8. Instruções compostas
***********************

Instruções compostas contém (grupos de) outras instruções; Elas afetam
ou controlam a execução dessas outras instruções de alguma maneira. Em
geral, instruções compostas abrangem múltiplas linhas, no entanto em
algumas manifestações simples uma instrução composta inteira pode
estar contida em uma linha.

As instruções "if", "while" e "for" implementam construções
tradicionais de controle do fluxo de execução. "try" especifica
tratadores de exceção e/ou código de limpeza para uma instrução ou
grupo de instruções, enquanto a palavra reservada "with" permite a
execução de código de inicialização e finalização em volta de um bloco
de código. Definições de função e classe também são sintaticamente
instruções compostas.

Uma instrução composta consiste em uma ou mais "cláusulas". Uma
cláusula consiste em um cabeçalho e um "conjunto". Os cabeçalhos das
cláusulas de uma instrução composta específica estão todos no mesmo
nível de indentação. Cada cabeçalho de cláusula começa com uma palavra
reservada de identificação exclusiva e termina com dois pontos. Um
conjunto é um grupo de instruções controladas por uma cláusula. Um
conjunto pode ser uma ou mais instruções simples separadas por ponto e
vírgula na mesma linha do cabeçalho, após os dois pontos do cabeçalho,
ou pode ser uma ou mais instruções indentadas nas linhas subsequentes.
Somente a última forma de conjunto pode conter instruções compostas
aninhadas; o seguinte é ilegal, principalmente porque não ficaria
claro a qual cláusula "if" a seguinte cláusula "else" pertenceria:

   if test1: if test2: print(x)

Observe também que o ponto e vírgula é mais vinculado que os dois
pontos neste contexto, de modo que no exemplo a seguir, todas ou
nenhuma das chamadas "print()" são executadas:

   if x < y < z: print(x); print(y); print(z)

Resumindo:

   compound_stmt ::= if_stmt
                     | while_stmt
                     | for_stmt
                     | try_stmt
                     | with_stmt
                     | match_stmt
                     | funcdef
                     | classdef
                     | async_with_stmt
                     | async_for_stmt
                     | async_funcdef
   suite         ::= stmt_list NEWLINE | NEWLINE INDENT statement+ DEDENT
   statement     ::= stmt_list NEWLINE | compound_stmt
   stmt_list     ::= simple_stmt (";" simple_stmt)* [";"]

Note que instruções sempre terminam em uma "NEWLINE" possivelmente
seguida por uma "DEDENT". Note também que cláusulas de continuação
sempre começam com uma palavra reservada que não pode iniciar uma
instrução, desta forma não há ambiguidades (o problema do ""else"
pendurado" é resolvido em Python obrigando que instruções "if"
aninhadas tenham indentação)

A formatação das regras de gramática nas próximas seções põe cada
cláusula em uma linha separada para as tornar mais claras.


8.1. A instrução "if"
=====================

A instrução "if" é usada para execução condicional:

   if_stmt ::= "if" assignment_expression ":" suite
               ("elif" assignment_expression ":" suite)*
               ["else" ":" suite]

Ele seleciona exatamente um dos conjuntos avaliando as expressões uma
por uma até que uma seja considerada verdadeira (veja a seção
Operações booleanas para a definição de verdadeiro e falso); então
esse conjunto é executado (e nenhuma outra parte da instrução "if" é
executada ou avaliada). Se todas as expressões forem falsas, o
conjunto da cláusula "else", se presente, é executado.


8.2. A instrução "while"
========================

A instrução "while" é usada para execução repetida desde que uma
expressão seja verdadeira:

   while_stmt ::= "while" assignment_expression ":" suite
                  ["else" ":" suite]

Isto testa repetidamente a expressão e, se for verdadeira, executa o
primeiro conjunto; se a expressão for falsa (o que pode ser a primeira
vez que ela é testada) o conjunto da cláusula "else", se presente, é
executado e o laço termina.

Uma instrução "break" executada no primeiro conjunto termina o loop
sem executar o conjunto da cláusula "else". Uma instrução "continue"
executada no primeiro conjunto ignora o resto do conjunto e volta a
testar a expressão.


8.3. A instrução "for"
======================

A instrução "for" é usada para iterar sobre os elementos de uma
sequência (como uma string, tupla ou lista) ou outro objeto iterável:

   for_stmt ::= "for" target_list "in" starred_list ":" suite
                ["else" ":" suite]

A expressão "starred_list" é avaliada uma vez; deve produzir um objeto
*iterável*. Um *iterador* é criado para esse iterável. O primeiro item
fornecido pelo iterador é então atribuído à lista de alvos usando as
regras padrão para atribuições (veja Instruções de atribuição), e o
conjunto é executado. Isso se repete para cada item fornecido pelo
iterador. Quando o iterador se esgota, o conjunto na cláusula "else",
se presente, é executado e o loop termina.

Uma instrução "break" executada no primeiro conjunto termina o loop
sem executar o conjunto da cláusula "else". Uma instrução "continue"
executada no primeiro conjunto pula o resto do conjunto e continua com
o próximo item, ou com a cláusula "else" se não houver próximo item.

O laço for faz atribuições às variáveis na lista de destino. Isso
substitui todas as atribuições anteriores a essas variáveis, incluindo
aquelas feitas no conjunto do laço for:

   for i in range(10):
       print(i)
       i = 5             # this will not affect the for-loop
                         # because i will be overwritten with the next
                         # index in the range

Os nomes na lista de destinos não são excluídos quando o laço termina,
mas se a sequência estiver vazia, eles não serão atribuídos pelo laço.
Dica: o tipo embutido "range()" representa sequências aritméticas
imutáveis de inteiros. Por exemplo, iterar "range(3)" sucessivamente
produz 0, 1 e depois 2.

Alterado na versão 3.11: Elementos marcados com estrela agora são
permitidos na lista de expressões.


8.4. A instrução "try"
======================

A instrução "try" especifica manipuladores de exceção e/ou código de
limpeza para um grupo de instruções:

   try_stmt  ::= try1_stmt | try2_stmt | try3_stmt
   try1_stmt ::= "try" ":" suite
                 ("except" [expression ["as" identifier]] ":" suite)+
                 ["else" ":" suite]
                 ["finally" ":" suite]
   try2_stmt ::= "try" ":" suite
                 ("except" "*" expression ["as" identifier] ":" suite)+
                 ["else" ":" suite]
                 ["finally" ":" suite]
   try3_stmt ::= "try" ":" suite
                 "finally" ":" suite

Informações adicionais sobre exceções podem ser encontradas na seção
Exceções, e informações sobre como usar a instrução "raise" para gerar
exceções podem ser encontradas na seção A instrução raise.


8.4.1. Cláusula "except"
------------------------

The "except" clause(s) specify one or more exception handlers. When no
exception occurs in the "try" clause, no exception handler is
executed. When an exception occurs in the "try" suite, a search for an
exception handler is started. This search inspects the "except"
clauses in turn until one is found that matches the exception. An
expression-less "except" clause, if present, must be last; it matches
any exception.

For an "except" clause with an expression, the expression must
evaluate to an exception type or a tuple of exception types. The
raised exception matches an "except" clause whose expression evaluates
to the class or a *non-virtual base class* of the exception object, or
to a tuple that contains such a class.

Se nenhuma cláusula "except" corresponder à exceção, a busca por um
manipulador de exceção continua no código circundante e na pilha de
invocação. [1]

Se a avaliação de uma expressão no cabeçalho de uma cláusula "except"
levantar uma exceção, a busca original por um manipulador será
cancelada e uma busca pela nova exceção será iniciada no código
circundante e na pilha de chamadas (ela é tratado como se toda a
instrução "try" levantasse a exceção).

Quando uma cláusula "except" correspondente é encontrada, a exceção é
atribuída ao destino especificado após a palavra reservada "as" nessa
cláusula "except", se presente, e o conjunto da cláusula "except" é
executado. Todas as cláusulas "except" devem ter um bloco executável.
Quando o final deste bloco é atingido, a execução continua normalmente
após toda a instrução "try". (Isso significa que se existirem dois
manipuladores aninhados para a mesma exceção, e a exceção ocorrer na
cláusula "try" do manipulador interno, o manipulador externo não
tratará a exceção.)

Quando uma exceção foi atribuída usando "as target", ela é limpa no
final da cláusula "except". É como se

   except E as N:
       foo

fosse traduzido para

   except E as N:
       try:
           foo
       finally:
           del N

Isso significa que a exceção deve ser atribuída a um nome diferente
para poder referenciá-la após a cláusula "except". As exceções são
limpas porque, com o traceback (situação da pilha de execução) anexado
a elas, elas formam um ciclo de referência com o quadro de pilha,
mantendo todos os locais nesse quadro vivos até que ocorra a próxima
coleta de lixo.

Antes de um conjunto de cláusulas "except" ser executado, a exceção é
armazenada no módulo "sys", onde pode ser acessada de dentro do corpo
da cláusula "except" chamando "sys.exception()". Ao sair de um
manipulador de exceções, a exceção armazenada no módulo "sys" é
redefinida para seu valor anterior:

   >>> print(sys.exception())
   None
   >>> try:
   ...     raise TypeError
   ... except:
   ...     print(repr(sys.exception()))
   ...     try:
   ...          raise ValueError
   ...     except:
   ...         print(repr(sys.exception()))
   ...     print(repr(sys.exception()))
   ...
   TypeError()
   ValueError()
   TypeError()
   >>> print(sys.exception())
   None


8.4.2. "except*" clause
-----------------------

The "except*" clause(s) are used for handling "ExceptionGroup"s. The
exception type for matching is interpreted as in the case of "except",
but in the case of exception groups we can have partial matches when
the type matches some of the exceptions in the group. This means that
multiple "except*" clauses can execute, each handling part of the
exception group. Each clause executes at most once and handles an
exception group of all matching exceptions.  Each exception in the
group is handled by at most one "except*" clause, the first that
matches it.

   >>> try:
   ...     raise ExceptionGroup("eg",
   ...         [ValueError(1), TypeError(2), OSError(3), OSError(4)])
   ... except* TypeError as e:
   ...     print(f'caught {type(e)} with nested {e.exceptions}')
   ... except* OSError as e:
   ...     print(f'caught {type(e)} with nested {e.exceptions}')
   ...
   caught <class 'ExceptionGroup'> with nested (TypeError(2),)
   caught <class 'ExceptionGroup'> with nested (OSError(3), OSError(4))
     + Exception Group Traceback (most recent call last):
     |   File "<stdin>", line 2, in <module>
     | ExceptionGroup: eg
     +-+---------------- 1 ----------------
       | ValueError: 1
       +------------------------------------

Any remaining exceptions that were not handled by any "except*" clause
are re-raised at the end, along with all exceptions that were raised
from within the "except*" clauses. If this list contains more than one
exception to reraise, they are combined into an exception group.

If the raised exception is not an exception group and its type matches
one of the "except*" clauses, it is caught and wrapped by an exception
group with an empty message string.

   >>> try:
   ...     raise BlockingIOError
   ... except* BlockingIOError as e:
   ...     print(repr(e))
   ...
   ExceptionGroup('', (BlockingIOError()))

An "except*" clause must have a matching expression; it cannot be
"except*:". Furthermore, this expression cannot contain exception
group types, because that would have ambiguous semantics.

It is not possible to mix "except" and "except*" in the same "try".
"break", "continue" and "return" cannot appear in an "except*" clause.


8.4.3. "else" clause
--------------------

The optional "else" clause is executed if the control flow leaves the
"try" suite, no exception was raised, and no "return", "continue", or
"break" statement was executed.  Exceptions in the "else" clause are
not handled by the preceding "except" clauses.


8.4.4. "finally" clause
-----------------------

If "finally" is present, it specifies a 'cleanup' handler.  The "try"
clause is executed, including any "except" and "else" clauses.  If an
exception occurs in any of the clauses and is not handled, the
exception is temporarily saved. The "finally" clause is executed.  If
there is a saved exception it is re-raised at the end of the "finally"
clause.  If the "finally" clause raises another exception, the saved
exception is set as the context of the new exception. If the "finally"
clause executes a "return", "break" or "continue" statement, the saved
exception is discarded:

   >>> def f():
   ...     try:
   ...         1/0
   ...     finally:
   ...         return 42
   ...
   >>> f()
   42

The exception information is not available to the program during
execution of the "finally" clause.

When a "return", "break" or "continue" statement is executed in the
"try" suite of a "try"..."finally" statement, the "finally" clause is
also executed 'on the way out.'

The return value of a function is determined by the last "return"
statement executed.  Since the "finally" clause always executes, a
"return" statement executed in the "finally" clause will always be the
last one executed:

   >>> def foo():
   ...     try:
   ...         return 'try'
   ...     finally:
   ...         return 'finally'
   ...
   >>> foo()
   'finally'

Alterado na versão 3.8: Prior to Python 3.8, a "continue" statement
was illegal in the "finally" clause due to a problem with the
implementation.


8.5. The "with" statement
=========================

The "with" statement is used to wrap the execution of a block with
methods defined by a context manager (see section Gerenciadores de
contexto da instrução with). This allows common
"try"..."except"..."finally" usage patterns to be encapsulated for
convenient reuse.

   with_stmt          ::= "with" ( "(" with_stmt_contents ","? ")" | with_stmt_contents ) ":" suite
   with_stmt_contents ::= with_item ("," with_item)*
   with_item          ::= expression ["as" target]

The execution of the "with" statement with one "item" proceeds as
follows:

1. The context expression (the expression given in the "with_item") is
   evaluated to obtain a context manager.

2. The context manager's "__enter__()" is loaded for later use.

3. The context manager's "__exit__()" is loaded for later use.

4. The context manager's "__enter__()" method is invoked.

5. If a target was included in the "with" statement, the return value
   from "__enter__()" is assigned to it.

   Nota:

     The "with" statement guarantees that if the "__enter__()" method
     returns without an error, then "__exit__()" will always be
     called. Thus, if an error occurs during the assignment to the
     target list, it will be treated the same as an error occurring
     within the suite would be. See step 7 below.

6. The suite is executed.

7. The context manager's "__exit__()" method is invoked.  If an
   exception caused the suite to be exited, its type, value, and
   traceback are passed as arguments to "__exit__()". Otherwise, three
   "None" arguments are supplied.

   If the suite was exited due to an exception, and the return value
   from the "__exit__()" method was false, the exception is reraised.
   If the return value was true, the exception is suppressed, and
   execution continues with the statement following the "with"
   statement.

   If the suite was exited for any reason other than an exception, the
   return value from "__exit__()" is ignored, and execution proceeds
   at the normal location for the kind of exit that was taken.

The following code:

   with EXPRESSION as TARGET:
       SUITE

is semantically equivalent to:

   manager = (EXPRESSION)
   enter = type(manager).__enter__
   exit = type(manager).__exit__
   value = enter(manager)
   hit_except = False

   try:
       TARGET = value
       SUITE
   except:
       hit_except = True
       if not exit(manager, *sys.exc_info()):
           raise
   finally:
       if not hit_except:
           exit(manager, None, None, None)

With more than one item, the context managers are processed as if
multiple "with" statements were nested:

   with A() as a, B() as b:
       SUITE

is semantically equivalent to:

   with A() as a:
       with B() as b:
           SUITE

You can also write multi-item context managers in multiple lines if
the items are surrounded by parentheses. For example:

   with (
       A() as a,
       B() as b,
   ):
       SUITE

Alterado na versão 3.1: Support for multiple context expressions.

Alterado na versão 3.10: Support for using grouping parentheses to
break the statement in multiple lines.

Ver também:

  **PEP 343** - A instrução "with"
     A especificação, o histórico e os exemplos para a instrução
     Python "with".


8.6. The "match" statement
==========================

Adicionado na versão 3.10.

The match statement is used for pattern matching.  Syntax:

   match_stmt   ::= 'match' subject_expr ":" NEWLINE INDENT case_block+ DEDENT
   subject_expr ::= star_named_expression "," star_named_expressions?
                    | named_expression
   case_block   ::= 'case' patterns [guard] ":" block

Nota:

  This section uses single quotes to denote soft keywords.

Pattern matching takes a pattern as input (following "case") and a
subject value (following "match").  The pattern (which may contain
subpatterns) is matched against the subject value.  The outcomes are:

* A match success or failure (also termed a pattern success or
  failure).

* Possible binding of matched values to a name.  The prerequisites for
  this are further discussed below.

The "match" and "case" keywords are soft keywords.

Ver também:

  * **PEP 634** -- Structural Pattern Matching: Specification

  * **PEP 636** -- Structural Pattern Matching: Tutorial


8.6.1. Visão Geral
------------------

Here's an overview of the logical flow of a match statement:

1. The subject expression "subject_expr" is evaluated and a resulting
   subject value obtained. If the subject expression contains a comma,
   a tuple is constructed using the standard rules.

2. Each pattern in a "case_block" is attempted to match with the
   subject value. The specific rules for success or failure are
   described below. The match attempt can also bind some or all of the
   standalone names within the pattern. The precise pattern binding
   rules vary per pattern type and are specified below.  **Name
   bindings made during a successful pattern match outlive the
   executed block and can be used after the match statement**.

   Nota:

     During failed pattern matches, some subpatterns may succeed.  Do
     not rely on bindings being made for a failed match.  Conversely,
     do not rely on variables remaining unchanged after a failed
     match.  The exact behavior is dependent on implementation and may
     vary.  This is an intentional decision made to allow different
     implementations to add optimizations.

3. If the pattern succeeds, the corresponding guard (if present) is
   evaluated. In this case all name bindings are guaranteed to have
   happened.

   * If the guard evaluates as true or is missing, the "block" inside
     "case_block" is executed.

   * Otherwise, the next "case_block" is attempted as described above.

   * If there are no further case blocks, the match statement is
     completed.

Nota:

  Users should generally never rely on a pattern being evaluated.
  Depending on implementation, the interpreter may cache values or use
  other optimizations which skip repeated evaluations.

A sample match statement:

   >>> flag = False
   >>> match (100, 200):
   ...    case (100, 300):  # Mismatch: 200 != 300
   ...        print('Case 1')
   ...    case (100, 200) if flag:  # Successful match, but guard fails
   ...        print('Case 2')
   ...    case (100, y):  # Matches and binds y to 200
   ...        print(f'Case 3, y: {y}')
   ...    case _:  # Pattern not attempted
   ...        print('Case 4, I match anything!')
   ...
   Case 3, y: 200

In this case, "if flag" is a guard.  Read more about that in the next
section.


8.6.2. Guards
-------------

   guard ::= "if" named_expression

A "guard" (which is part of the "case") must succeed for code inside
the "case" block to execute.  It takes the form: "if" followed by an
expression.

The logical flow of a "case" block with a "guard" follows:

1. Check that the pattern in the "case" block succeeded.  If the
   pattern failed, the "guard" is not evaluated and the next "case"
   block is checked.

2. If the pattern succeeded, evaluate the "guard".

   * If the "guard" condition evaluates as true, the case block is
     selected.

   * If the "guard" condition evaluates as false, the case block is
     not selected.

   * If the "guard" raises an exception during evaluation, the
     exception bubbles up.

Guards are allowed to have side effects as they are expressions.
Guard evaluation must proceed from the first to the last case block,
one at a time, skipping case blocks whose pattern(s) don't all
succeed. (I.e., guard evaluation must happen in order.) Guard
evaluation must stop once a case block is selected.


8.6.3. Irrefutable Case Blocks
------------------------------

An irrefutable case block is a match-all case block.  A match
statement may have at most one irrefutable case block, and it must be
last.

A case block is considered irrefutable if it has no guard and its
pattern is irrefutable.  A pattern is considered irrefutable if we can
prove from its syntax alone that it will always succeed.  Only the
following patterns are irrefutable:

* AS Patterns whose left-hand side is irrefutable

* OR Patterns containing at least one irrefutable pattern

* Capture Patterns

* Wildcard Patterns

* parenthesized irrefutable patterns


8.6.4. Patterns
---------------

Nota:

  This section uses grammar notations beyond standard EBNF:

  * the notation "SEP.RULE+" is shorthand for "RULE (SEP RULE)*"

  * the notation "!RULE" is shorthand for a negative lookahead
    assertion

The top-level syntax for "patterns" is:

   patterns       ::= open_sequence_pattern | pattern
   pattern        ::= as_pattern | or_pattern
   closed_pattern ::= | literal_pattern
                      | capture_pattern
                      | wildcard_pattern
                      | value_pattern
                      | group_pattern
                      | sequence_pattern
                      | mapping_pattern
                      | class_pattern

The descriptions below will include a description "in simple terms" of
what a pattern does for illustration purposes (credits to Raymond
Hettinger for a document that inspired most of the descriptions). Note
that these descriptions are purely for illustration purposes and **may
not** reflect the underlying implementation.  Furthermore, they do not
cover all valid forms.


8.6.4.1. OR Patterns
~~~~~~~~~~~~~~~~~~~~

An OR pattern is two or more patterns separated by vertical bars "|".
Syntax:

   or_pattern ::= "|".closed_pattern+

Only the final subpattern may be irrefutable, and each subpattern must
bind the same set of names to avoid ambiguity.

An OR pattern matches each of its subpatterns in turn to the subject
value, until one succeeds.  The OR pattern is then considered
successful.  Otherwise, if none of the subpatterns succeed, the OR
pattern fails.

In simple terms, "P1 | P2 | ..." will try to match "P1", if it fails
it will try to match "P2", succeeding immediately if any succeeds,
failing otherwise.


8.6.4.2. AS Patterns
~~~~~~~~~~~~~~~~~~~~

An AS pattern matches an OR pattern on the left of the "as" keyword
against a subject.  Syntax:

   as_pattern ::= or_pattern "as" capture_pattern

If the OR pattern fails, the AS pattern fails.  Otherwise, the AS
pattern binds the subject to the name on the right of the as keyword
and succeeds. "capture_pattern" cannot be a "_".

In simple terms "P as NAME" will match with "P", and on success it
will set "NAME = <subject>".


8.6.4.3. Literal Patterns
~~~~~~~~~~~~~~~~~~~~~~~~~

A literal pattern corresponds to most literals in Python.  Syntax:

   literal_pattern ::= signed_number
                       | signed_number "+" NUMBER
                       | signed_number "-" NUMBER
                       | strings
                       | "None"
                       | "True"
                       | "False"
                       | signed_number: NUMBER | "-" NUMBER

The rule "strings" and the token "NUMBER" are defined in the standard
Python grammar.  Triple-quoted strings are supported.  Raw strings and
byte strings are supported.  Literais de strings formatadas are not
supported.

The forms "signed_number '+' NUMBER" and "signed_number '-' NUMBER"
are for expressing complex numbers; they require a real number on the
left and an imaginary number on the right. E.g. "3 + 4j".

In simple terms, "LITERAL" will succeed only if "<subject> ==
LITERAL". For the singletons "None", "True" and "False", the "is"
operator is used.


8.6.4.4. Capture Patterns
~~~~~~~~~~~~~~~~~~~~~~~~~

A capture pattern binds the subject value to a name. Syntax:

   capture_pattern ::= !'_' NAME

A single underscore "_" is not a capture pattern (this is what "!'_'"
expresses). It is instead treated as a "wildcard_pattern".

In a given pattern, a given name can only be bound once.  E.g. "case
x, x: ..." is invalid while "case [x] | x: ..." is allowed.

Capture patterns always succeed.  The binding follows scoping rules
established by the assignment expression operator in **PEP 572**; the
name becomes a local variable in the closest containing function scope
unless there's an applicable "global" or "nonlocal" statement.

In simple terms "NAME" will always succeed and it will set "NAME =
<subject>".


8.6.4.5. Wildcard Patterns
~~~~~~~~~~~~~~~~~~~~~~~~~~

A wildcard pattern always succeeds (matches anything) and binds no
name.  Syntax:

   wildcard_pattern ::= '_'

"_" is a soft keyword within any pattern, but only within patterns.
It is an identifier, as usual, even within "match" subject
expressions, "guard"s, and "case" blocks.

In simple terms, "_" will always succeed.


8.6.4.6. Value Patterns
~~~~~~~~~~~~~~~~~~~~~~~

A value pattern represents a named value in Python. Syntax:

   value_pattern ::= attr
   attr          ::= name_or_attr "." NAME
   name_or_attr  ::= attr | NAME

The dotted name in the pattern is looked up using standard Python name
resolution rules.  The pattern succeeds if the value found compares
equal to the subject value (using the "==" equality operator).

In simple terms "NAME1.NAME2" will succeed only if "<subject> ==
NAME1.NAME2"

Nota:

  If the same value occurs multiple times in the same match statement,
  the interpreter may cache the first value found and reuse it rather
  than repeat the same lookup.  This cache is strictly tied to a given
  execution of a given match statement.


8.6.4.7. Group Patterns
~~~~~~~~~~~~~~~~~~~~~~~

A group pattern allows users to add parentheses around patterns to
emphasize the intended grouping.  Otherwise, it has no additional
syntax. Syntax:

   group_pattern ::= "(" pattern ")"

In simple terms "(P)" has the same effect as "P".


8.6.4.8. Sequence Patterns
~~~~~~~~~~~~~~~~~~~~~~~~~~

A sequence pattern contains several subpatterns to be matched against
sequence elements. The syntax is similar to the unpacking of a list or
tuple.

   sequence_pattern       ::= "[" [maybe_sequence_pattern] "]"
                        | "(" [open_sequence_pattern] ")"
   open_sequence_pattern  ::= maybe_star_pattern "," [maybe_sequence_pattern]
   maybe_sequence_pattern ::= ",".maybe_star_pattern+ ","?
   maybe_star_pattern     ::= star_pattern | pattern
   star_pattern           ::= "*" (capture_pattern | wildcard_pattern)

There is no difference if parentheses  or square brackets are used for
sequence patterns (i.e. "(...)" vs "[...]" ).

Nota:

  A single pattern enclosed in parentheses without a trailing comma
  (e.g. "(3 | 4)") is a group pattern. While a single pattern enclosed
  in square brackets (e.g. "[3 | 4]") is still a sequence pattern.

At most one star subpattern may be in a sequence pattern.  The star
subpattern may occur in any position. If no star subpattern is
present, the sequence pattern is a fixed-length sequence pattern;
otherwise it is a variable-length sequence pattern.

The following is the logical flow for matching a sequence pattern
against a subject value:

1. If the subject value is not a sequence [2], the sequence pattern
   fails.

2. If the subject value is an instance of "str", "bytes" or
   "bytearray" the sequence pattern fails.

3. The subsequent steps depend on whether the sequence pattern is
   fixed or variable-length.

   If the sequence pattern is fixed-length:

   1. If the length of the subject sequence is not equal to the number
      of subpatterns, the sequence pattern fails

   2. Subpatterns in the sequence pattern are matched to their
      corresponding items in the subject sequence from left to right.
      Matching stops as soon as a subpattern fails.  If all
      subpatterns succeed in matching their corresponding item, the
      sequence pattern succeeds.

   Otherwise, if the sequence pattern is variable-length:

   1. If the length of the subject sequence is less than the number of
      non-star subpatterns, the sequence pattern fails.

   2. The leading non-star subpatterns are matched to their
      corresponding items as for fixed-length sequences.

   3. If the previous step succeeds, the star subpattern matches a
      list formed of the remaining subject items, excluding the
      remaining items corresponding to non-star subpatterns following
      the star subpattern.

   4. Remaining non-star subpatterns are matched to their
      corresponding subject items, as for a fixed-length sequence.

   Nota:

     The length of the subject sequence is obtained via "len()" (i.e.
     via the "__len__()" protocol).  This length may be cached by the
     interpreter in a similar manner as value patterns.

In simple terms "[P1, P2, P3," ... ", P<N>]" matches only if all the
following happens:

* check "<subject>" is a sequence

* "len(subject) == <N>"

* "P1" matches "<subject>[0]" (note that this match can also bind
  names)

* "P2" matches "<subject>[1]" (note that this match can also bind
  names)

* ... and so on for the corresponding pattern/element.


8.6.4.9. Mapping Patterns
~~~~~~~~~~~~~~~~~~~~~~~~~

A mapping pattern contains one or more key-value patterns.  The syntax
is similar to the construction of a dictionary. Syntax:

   mapping_pattern     ::= "{" [items_pattern] "}"
   items_pattern       ::= ",".key_value_pattern+ ","?
   key_value_pattern   ::= (literal_pattern | value_pattern) ":" pattern
                         | double_star_pattern
   double_star_pattern ::= "**" capture_pattern

At most one double star pattern may be in a mapping pattern.  The
double star pattern must be the last subpattern in the mapping
pattern.

Duplicate keys in mapping patterns are disallowed. Duplicate literal
keys will raise a "SyntaxError". Two keys that otherwise have the same
value will raise a "ValueError" at runtime.

The following is the logical flow for matching a mapping pattern
against a subject value:

1. If the subject value is not a mapping [3],the mapping pattern
   fails.

2. If every key given in the mapping pattern is present in the subject
   mapping, and the pattern for each key matches the corresponding
   item of the subject mapping, the mapping pattern succeeds.

3. If duplicate keys are detected in the mapping pattern, the pattern
   is considered invalid. A "SyntaxError" is raised for duplicate
   literal values; or a "ValueError" for named keys of the same value.

Nota:

  Key-value pairs are matched using the two-argument form of the
  mapping subject's "get()" method.  Matched key-value pairs must
  already be present in the mapping, and not created on-the-fly via
  "__missing__()" or "__getitem__()".

In simple terms "{KEY1: P1, KEY2: P2, ... }" matches only if all the
following happens:

* check "<subject>" is a mapping

* "KEY1 in <subject>"

* "P1" matches "<subject>[KEY1]"

* ... and so on for the corresponding KEY/pattern pair.


8.6.4.10. Class Patterns
~~~~~~~~~~~~~~~~~~~~~~~~

A class pattern represents a class and its positional and keyword
arguments (if any).  Syntax:

   class_pattern       ::= name_or_attr "(" [pattern_arguments ","?] ")"
   pattern_arguments   ::= positional_patterns ["," keyword_patterns]
                         | keyword_patterns
   positional_patterns ::= ",".pattern+
   keyword_patterns    ::= ",".keyword_pattern+
   keyword_pattern     ::= NAME "=" pattern

The same keyword should not be repeated in class patterns.

The following is the logical flow for matching a class pattern against
a subject value:

1. If "name_or_attr" is not an instance of the builtin "type" , raise
   "TypeError".

2. If the subject value is not an instance of "name_or_attr" (tested
   via "isinstance()"), the class pattern fails.

3. If no pattern arguments are present, the pattern succeeds.
   Otherwise, the subsequent steps depend on whether keyword or
   positional argument patterns are present.

   For a number of built-in types (specified below), a single
   positional subpattern is accepted which will match the entire
   subject; for these types keyword patterns also work as for other
   types.

   If only keyword patterns are present, they are processed as
   follows, one by one:

   I. The keyword is looked up as an attribute on the subject.

      * If this raises an exception other than "AttributeError", the
        exception bubbles up.

      * If this raises "AttributeError", the class pattern has failed.

      * Else, the subpattern associated with the keyword pattern is
        matched against the subject's attribute value.  If this fails,
        the class pattern fails; if this succeeds, the match proceeds
        to the next keyword.

   II. If all keyword patterns succeed, the class pattern succeeds.

   If any positional patterns are present, they are converted to
   keyword patterns using the "__match_args__" attribute on the class
   "name_or_attr" before matching:

   I. The equivalent of "getattr(cls, "__match_args__", ())" is
   called.

      * If this raises an exception, the exception bubbles up.

      * If the returned value is not a tuple, the conversion fails and
        "TypeError" is raised.

      * If there are more positional patterns than
        "len(cls.__match_args__)", "TypeError" is raised.

      * Otherwise, positional pattern "i" is converted to a keyword
        pattern using "__match_args__[i]" as the keyword.
        "__match_args__[i]" must be a string; if not "TypeError" is
        raised.

      * If there are duplicate keywords, "TypeError" is raised.

      Ver também:

        Customizando argumentos posicionais na classe correspondência
        de padrão

   II. Once all positional patterns have been converted to keyword
   patterns,
      the match proceeds as if there were only keyword patterns.

   For the following built-in types the handling of positional
   subpatterns is different:

   * "bool"

   * "bytearray"

   * "bytes"

   * "dict"

   * "float"

   * "frozenset"

   * "int"

   * "list"

   * "set"

   * "str"

   * "tuple"

   These classes accept a single positional argument, and the pattern
   there is matched against the whole object rather than an attribute.
   For example "int(0|1)" matches the value "0", but not the value
   "0.0".

In simple terms "CLS(P1, attr=P2)" matches only if the following
happens:

* "isinstance(<subject>, CLS)"

* convert "P1" to a keyword pattern using "CLS.__match_args__"

* For each keyword argument "attr=P2":

  * "hasattr(<subject>, "attr")"

  * "P2" matches "<subject>.attr"

* ... and so on for the corresponding keyword argument/pattern pair.

Ver também:

  * **PEP 634** -- Structural Pattern Matching: Specification

  * **PEP 636** -- Structural Pattern Matching: Tutorial


8.7. Definições de função
=========================

A function definition defines a user-defined function object (see
section A hierarquia de tipos padrão):

   funcdef                   ::= [decorators] "def" funcname [type_params] "(" [parameter_list] ")"
               ["->" expression] ":" suite
   decorators                ::= decorator+
   decorator                 ::= "@" assignment_expression NEWLINE
   parameter_list            ::= defparameter ("," defparameter)* "," "/" ["," [parameter_list_no_posonly]]
                        | parameter_list_no_posonly
   parameter_list_no_posonly ::= defparameter ("," defparameter)* ["," [parameter_list_starargs]]
                                 | parameter_list_starargs
   parameter_list_starargs   ::= "*" [parameter] ("," defparameter)* ["," ["**" parameter [","]]]
                               | "**" parameter [","]
   parameter                 ::= identifier [":" expression]
   defparameter              ::= parameter ["=" expression]
   funcname                  ::= identifier

A function definition is an executable statement.  Its execution binds
the function name in the current local namespace to a function object
(a wrapper around the executable code for the function).  This
function object contains a reference to the current global namespace
as the global namespace to be used when the function is called.

The function definition does not execute the function body; this gets
executed only when the function is called. [4]

A function definition may be wrapped by one or more *decorator*
expressions. Decorator expressions are evaluated when the function is
defined, in the scope that contains the function definition.  The
result must be a callable, which is invoked with the function object
as the only argument. The returned value is bound to the function name
instead of the function object.  Multiple decorators are applied in
nested fashion. For example, the following code

   @f1(arg)
   @f2
   def func(): pass

is roughly equivalent to

   def func(): pass
   func = f1(arg)(f2(func))

except that the original function is not temporarily bound to the name
"func".

Alterado na versão 3.9: Functions may be decorated with any valid
"assignment_expression". Previously, the grammar was much more
restrictive; see **PEP 614** for details.

A list of type parameters may be given in square brackets between the
function's name and the opening parenthesis for its parameter list.
This indicates to static type checkers that the function is generic.
At runtime, the type parameters can be retrieved from the function's
"__type_params__" attribute. See Generic functions for more.

Alterado na versão 3.12: Type parameter lists are new in Python 3.12.

When one or more *parameters* have the form *parameter* "="
*expression*, the function is said to have "default parameter values."
For a parameter with a default value, the corresponding *argument* may
be omitted from a call, in which case the parameter's default value is
substituted.  If a parameter has a default value, all following
parameters up until the ""*"" must also have a default value --- this
is a syntactic restriction that is not expressed by the grammar.

**Default parameter values are evaluated from left to right when the
function definition is executed.** This means that the expression is
evaluated once, when the function is defined, and that the same "pre-
computed" value is used for each call.  This is especially important
to understand when a default parameter value is a mutable object, such
as a list or a dictionary: if the function modifies the object (e.g.
by appending an item to a list), the default parameter value is in
effect modified.  This is generally not what was intended.  A way
around this is to use "None" as the default, and explicitly test for
it in the body of the function, e.g.:

   def whats_on_the_telly(penguin=None):
       if penguin is None:
           penguin = []
       penguin.append("property of the zoo")
       return penguin

Function call semantics are described in more detail in section
Chamadas. A function call always assigns values to all parameters
mentioned in the parameter list, either from positional arguments,
from keyword arguments, or from default values.  If the form
""*identifier"" is present, it is initialized to a tuple receiving any
excess positional parameters, defaulting to the empty tuple. If the
form ""**identifier"" is present, it is initialized to a new ordered
mapping receiving any excess keyword arguments, defaulting to a new
empty mapping of the same type.  Parameters after ""*"" or
""*identifier"" are keyword-only parameters and may only be passed by
keyword arguments.  Parameters before ""/"" are positional-only
parameters and may only be passed by positional arguments.

Alterado na versão 3.8: The "/" function parameter syntax may be used
to indicate positional-only parameters. See **PEP 570** for details.

Parameters may have an *annotation* of the form "": expression""
following the parameter name.  Any parameter may have an annotation,
even those of the form "*identifier" or "**identifier".  Functions may
have "return" annotation of the form ""-> expression"" after the
parameter list.  These annotations can be any valid Python expression.
The presence of annotations does not change the semantics of a
function.  The annotation values are available as values of a
dictionary keyed by the parameters' names in the "__annotations__"
attribute of the function object.  If the "annotations" import from
"__future__" is used, annotations are preserved as strings at runtime
which enables postponed evaluation.  Otherwise, they are evaluated
when the function definition is executed.  In this case annotations
may be evaluated in a different order than they appear in the source
code.

It is also possible to create anonymous functions (functions not bound
to a name), for immediate use in expressions.  This uses lambda
expressions, described in section Lambdas.  Note that the lambda
expression is merely a shorthand for a simplified function definition;
a function defined in a ""def"" statement can be passed around or
assigned to another name just like a function defined by a lambda
expression.  The ""def"" form is actually more powerful since it
allows the execution of multiple statements and annotations.

**Programmer's note:** Functions are first-class objects.  A ""def""
statement executed inside a function definition defines a local
function that can be returned or passed around.  Free variables used
in the nested function can access the local variables of the function
containing the def.  See section Nomeação e ligação for details.

Ver também:

  **PEP 3107** - Function Annotations
     The original specification for function annotations.

  **PEP 484** - Dicas de tipos
     Definition of a standard meaning for annotations: type hints.

  **PEP 526** - Sintaxe para Anotações de Variáveis
     Ability to type hint variable declarations, including class
     variables and instance variables.

  **PEP 563** - Postponed Evaluation of Annotations
     Support for forward references within annotations by preserving
     annotations in a string form at runtime instead of eager
     evaluation.

  **PEP 318** - Decorators for Functions and Methods
     Function and method decorators were introduced. Class decorators
     were introduced in **PEP 3129**.


8.8. Definições de classe
=========================

A class definition defines a class object (see section A hierarquia de
tipos padrão):

   classdef    ::= [decorators] "class" classname [type_params] [inheritance] ":" suite
   inheritance ::= "(" [argument_list] ")"
   classname   ::= identifier

A class definition is an executable statement.  The inheritance list
usually gives a list of base classes (see Metaclasses for more
advanced uses), so each item in the list should evaluate to a class
object which allows subclassing.  Classes without an inheritance list
inherit, by default, from the base class "object"; hence,

   class Foo:
       pass

é equivalente a:

   class Foo(object):
       pass

The class's suite is then executed in a new execution frame (see
Nomeação e ligação), using a newly created local namespace and the
original global namespace. (Usually, the suite contains mostly
function definitions.)  When the class's suite finishes execution, its
execution frame is discarded but its local namespace is saved. [5] A
class object is then created using the inheritance list for the base
classes and the saved local namespace for the attribute dictionary.
The class name is bound to this class object in the original local
namespace.

The order in which attributes are defined in the class body is
preserved in the new class's "__dict__".  Note that this is reliable
only right after the class is created and only for classes that were
defined using the definition syntax.

Class creation can be customized heavily using metaclasses.

Classes can also be decorated: just like when decorating functions,

   @f1(arg)
   @f2
   class Foo: pass

is roughly equivalent to

   class Foo: pass
   Foo = f1(arg)(f2(Foo))

The evaluation rules for the decorator expressions are the same as for
function decorators.  The result is then bound to the class name.

Alterado na versão 3.9: Classes may be decorated with any valid
"assignment_expression". Previously, the grammar was much more
restrictive; see **PEP 614** for details.

A list of type parameters may be given in square brackets immediately
after the class's name. This indicates to static type checkers that
the class is generic. At runtime, the type parameters can be retrieved
from the class's "__type_params__" attribute. See Generic classes for
more.

Alterado na versão 3.12: Type parameter lists are new in Python 3.12.

**Programmer's note:** Variables defined in the class definition are
class attributes; they are shared by instances.  Instance attributes
can be set in a method with "self.name = value".  Both class and
instance attributes are accessible through the notation ""self.name"",
and an instance attribute hides a class attribute with the same name
when accessed in this way.  Class attributes can be used as defaults
for instance attributes, but using mutable values there can lead to
unexpected results.  Descriptors can be used to create instance
variables with different implementation details.

Ver também:

  **PEP 3115** - Metaclasses no Python 3000
     The proposal that changed the declaration of metaclasses to the
     current syntax, and the semantics for how classes with
     metaclasses are constructed.

  **PEP 3129** - Class Decorators
     The proposal that added class decorators.  Function and method
     decorators were introduced in **PEP 318**.


8.9. Corrotinas
===============

Adicionado na versão 3.5.


8.9.1. Coroutine function definition
------------------------------------

   async_funcdef ::= [decorators] "async" "def" funcname "(" [parameter_list] ")"
                     ["->" expression] ":" suite

Execution of Python coroutines can be suspended and resumed at many
points (see *coroutine*). "await" expressions, "async for" and "async
with" can only be used in the body of a coroutine function.

Functions defined with "async def" syntax are always coroutine
functions, even if they do not contain "await" or "async" keywords.

It is a "SyntaxError" to use a "yield from" expression inside the body
of a coroutine function.

An example of a coroutine function:

   async def func(param1, param2):
       do_stuff()
       await some_coroutine()

Alterado na versão 3.7: "await" and "async" are now keywords;
previously they were only treated as such inside the body of a
coroutine function.


8.9.2. The "async for" statement
--------------------------------

   async_for_stmt ::= "async" for_stmt

An *asynchronous iterable* provides an "__aiter__" method that
directly returns an *asynchronous iterator*, which can call
asynchronous code in its "__anext__" method.

The "async for" statement allows convenient iteration over
asynchronous iterables.

The following code:

   async for TARGET in ITER:
       SUITE
   else:
       SUITE2

Is semantically equivalent to:

   iter = (ITER)
   iter = type(iter).__aiter__(iter)
   running = True

   while running:
       try:
           TARGET = await type(iter).__anext__(iter)
       except StopAsyncIteration:
           running = False
       else:
           SUITE
   else:
       SUITE2

See also "__aiter__()" and "__anext__()" for details.

It is a "SyntaxError" to use an "async for" statement outside the body
of a coroutine function.


8.9.3. The "async with" statement
---------------------------------

   async_with_stmt ::= "async" with_stmt

An *asynchronous context manager* is a *context manager* that is able
to suspend execution in its *enter* and *exit* methods.

The following code:

   async with EXPRESSION as TARGET:
       SUITE

is semantically equivalent to:

   manager = (EXPRESSION)
   aenter = type(manager).__aenter__
   aexit = type(manager).__aexit__
   value = await aenter(manager)
   hit_except = False

   try:
       TARGET = value
       SUITE
   except:
       hit_except = True
       if not await aexit(manager, *sys.exc_info()):
           raise
   finally:
       if not hit_except:
           await aexit(manager, None, None, None)

See also "__aenter__()" and "__aexit__()" for details.

It is a "SyntaxError" to use an "async with" statement outside the
body of a coroutine function.

Ver também:

  **PEP 492** - Coroutines with async and await syntax
     The proposal that made coroutines a proper standalone concept in
     Python, and added supporting syntax.


8.10. Type parameter lists
==========================

Adicionado na versão 3.12.

Alterado na versão 3.13: Support for default values was added (see
**PEP 696**).

   type_params  ::= "[" type_param ("," type_param)* "]"
   type_param   ::= typevar | typevartuple | paramspec
   typevar      ::= identifier (":" expression)? ("=" expression)?
   typevartuple ::= "*" identifier ("=" expression)?
   paramspec    ::= "**" identifier ("=" expression)?

Functions (including coroutines), classes and type aliases may contain
a type parameter list:

   def max[T](args: list[T]) -> T:
       ...

   async def amax[T](args: list[T]) -> T:
       ...

   class Bag[T]:
       def __iter__(self) -> Iterator[T]:
           ...

       def add(self, arg: T) -> None:
           ...

   type ListOrSet[T] = list[T] | set[T]

Semantically, this indicates that the function, class, or type alias
is generic over a type variable. This information is primarily used by
static type checkers, and at runtime, generic objects behave much like
their non-generic counterparts.

Type parameters are declared in square brackets ("[]") immediately
after the name of the function, class, or type alias. The type
parameters are accessible within the scope of the generic object, but
not elsewhere. Thus, after a declaration "def func[T](): pass", the
name "T" is not available in the module scope. Below, the semantics of
generic objects are described with more precision. The scope of type
parameters is modeled with a special function (technically, an
annotation scope) that wraps the creation of the generic object.

Generic functions, classes, and type aliases have a "__type_params__"
attribute listing their type parameters.

Type parameters come in three kinds:

* "typing.TypeVar", introduced by a plain name (e.g., "T").
  Semantically, this represents a single type to a type checker.

* "typing.TypeVarTuple", introduced by a name prefixed with a single
  asterisk (e.g., "*Ts"). Semantically, this stands for a tuple of any
  number of types.

* "typing.ParamSpec", introduced by a name prefixed with two asterisks
  (e.g., "**P"). Semantically, this stands for the parameters of a
  callable.

"typing.TypeVar" declarations can define *bounds* and *constraints*
with a colon (":") followed by an expression. A single expression
after the colon indicates a bound (e.g. "T: int"). Semantically, this
means that the "typing.TypeVar" can only represent types that are a
subtype of this bound. A parenthesized tuple of expressions after the
colon indicates a set of constraints (e.g. "T: (str, bytes)"). Each
member of the tuple should be a type (again, this is not enforced at
runtime). Constrained type variables can only take on one of the types
in the list of constraints.

For "typing.TypeVar"s declared using the type parameter list syntax,
the bound and constraints are not evaluated when the generic object is
created, but only when the value is explicitly accessed through the
attributes "__bound__" and "__constraints__". To accomplish this, the
bounds or constraints are evaluated in a separate annotation scope.

"typing.TypeVarTuple"s and "typing.ParamSpec"s cannot have bounds or
constraints.

All three flavors of type parameters can also have a *default value*,
which is used when the type parameter is not explicitly provided. This
is added by appending a single equals sign ("=") followed by an
expression. Like the bounds and constraints of type variables, the
default value is not evaluated when the object is created, but only
when the type parameter's "__default__" attribute is accessed. To this
end, the default value is evaluated in a separate annotation scope. If
no default value is specified for a type parameter, the "__default__"
attribute is set to the special sentinel object "typing.NoDefault".

The following example indicates the full set of allowed type parameter
declarations:

   def overly_generic[
      SimpleTypeVar,
      TypeVarWithDefault = int,
      TypeVarWithBound: int,
      TypeVarWithConstraints: (str, bytes),
      *SimpleTypeVarTuple = (int, float),
      **SimpleParamSpec = (str, bytearray),
   ](
      a: SimpleTypeVar,
      b: TypeVarWithDefault,
      c: TypeVarWithBound,
      d: Callable[SimpleParamSpec, TypeVarWithConstraints],
      *e: SimpleTypeVarTuple,
   ): ...


8.10.1. Generic functions
-------------------------

Generic functions are declared as follows:

   def func[T](arg: T): ...

This syntax is equivalent to:

   annotation-def TYPE_PARAMS_OF_func():
       T = typing.TypeVar("T")
       def func(arg: T): ...
       func.__type_params__ = (T,)
       return func
   func = TYPE_PARAMS_OF_func()

Here "annotation-def" indicates an annotation scope, which is not
actually bound to any name at runtime. (One other liberty is taken in
the translation: the syntax does not go through attribute access on
the "typing" module, but creates an instance of "typing.TypeVar"
directly.)

The annotations of generic functions are evaluated within the
annotation scope used for declaring the type parameters, but the
function's defaults and decorators are not.

The following example illustrates the scoping rules for these cases,
as well as for additional flavors of type parameters:

   @decorator
   def func[T: int, *Ts, **P](*args: *Ts, arg: Callable[P, T] = some_default):
       ...

Except for the lazy evaluation of the "TypeVar" bound, this is
equivalent to:

   DEFAULT_OF_arg = some_default

   annotation-def TYPE_PARAMS_OF_func():

       annotation-def BOUND_OF_T():
           return int
       # In reality, BOUND_OF_T() is evaluated only on demand.
       T = typing.TypeVar("T", bound=BOUND_OF_T())

       Ts = typing.TypeVarTuple("Ts")
       P = typing.ParamSpec("P")

       def func(*args: *Ts, arg: Callable[P, T] = DEFAULT_OF_arg):
           ...

       func.__type_params__ = (T, Ts, P)
       return func
   func = decorator(TYPE_PARAMS_OF_func())

The capitalized names like "DEFAULT_OF_arg" are not actually bound at
runtime.


8.10.2. Generic classes
-----------------------

Generic classes are declared as follows:

   class Bag[T]: ...

This syntax is equivalent to:

   annotation-def TYPE_PARAMS_OF_Bag():
       T = typing.TypeVar("T")
       class Bag(typing.Generic[T]):
           __type_params__ = (T,)
           ...
       return Bag
   Bag = TYPE_PARAMS_OF_Bag()

Here again "annotation-def" (not a real keyword) indicates an
annotation scope, and the name "TYPE_PARAMS_OF_Bag" is not actually
bound at runtime.

Generic classes implicitly inherit from "typing.Generic". The base
classes and keyword arguments of generic classes are evaluated within
the type scope for the type parameters, and decorators are evaluated
outside that scope. This is illustrated by this example:

   @decorator
   class Bag(Base[T], arg=T): ...

Isso equivale a:

   annotation-def TYPE_PARAMS_OF_Bag():
       T = typing.TypeVar("T")
       class Bag(Base[T], typing.Generic[T], arg=T):
           __type_params__ = (T,)
           ...
       return Bag
   Bag = decorator(TYPE_PARAMS_OF_Bag())


8.10.3. Generic type aliases
----------------------------

The "type" statement can also be used to create a generic type alias:

   type ListOrSet[T] = list[T] | set[T]

Except for the lazy evaluation of the value, this is equivalent to:

   annotation-def TYPE_PARAMS_OF_ListOrSet():
       T = typing.TypeVar("T")

       annotation-def VALUE_OF_ListOrSet():
           return list[T] | set[T]
       # In reality, the value is lazily evaluated
       return typing.TypeAliasType("ListOrSet", VALUE_OF_ListOrSet(), type_params=(T,))
   ListOrSet = TYPE_PARAMS_OF_ListOrSet()

Here, "annotation-def" (not a real keyword) indicates an annotation
scope. The capitalized names like "TYPE_PARAMS_OF_ListOrSet" are not
actually bound at runtime.

-[ Notas de rodapé ]-

[1] The exception is propagated to the invocation stack unless there
    is a "finally" clause which happens to raise another exception.
    That new exception causes the old one to be lost.

[2] In pattern matching, a sequence is defined as one of the
    following:

    * a class that inherits from "collections.abc.Sequence"

    * a Python class that has been registered as
      "collections.abc.Sequence"

    * a builtin class that has its (CPython) "Py_TPFLAGS_SEQUENCE" bit
      set

    * a class that inherits from any of the above

    The following standard library classes are sequences:

    * "array.array"

    * "collections.deque"

    * "list"

    * "memoryview"

    * "range"

    * "tuple"

    Nota:

      Subject values of type "str", "bytes", and "bytearray" do not
      match sequence patterns.

[3] In pattern matching, a mapping is defined as one of the following:

    * a class that inherits from "collections.abc.Mapping"

    * a Python class that has been registered as
      "collections.abc.Mapping"

    * a builtin class that has its (CPython) "Py_TPFLAGS_MAPPING" bit
      set

    * a class that inherits from any of the above

    The standard library classes "dict" and "types.MappingProxyType"
    are mappings.

[4] A string literal appearing as the first statement in the function
    body is transformed into the function's "__doc__" attribute and
    therefore the function's *docstring*.

[5] A string literal appearing as the first statement in the class
    body is transformed into the namespace's "__doc__" item and
    therefore the class's *docstring*.
