


import os
import socket
from contextlib import contextmanager

import torch

from ..commands.config.default import write_basic_config  # noqa: F401
from ..state import PartialState
from .dataclasses import DistributedType
from .imports import is_deepspeed_available, is_tpu_available
from .transformer_engine import convert_model
from .versions import is_torch_version


if is_deepspeed_available():
	from deepspeed import DeepSpeedEngine

if is_tpu_available(check_device=False):
	import torch_xla.core.xla_model as xm






def 			UpperCamelCase (     __magic_name__					:							Tuple					)       ->  Union[str, Any]:
		"""simple docstring"""




		if is_torch_version("""<"""   ,   """2.0.0"""					) or not hasattr(__magic_name__   ,   """_dynamo"""					):
				return False
		return isinstance(__magic_name__   ,   torch._dynamo.eval_frame.OptimizedModule					)






def 			UpperCamelCase (     __magic_name__					:							List[Any]   ,   __magic_name__					:							bool = True					)       ->  Union[str, Any]:
		"""simple docstring"""




		lowercase__					      =		(torch.nn.parallel.DistributedDataParallel, torch.nn.DataParallel)

		lowercase__					      =		is_compiled_module(__magic_name__					)
		if is_compiled:
				lowercase__					      =		model
				lowercase__					      =		model._orig_mod

		if is_deepspeed_available():
				options += (DeepSpeedEngine,)

		while isinstance(__magic_name__   ,   __magic_name__					):
				lowercase__					      =		model.module

		if not keep_fpaa_wrapper:
				lowercase__					      =		getattr(__magic_name__   ,   """forward"""					)
				lowercase__					      =		model.__dict__.pop("""_original_forward"""   ,   __magic_name__					)
				if original_forward is not None:
						while hasattr(__magic_name__   ,   """__wrapped__"""					):
								lowercase__					      =		forward.__wrapped__
								if forward == original_forward:
										break
						lowercase__					      =		forward
				if getattr(__magic_name__   ,   """_converted_to_transformer_engine"""   ,   __magic_name__					):
						convert_model(__magic_name__   ,   to_transformer_engine=__magic_name__					)

		if is_compiled:
				lowercase__					      =		model
				lowercase__					      =		compiled_model

		return model






def 			UpperCamelCase (     )       ->  Optional[Any]:
		"""simple docstring"""




		PartialState().wait_for_everyone()






def 			UpperCamelCase (     __magic_name__					:							Dict   ,   __magic_name__					:							Optional[int]					)       ->  Union[str, Any]:
		"""simple docstring"""




		if PartialState().distributed_type == DistributedType.TPU:
				xm.save(__magic_name__   ,   __magic_name__					)
		elif PartialState().local_process_index == 0:
				torch.save(__magic_name__   ,   __magic_name__					)






@contextmanager
def 			UpperCamelCase (     **__magic_name__					:							List[str]					)       ->  str:
		"""simple docstring"""




		for key, value in kwargs.items():
				lowercase__					      =		str(__magic_name__					)

		yield

		for key in kwargs:
				if key.upper() in os.environ:
						del os.environ[key.upper()]






def 			UpperCamelCase (     __magic_name__					:							List[str]					)       ->  int:
		"""simple docstring"""




		if not hasattr(__magic_name__   ,   """__qualname__"""					) and not hasattr(__magic_name__   ,   """__name__"""					):
				lowercase__					      =		getattr(__magic_name__   ,   """__class__"""   ,   __magic_name__					)
		if hasattr(__magic_name__   ,   """__qualname__"""					):
				return obj.__qualname__
		if hasattr(__magic_name__   ,   """__name__"""					):
				return obj.__name__
		return str(__magic_name__					)






def 			UpperCamelCase (     __magic_name__					:							int   ,   __magic_name__					:							Optional[Any]					)       ->  str:
		"""simple docstring"""




		for key, value in source.items():
				if isinstance(__magic_name__   ,   __magic_name__					):
						lowercase__					      =		destination.setdefault(__magic_name__   ,   {}					)
						merge_dicts(__magic_name__   ,   __magic_name__					)
				else:
						lowercase__					      =		value

		return destination







def 			UpperCamelCase (     __magic_name__					:							int = None					)       ->  bool:
		"""simple docstring"""




		if port is None:
				lowercase__					      =		2_9500
		with socket.socket(socket.AF_INET   ,   socket.SOCK_STREAM					) as s:
				return s.connect_ex(("""localhost""", port)					) == 0





def 			UpperCamelCase (     __magic_name__					:							str					)       ->  List[str]:  # noqa: E741
		"""simple docstring"""




		lowercase__					      =		len(__magic_name__					)
		lowercase__					      =		0
		lowercase__					      =		[0] * n
		lowercase__					      =		[False] * n
		lowercase__					      =		[False] * n

		def dfs(__magic_name__					:							str   ,   __magic_name__					:							List[str]   ,   __magic_name__					:							str   ,   __magic_name__					:							Any					):
				if parent == root:
						out_edge_count += 1
				lowercase__					      =		True
				lowercase__					      =		at

				for to in l[at]:
						if to == parent:
								pass
						elif not visited[to]:
								lowercase__					      =		dfs(__magic_name__   ,   __magic_name__   ,   __magic_name__   ,   __magic_name__					)
								lowercase__					      =		min(low[at]   ,   low[to]					)

								# AP found via bridge
								if at < low[to]:
										lowercase__					      =		True
								# AP found via cycle
								if at == low[to]:
										lowercase__					      =		True
						else:
								lowercase__					      =		min(low[at]   ,   __magic_name__					)
				return out_edge_count

		for i in range(__magic_name__					):
				if not visited[i]:
						lowercase__					      =		0
						lowercase__					      =		dfs(__magic_name__   ,   __magic_name__   ,   -1   ,   __magic_name__					)
						lowercase__					      =		out_edge_count > 1

		for x in range(len(__magic_name__					)					):
				if is_art[x] is True:
						print(__magic_name__					)


# Adjacency list of graph
A	:		List[str]    =       {
    0: [1, 2],
    1: [0, 2],
    2: [0, 1, 3, 5],
    3: [2, 4],
    4: [3],
    5: [2, 6, 8],
    6: [5, 7],
    7: [6, 8],
    8: [5, 7],
}







compute_ap(data)

