You are working in `/workspace/cpython`, a source tree checked out at
the base commit for this task. Implement the requested behavior in the source
tree, then run:

```bash
lolbench-submit
```

Do not stop after editing files, running tests, or describing the solution. The
task is complete only when `lolbench-submit` has created
`/logs/artifacts/solution.patch`. If that file does not exist, continue
working and run `lolbench-submit` again.

That command writes your implementation diff to
`/logs/artifacts/solution.patch`, which is the artifact the Harbor verifier
will grade. Before running `lolbench-submit`, clean or revert any test files
you created or modified; test files must not be included in the final
`solution.patch`.

This environment has no outbound internet access — `curl`/`wget`, `git fetch`/`clone`, package installs, and web fetch/search will all fail. Implement the requirements using only the code already in the workspace and your own knowledge; do not attempt to fetch or search external resources.

Now please implement the following requirements in the source tree:

---

## Abstract
This PEP introduces template strings for custom string processing.

Template strings are a generalization of f-strings, using a `t` in place of
the `f` prefix. Instead of evaluating to `str`, t-strings evaluate to a new
type, `Template`:

```python
template: Template = t"Hello {name}"
```
Templates provide developers with access to the string and its interpolated
values *before* they are combined. This brings native flexible string
processing to the Python language and enables safety checks, web templating,
domain-specific languages, and more.

## Relationship With Other PEPs
Python introduced f-strings in Python 3.6 with PEP 498. The grammar was
then formalized in PEP 701 which also lifted some restrictions. This PEP
is based on PEP 701.

At nearly the same time PEP 498 arrived, PEP 501 was written to provide
"i-strings" -- that is, "interpolation template strings". The PEP was
deferred pending further experience with f-strings. Work on this PEP was
resumed by a different author in March 2023, introducing "t-strings" as template
literal strings, and built atop PEP 701.

The authors of this PEP consider it to be a generalization and simplification
of the updated work in PEP 501. (That PEP has also recently been updated to
reflect the new ideas in this PEP.)

## Motivation
Python f-strings are easy to use and very popular. Over time, however, developers
have encountered limitations that make them
[unsuitable for certain use cases](https://docs.djangoproject.com/en/5.1/ref/utils/#django.utils.html.format_html).
In particular, f-strings provide no way to intercept and transform interpolated
values before they are combined into a final string.

As a result, incautious use of f-strings can lead to security vulnerabilities.
For example, a user executing a SQL query with `python:sqlite3`
may be tempted to use an f-string to embed values into their SQL expression,
which could lead to a [SQL injection attack](https://en.wikipedia.org/wiki/SQL_injection).
Or, a developer building HTML may include unescaped user input in the string,
leading to a [cross-site scripting (XSS)](https://en.wikipedia.org/wiki/Cross-site_scripting)
vulnerability.

More broadly, the inability to transform interpolated values before they are
combined into a final string limits the utility of f-strings in more complex
string processing tasks.

Template strings address these problems by providing
developers with access to the string and its interpolated values.

For example, imagine we want to generate some HTML. Using template strings,
we can define an `html()` function that allows us to automatically sanitize
content:

```python
evil = "<script>alert('evil')</script>"
template = t"<p>{evil}</p>"
assert html(template) == "<p>&lt;script&gt;alert('evil')&lt;/script&gt;</p>"
```
Likewise, our hypothetical `html()` function can make it easy for developers
to add attributes to HTML elements using a dictionary:

```python
attributes = {"src": "shrubbery.jpg", "alt": "looks nice"}
template = t"<img {attributes} />"
assert html(template) == '<img src="shrubbery.jpg" alt="looks nice" />'
```
Neither of these examples is possible with f-strings. By providing a
mechanism to intercept and transform interpolated values, template strings
enable a wide range of string processing use cases.

## Specification
### Template String Literals
This PEP introduces a new string prefix, `t`, to define template string literals.
These literals resolve to a new type, `Template`, found in the standard library
module `!string.templatelib`.

The following code creates a `Template` instance:

```python
from string.templatelib import Template
template = t"This is a template string."
assert isinstance(template, Template)
```
Template string literals support the full syntax of PEP 701. This includes
the ability to nest template strings within interpolations, as well as the ability
to use all valid quote marks (`'`, `"`, `'''`, and `"""`). Like other string
prefixes, the `t` prefix must immediately precede the quote. Like f-strings,
both lowercase `t` and uppercase `T` prefixes are supported. Like
f-strings, t-strings may not be combined with `u` or the `b` prefix.

Additionally, f-strings and t-strings cannot be combined, so the `ft`
prefix is invalid. t-strings *may* be combined with the `r` prefix;
see the `Raw Template Strings`_ section below for more information.

### The `Template` Type
Template strings evaluate to an instance of a new immutable type,
`!string.templatelib.Template`:

```python
class Template:
    strings: tuple[str, ...]
    """
    A non-empty tuple of the string parts of the template,
    with N+1 items, where N is the number of interpolations
    in the template.
    """

    interpolations: tuple[Interpolation, ...]
    """
    A tuple of the interpolation parts of the template.
    This will be an empty tuple if there are no interpolations.
    """

    def __new__(cls, *args: str | Interpolation):
        """
        Create a new Template instance.

        Arguments can be provided in any order.
        """
        ...

    @property
    def values(self) -> tuple[object, ...]:
        """
        Return a tuple of the `value` attributes of each Interpolation
        in the template.
        This will be an empty tuple if there are no interpolations.
        """
        ...

    def __iter__(self) -> Iterator[str | Interpolation]:
        """
        Iterate over the string parts and interpolations in the template.

        These may appear in any order. Empty strings will not be included.
        """
        ...
```
The `strings` and `interpolations` attributes provide access to the string
parts and any interpolations in the literal:

```python
name = "World"
template = t"Hello {name}"
assert template.strings[0] == "Hello "
assert template.interpolations[0].value == "World"
```
### The `Interpolation` Type
The `Interpolation` type represents an expression inside a template string.
Like `Template`, it is a new class found in the `!string.templatelib` module:

```python
class Interpolation:
    value: object
    expression: str
    conversion: Literal["a", "r", "s"] | None
    format_spec: str

    __match_args__ = ("value", "expression", "conversion", "format_spec")

    def __new__(
        cls,
        value: object,
        expression: str = "",
        conversion: Literal["a", "r", "s"] | None = None,
        format_spec: str = "",
    ):
        ...
```
The `Interpolation` type is shallow immutable. Its attributes
cannot be reassigned.

The `value` attribute is the evaluated result of the interpolation:

```python
name = "World"
template = t"Hello {name}"
assert template.interpolations[0].value == "World"
```
When interpolations are created from a template string literal, the
`expression` attribute contains the *original text* of the interpolation:

```python
name = "World"
template = t"Hello {name}"
assert template.interpolations[0].expression == "name"
```
When developers explicitly construct an `Interpolation`, they may optionally
provide a value for the `expression` attribute. Even though it is stored as
a string, this *should* be a valid Python expression. If no value is provided,
the `expression` attribute defaults to the empty string (`""`).

We expect that the `expression` attribute will not be used in most template
processing code. It is provided for completeness and for use in debugging and
introspection. See both the `Common Patterns Seen in Processing Templates`_
section and the `Examples`_ section for more information on how to process
template strings.

The `conversion` attribute is the `optional conversion <python:formatstrings>`
to be used, one of `r`, `s`, and `a`, corresponding to `repr()`,
`str()`, and `ascii()` conversions. As with f-strings, no other conversions
are supported:

```python
name = "World"
template = t"Hello {name!r}"
assert template.interpolations[0].conversion == "r"
```
If no conversion is provided, `conversion` is `None`.

The `format_spec` attribute is the `format specification <python:formatspec>`.
As with f-strings, this is an arbitrary string that defines how to present the value:

```python
value = 42
template = t"Value: {value:.2f}"
assert template.interpolations[0].format_spec == ".2f"
```
Format specifications in f-strings can themselves contain interpolations. This
is permitted in template strings as well; `format_spec` is set to the eagerly
evaluated result:

```python
value = 42
precision = 2
template = t"Value: {value:.{precision}f}"
assert template.interpolations[0].format_spec == ".2f"
```
If no format specification is provided, `format_spec` defaults to an empty
string (`""`). This matches the `format_spec` parameter of Python's
`python:format` built-in.

Unlike f-strings, it is up to code that processes the template to determine how to
interpret the `conversion` and `format_spec` attributes.
Such code is not required to use these attributes, but when present they should
be respected, and to the extent possible match the behavior of f-strings.
It would be surprising if, for example, a template string that uses `{value:.2f}`
did not round the value to two decimal places when processed.

### The `Template.values` Property
The `Template.values` property is a shortcut for accessing the `value`
attribute of each `Interpolation` in the template and is equivalent to:

```python
@property
def values(self) -> tuple[object, ...]:
    return tuple(i.value for i in self.interpolations)
```
### Iterating `Template` Contents
The `Template.__iter__()` method provides a simple way to access the full
contents of a template. It yields the string parts and interpolations in
the order they appear, with empty strings omitted.

The `__iter__()` method is equivalent to:

```python
def __iter__(self) -> Iterator[str | Interpolation]:
    for s, i in zip_longest(self.strings, self.interpolations):
        if s:
            yield s
        if i:
            yield i
```
The following examples show the `__iter__()` method in action:

```python
assert list(t"") == []

assert list(t"Hello") == ["Hello"]

name = "World"
template = t"Hello {name}!"
contents = list(template)
assert len(contents) == 3
assert contents[0] == "Hello "
assert contents[1].value == "World"
assert contents[1].expression == "name"
assert contents[2] == "!"
```
Empty strings, which may be present in `Template.strings`, are not included
in the output of the `__iter__()` method:

```python
first = "Eat"
second = "Red Leicester"
template = t"{first}{second}"
contents = list(template)
assert len(contents) == 2
assert contents[0].value == "Eat"
assert contents[0].expression == "first"
assert contents[1].value == "Red Leicester"
assert contents[1].expression == "second"

## However, the strings attribute contains empty strings:
assert template.strings == ("", "", "")
```
Template processing code can choose to work with any combination of
`strings`, `interpolations`, `values`, and `__iter__()` based on
requirements and convenience.

### Processing Template Strings
Developers can write arbitrary code to process template strings. For example,
the following function renders static parts of the template in lowercase and
interpolations in uppercase:

```python
from string.templatelib import Template, Interpolation

def lower_upper(template: Template) -> str:
    """Render static parts lowercased and interpolations uppercased."""
    parts: list[str] = []
    for item in template:
        if isinstance(item, Interpolation):
            parts.append(str(item.value).upper())
        else:
            parts.append(item.lower())
    return "".join(parts)

name = "world"
assert lower_upper(t"HELLO {name}") == "hello WORLD"
```
There is no requirement that template strings are processed in any particular
way. Code that processes templates has no obligation to return a string.
Template strings are a flexible, general-purpose feature.

See the `Common Patterns Seen in Processing Templates`_ section for more
information on how to process template strings. See the `Examples`_ section
for detailed working examples.

### Template String Concatenation
Template strings support explicit concatenation using `+`. Concatenation is
supported for two `Template` instances via `Template.__add__()`:

```python
name = "World"

assert isinstance(t"Hello " + t"{name}", Template)
assert (t"Hello " + t"{name}").strings == ("Hello ", "")
assert (t"Hello " + t"{name}").values[0] == "World"
```
Implicit concatenation of two template string literals is also supported:

```python
name = "World"
assert isinstance(t"Hello " t"{name}", Template)
assert (t"Hello " t"{name}").strings == ("Hello ", "")
assert (t"Hello " t"{name}").values[0] == "World"
```
`Template` and `str` can be concatenated with `+`: `Template + str` appends
the `str` as a static string part (extending the `Template`'s last string),
and `str + Template` prepends it as a static string part; both return a new
`Template`. Adjacent t-string and `str`/`f`/`r` string literals implicitly
concatenate into a single `Template`, with the literal parts folded into the
static strings. (This task targets the PR-132662 semantics, which permit `+`
concatenation.)

A `str` can also be incorporated explicitly through the `Template` constructor
to make its role unambiguous. If the `str` is intended to be a static string
part, it should be wrapped in a `Template`. If the `str` is intended to be an
interpolation value, it should be wrapped in an `Interpolation` and passed to
the `Template` constructor. For example:

```python
name = "World"

## Treat `name` as a static string part
template = t"Hello " + Template(name)

## Treat `name` as an interpolation
template = t"Hello " + Template(Interpolation(name, "name"))
```
### Template and Interpolation Equality
`Template` and `Interpolation` instances compare with object identity
(`is`).

`Template` instances are intended to be used by template processing code,
which may return a string or any other type. Those types can provide their
own equality semantics as needed.

### No Support for Ordering
The `Template` and `Interpolation` types do not support ordering. This is
unlike all other string literal types in Python, which support lexicographic
ordering. Because interpolations can contain arbitrary values, there is no
natural ordering for them. As a result, neither the `Template` nor the
`Interpolation` type implements the standard comparison methods.

### Support for the debug specifier (`=`)
The debug specifier, `=`, is supported in template strings and behaves similarly
to how it behaves in f-strings, though due to limitations of the implementation
there is a slight difference.

In particular, `t'{value=}'` is treated as `t'value={value!r}'`. The first
static string is rewritten from `""` to `"value="` and the `conversion`
defaults to `r`:

```python
name = "World"
template = t"Hello {name=}"
assert template.strings[0] == "Hello name="
assert template.interpolations[0].value == "World"
assert template.interpolations[0].conversion == "r"
```
If a conversion is explicitly provided, it is kept: `t'{value=!s}'`
is treated as `t'value={value!s}'`.

If a format string is provided without a conversion, the `conversion`
is set to `None`: `t'{value=:fmt}'` is treated as `t'value={value:fmt}'`.

Whitespace is preserved in the debug specifier, so `t'{value = }'` is
treated as `t'value = {value!r}'`.

### Raw Template Strings
Raw template strings are supported using the `rt` (or `tr`) prefix:

```python
trade = 'shrubberies'
template = rt'Did you say "{trade}"?\n'
assert template.strings[0] == r'Did you say "'
assert template.strings[1] == r'"?\n'
```
In this example, the `\n` is treated as two separate characters
(a backslash followed by 'n') rather than a newline character. This is
consistent with Python's raw string behavior.

As with regular template strings, interpolations in raw template strings are
processed normally, allowing for the combination of raw string behavior and
dynamic content.

### Interpolation Expression Evaluation
Expression evaluation for interpolations is the same as in `498#expression-evaluation`:

    The expressions that are extracted from the string are evaluated in the context
    where the template string appeared. This means the expression has full access to its
    lexical scope, including local and global variables. Any valid Python expression
    can be used, including function and method calls.

Template strings are evaluated eagerly from left to right, just like f-strings. This means that
interpolations are evaluated immediately when the template string is processed, not deferred
or wrapped in lambdas.  In the compiler this eager evaluation is driven through the abstract
syntax tree and bytecode: a template literal is represented as an `ast.TemplateStr` node and
each replacement field as an `ast.Interpolation` node whose evaluated expression is held in
its `value` field, and the code generator emits two dedicated opcodes to assemble the objects
at run time — `BUILD_INTERPOLATION` builds each `Interpolation` from its value, expression,
conversion, and format spec, and `BUILD_TEMPLATE` combines the string parts and interpolations
into the final `Template`.

### Exceptions
Exceptions raised in t-string literals are the same as those raised in f-string
literals.

### No `Template.__str__()` Implementation
The `Template` type does not provide a specialized `__str__()` implementation.

This is because `Template` instances are intended to be used by template processing
code, which may return a string or any other type. There is no canonical way to
convert a Template to a string.

The `Template` and `Interpolation` types both provide useful `__repr__()`
implementations.

### The `string.templatelib` Module
The `string` module will be converted into a package, with a new
`templatelib` submodule containing the `Template` and `Interpolation`
types. Following the implementation of this PEP, this new module may be used
for related functions, such as `!convert`, or potential future template
processing code, such as shell script helpers.

## Examples
All examples in this section of the PEP have fully tested reference implementations
available in the public [pep750-examples](https://github.com/t-strings/pep750-examples)
git repository.

### Example: Implementing f-strings with t-strings
It is easy to "implement" f-strings using t-strings. That is, we can
write a function `f(template: Template) -> str` that processes a `Template`
in much the same way as an f-string literal, returning the same result:

```python
name = "World"
value = 42
templated = t"Hello {name!r}, value: {value:.2f}"
formatted = f"Hello {name!r}, value: {value:.2f}"
assert f(templated) == formatted
```
The `f()` function supports both conversion specifiers like `!r` and format
specifiers like `:.2f`. The full code is fairly simple:

```python
from string.templatelib import Template, Interpolation

def convert(value: object, conversion: Literal["a", "r", "s"] | None) -> object:
    if conversion == "a":
        return ascii(value)
    elif conversion == "r":
        return repr(value)
    elif conversion == "s":
        return str(value)
    return value

def f(template: Template) -> str:
    parts = []
    for item in template:
        match item:
            case str() as s:
                parts.append(s)
            case Interpolation(value, _, conversion, format_spec):
                value = convert(value, conversion)
                value = format(value, format_spec)
                parts.append(value)
    return "".join(parts)
```
> **Note:** Example code

   See `fstring.py`__ and `test_fstring.py`__.

   __ https://github.com/t-strings/pep750-examples/blob/main/pep/fstring.py
   __ https://github.com/t-strings/pep750-examples/blob/main/pep/test_fstring.py

### Example: Structured Logging
Structured logging allows developers to log data in machine-readable
formats like JSON. With t-strings, developers can easily log structured data
alongside human-readable messages using just a single log statement.

We present two different approaches to implementing structured logging with
template strings.

#### Approach 1: Custom Log Messages
The `Python Logging Cookbook <python:logging-cookbook>`
has a short section on [how to implement structured logging](https://docs.python.org/3/howto/logging-cookbook.html#implementing-structured-logging).

The logging cookbook suggests creating a new "message" class, `StructuredMessage`,
that is constructed with a simple text message and a separate dictionary of values:

```python
message = StructuredMessage("user action", {
    "action": "traded",
    "amount": 42,
    "item": "shrubs"
})
logging.info(message)

## Outputs:
## user action >>> {"action": "traded", "amount": 42, "item": "shrubs"}
```
The `StructuredMessage.__str__()` method formats both the human-readable
message *and* the values, combining them into a final string. (See the
[logging cookbook](https://docs.python.org/3/howto/logging-cookbook.html#implementing-structured-logging)
for its full example.)

We can implement an improved version of `StructuredMessage` using template strings:

```python
import json
from string.templatelib import Interpolation, Template
from typing import Mapping

class TemplateMessage:
    def __init__(self, template: Template) -> None:
        self.template = template

    @property
    def message(self) -> str:
        # Use the f() function from the previous example
        return f(self.template)

    @property
    def values(self) -> Mapping[str, object]:
        return {
            item.expression: item.value
            for item in self.template
            if isinstance(item, Interpolation)
        }

    def __str__(self) -> str:
        return f"{self.message} >>> {json.dumps(self.values)}"

_ = TemplateMessage  # optional, to improve readability
action, amount, item = "traded", 42, "shrubs"
logging.info(_(t"User {action}: {amount:.2f} {item}"))

## Outputs:
## User traded: 42.00 shrubs >>> {"action": "traded", "amount": 42, "item": "shrubs"}
```
Template strings give us a more elegant way to define the custom message
class. With template strings it is no longer necessary for developers to make
sure that their format string and values dictionary are kept in sync; a single
template string literal is all that is needed. The `TemplateMessage`
implementation can automatically extract structured keys and values from
the `Interpolation.expression` and `Interpolation.value` attributes,
respectively.

#### Approach 2: Custom Formatters
Custom messages are a reasonable approach to structured logging but can be a
little awkward. To use them, developers must wrap every log message they write
in a custom class. This can be easy to forget.

An alternative approach is to define custom `logging.Formatter` classes. This
approach is more flexible and allows for more control over the final output. In
particular, it's possible to take a single template string and output it in
multiple formats (human-readable and JSON) to separate log streams.

We define two simple formatters, a `MessageFormatter` for human-readable output
and a `ValuesFormatter` for JSON output:

```python
import json
from logging import Formatter, LogRecord
from string.templatelib import Interpolation, Template
from typing import Any, Mapping

class MessageFormatter(Formatter):
    def message(self, template: Template) -> str:
        # Use the f() function from the previous example
        return f(template)

    def format(self, record: LogRecord) -> str:
        msg = record.msg
        if not isinstance(msg, Template):
            return super().format(record)
        return self.message(msg)

class ValuesFormatter(Formatter):
    def values(self, template: Template) -> Mapping[str, Any]:
        return {
            item.expression: item.value
            for item in template
            if isinstance(item, Interpolation)
        }

    def format(self, record: LogRecord) -> str:
        msg = record.msg
        if not isinstance(msg, Template):
            return super().format(record)
        return json.dumps(self.values(msg))
```
We can then use these formatters when configuring our logger:

```python
import logging
import sys

logger = logging.getLogger(__name__)
message_handler = logging.StreamHandler(sys.stdout)
message_handler.setFormatter(MessageFormatter())
logger.addHandler(message_handler)

values_handler = logging.StreamHandler(sys.stderr)
values_handler.setFormatter(ValuesFormatter())
logger.addHandler(values_handler)

action, amount, item = "traded", 42, "shrubs"
logger.info(t"User {action}: {amount:.2f} {item}")

## Outputs to sys.stdout:
## User traded: 42.00 shrubs

## At the same time, outputs to sys.stderr:
## {"action": "traded", "amount": 42, "item": "shrubs"}
```
This approach has a couple advantages over the custom message approach to structured
logging:

- Developers can log a t-string directly without wrapping it in a custom class.
- Human-readable and structured output can be sent to separate log streams. This
  is useful for log aggregation systems that process structured data independently
  from human-readable data.

> **Note:** Example code

   See `logging.py`__ and `test_logging.py`__.

   __ https://github.com/t-strings/pep750-examples/blob/main/pep/logging.py
   __ https://github.com/t-strings/pep750-examples/blob/main/pep/test_logging.py

### Example: HTML Templating
This PEP contains several short HTML templating examples. It turns out that the
"hypothetical" `html()` function mentioned in the  `Motivation`_ section
(and a few other places in this PEP) exists and is available in the
[pep750-examples repository](https://github.com/t-strings/pep750-examples/).
If you're thinking about parsing a complex grammar with template strings, we
hope you'll find it useful.

## Backwards Compatibility
Like f-strings, use of template strings will be a syntactic backwards incompatibility
with previous versions.

## Security Implications
The security implications of working with template strings, with respect to
interpolations, are as follows:

1. Scope lookup is the same as f-strings (lexical scope). This model has been
   shown to work well in practice.

2. Code that processes `Template` instances can ensure that any interpolations
   are processed in a safe fashion, including respecting the context in which
   they appear.

## How To Teach This
Template strings have several audiences:

- Developers using template strings and processing functions
- Authors of template processing code
- Framework authors who build interesting machinery with template strings

We hope that teaching developers will be straightforward. At a glance,
template strings look just like f-strings. Their syntax is familiar and the
scoping rules remain the same.

The first thing developers must learn is that template string literals don't
evaluate to strings; instead, they evaluate to a new type, `Template`. This
is a simple type intended to be used by template processing code. It's not until
developers call a processing function that they get the result they want:
typically, a string, although processing code can of course return any arbitrary
type.

Developers will also want to understand how template strings relate to other
string formatting methods like f-strings and `str.format`. They will need
to decide when to use each method. If a simple string is all that is needed, and
there are no security implications, f-strings are likely the best choice. For
most cases where a format string is used, it can be replaced with a function
wrapping the creation of a template string. In cases where the format string is
obtained from user input, the filesystem, or databases, it is possible to write
code to convert it into a `Template` instance if desired.

Because developers will learn that t-strings are nearly always used in tandem
with processing functions, they don't necessarily need to understand the details
of the `Template` type. As with descriptors and decorators, we expect many more
developers will use t-strings than write t-string processing functions.

Over time, a small number of more advanced developers *will* wish to author their
own template processing code. Writing processing code often requires thinking
in terms of formal grammars. Developers will need to learn how to work with the
`strings` and `interpolation` attributes of a `Template` instance and how
to process interpolations in a context-sensitive fashion. More sophisticated
grammars will likely require parsing to intermediate representations like an
abstract syntax tree (AST). Great template processing code will handle format
specifiers and conversions when appropriate. Writing production-grade template
processing code -- for instance, to support HTML templates -- can be a large
undertaking.

We expect that template strings will provide framework authors with a powerful
new tool in their toolbox. While the functionality of template strings overlaps
with existing tools like template engines, t-strings move that logic into
the language itself. Bringing the full power and generality of Python to bear on
string processing tasks opens new possibilities for framework authors.

## Why another templating approach?
The world of Python already has mature templating languages with wide adoption,
such as Jinja. Why build support for creating new templating systems?

Projects such as Jinja are still needed in cases where the template is less part
of the software by the developers, and more part of customization by designers
or even content created by users, for example in a CMS.

The trends in frontend development have treated templating as part of the
software and written by developers. They want modern language features and a
good tooling experience. PEP 750 envisions DSLs where the non-static parts are
Python: same scope rules, typing, expression syntax, and the like.

## Common Patterns Seen in Processing Templates
### Structural Pattern Matching
Iterating over the `Template` with structural pattern matching is the expected
best practice for many template function implementations:

```python
from string.templatelib import Template, Interpolation

def process(template: Template) -> Any:
    for item in template:
        match item:
            case str() as s:
                ... # handle each string part
            case Interpolation() as interpolation:
                ... # handle each interpolation
```
Processing code may also commonly sub-match on attributes of the `Interpolation` type:

```python
match arg:
    case Interpolation(int()):
        ... # handle interpolations with integer values
    case Interpolation(value=str() as s):
        ... # handle interpolations with string values
    # etc.
```
### Memoizing
Template functions can efficiently process both static and dynamic parts of templates.
The structure of `Template` objects allows for effective memoization:

```python
strings = template.strings  # Static string parts
values = template.values  # Dynamic interpolated values
```
This separation enables caching of processed static parts while dynamic parts
can be inserted as needed. Authors of template processing code can use the static
`strings` as cache keys, leading to significant performance improvements when
similar templates are used repeatedly.

### Parsing to Intermediate Representations
Code that processes templates can parse the template string into intermediate
representations, like an AST. We expect that many template processing libraries
will use this approach.

For instance, rather than returning a `str`, our theoretical `html()` function
(see the `Motivation`_ section) could return an HTML `Element` defined in the
same package:

```python
@dataclass(frozen=True)
class Element:
    tag: str
    attributes: Mapping[str, str | bool]
    children: Sequence[str | Element]

    def __str__(self) -> str:
        ...

def html(template: Template) -> Element:
    ...
```
Calling `str(element)` would then render the HTML but, in the meantime, the
`Element` could be manipulated in a variety of ways.

### Context-sensitive Processing of Interpolations
Continuing with our hypothetical `html()` function, it could be made
context-sensitive. Interpolations could be processed differently depending
on where they appear in the template.

For example, our `html()` function could support multiple kinds of
interpolations:

```python
attributes = {"id": "main"}
attribute_value = "shrubbery"
content = "hello"
template = t"<div {attributes} data-value={attribute_value}>{content}</div>"
element = html(template)
assert str(element) == '<div id="main" data-value="shrubbery">hello</div>'
```
Because the `{attributes}` interpolation occurs in the context of an HTML tag,
and because there is no corresponding attribute name, it is treated as a dictionary
of attributes. The `{attribute_value}` interpolation is treated as a simple
string value and is quoted before inclusion in the final string. The
`{content}` interpolation is treated as potentially unsafe content and is
escaped before inclusion in the final string.

### Nested Template Strings
Going a step further with our `html()` function, we could support nested
template strings. This would allow for more complex HTML structures to be
built up from simpler templates:

```python
name = "World"
content = html(t"<p>Hello {name}</p>")
template = t"<div>{content}</div>"
element = html(template)
assert str(element) == '<div><p>Hello World</p></div>'
```
Because the `{content}` interpolation is an `Element` instance, it does
not need to be escaped before inclusion in the final string.

One could imagine a nice simplification: if the `html()` function is passed
a `Template` instance, it could automatically convert it to an `Element`
by recursively calling itself on the nested template.

We expect that nesting and composition of templates will be a common pattern
in template processing code and, where appropriate, used in preference to
simple string concatenation.

### Approaches to Lazy Evaluation
Like f-strings, interpolations in t-string literals are eagerly evaluated. However,
there are cases where lazy evaluation may be desirable.

If a single interpolation is expensive to evaluate, it can be explicitly wrapped
in a `lambda` in the template string literal:

```python
name = "World"
template = t"Hello {(lambda: name)}"
assert callable(template.interpolations[0].value)
assert template.interpolations[0].value() == "World"
```
This assumes, of course, that template processing code anticipates and handles
callable interpolation values. (One could imagine also supporting iterators,
awaitables, etc.) This is not a requirement of the PEP, but it is a common
pattern in template processing code.

In general, we hope that the community will develop best practices for lazy
evaluation of interpolations in template strings and that, when it makes sense,
common libraries will provide support for callable or awaitable values in
their template processing code.

### Approaches to Asynchronous Evaluation
Closely related to lazy evaluation is asynchronous evaluation.

As with f-strings, the `await` keyword is allowed in interpolations:

```python
async def example():
    async def get_name() -> str:
        await asyncio.sleep(1)
        return "Sleepy"

    template = t"Hello {await get_name()}"
    # Use the f() function from the f-string example, above
    assert f(template) == "Hello Sleepy"
```
More sophisticated template processing code can take advantage of this to
perform asynchronous operations in interpolations. For example, a "smart"
processing function could anticipate that an interpolation is an awaitable
and await it before processing the template string:

```python
async def example():
    async def get_name() -> str:
        await asyncio.sleep(1)
        return "Sleepy"

    template = t"Hello {get_name}"
    assert await async_f(template) == "Hello Sleepy"
```
This assumes that the template processing code in `async_f()` is asynchronous
and is able to `await` an interpolation's value.

> **Note:** Example code

   See `afstring.py`__ and `test_afstring.py`__.

   __ https://github.com/t-strings/pep750-examples/blob/main/pep/afstring.py
   __ https://github.com/t-strings/pep750-examples/blob/main/pep/test_afstring.py

### Approaches to Template Reuse
If developers wish to reuse template strings multiple times with different
values, they can write a function to return a `Template` instance:

```python
def reusable(name: str, question: str) -> Template:
    return t"Hello {name}, {question}?"

template = reusable("friend", "how are you")
template = reusable("King Arthur", "what is your quest")
```
This is, of course, no different from how f-strings can be reused.

### Relation to Format Strings
The venerable `str.format` method accepts format strings that can later
be used to format values:

```python
alas_fmt = "We're all out of {cheese}."
assert alas_fmt.format(cheese="Red Leicester") == "We're all out of Red Leicester."
```
If one squints, one can think of format strings as a kind of function definition.
The *call* to `str.format` can be seen as a kind of function call. The
t-string equivalent is to simply define a standard Python function that returns
a `Template` instance:

```python
def make_template(*, cheese: str) -> Template:
    return t"We're all out of {cheese}."

template = make_template(cheese="Red Leicester")
## Using the f() function from the f-string example, above
assert f(template) == "We're all out of Red Leicester."
```
The `make_template()` function itself can be thought of as analogous to the
format string. The call to `make_template()` is analogous to the call to
`str.format`.

Of course, it is common to load format strings from external sources like a
filesystem or database. Thankfully, because `Template` and `Interpolation`
are simple Python types, it is possible to write a function that takes an
old-style format string and returns an equivalent `Template` instance:

```python
def from_format(fmt: str, /, *args: object, **kwargs: object) -> Template:
     """Parse `fmt` and return a `Template` instance."""
     ...

 # Load this from a file, database, etc.
 fmt = "We're all out of {cheese}."
 template = from_format(fmt, cheese="Red Leicester")
 # Using the f() function from the f-string example, above
 assert f(template) == "We're all out of Red Leicester."
```
This is a powerful pattern that allows developers to use template strings in
places where they might have previously used format strings. A full implementation
of `from_format()` is available in the examples repository,
which supports the full grammar of format strings.

> **Note:** Example code

   See `format.py`__ and `test_format.py`__.

   __ https://github.com/t-strings/pep750-examples/blob/main/pep/format.py
   __ https://github.com/t-strings/pep750-examples/blob/main/pep/test_format.py

## Rejected Ideas
This PEP has been through several significant revisions. In addition, quite a few interesting
ideas were considered both in revisions of PEP 501 and in the [Discourse discussion](https://discuss.python.org/t/pep-750-tag-strings-for-writing-domain-specific-languages/60408/196).

We attempt to document the most significant ideas that were considered and rejected.

### Arbitrary String Literal Prefixes
Inspired by [JavaScript tagged template literals](https://developer.mozilla.org/en-US/docs/Web/JavaScript/Reference/Template_literals#tagged_templates),
an earlier version of this PEP allowed for arbitrary "tag" prefixes in front
of literal strings:

```python
my_tag'Hello {name}'
```
The prefix was a special callable called a "tag function". Tag functions
received the parts of the template string in an argument list. They could then
process the string and return an arbitrary value:

```python
def my_tag(*args: str | Interpolation) -> Any:
    ...
```
This approach was rejected for several reasons:

- It was deemed too complex to build in full generality. JavaScript allows for
  arbitrary expressions to precede a template string, which is a significant
  challenge to implement in Python.
- It precluded future introduction of new string prefixes.
- It seemed to needlessly pollute the namespace.

Use of a single `t` prefix was chosen as a simpler, more Pythonic approach and
more in keeping with template strings' role as a generalization of f-strings.

### Delayed Evaluation of Interpolations
An early version of this PEP proposed that interpolations should be lazily
evaluated. All interpolations were "wrapped" in implicit lambdas. Instead of
having an eagerly evaluated `value` attribute, interpolations had a
`getvalue()` method that would resolve the value of the interpolation:

```python
class Interpolation:
    ...
    _value: Callable[[], object]

    def getvalue(self) -> object:
        return self._value()
```
This was rejected for several reasons:

- The overwhelming majority of use cases for template strings naturally call
  for immediate evaluation.
- Delayed evaluation would be a significant departure from the behavior of
  f-strings.
- Implicit lambda wrapping leads to difficulties with type hints and
  static analysis.

Most importantly, there are viable (if imperfect) alternatives to implicit
lambda wrapping in many cases where lazy evaluation is desired. See the section
on `Approaches to Lazy Evaluation`_, above, for more information.

While delayed evaluation was rejected for *this* PEP, we hope that the community
continues to explore the idea.

### Making `Template` and `Interpolation` Into Protocols
An early version of this PEP proposed that the `Template` and `Interpolation`
types be runtime checkable protocols rather than classes.

In the end, we felt that using classes was more straightforward.

### Overridden `__eq__` and `__hash__` for `Template` and `Interpolation`
Earlier versions of this PEP proposed that the `Template` and `Interpolation`
types should have their own implementations of `__eq__` and `__hash__`.

`Templates` were considered equal if their `strings` and `interpolations`
were equal; `Interpolations` were considered equal if their `value`,
`expression`, `conversion`, and `format_spec` were equal. Interpolation
hashing was similar to tuple hashing: an `Interpolation` was hashable if and
only if its `value` was hashable.

This was rejected because `Template.__hash__` so defined was not useful as a
cache key in template processing code; we were concerned that it would be
confusing to developers.

By dropping these implementations of `__eq__` and `__hash__`, we lose the
ability to write asserts such as:

```python
name = "World"
assert t"Hello " + t"{name}" == t"Hello {name}"
```
Because `Template` instances are intended to be quickly processed by further
code, we felt that the utility of these asserts was limited.

### An Additional `Decoded` Type
An early version of this PEP proposed an additional type, `Decoded`, to represent
the "static string" parts of a template string. This type derived from `str` and
had a single extra `raw` attribute that provided the original text of the string.
We rejected this in favor of the simpler approach of using plain `str` and
allowing combination of `r` and `t` prefixes.

### The Final Home for `Template` and `Interpolation`
Previous versions of this PEP proposed placing the `Template` and
`Interpolation` types in: `types`, `collections`, `collections.abc`,
and even in a new top-level module, `templatelib`. The final decision was to
place them in `string.templatelib`.

### Enable Full Reconstruction of Original Template Literal
Earlier versions of this PEP attempted to make it possible to fully reconstruct
the text of the original template string from a `Template` instance. This was
rejected as being overly complex. The mapping between template literal source
and the underlying AST is not one-to-one and there are several limitations with
respect to round-tripping to the original source text.

First, `Interpolation.format_spec` defaults to `""` if not provided:

```python
value = 42
template1 = t"{value}"
template2 = t"{value:}"
assert template1.interpolations[0].format_spec == ""
assert template2.interpolations[0].format_spec == ""
```
Next, the debug specifier, `=`, is treated as a special case and is processed
before the AST is created. It is therefore not possible to distinguish
`t"{value=}"` from `t"value={value!r}"`:

```python
value = 42
template1 = t"{value=}"
template2 = t"value={value!r}"
assert template1.strings[0] == "value="
assert template1.interpolations[0].expression == "value"
assert template1.interpolations[0].conversion == "r"
assert template2.strings[0] == "value="
assert template2.interpolations[0].expression == "value"
assert template2.interpolations[0].conversion == "r"
```
Finally, format specifiers in f-strings allow arbitrary nesting. In this PEP
and in the reference implementation, the specifier is eagerly evaluated to
set the `format_spec` in the `Interpolation`, thereby losing the original
expressions. For example:

```python
value = 42
precision = 2
template1 = t"{value:.2f}"
template2 = t"{value:.{precision}f}"
assert template1.interpolations[0].format_spec == ".2f"
assert template2.interpolations[0].format_spec == ".2f"
```
We do not anticipate that these limitations will be a significant issue in practice.
Developers who need to obtain the original template string literal can always
use `inspect.getsource()` or similar tools.

### Disallowing Template Concatenation
Earlier versions of this PEP proposed that `Template` instances should not
support concatenation. This was rejected in favor of allowing concatenating
multiple `Template` instances.

There are reasonable arguments in favor of rejecting one or all forms of
concatenation: namely, that it cuts off a class of potential bugs, particularly
when one takes the view that template strings will often contain complex grammars
for which concatenation doesn't always have the same meaning (or any meaning).

Moreover, the earliest versions of this PEP proposed a syntax closer to
JavaScript's tagged template literals, where an arbitrary callable could be used
as a prefix to a string literal. There was no guarantee that the callable would
return a type that supported concatenation.

In the end, we decided that the surprise to developers of a new string type
*not* supporting concatenation was likely to be greater than the theoretical
harm caused by supporting it.

While the final version of this PEP disallows concatenation of a `Template` and a
`str`, the implementation targeted by this task (PR-132662) *does* support it:
`Template + str` appends the `str` as a static string part and `str + Template`
prepends it, both returning a new `Template` (see the Template String Concatenation
section above).

We expect that code that uses template strings will more commonly build up
larger templates through nesting and composition rather than concatenation.

### Arbitrary Conversion Values
Python allows only `r`, `s`, or `a` as possible conversion type values.
Trying to assign a different value results in `SyntaxError`.

In theory, template functions could choose to handle other conversion types. But this
PEP adheres closely to PEP 701. Any changes to allowed values should be in a
separate PEP.

### Removing `conversion` From `Interpolation`
While drafting this PEP, we considered removing the `conversion`
attribute from `Interpolation` and specifying that the conversion should be
performed eagerly, before `Interpolation.value` is set.

This was done to simplify the work of writing template processing code. The
`conversion` attribute is of limited extensibility (it is typed as
`Literal["r", "s", "a"] | None`). It is not clear that it adds significant
value or flexibility to template strings that couldn't better be achieved with
custom format specifiers. Unlike with format specifiers, there is no
equivalent to Python's `python:format` built-in. (Instead, we include a
sample implementation of `convert()` in the `Examples`_ section.)

Ultimately we decided to keep the `conversion` attribute in the
`Interpolation` type to maintain compatibility with f-strings and to allow
for future extensibility.

### Alternate Interpolation Symbols
In the early stages of this PEP, we considered allowing alternate symbols for
interpolations in template strings. For example, we considered allowing
`${name}` as an alternative to `{name}` with the idea that it might be useful
for i18n or other purposes. See the
[Discourse thread](https://discuss.python.org/t/pep-750-tag-strings-for-writing-domain-specific-languages/60408/122)
for more information.

This was rejected in favor of keeping t-string syntax as close to f-string syntax
as possible.

### Alternate Layouts for `Template`
During the development of this PEP, we considered several alternate layouts for
the `Template` type. Many focused on a single `args` tuple that contained
both strings and interpolations. Variants included:

- `args` was a `tuple[str | Interpolation, ...]`` with the promise that
  its first and last items were strings and that strings and interpolations
  always alternated. This implied that `args` was always non-empty and that
  empty strings would be inserted between neighboring interpolations. This was
  rejected because alternation could not be captured by the type system and was
  not a guarantee we wished to make.

- `args` remained a `tuple[str | Interpolation, ...]` but did not support
  interleaving. As a result, empty strings were not added to the sequence. It
  was no longer possible to obtain static strings with `args[::2]`; instead,
  instance checks or structural pattern matching had to be used to distinguish
  between strings and interpolations. This approach was rejected as offering
  less future opportunity for performance optimization.

- `args` was typed as a `Sequence[tuple[str, Interpolation | None]]`. Each
  static string was paired with is neighboring interpolation. The final
  string part had no corresponding interpolation. This was rejected as being
  overly complex.

### Mechanism to Describe the "Kind" of Template
If t-strings prove popular, it may be useful to have a way to describe the
"kind" of content found in a template string: "sql", "html", "css", etc.
This could enable powerful new features in tools such as linters, formatters,
type checkers, and IDEs. (Imagine, for example, `black` formatting HTML in
t-strings, or `mypy` checking whether a given attribute is valid for an HTML
tag.) While exciting, this PEP does not propose any specific mechanism. It is
our hope that, over time, the community will develop conventions for this purpose.

### Binary Template Strings
The combination of t-strings and bytes (`tb`) is considered out of scope for
this PEP. However, unlike f-strings, there is no fundamental reason why t-strings
and bytes cannot be combined. Support could be considered in a future PEP.