### FlexibleAlias for Generic Type Aliases in mypy_extensions

Source: https://context7.com/python/mypy_extensions/llms.txt

Demonstrates the use of FlexibleAlias for creating generic type aliases that can accommodate extra type parameters. This is useful for advanced typing scenarios where parameters might be ignored.

```python
from mypy_extensions import FlexibleAlias
from typing import TypeVar, List

T = TypeVar('T')

# Create a flexible alias that ignores extra type parameters
# FlexibleAlias[T, List[T]] creates an alias where extra params are dropped
MyList = FlexibleAlias[T, List[T]]

# Usage in type hints (the extra parameter is accepted but ignored at runtime)
def process_items(items: MyList[int]) -> int:
    return sum(items)

result = process_items([1, 2, 3, 4, 5])
print(result)  # 15
```

--------------------------------

### Variadic Keyword Arguments (KwArg) with mypy_extensions

Source: https://context7.com/python/mypy_extensions/llms.txt

Demonstrates using KwArg from mypy_extensions to define function types that accept variable keyword arguments. This allows for more precise type hinting of functions with dynamic keyword parameters.

```python
from typing import Callable
from mypy_extensions import KwArg

# Function type accepting variable keyword arguments
ConfigBuilderType = Callable[[KwArg(str)], dict]

def build_config(**options: str) -> dict:
    return {k: v for k, v in options.items()}

def setup(builder: ConfigBuilderType) -> None:
    config = builder(host="localhost", port="8080", debug="true")
    print(config)  # {'host': 'localhost', 'port': '8080', 'debug': 'true'}

setup(build_config)
```

--------------------------------

### TypedDict Definition and Usage (Deprecated)

Source: https://context7.com/python/mypy_extensions/llms.txt

Demonstrates how to define dictionary types with fixed keys and specific value types using `mypy_extensions.TypedDict`. Note that this feature is deprecated in favor of standard library or `typing_extensions` equivalents. It shows both functional and class-based syntax, instance creation, and accessing annotations.

```python
import warnings
from mypy_extensions import TypedDict

# Suppress deprecation warnings for demonstration
warnings.filterwarnings("ignore", category=DeprecationWarning)

# Functional syntax with dictionary
Employee = TypedDict('Employee', {'name': str, 'id': int, 'department': str})

# Functional syntax with keyword arguments
Point = TypedDict('Point', x=int, y=int)

# Class syntax (Python 3.6+)
class UserProfile(TypedDict):
    username: str
    email: str
    age: int

# Creating instances (returns regular dict at runtime)
emp = Employee(name='Alice', id=42, department='Engineering')
print(emp)  # {'name': 'Alice', 'id': 42, 'department': 'Engineering'}
print(type(emp))  # <class 'dict'>

# Access annotations
print(Employee.__annotations__)  # {'name': <class 'str'>, 'id': <class 'int'>, 'department': <class 'str'>}
print(Employee.__total__)  # True

# Optional fields with total=False
class OptionalConfig(TypedDict, total=False):
    debug: bool
    log_level: int
    log_path: str

config = OptionalConfig()  # Empty dict is valid
config_with_debug = OptionalConfig(debug=True)  # Partial dict is valid
print(config)  # {}
print(config_with_debug)  # {'debug': True}
```

--------------------------------

### VarArg: Type Hint for Variadic Positional Arguments (*args)

Source: https://context7.com/python/mypy_extensions/llms.txt

Defines a type hint for functions that accept an arbitrary number of positional arguments. `mypy_extensions.VarArg` is used within `typing.Callable` to specify the type of elements within the `*args` tuple, allowing mypy to check the types of all passed positional arguments.

```python
from typing import Callable
from mypy_extensions import VarArg

# Function type accepting variable positional arguments
SumFunctionType = Callable[[VarArg(int)], int]

def sum_all(*numbers: int) -> int:
    return sum(numbers)

def calculate(fn: SumFunctionType) -> None:
    print(fn(1, 2, 3))        # 6
    print(fn(10, 20, 30, 40)) # 100

calculate(sum_all)
```

--------------------------------

### Native Integer Types for mypyc Compilation

Source: https://context7.com/python/mypy_extensions/llms.txt

Introduces native integer types (i64, i32, i16, u8) from mypy_extensions for mypyc-compiled code. These types represent fixed-width integers for performance, behaving like standard Python ints at runtime.

```python
from mypy_extensions import i64, i32, i16, u8

# Construction - behaves like int()
a = i64(100)
b = i32(-50)
c = i16(255)
d = u8(42)

print(a, b, c, d)  # 100 -50 255 42
print(type(a))     # <class 'int'>

# From string with base
hex_val = i64("ff", 16)
bin_val = i32("1010", base=2)
print(hex_val)  # 255
print(bin_val)  # 10

# From float (truncates)
from_float = i64(3.7)
print(from_float)  # 3

# isinstance checks (matches int behavior at runtime)
print(isinstance(100, i64))   # True
print(isinstance(100, i32))   # True
print(isinstance(100, u8))    # True
print(isinstance(3.14, i64))  # False

# Type annotations for mypyc-compiled code
def fast_compute(x: i64, y: i64) -> i64:
    return i64(x * y + 1)

result = fast_compute(i64(10), i64(20))
print(result)  # 201
```

--------------------------------

### Using the 'trait' Decorator for mypyc Classes

Source: https://context7.com/python/mypy_extensions/llms.txt

Explains the @trait decorator from mypy_extensions, used to mark classes as traits for mypyc compilation. At runtime, it functions as a no-op, returning the class unchanged.

```python
from mypy_extensions import trait

@trait
class Drawable:
    def draw(self) -> None:
        pass

@trait
class Clickable:
    def on_click(self) -> None:
        pass

# In mypyc-compiled code, traits enable efficient multiple inheritance
class Button(Drawable, Clickable):
    def draw(self) -> None:
        print("Drawing button")

    def on_click(self) -> None:
        print("Button clicked")

btn = Button()
btn.draw()      # Drawing button
btn.on_click()  # Button clicked
```

--------------------------------

### DefaultNamedArg: Type Hint for Optional Keyword-Only Arguments

Source: https://context7.com/python/mypy_extensions/llms.txt

Enables type hinting for callables that accept keyword-only arguments with default values. `mypy_extensions.DefaultNamedArg` is used within `typing.Callable` to specify the type, name, and default value of optional keyword arguments, improving type safety for flexible function signatures.

```python
from typing import Callable
from mypy_extensions import DefaultNamedArg

# Callback with optional keyword-only argument
LoggerType = Callable[[str, DefaultNamedArg(str, 'level')], None]

def log_message(msg: str, *, level: str = "INFO") -> None:
    print(f"[{level}] {msg}")

def run_with_logger(logger: LoggerType) -> None:
    logger("Starting process")           # [INFO] Starting process
    logger("Error occurred", level="ERROR")  # [ERROR] Error occurred

run_with_logger(log_message)
```

--------------------------------

### Applying 'mypyc_attr' Decorator with mypy_extensions

Source: https://context7.com/python/mypy_extensions/llms.txt

Details the @mypyc_attr decorator for applying mypyc-specific attributes to classes or functions. This decorator is a runtime no-op, affecting only mypyc compilation behavior.

```python
from mypy_extensions import mypyc_attr

# Mark a class for specific mypyc behavior
@mypyc_attr(allow_interpreted_subclasses=True)
class BasePlugin:
    def execute(self) -> str:
        return "base"

# Can be subclassed even from non-compiled code
class CustomPlugin(BasePlugin):
    def execute(self) -> str:
        return "custom"

plugin = CustomPlugin()
print(plugin.execute())  # custom

# Multiple attributes
@mypyc_attr(serializable=True, allow_interpreted_subclasses=True)
class DataModel:
    def __init__(self, value: int) -> None:
        self.value = value

model = DataModel(42)
print(model.value)  # 42
```

--------------------------------

### Arg: Type Hint for Required Positional Arguments

Source: https://context7.com/python/mypy_extensions/llms.txt

Defines a type hint for a callable that expects a single, required positional argument of a specific type. The `Arg` constructor from `mypy_extensions` is used within a `typing.Callable` to specify the argument's type and name, aiding mypy in type checking.

```python
from typing import Callable
from mypy_extensions import Arg

# Function that takes a callback with a required positional 'x' argument
def process(callback: Callable[[Arg(int, 'x')], str]) -> str:
    return callback(42)

def my_callback(x: int) -> str:
    return f"Received: {x}"

result = process(my_callback)
print(result)  # Received: 42
```

--------------------------------

### DefaultArg: Type Hint for Positional Arguments with Defaults

Source: https://context7.com/python/mypy_extensions/llms.txt

Specifies a type hint for a callable that accepts a positional argument which has a default value. `mypy_extensions.DefaultArg` is used within `typing.Callable` to denote the type, name, and default nature of the argument, enabling mypy to understand optional positional parameters.

```python
from typing import Callable
from mypy_extensions import DefaultArg

# Callback type where 'count' has a default value
ProcessorType = Callable[[str, DefaultArg(int, 'count')], list]

def repeat_string(text: str, count: int = 3) -> list:
    return [text] * count

def use_processor(fn: ProcessorType) -> None:
    print(fn("hello"))       # Uses default: ['hello', 'hello', 'hello']
    print(fn("world", 2))    # Override: ['world', 'world']

use_processor(repeat_string)
```

--------------------------------

### NamedArg: Type Hint for Keyword-Only Arguments

Source: https://context7.com/python/mypy_extensions/llms.txt

Provides a type hint for callables that require specific keyword-only arguments. `mypy_extensions.NamedArg` is used within `typing.Callable` to define the type and name of these arguments, ensuring they are passed by keyword and are mandatory.

```python
from typing import Callable
from mypy_extensions import NamedArg

# Function type with keyword-only argument
FormatterType = Callable[[str, NamedArg(bool, 'uppercase')], str]

def format_text(text: str, *, uppercase: bool) -> str:
    return text.upper() if uppercase else text.lower()

def apply_formatter(fn: FormatterType, value: str) -> None:
    print(fn(value, uppercase=True))   # HELLO
    print(fn(value, uppercase=False))  # hello

apply_formatter(format_text, "Hello")
```

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