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"""Logging utilities for Dynamo and Inductor.
This module provides specialized logging functionality including:
- Step-based logging that prepends step numbers to log messages
- Progress bar management for compilation phases
- Centralized logger management for Dynamo and Inductor components
The logging system helps track the progress of compilation phases and provides structured
logging output for debugging and monitoring.
"""

import itertools
import logging
from typing import Any, Callable

from torch.hub import _Faketqdm, tqdm


# Disable progress bar by default, not in dynamo config because otherwise get a circular import
disable_progress = True


# Return all loggers that torchdynamo/torchinductor is responsible for
def get_loggers() -> list[logging.Logger]:
    return [
        logging.getLogger("torch.fx.experimental.symbolic_shapes"),
        logging.getLogger("torch._dynamo"),
        logging.getLogger("torch._inductor"),
    ]


# Creates a logging function that logs a message with a step # prepended.
# get_step_logger should be lazily called (i.e. at runtime, not at module-load time)
# so that step numbers are initialized properly. e.g.:

# @functools.cache
# def _step_logger():
#     return get_step_logger(logging.getLogger(...))

# def fn():
#     _step_logger()(logging.INFO, "msg")

_step_counter = itertools.count(1)

# Update num_steps if more phases are added: Dynamo, AOT, Backend
# This is very inductor centric
# _inductor.utils.has_triton() gives a circular import error here

if not disable_progress:
    try:
        import triton  # noqa: F401

        num_steps = 3
    except ImportError:
        num_steps = 2
    pbar = tqdm(total=num_steps, desc="torch.compile()", delay=0)


def get_step_logger(logger: logging.Logger) -> Callable[..., None]:
    if not disable_progress:
        pbar.update(1)
        if not isinstance(pbar, _Faketqdm):
            pbar.set_postfix_str(f"{logger.name}")

    step = next(_step_counter)

    def log(level: int, msg: str, **kwargs: Any) -> None:
        if "stacklevel" not in kwargs:
            kwargs["stacklevel"] = 2
        logger.log(level, "Step %s: %s", step, msg, **kwargs)

    return log

	"""Logging utilities for Dynamo and Inductor.

	This module provides specialized logging functionality including:
	- Step-based logging that prepends step numbers to log messages
	- Progress bar management for compilation phases
	- Centralized logger management for Dynamo and Inductor components

	The logging system helps track the progress of compilation phases and provides structured
	logging output for debugging and monitoring.
	"""

	import itertools
	import logging
	from typing import Any, Callable

	from torch.hub import _Faketqdm, tqdm


	# Disable progress bar by default, not in dynamo config because otherwise get a circular import
	disable_progress = True


	# Return all loggers that torchdynamo/torchinductor is responsible for
	def get_loggers() -> list[logging.Logger]:
	return [
	logging.getLogger("torch.fx.experimental.symbolic_shapes"),
	logging.getLogger("torch._dynamo"),
	logging.getLogger("torch._inductor"),
	]


	# Creates a logging function that logs a message with a step # prepended.
	# get_step_logger should be lazily called (i.e. at runtime, not at module-load time)
	# so that step numbers are initialized properly. e.g.:

	# @functools.cache
	# def _step_logger():
	# return get_step_logger(logging.getLogger(...))

	# def fn():
	# _step_logger()(logging.INFO, "msg")

	_step_counter = itertools.count(1)

	# Update num_steps if more phases are added: Dynamo, AOT, Backend
	# This is very inductor centric
	# _inductor.utils.has_triton() gives a circular import error here

	if not disable_progress:
	try:
	import triton # noqa: F401

	num_steps = 3
	except ImportError:
	num_steps = 2
	pbar = tqdm(total=num_steps, desc="torch.compile()", delay=0)


	def get_step_logger(logger: logging.Logger) -> Callable[..., None]:
	if not disable_progress:
	pbar.update(1)
	if not isinstance(pbar, _Faketqdm):
	pbar.set_postfix_str(f"{logger.name}")

	step = next(_step_counter)

	def log(level: int, msg: str, **kwargs: Any) -> None:
	if "stacklevel" not in kwargs:
	kwargs["stacklevel"] = 2
	logger.log(level, "Step %s: %s", step, msg, **kwargs)

	return log