"""Retry decorators with exponential backoff for network operations.
Tiger Style compliant: Use only at external boundaries (network I/O, file I/O).
Internal code should use assertions and let it crash.
This is a lightweight alternative to tenacity with the essential features:
- Composable retry/stop/wait strategies
- Retry on exceptions OR results
- Exponential backoff with optional jitter
- Before/after/on_retry callbacks
- Async support via anyio (works with trio or asyncio)
"""
import functools
import inspect
import logging
import random
import time
from collections.abc import Callable
from dataclasses import dataclass
from typing import Any, TypeVar, cast
# Type variable for the decorated function
F = TypeVar("F", bound=Callable[..., Any])
@dataclass
class RetryState:
    """State passed to callbacks during retry attempts."""
    attempt: int
    delay: float
    exception: Exception | None = None
    result: Any = None
# Pure helper functions (no control flow, just computation)
def _calculate_sleep_time(base_delay: float, jitter: bool, max_delay: float | None) -> float:
    """Calculate sleep time with optional jitter and cap. Pure function."""
    assert base_delay > 0, f"base_delay must be > 0, got {base_delay}"
    assert isinstance(jitter, bool), f"jitter must be bool, got {type(jitter)}"
    sleep_time = base_delay
    if jitter:

        sleep_time = base_delay * (0.5 + random.random())
    if max_delay:
        sleep_time = min(sleep_time, max_delay)
    assert sleep_time > 0, f"sleep_time must be > 0, got {sleep_time}"
    return sleep_time
def _should_retry_on_result(result: Any, retry_predicate: Callable[[Any], bool] | None) -> bool:
    """Check if result should trigger retry. Pure function."""
    if retry_predicate is None:
        return False
    return retry_predicate(result)
def _log_retry_attempt(
    func_name: str,
    module_name: str,
    attempt: int,
    max_attempts: int,
    sleep_time: float,
    exception: Exception | None,
    has_result_predicate: bool,
) -> None:
    """Log retry attempt. Leaf function with no control flow."""
    assert isinstance(func_name, str), f"func_name must be str, got {type(func_name)}"
    assert isinstance(module_name, str), f"module_name must be str, got {type(module_name)}"
    assert attempt > 0, f"attempt must be > 0, got {attempt}"
    assert attempt <= max_attempts, f"attempt must be <= max_attempts, got {attempt} > {max_attempts}"
    assert sleep_time > 0, f"sleep_time must be > 0, got {sleep_time}"
    logger = logging.getLogger(module_name)
    if exception:
        logger.warning(
            f"{func_name}() attempt {attempt}/{max_attempts} failed: {exception}. "
            f"Retrying in {sleep_time:.2f}s..."
        )
    else:
        logger.warning(
            f"{func_name}() attempt {attempt}/{max_attempts} returned retriable result. "
            f"Retrying in {sleep_time:.2f}s..."
        )
def retry(
    max_attempts: int = 3,
    delay: float = 1,
    backoff: float = 2,
    exceptions: tuple[type[Exception], ...] = (Exception,),
) -> Callable[[F], F]:
    """Retry decorator with exponential backoff.
    Use this ONLY at external boundaries (network I/O, API calls, file transfers).
    Internal code should use assertions and fail fast.
    Args:
        max_attempts: Maximum number of attempts (default: 3)
        delay: Initial delay in seconds (default: 1)
        backoff: Backoff multiplier (default: 2, gives 1s, 2s, 4s)
        exceptions: Tuple of exceptions to catch (default: all Exception)
    Example:
        @retry(max_attempts=3, delay=1, backoff=2, exceptions=(requests.RequestException,))
        def make_api_call():
            return requests.get("https://api.example.com")
    """
    # Tiger Style: Assert all inputs
    assert max_attempts >= 1, f"max_attempts must be >= 1, got {max_attempts}"
    assert delay > 0, f"delay must be > 0, got {delay}"
    assert backoff >= 1, f"backoff must be >= 1, got {backoff}"
    assert isinstance(exceptions, tuple), f"exceptions must be tuple, got {type(exceptions)}"
    assert len(exceptions) > 0, "exceptions tuple cannot be empty"
    def decorator(func: F) -> F:
        @functools.wraps(func)
        def wrapper(*args: Any, **kwargs: Any) -> Any:
            attempt = 0
            current_delay = delay
            while attempt < max_attempts:
                try:
                    return func(*args, **kwargs)
                except exceptions as e:
                    attempt += 1
                    if attempt >= max_attempts:
                        # Let the final exception propagate
                        raise
                    # Log the retry for debugging
                    logger = logging.getLogger(getattr(func, "__module__", "unknown"))
                    logger.warning(
                        f"{getattr(func, '__name__', '<function>')}() attempt {attempt}/{max_attempts} failed: {e}. "
                        f"Retrying in {current_delay}s..."
                    )
                    time.sleep(current_delay)
                    current_delay *= backoff
            # Tiger Style: Assert impossible states
            raise AssertionError("Retry loop exited without return or raise")
        return cast(F, wrapper)
    return decorator
def retry_v2(
    max_attempts: int = 3,
    delay: float = 1,
    backoff: float = 2,
    max_delay: float | None = None,
    jitter: bool = False,
    exceptions: tuple[type[Exception], ...] = (Exception,),
    retry_on_result: Callable[[Any], bool] | None = None,
    on_retry: Callable[[RetryState], None] | None = None,
) -> Callable[[F], F]:
    """Enhanced retry decorator with composable strategies (lightweight tenacity).
    Use this ONLY at external boundaries (network I/O, API calls, file transfers).
    Internal code should use assertions and fail fast.
    Args:
        max_attempts: Maximum number of attempts (default: 3)
        delay: Initial delay in seconds (default: 1)
        backoff: Backoff multiplier (default: 2, gives 1s, 2s, 4s)
        max_delay: Maximum delay cap (default: None = no cap)
        jitter: Add randomness to delay (default: False)
        exceptions: Tuple of exceptions to catch (default: all Exception)
        retry_on_result: Optional predicate to retry based on result (e.g., lambda x: x is None)
        on_retry: Optional callback called before each retry with RetryState
    Example:
        # Retry on rate limit, with jitter
        @retry_v2(
            max_attempts=5,
            delay=1,
            backoff=2,
            max_delay=60,
            jitter=True,
            exceptions=(openai.RateLimitError,),
            on_retry=lambda state: logger.info(f"Retry {state.attempt}, waiting {state.delay}s")
        )
        def make_api_call():
            return requests.get("https://api.example.com")
        # Retry until non-None result
        @retry_v2(
            max_attempts=3,
            retry_on_result=lambda x: x is None
        )
        def fetch_data():
            return db.get_value()  # Returns None if not ready
    """
    # Tiger Style: Assert all inputs
    assert max_attempts >= 1, f"max_attempts must be >= 1, got {max_attempts}"
    assert delay > 0, f"delay must be > 0, got {delay}"
    assert backoff >= 1, f"backoff must be >= 1, got {backoff}"
    assert isinstance(exceptions, tuple), f"exceptions must be tuple, got {type(exceptions)}"
    assert len(exceptions) > 0, "exceptions tuple cannot be empty"
    def decorator(func: F) -> F:
        @functools.wraps(func)
        def wrapper(*args: Any, **kwargs: Any) -> Any:
            attempt = 0
            current_delay = delay
            while attempt < max_attempts:
                exception_to_retry: Exception | None = None
                result: Any = None
                # Execute function
                try:
                    result = func(*args, **kwargs)
                except exceptions as e:
                    exception_to_retry = e
                # Tiger Style: Centralize control flow (push ifs up)

                should_retry = exception_to_retry is not None or _should_retry_on_result(
                    result, retry_on_result
                )
                if not should_retry:
                    # Success - return result
                    return result
                # Prepare for retry
                attempt += 1
                if attempt >= max_attempts:
                    # Final attempt exhausted
                    if exception_to_retry:
                        raise exception_to_retry
                    # Result failed predicate but no more retries
                    return result

                sleep_time = _calculate_sleep_time(current_delay, jitter, max_delay)

                if on_retry:
                    state = RetryState(
                        attempt=attempt,
                        delay=sleep_time,
                        exception=exception_to_retry,
                        result=result,
                    )
                    on_retry(state)
                else:

                    _log_retry_attempt(
                        func_name=getattr(func, "__name__", "<function>"),
                        module_name=getattr(func, "__module__", "unknown"),
                        attempt=attempt,
                        max_attempts=max_attempts,
                        sleep_time=sleep_time,
                        exception=exception_to_retry,
                        has_result_predicate=retry_on_result is not None,
                    )
                time.sleep(sleep_time)
                current_delay *= backoff
            # Tiger Style: Assert impossible states
            raise AssertionError("Retry loop exited without return or raise")
        return cast(F, wrapper)
    return decorator
def async_retry(
    max_attempts: int = 3,
    delay: float = 1,
    backoff: float = 2,
    max_delay: float | None = None,
    jitter: bool = False,
    exceptions: tuple[type[Exception], ...] = (Exception,),
    retry_on_result: Callable[[Any], bool] | None = None,
    on_retry: Callable[[RetryState], None] | None = None,
) -> Callable[[F], F]:
    """Async retry decorator with exponential backoff using anyio.
    Works with both trio and asyncio backends via anyio.
    Use this ONLY at external boundaries (network I/O, API calls).
    Internal code should use assertions and fail fast.
    Args:
        max_attempts: Maximum number of attempts (default: 3)
        delay: Initial delay in seconds (default: 1)
        backoff: Backoff multiplier (default: 2, gives 1s, 2s, 4s)
        max_delay: Maximum delay cap (default: None = no cap)
        jitter: Add randomness to delay (default: False)
        exceptions: Tuple of exceptions to catch (default: all Exception)
        retry_on_result: Optional predicate to retry based on result (e.g., lambda x: x is None)
        on_retry: Optional callback called before each retry with RetryState
    Example:
        @async_retry(
            max_attempts=5,
            delay=1,
            backoff=2,
            max_delay=60,
            jitter=True,
            exceptions=(openai.RateLimitError,),
            on_retry=lambda state: logger.info(f"Retry {state.attempt}")
        )
        async def make_api_call():
            return await client.chat.completions.create(...)
    """
    # Tiger Style: Assert all inputs
    assert max_attempts >= 1, f"max_attempts must be >= 1, got {max_attempts}"
    assert delay > 0, f"delay must be > 0, got {delay}"
    assert backoff >= 1, f"backoff must be >= 1, got {backoff}"
    assert isinstance(exceptions, tuple), f"exceptions must be tuple, got {type(exceptions)}"
    assert len(exceptions) > 0, "exceptions tuple cannot be empty"
    def decorator(func: F) -> F:
        # Verify function is async
        assert inspect.iscoroutinefunction(func), f"{func.__name__} must be async for async_retry"
        @functools.wraps(func)
        async def wrapper(*args: Any, **kwargs: Any) -> Any:
            import anyio  # Import here to avoid hard dependency
            attempt = 0
            current_delay = delay
            while attempt < max_attempts:
                exception_to_retry: Exception | None = None
                result: Any = None
                # Execute function
                try:
                    result = await func(*args, **kwargs)
                except exceptions as e:
                    exception_to_retry = e
                # Tiger Style: Centralize control flow (push ifs up)

                should_retry = exception_to_retry is not None or _should_retry_on_result(
                    result, retry_on_result
                )
                if not should_retry:
                    # Success - return result
                    return result
                # Prepare for retry
                attempt += 1
                if attempt >= max_attempts:
                    # Final attempt exhausted
                    if exception_to_retry:
                        raise exception_to_retry
                    # Result failed predicate but no more retries
                    return result

                sleep_time = _calculate_sleep_time(current_delay, jitter, max_delay)

                if on_retry:
                    state = RetryState(
                        attempt=attempt,
                        delay=sleep_time,
                        exception=exception_to_retry,
                        result=result,
                    )
                    on_retry(state)
                else:

                    _log_retry_attempt(
                        func_name=getattr(func, "__name__", "<function>"),
                        module_name=getattr(func, "__module__", "unknown"),
                        attempt=attempt,
                        max_attempts=max_attempts,
                        sleep_time=sleep_time,
                        exception=exception_to_retry,
                        has_result_predicate=retry_on_result is not None,
                    )
                await anyio.sleep(sleep_time)
                current_delay *= backoff
            # Tiger Style: Assert impossible states
            raise AssertionError("Retry loop exited without return or raise")
        return cast(F, wrapper)
    return decorator
