8. Compound statements
**********************

Compound statements contain (groups of) other statements; they affect
or control the execution of those other statements in some way.  In
general, compound statements span multiple lines, although in simple
incarnations a whole compound statement may be contained in one line.

The "if", "while" and "for" statements implement traditional control
flow constructs.  "try" specifies exception handlers and/or cleanup
code for a group of statements, while the "with" statement allows the
execution of initialization and finalization code around a block of
code.  Function and class definitions are also syntactically compound
statements.

A compound statement consists of one or more ‘clauses.’  A clause
consists of a header and a ‘suite.’  The clause headers of a
particular compound statement are all at the same indentation level.
Each clause header begins with a uniquely identifying keyword and ends
with a colon.  A suite is a group of statements controlled by a
clause.  A suite can be one or more semicolon-separated simple
statements on the same line as the header, following the header’s
colon, or it can be one or more indented statements on subsequent
lines.  Only the latter form of a suite can contain nested compound
statements; the following is illegal, mostly because it wouldn’t be
clear to which "if" clause a following "else" clause would belong:

   if test1: if test2: print(x)

Also note that the semicolon binds tighter than the colon in this
context, so that in the following example, either all or none of the
"print()" calls are executed:

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

Summarizing:

   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 that statements always end in a "NEWLINE" possibly followed by a
"DEDENT".  Also note that optional continuation clauses always begin
with a keyword that cannot start a statement, thus there are no
ambiguities (the ‘dangling "else"’ problem is solved in Python by
requiring nested "if" statements to be indented).

The formatting of the grammar rules in the following sections places
each clause on a separate line for clarity.


8.1. The "if" statement
=======================

The "if" statement is used for conditional execution:

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

It selects exactly one of the suites by evaluating the expressions one
by one until one is found to be true (see section Boolean operations
for the definition of true and false); then that suite is executed
(and no other part of the "if" statement is executed or evaluated).
If all expressions are false, the suite of the "else" clause, if
present, is executed.


8.2. The "while" statement
==========================

The "while" statement is used for repeated execution as long as an
expression is true:

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

This repeatedly tests the expression and, if it is true, executes the
first suite; if the expression is false (which may be the first time
it is tested) the suite of the "else" clause, if present, is executed
and the loop terminates.

A "break" statement executed in the first suite terminates the loop
without executing the "else" clause’s suite.  A "continue" statement
executed in the first suite skips the rest of the suite and goes back
to testing the expression.


8.3. The "for" statement
========================

The "for" statement is used to iterate over the elements of a sequence
(such as a string, tuple or list) or other iterable object:

   for_stmt: "for" target_list "in" starred_expression_list ":" suite
             ["else" ":" suite]

The "starred_expression_list" expression is evaluated once; it should
yield an *iterable* object. An *iterator* is created for that
iterable. The first item provided by the iterator is then assigned to
the target list using the standard rules for assignments (see
Assignment statements), and the suite is executed. This repeats for
each item provided by the iterator. When the iterator is exhausted,
the suite in the "else" clause, if present, is executed, and the loop
terminates.

A "break" statement executed in the first suite terminates the loop
without executing the "else" clause’s suite.  A "continue" statement
executed in the first suite skips the rest of the suite and continues
with the next item, or with the "else" clause if there is no next
item.

The for-loop makes assignments to the variables in the target list.
This overwrites all previous assignments to those variables including
those made in the suite of the for-loop:

   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

Names in the target list are not deleted when the loop is finished,
but if the sequence is empty, they will not have been assigned to at
all by the loop.  Hint: the built-in type "range()" represents
immutable arithmetic sequences of integers. For instance, iterating
"range(3)" successively yields 0, 1, and then 2.

Changed in version 3.11: Starred elements are now allowed in the
expression list.


8.4. The "try" statement
========================

The "try" statement specifies exception handlers and/or cleanup code
for a group of statements:

   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

Additional information on exceptions can be found in section
Exceptions, and information on using the "raise" statement to generate
exceptions may be found in section The raise statement.

Changed in version 3.14: Support for optionally dropping grouping
parentheses when using multiple exception types. See **PEP 758**.


8.4.1. "except" clause
----------------------

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.
Parentheses can be dropped if multiple exception types are provided
and the "as" clause is not used. 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.

If no "except" clause matches the exception, the search for an
exception handler continues in the surrounding code and on the
invocation stack.  [1]

If the evaluation of an expression in the header of an "except" clause
raises an exception, the original search for a handler is canceled and
a search starts for the new exception in the surrounding code and on
the call stack (it is treated as if the entire "try" statement raised
the exception).

When a matching "except" clause is found, the exception is assigned to
the target specified after the "as" keyword in that "except" clause,
if present, and the "except" clause’s suite is executed. All "except"
clauses must have an executable block. When the end of this block is
reached, execution continues normally after the entire "try"
statement. (This means that if two nested handlers exist for the same
exception, and the exception occurs in the "try" clause of the inner
handler, the outer handler will not handle the exception.)

When an exception has been assigned using "as target", it is cleared
at the end of the "except" clause.  This is as if:

   except E as N:
       foo

was translated to:

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

This means the exception must be assigned to a different name to be
able to refer to it after the "except" clause. Exceptions are cleared
because with the traceback attached to them, they form a reference
cycle with the stack frame, keeping all locals in that frame alive
until the next garbage collection occurs.

Before an "except" clause’s suite is executed, the exception is stored
in the "sys" module, where it can be accessed from within the body of
the "except" clause by calling "sys.exception()". When leaving an
exception handler, the exception stored in the "sys" module is reset
to its previous value:

   >>> 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) specify one or more handlers for groups of
exceptions ("BaseExceptionGroup" instances). A "try" statement can
have either "except" or "except*" clauses, but not both. The exception
type for matching is mandatory in the case of "except*", so "except*:"
is a syntax error. The type is interpreted as in the case of "except",
but matching is performed on the exceptions contained in the group
that is being handled. A "TypeError" is raised if a matching type is a
subclass of "BaseExceptionGroup", because that would have ambiguous
semantics.

When an exception group is raised in the try block, each "except*"
clause splits (see "split()") it into the subgroups of matching and
non-matching exceptions. If the matching subgroup is not empty, it
becomes the handled exception (the value returned from
"sys.exception()") and assigned to the target of the "except*" clause
(if there is one). Then, the body of the "except*" clause executes. If
the non-matching subgroup is not empty, it is processed by the next
"except*" in the same manner. This continues until all exceptions in
the group have been matched, or the last "except*" clause has run.

After all "except*" clauses execute, the group of unhandled exceptions
is merged with any exceptions that were raised or re-raised from
within "except*" clauses. This merged exception group propagates on:

   >>> 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 "<doctest default[0]>", line 2, in <module>
     |     raise ExceptionGroup("eg",
     |         [ValueError(1), TypeError(2), OSError(3), OSError(4)])
     | ExceptionGroup: eg (1 sub-exception)
     +-+---------------- 1 ----------------
       | ValueError: 1
       +------------------------------------

If the exception raised from the "try" block 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. This
ensures that the type of the target "e" is consistently
"BaseExceptionGroup":

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

"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. For example, this function returns 42.

   def f():
       try:
           1/0
       finally:
           return 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. The following function returns ‘finally’.

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

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

Changed in version 3.14: The compiler emits a "SyntaxWarning" when a
"return", "break" or "continue" appears in a "finally" block (see
**PEP 765**).


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 With Statement
Context Managers). 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.

   Note:

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

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

except that implicit special method lookup is used for "__enter__()"
and "__exit__()".

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

Changed in version 3.1: Support for multiple context expressions.

Changed in version 3.10: Support for using grouping parentheses to
break the statement in multiple lines.

See also:

  **PEP 343** - The “with” statement
     The specification, background, and examples for the Python "with"
     statement.


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

Added in version 3.10.

The match statement is used for pattern matching.  Syntax:

   match_stmt:   'match' subject_expr ":" NEWLINE INDENT case_block+ DEDENT
   subject_expr: flexible_expression "," [flexible_expression_list [',']]
                 | assignment_expression
   case_block:   'case' patterns [guard] ":" suite

Note:

  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.

See also:

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

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


8.6.1. Overview
---------------

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**.

   Note:

     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.

Note:

  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" assignment_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
---------------

Note:

  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

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.  f-strings and t-strings 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"

Note:

  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 "[...]" ).

Note:

  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.

   Note:

     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.

Note:

  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:

   1. 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.

   2. 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:

   1. 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.

      See also:

        Customizing positional arguments in class pattern matching

   2. 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"

   * "frozendict"

   * "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.

See also:

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

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


8.7. Function definitions
=========================

A function definition defines a user-defined function object (see
section The standard type hierarchy):

   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:   "*" star_parameter ("," defparameter)* ["," [parameter_star_kwargs]]
                              | "*" ("," defparameter)+ ["," [parameter_star_kwargs]]
                              | parameter_star_kwargs
   parameter_star_kwargs:     "**" parameter [","]
   parameter:                 identifier [":" expression]
   star_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".

Changed in version 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.

Changed in version 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, for example:

   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 Calls.
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.

Changed in version 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". (As a special
case, parameters of the form "*identifier" may have an annotation “":
*expression"”.) 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. See Annotations for more
information on annotations.

Changed in version 3.11: Parameters of the form “"*identifier"” may
have an annotation “": *expression"”. See **PEP 646**.

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 Naming and binding for details.

See also:

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

  **PEP 484** - Type Hints
     Definition of a standard meaning for annotations: type hints.

  **PEP 526** - Syntax for Variable Annotations
     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. Class definitions
======================

A class definition defines a class object (see section The standard
type hierarchy):

   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

is equivalent to

   class Foo(object):
       pass

There may be one or more base classes; see Multiple inheritance below
for more information.

The class’s suite is then executed in a new execution frame (see
Naming and binding), 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.

Changed in version 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.

Changed in version 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.

See also:

  **PEP 3115** - Metaclasses in 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**.


8.8.1. Multiple inheritance
---------------------------

Python classes may have multiple base classes, a technique known as
*multiple inheritance*.  The base classes are specified in the class
definition by listing them in parentheses after the class name,
separated by commas. For example, the following class definition:

   >>> class A: pass
   >>> class B: pass
   >>> class C(A, B): pass

defines a class "C" that inherits from classes "A" and "B".

The *method resolution order* (MRO) is the order in which base classes
are searched when looking up an attribute on a class. See The Python
2.3 Method Resolution Order for a description of how Python determines
the MRO for a class.

Multiple inheritance is not always allowed. Attempting to define a
class with multiple inheritance will raise an error if one of the
bases does not allow subclassing, if a consistent MRO cannot be
created, if no valid metaclass can be determined, or if there is an
instance layout conflict. We’ll discuss each of these in turn.

First, all base classes must allow subclassing. While most classes
allow subclassing, some built-in classes do not, such as "bool":

   >>> class SubBool(bool):  # TypeError
   ...    pass
   Traceback (most recent call last):
      ...
   TypeError: type 'bool' is not an acceptable base type

In the resolved MRO of a class, the class’s bases appear in the order
they were specified in the class’s bases list. Additionally, the MRO
always lists a child class before any of its bases. A class definition
will fail if it is impossible to resolve a consistent MRO that
satisfies these rules from the list of bases provided:

   >>> class Base: pass
   >>> class Child(Base): pass
   >>> class Grandchild(Base, Child): pass  # TypeError
   Traceback (most recent call last):
      ...
   TypeError: Cannot create a consistent method resolution order (MRO) for bases Base, Child

In the MRO of "Grandchild", "Base" must appear before "Child" because
it is first in the base class list, but it must also appear after
"Child" because it is a parent of "Child". This is a contradiction, so
the class cannot be defined.

If some of the bases have a custom *metaclass*, the metaclass of the
resulting class is chosen among the metaclasses of the bases and the
explicitly specified metaclass of the child class. It must be a
metaclass that is a subclass of all other candidate metaclasses. If no
such metaclass exists among the candidates, the class cannot be
created, as explained in Determining the appropriate metaclass.

Finally, the instance layouts of the bases must be compatible. This
means that it must be possible to compute a *solid base* for the
class. Exactly which classes are solid bases depends on the Python
implementation.

**CPython implementation detail:** In CPython, a class is a solid base
if it has a nonempty "__slots__" definition. Many but not all classes
defined in C are also solid bases, including most builtins (such as
"int" or "BaseException") but excluding most concrete "Exception"
classes. Generally, a C class is a solid base if its underlying struct
is different in size from its base class.

Every class has a solid base. "object", the base class, has itself as
its solid base. If there is a single base, the child class’s solid
base is that class if it is a solid base, or else the base class’s
solid base. If there are multiple bases, we first find the solid base
for each base class to produce a list of candidate solid bases. If
there is a unique solid base that is a subclass of all others, then
that class is the solid base. Otherwise, class creation fails.

Example:

   >>> class Solid1:
   ...    __slots__ = ("solid1",)
   >>>
   >>> class Solid2:
   ...    __slots__ = ("solid2",)
   >>>
   >>> class SolidChild(Solid1):
   ...    __slots__ = ("solid_child",)
   >>>
   >>> class C1:  # solid base is `object`
   ...    pass
   >>>
   >>> # OK: solid bases are `Solid1` and `object`, and `Solid1` is a subclass of `object`.
   >>> class C2(Solid1, C1):  # solid base is `Solid1`
   ...    pass
   >>>
   >>> # OK: solid bases are `SolidChild` and `Solid1`, and `SolidChild` is a subclass of `Solid1`.
   >>> class C3(SolidChild, Solid1):  # solid base is `SolidChild`
   ...    pass
   >>>
   >>> # Error: solid bases are `Solid1` and `Solid2`, but neither is a subclass of the other.
   >>> class C4(Solid1, Solid2):  # error: no single solid base
   ...    pass
   Traceback (most recent call last):
     ...
   TypeError: multiple bases have instance lay-out conflict


8.9. Coroutines
===============

Added in version 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()

Changed in version 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).__aiter__()
   running = True

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

except that implicit special method lookup is used for "__aiter__()"
and "__anext__()".

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

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

except that implicit special method lookup is used for "__aenter__()"
and "__aexit__()".

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

See also:

  **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
==========================

Added in version 3.12.

Changed in version 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): ...

This is equivalent to:

   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.


8.11. Annotations
=================

Changed in version 3.14: Annotations are now lazily evaluated by
default.

Variables and function parameters may carry *annotations*, created by
adding a colon after the name, followed by an expression:

   x: annotation = 1
   def f(param: annotation): ...

Functions may also carry a return annotation following an arrow:

   def f() -> annotation: ...

Annotations are conventionally used for *type hints*, but this is not
enforced by the language, and in general annotations may contain
arbitrary expressions. The presence of annotations does not change the
runtime semantics of the code, except if some mechanism is used that
introspects and uses the annotations (such as "dataclasses" or
"@functools.singledispatch").

By default, annotations are lazily evaluated in an annotation scope.
This means that they are not evaluated when the code containing the
annotation is evaluated. Instead, the interpreter saves information
that can be used to evaluate the annotation later if requested. The
"annotationlib" module provides tools for evaluating annotations.

If the future statement "from __future__ import annotations" is
present, all annotations are instead stored as strings:

   >>> from __future__ import annotations
   >>> def f(param: annotation): ...
   >>> f.__annotations__
   {'param': 'annotation'}

This future statement will be deprecated and removed in a future
version of Python, but not before Python 3.13 reaches its end of life
(see **PEP 749**). When it is used, introspection tools like
"annotationlib.get_annotations()" and "typing.get_type_hints()" are
less likely to be able to resolve annotations at runtime.

-[ Footnotes ]-

[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"

    Note:

      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*.
