached instances of this class by the usual Python syntax:

    sage: G = SymmetricGroup(6)
    sage: issubclass(SymmetricGroup, sage.structure.unique_representation.CachedRepresentation)
    True
    sage: isinstance(G, SymmetricGroup)
    True

In contrast, a factory is just a callable object that returns something that has absolutely nothing to do with the factory, and may in fact return instances of quite different classes:

    sage: isinstance(GF, sage.structure.factory.UniqueFactory)
    True
    sage: K5 = GF(5)
    sage: type(K5)
    <class 'sage.rings.finite_rings.finite_field_prime_modn.FiniteField_prime_modn_with_category'>
    sage: K25 = GF(25, 'x')
    sage: type(K25)
    <class 'sage.rings.finite_rings.finite_field_givaro.FiniteField_givaro_with_category'>
    sage: Kp = GF(next_prime_power(1000000)^2, 'x')
    sage: type(Kp)
    <class 'sage.rings.finite_rings.finite_field_pari_ffelt.FiniteField_pari_ffelt_with_category'>

This can be confusing to the user. Namely, the user might determine the class of an instance and try to create further instances by calling the class rather than the factory—which is a mistake since it works around the cache (and also since the class might be more restrictive than the factory – i. e., the type of `K5` in the above doctest cannot be called on a prime power which is not a prime). This mistake can more easily be avoided by using `CachedRepresentation`.

We have seen above that one can easily create new cached-representation classes by subclassing an existing cached-representation class, even making use of an existing argument preprocess. This would be much more complicated with a factory. Namely, one would need to rewrite old factories making them aware of the new classes, and/or write new factories for the new classes.

#### Python versus extension classes

`CachedRepresentation` uses a metaclass, namely `ClasscallMetaclass`. Hence, it can currently not be a Cython extension class. Moreover, it is supposed to be used by providing it as a base class. But in typical applications, one also has another base class, say, `Parent`. Hence, one would like to create a class with at least two base classes, which is currently impossible in Cython extension classes.

In other words, when using `CachedRepresentation`, one must work with Python classes. These can be defined in Cython code (`.pyx` files) and can thus benefit from Cython’s speed inside of their methods, but they must not be `cdef class` and can thus not use `cdef` attributes or methods.

Such restrictions do not exist when using a factory. However, if attribute assignment does not work, then the automatic pickling provided by `UniqueFactory` will not be available.

## What is a unique representation?

Instances of a class have a unique instance behavior when instances of this class evaluate equal if and only if they are identical. Sage provides the base class `WithEqualityById`, which provides comparison by identity and a hash that is determined by the memory address of the instance. Both the equality test and the hash are implemented in Cython and are very fast, even when one has a Python class inheriting from `WithEqualityById`.

In many applications, one wants to combine unique instance and cached representation behaviour. This is called unique representation behaviour. We have seen above that symmetric groups have a cached representation behaviour. However, they do not show the unique representation behaviour, since they are equal to groups created in a totally different way, namely to subgroups:

    sage: G = SymmetricGroup(6)
    sage: G3 = G.subgroup([G((1,2,3,4,5,6)),G((1,2))])
    sage: G is G3
    False
    sage: type(G) == type(G3)
    False
    sage: G == G3
    True

The unique representation behaviour can conveniently be implemented with a class that inherits from `UniqueRepresentation`: By adding `UniqueRepresentation` to the base classes, the class will simultaneously inherit from `CachedRepresentation` and from `WithEqualityById`.

For example, a symmetric function algebra is uniquely determined by the base ring. Thus, it is reasonable to use `UniqueRepresentation` in this case:

    sage: isinstance(SymmetricFunctions(CC), SymmetricFunctions)
    True
    sage: issubclass(SymmetricFunctions, UniqueRepresentation)
    True

`UniqueRepresentation` differs from `CachedRepresentation` only by adding `WithEqualityById` as a base class. Hence, the above examples of argument preprocessing work for `UniqueRepresentation` as well.

Note that a cached representation created with `UniqueFactory` does not automatically provide unique representation behaviour, in spite of its name! Hence, for unique representation behaviour, one has to implement hash and equality test accordingly, for example by inheriting from `WithEqualityById`.

class `sage.structure.unique_representation.``CachedRepresentation`

  Bases: `object`

  Classes derived from CachedRepresentation inherit a weak cache for their instances.

  Note

  If this class is used as a base class, then instances are (weakly) cached, according to the arguments used to create the instance. Pickling is provided, of course by using the cache.

  Note

  Using this class, one can have arbitrary hash and comparison. Hence, unique representation behaviour is not provided.

  See also

  `UniqueRepresentation`, `unique_representation`

  EXAMPLES:

  Providing a class with a weak cache for the instances is easy: Just inherit from `CachedRepresentation`:

      sage: from sage.structure.unique_representation import CachedRepresentation
      sage: class MyClass(CachedRepresentation):
      ....:     # all the rest as usual
      ....:     pass

  We start with a simple class whose constructor takes a single value as argument (TODO: find a more meaningful example):

      sage: class MyClass(CachedRepresentation):
      ....:     def __init__(self, value):
      ....:         self.value = value
      ....:     def __cmp__(self, other):
      ....:         c = cmp(type(self),type(other))
      ....:         if c: return c
      ....:         return cmp(self.value, other.value)

  Two coexisting instances of `MyClass` created with the same argument data are guaranteed to share the same identity. Since trac ticket #12215, this is only the case if there is some strong reference to the returned instance, since otherwise it may be garbage collected:

      sage: x = MyClass(1)
      sage: y = MyClass(1)
      sage: x is y               # There is a strong reference
      True
      sage: z = MyClass(2)
      sage: x is z
      False

  In particular, modifying any one of them modifies the other (reference effect):

      sage: x.value = 3
      sage: x.value, y.value
      (3, 3)
      sage: y.value = 1
      sage: x.value, y.value
      (1, 1)

  The arguments can consist of any combination of positional or keyword arguments, as taken by a usual `__init__` function. However, all values passed in should be hashable:

      sage: MyClass(value = [1,2,3])
      Traceback (most recent call last):
      ...
      TypeError: unhashable type: 'list'

  Argument preprocessing

  Sometimes, one wants to do some preprocessing on the arguments, to put them in some canonical form. The following example illustrates how to achieve this; it takes as argument any iterable, and canonicalizes it into a tuple (which is hashable!):

      sage: class MyClass2(CachedRepresentation):
      ....:     @staticmethod
      ....:     def __classcall__(cls, iterable):
      ....:         t = tuple(iterable)
      ....:         return super(MyClass2, cls).__classcall__(cls, t)
      ....:
      ....:     def __init__(self, value):
      ....:         self.value = value
      ....:
      sage: x = MyClass2([1,2,3])
      sage: y = MyClass2(tuple([1,2,3]))
      sage: z = MyClass2(i for i in [1,2,3])
      sage: x.value
      (1, 2, 3)
      sage: x is y, y is z
      (True, True)

  A similar situation arises when the constructor accepts default values for some of its parameters. Alas, the obvious implementation does not work:

      sage: class MyClass3(CachedRepresentation):
      ....:     def __init__(self, value = 3):
      ....:         self.value = value
      ....:
      sage: MyClass3(3) is MyClass3()
      False

  Instead, one should do:

      sage: class MyClass3(UniqueRepresentation):
      ....:     @staticmethod
      ....:     def __classcall__(cls, value = 3):
      ....:         return super(MyClass3, cls).__classcall__(cls, value)
      ....:
      ....:     def __init__(self, value):
      ....:         self.value = value
      ....:
      sage: MyClass3(3) is MyClass3()
      True

  A bit of explanation is in order. First, the call `MyClass2([1,2,3])` triggers a call to `MyClass2.__classcall__(MyClass2, [1,2,3])`. This is an extension of the standard Python behavior, needed by `CachedRepresentation`, and implemented by the `ClasscallMetaclass`. Then, `MyClass2.__classcall__` does the desired transformations on the arguments. Finally, it uses `super` to call the default implementation of `__classcall__` provided by `CachedRepresentation`. This one in turn handles the caching and, if needed, constructs and initializes a new object in the class using `__new__` and `__init__` as usual.

  Constraints:

  - `__classcall__()` is a staticmethod (like, implicitly, `__new__`)
  - the preprocessing on the arguments should be idempotent. That is, if `MyClass2.__classcall__(<arguments>)` calls `CachedRepresentation.__classcall__(<preprocessed_arguments>)`, then `MyClass2.__classcall__(<preprocessed_arguments>)` should also result in a call to `CachedRepresentation.__classcall__(<preprocessed_arguments>)`.
  - `MyClass2.__classcall__` should return the result of `CachedRepresentation.__classcall__()` without modifying it.

  Other than that `MyClass2.__classcall__` may play any tricks, like acting as a factory and returning objects from other classes.

  Warning

  It is possible, but strongly discouraged, to let the `__classcall__` method of a class `C` return objects that are not instances of `C`. Of course, instances of a subclass of `C` are fine. Compare the examples in `unique_representation`.

  We illustrate what is meant by an “idempotent” preprocessing. Imagine that one has instances that are created with an integer-valued argument, but only depend on the square of the argument. It would be a mistake to square the given argument during preprocessing:

      sage: class WrongUsage(CachedRepresentation):
      ....:     @staticmethod
      ....:     def __classcall__(cls, n):
      ....:         return super(WrongUsage,cls).__classcall__(cls, n^2)
      ....:     def __init__(self, n):
      ....:         self.n = n
      ....:     def __repr__(self):
      ....:         return "Something(%d)"%self.n
      ....:
      sage: import __main__
      sage: __main__.WrongUsage = WrongUsage # This is only needed in doctests
      sage: w = WrongUsage(3); w
      Something(9)
      sage: w._reduction
      (<class '__main__.WrongUsage'>, (9,), {})

  Indeed, the reduction data are obtained from the preprocessed arguments. By consequence, if the resulting instance is pickled and unpickled, the argument gets squared again:

      sage: loads(dumps(w))
      Something(81)

  Instead, the preprocessing should only take the absolute value of the given argument, while the squaring should happen inside of the `__init__` method, where it won’t mess with the cache:

      sage: class BetterUsage(CachedRepresentation):
      ....:     @staticmethod
      ....:     def __classcall__(cls, n):
      ....:         return super(BetterUsage, cls).__classcall__(cls, abs(n))
      ....:     def __init__(self, n):
      ....:         self.n = n^2
      ....:     def __repr__(self):
      ....:         return "SomethingElse(%d)"%self.n
      ....:
      sage: __main__.BetterUsage = BetterUsage # This is only needed in doctests
      sage: b = BetterUsage(3); b
      SomethingElse(9)
      sage: loads(dumps(b)) is b
      True
      sage: b is BetterUsage(-3)
      True

  Cached representation and mutability

  `CachedRepresentation` is primarily intended for implementing objects which are (at least semantically) immutable. This is in particular assumed by the default implementations of `copy` and `deepcopy`:

      sage: copy(x) is x
      True
      sage: from copy import deepcopy
      sage: deepcopy(x) is x
      True

  However, in contrast to `UniqueRepresentation`, using `CachedRepresentation` allows for a comparison that is not by identity:

      sage: t = MyClass(3)
      sage: z = MyClass(2)
      sage: t.value = 2

  Now `t` and `z` are non-identical, but equal:

      sage: t.value == z.value
      True
      sage: t == z
      True
      sage: t is z
      False

  More on cached representation and identity

  `CachedRepresentation` is implemented by means of a cache. This cache uses weak references. Hence, when all other references to, say, `MyClass(1)` have been deleted, the instance is actually deleted from memory. A later call to `MyClass(1)` reconstructs the instance from scratch.

      sage: class SomeClass(UniqueRepresentation):
      ....:     def __init__(self, i):
      ....:         print "creating new instance for argument %s"%i
      ....:         self.i = i
      ....:     def __del__(self):
      ....:         print "deleting instance for argument %s"%self.i
      ....:
      sage: O = SomeClass(1)
      creating new instance for argument 1
      sage: O is SomeClass(1)
      True
      sage: O is SomeClass(2)
      creating new instance for argument 2
      deleting instance for argument 2
      False
      sage: del O
      deleting instance for argument 1
      sage: O = SomeClass(1)
      creating new instance for argument 1
      sage: del O
      deleting instance for argument 1

  Cached representation and pickling

  The default Python pickling implementation (by reconstructing an object from its class and dictionary, see “The pickle protocol” in the Python Library Reference) does not preserve cached representation, as Python has no chance to know whether and where the same object already exists.

  `CachedRepresentation` tries to ensure appropriate pickling by implementing a `__reduce__` method returning the arguments passed to the constructor:

      sage: import __main__             # Fake MyClass being defined in a python module
      sage: __main__.MyClass = MyClass
      sage: x = MyClass(1)
      sage: loads(dumps(x)) is x
      True

  `CachedRepresentation` uses the `__reduce__` pickle protocol rather than `__getnewargs__` because the latter does not handle keyword arguments:

      sage: x = MyClass(value = 1)
      sage: x.__reduce__()
      (<function unreduce at ...>, (<class '__main__.MyClass'>, (), {'value': 1}))
      sage: x is loads(dumps(x))
      True

  Note

  The default implementation of `__reduce__` in `CachedRepresentation` requires to store the constructor’s arguments in the instance dictionary upon construction:

      sage: x.__dict__
      {'_reduction': (<class '__main__.MyClass'>, (), {'value': 1}), 'value': 1}

  It is often easy in a derived subclass to reconstruct the constructor’s arguments from the instance data structure. When this is the case, `__reduce__` should be overridden; automagically the arguments won’t be stored anymore:

      sage: class MyClass3(UniqueRepresentation):
      ....:     def __init__(self, value):
      ....:         self.value = value
      ....:
      ....:     def __reduce__(self):
      ....:         return (MyClass3, (self.value,))
      ....:
      sage: import __main__; __main__.MyClass3 = MyClass3  # Fake MyClass3 being defined in a python module
      sage: x = MyClass3(1)
      sage: loads(dumps(x)) is x
      True
      sage: x.__dict__
      {'value': 1}

  Migrating classes to `CachedRepresentation` and unpickling

  We check that, when migrating a class to `CachedRepresentation`, older pickles can still be reasonably unpickled. Let us create a (new style) class, and pickle one of its instances:

      sage: class MyClass4(object):
      ....:     def __init__(self, value):
      ....:         self.value = value
      ....:
      sage: import __main__; __main__.MyClass4 = MyClass4  # Fake MyClass4 being defined in a python module
      sage: pickle = dumps(MyClass4(1))

  It can be unpickled:

      sage: y = loads(pickle)
      sage: y.value
      1

  Now, we upgrade the class to derive from `UniqueRepresentation`, which inherits from `CachedRepresentation`:

      sage: class MyClass4(UniqueRepresentation, object):
      ....:     def __init__(self, value):
      ....:         self.value = value
      sage: import __main__; __main__.MyClass4 = MyClass4  # Fake MyClass4 being defined in a python module
      sage: __main__.MyClass4 = MyClass4

  The pickle can still be unpickled:

      sage: y = loads(pickle)
      sage: y.value
      1

  Note however that, for the reasons explained above, unique representation is not guaranteed in this case:

      sage: y is MyClass4(1)
      False

  Todo

  Illustrate how this can be fixed on a case by case basis.

  Now, we redo the same test for a class deriving from SageObject:

      sage: class MyClass4(SageObject):
      ....:     def __init__(self, value):
      ....:         self.value = value
      sage: import __main__; __main__.MyClass4 = MyClass4  # Fake MyClass4 being defined in a python module
      sage: pickle = dumps(MyClass4(1))

      sage: class MyClass4(UniqueRepresentation, SageObject):
      ....:     def __init__(self, value):
      ....:         self.value = value
      sage: __main__.MyClass4 = MyClass4
      sage: y = loads(pickle)
      sage: y.value
      1

  Caveat: unpickling instances of a formerly old-style class is not supported yet by default:

      sage: class MyClass4:
      ....:     def __init__(self, value):
      ....:         self.value = value
      sage: import __main__; __main__.MyClass4 = MyClass4  # Fake MyClass4 being defined in a python module
      sage: pickle = dumps(MyClass4(1))

      sage: class MyClass4(UniqueRepresentation, SageObject):
      ....:     def __init__(self, value):
      ....:         self.value = value
      sage: __main__.MyClass4 = MyClass4
      sage: y = loads(pickle)  # todo: not implemented
      sage: y.value            # todo: not implemented
      1

  Rationale for the current implementation

  `CachedRepresentation` and derived classes use the `ClasscallMetaclass` of the standard Python type. The following example explains why.

  We define a variant of `MyClass` where the calls to `__init__` are traced:

      sage: class MyClass(CachedRepresentation):
      ....:     def __init__(self, value):
      ....:         print "initializing object"
      ....:         self.value = value
      ....:

  Let us create an object twice:

      sage: x = MyClass(1)
      initializing object
      sage: z = MyClass(1)

  As desired the `__init__` method was only called the first time, which is an important feature.

  As far as we can tell, this is not achievable while just using `__new__` and `__init__` (as defined by type; see Section Basic Customization in the Python Reference Manual). Indeed, `__init__` is called systematically on the result of `__new__` whenever the result is an instance of the class.

  Another difficulty is that argument preprocessing (as in the example above) cannot be handled by `__new__`, since the unprocessed arguments will be passed down to `__init__`.

class `sage.structure.unique_representation.``UniqueRepresentation`

  Bases: `sage.structure.unique_representation.CachedRepresentation`, `sage.misc.fast_methods.WithEqualityById`

  Classes derived from UniqueRepresentation inherit a unique representation behavior for their instances.

  See also

  `unique_representation`

  EXAMPLES:

  The short story: to construct a class whose instances have a unique representation behavior one just has to do:

      sage: class MyClass(UniqueRepresentation):
      ....:     # all the rest as usual
      ....:     pass

  Everything below is for the curious or for advanced usage.

  What is unique representation?

  Instances of a class have a unique representation behavior when instances evaluate equal if and only if they are identical (i.e., share the same memory representation), if and only if they were created using equal arguments. For example, calling twice:

      sage: f = SymmetricFunctions(QQ)
      sage: g = SymmetricFunctions(QQ)

  to create the symmetric function algebra over \(\QQ\) actually gives back the same object:

      sage: f == g
      True
      sage: f is g
      True

  This is a standard design pattern. It allows for sharing cached data (say representation theoretical information about a group) as well as for very fast hashing and equality testing. This behaviour is typically desirable for parents and categories. It can also be useful for intensive computations where one wants to cache all the operations on a small set of elements (say the multiplication table of a small group), and access this cache as quickly as possible.

  `UniqueRepresentation` is very easy to use: a class just needs to derive from it, or make sure some of its super classes does. Also, it groups together the class and the factory in a single gadget:

      sage: isinstance(SymmetricFunctions(CC), SymmetricFunctions)
      True
      sage: issubclass(SymmetricFunctions, UniqueRepresentation)
      True

  This nice behaviour is not available when one just uses a factory:

      sage: isinstance(GF(7), GF)
      Traceback (most recent call last):
      ...
      TypeError: isinstance() arg 2 must be a class, type, or tuple of classes and types
      sage: isinstance(GF, sage.structure.factory.UniqueFactory)
      True

  In addition, `UniqueFactory` only provides the cached representation behaviour, but not the unique representation behaviour—the examples in `unique_representation` explain this difference.

  On the other hand, the `UniqueRepresentation` class is more intrusive, as it imposes a behavior (and a metaclass) on all the subclasses. In particular, the unique representation behaviour is imposed on all subclasses (unless the `__classcall__` method is overloaded and not called in the subclass, which is not recommended). Its implementation is also more technical, which leads to some subtleties.

  EXAMPLES:

  We start with a simple class whose constructor takes a single value as argument. This pattern is similar to what is done in `sage.combinat.sf.sf.SymmetricFunctions`:

      sage: class MyClass(UniqueRepresentation):
      ....:     def __init__(self, value):
      ....:         self.value = value
      ....:     def __cmp__(self, other):
      ....:         c = cmp(type(self),type(other))
      ....:         if c: return c
      ....:         print "custom cmp"
      ....:         return cmp(self.value, other.value)
      ....:

  Two coexisting instances of `MyClass` created with the same argument data are guaranteed to share the same identity. Since trac ticket #12215, this is only the case if there is some strong reference to the returned instance, since otherwise it may be garbage collected:

      sage: x = MyClass(1)
      sage: y = MyClass(1)
      sage: x is y               # There is a strong reference
      True
      sage: z = MyClass(2)
      sage: x is z
      False

  In particular, modifying any one of them modifies the other (reference effect):

      sage: x.value = 3
      sage: x.value, y.value
      (3, 3)
      sage: y.value = 1
      sage: x.value, y.value
      (1, 1)

  Rich comparison by identity is used when possible (hence, for `==`, for `!=`, and for identical arguments in the case of `<`, `<=`, `>=` and `>`), which is as fast as it can get. Only if identity is not enough to decide the answer of a comparison, the custom comparison is called:

      sage: x == y
      True
      sage: z = MyClass(2)
      sage: x == z, x is z
      (False, False)
      sage: x <= x
      True
      sage: x != z
      True
      sage: x <= z
      custom cmp
      True
      sage: x > z
      custom cmp
      False

  A hash function equivalent to `object.__hash__()` is used, which is compatible with comparison by identity. However this means that the hash function may change in between Sage sessions, or even within the same Sage session.

      sage: hash(x) == object.__hash__(x)
      True

  Warning

  It is possible to inherit from `UniqueRepresentation` and then overload comparison in a way that destroys the unique representation property. We strongly recommend against it! You should use `CachedRepresentation` instead.

  Mixing super types and super classes

  TESTS:

  For the record, this test did fail with previous implementation attempts:

      sage: class bla(UniqueRepresentation, SageObject):
      ....:     pass
      ....:
      sage: b = bla()

`sage.structure.unique_representation.``unreduce`(cls, args, keywords)

  Calls a class on the given arguments:

      sage: sage.structure.unique_representation.unreduce(Integer, (1,), {})
      1

  Todo

  should reuse something preexisting ...

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