"""simple docstring"""







import importlib
import importlib.metadata
import os
import warnings
from distutils.util import strtobool
from functools import lru_cache

import torch
from packaging import version
from packaging.version import parse

from .environment import parse_flag_from_env
from .versions import compare_versions, is_torch_version


try:
						import torch_xla.core.xla_model as xm  # noqa: F401

						UpperCamelCase_    :    List[Any]											=						True
except ImportError:
						UpperCamelCase_    :    Optional[int]											=						False


# Cache this result has it's a C FFI call which can be pretty time-consuming
UpperCamelCase_    :    int											=						torch.distributed.is_available()


def 					a__ (						UpperCAmelCase__    :     Any     )->		Optional[int]:
						# Check we're not importing a "pkg_name" directory somewhere but the actual library by trying to grab the version
						UpperCAmelCase__   		=						importlib.util.find_spec(UpperCAmelCase__     ) is not None
						if package_exists:
												try:
																		UpperCAmelCase__   		=						importlib.metadata.metadata(UpperCAmelCase__     )
																		return True
												except importlib.metadata.PackageNotFoundError:
																		return False


def 					a__ (						)->		bool:
						return _torch_distributed_available


def 					a__ (						)->		List[Any]:
						try:
												pass
						except ImportError:
												print(
												    """Intel(R) oneCCL Bindings for PyTorch* is required to run DDP on Intel(R) GPUs, but it is not"""
												    """ detected. If you see \"ValueError: Invalid backend: 'ccl'\" error, please install Intel(R) oneCCL"""
												    """ Bindings for PyTorch*."""     )
						return (
						    importlib.util.find_spec("""torch_ccl"""     ) is not None
						    or importlib.util.find_spec("""oneccl_bindings_for_pytorch"""     ) is not None
						)


def 					a__ (						)->		Optional[Any]:
						return importlib.metadata.version("""oneccl_bind_pt"""     )


def 					a__ (						)->		Optional[Any]:
						return _is_package_available("""transformer_engine"""     )


@lru_cache
def 					a__ (						UpperCAmelCase__    :     List[Any]=True     )->		Any:
						# Due to bugs on the amp series GPUs, we disable torch-xla on them
						if torch.cuda.is_available():
												return False
						if _tpu_available and check_device:
												try:
																		# Will raise a RuntimeError if no XLA configuration is found
																		UpperCAmelCase__   		=						xm.xla_device()
																		return True
												except RuntimeError:
																		return False
						return _tpu_available


def 					a__ (						)->		Any:
						return _is_package_available("""deepspeed"""     )


def 					a__ (						UpperCAmelCase__    :     int=False     )->		Optional[Any]:
						if is_tpu_available():
												return not ignore_tpu
						if torch.cuda.is_available():
												return torch.cuda.is_bfaa_supported()
						if is_npu_available():
												return False
						return True


def 					a__ (						)->		int:
						UpperCAmelCase__   		=						_is_package_available("""bitsandbytes"""     )
						if package_exists:
												UpperCAmelCase__   		=						version.parse(importlib.metadata.version("""bitsandbytes"""     )     )
												return compare_versions(UpperCAmelCase__						,     """>="""						,     """0.39.0"""     )
						return False


def 					a__ (						)->		str:
						UpperCAmelCase__   		=						_is_package_available("""bitsandbytes"""     )
						if package_exists:
												UpperCAmelCase__   		=						version.parse(importlib.metadata.version("""bitsandbytes"""     )     )
												return compare_versions(UpperCAmelCase__						,     """>="""						,     """0.37.2"""     )
						return False


def 					a__ (						)->		Dict:
						return _is_package_available("""bitsandbytes"""     )


def 					a__ (						)->		int:
						if strtobool(os.environ.get("""ACCELERATE_USE_MEGATRON_LM"""						,     """False"""     )     ) == 1:
												UpperCAmelCase__   		=						importlib.util.find_spec("""megatron"""     ) is not None
												if package_exists:
																		try:
																								UpperCAmelCase__   		=						parse(importlib.metadata.version("""megatron-lm"""     )     )
																								return compare_versions(UpperCAmelCase__						,     """>="""						,     """2.2.0"""     )
																		except Exception as e:
																								warnings.warn(f'Parse Megatron version failed. Exception:{e}'     )
																								return False


def 					a__ (						)->		Optional[int]:
						return _is_package_available("""safetensors"""     )


def 					a__ (						)->		Optional[Any]:
						return _is_package_available("""transformers"""     )


def 					a__ (						)->		int:
						return _is_package_available("""datasets"""     )


def 					a__ (						)->		Any:
						return _is_package_available("""aim"""     )


def 					a__ (						)->		Union[str, Any]:
						return _is_package_available("""tensorboard"""     ) or _is_package_available("""tensorboardX"""     )


def 					a__ (						)->		Tuple:
						return _is_package_available("""wandb"""     )


def 					a__ (						)->		List[str]:
						return _is_package_available("""comet_ml"""     )


def 					a__ (						)->		Dict:
						return _is_package_available("""boto3"""     )


def 					a__ (						)->		Union[str, Any]:
						if _is_package_available("""rich"""     ):
												if "ACCELERATE_DISABLE_RICH" in os.environ:
																		warnings.warn(
																		    """`ACCELERATE_DISABLE_RICH` is deprecated and will be removed in v0.22.0 and deactivated by default. Please use `ACCELERATE_ENABLE_RICH` if you wish to use `rich`."""     )
																		return not parse_flag_from_env("""ACCELERATE_DISABLE_RICH"""						,     UpperCAmelCase__     )
												return parse_flag_from_env("""ACCELERATE_ENABLE_RICH"""						,     UpperCAmelCase__     )
						return False


def 					a__ (						)->		Dict:
						return _is_package_available("""sagemaker"""     )


def 					a__ (						)->		str:
						return _is_package_available("""tqdm"""     )


def 					a__ (						)->		List[str]:
						return _is_package_available("""mlflow"""     )


def 					a__ (						)->		List[str]:
						return is_torch_version(""">="""						,     """1.12"""     ) and torch.backends.mps.is_available() and torch.backends.mps.is_built()


def 					a__ (						)->		List[Any]:
						def get_major_and_minor_from_version(UpperCAmelCase__    :     List[Any]     ):
												return str(version.parse(UpperCAmelCase__     ).major     ) + "." + str(version.parse(UpperCAmelCase__     ).minor     )

						UpperCAmelCase__   		=						importlib.metadata.version("""torch"""     )
						if importlib.util.find_spec("""intel_extension_for_pytorch"""     ) is None:
												return False
						UpperCAmelCase__   		=						"""N/A"""
						try:
												UpperCAmelCase__   		=						importlib.metadata.version("""intel_extension_for_pytorch"""     )
						except importlib.metadata.PackageNotFoundError:
												return False
						UpperCAmelCase__   		=						get_major_and_minor_from_version(_torch_version     )
						UpperCAmelCase__   		=						get_major_and_minor_from_version(_ipex_version     )
						if torch_major_and_minor != ipex_major_and_minor:
												warnings.warn(
												    f'Intel Extension for PyTorch {ipex_major_and_minor} needs to work with PyTorch {ipex_major_and_minor}.*,'
												    f' but PyTorch {_torch_version} is found. Please switch to the matching version and run again.'     )
												return False
						return True


@lru_cache
def 					a__ (						UpperCAmelCase__    :     Dict=False     )->		str:
						if importlib.util.find_spec("""torch"""     ) is None or importlib.util.find_spec("""torch_npu"""     ) is None:
												return False

						import torch
						import torch_npu  # noqa: F401

						if check_device:
												try:
																		# Will raise a RuntimeError if no NPU is found
																		UpperCAmelCase__   		=						torch.npu.device_count()
																		return torch.npu.is_available()
												except RuntimeError:
																		return False
						return hasattr(UpperCAmelCase__						,     """npu"""     ) and torch.npu.is_available()







@lru_cache
def 					a__ (						UpperCAmelCase__    :     Union[str, Any]=False     )->		List[Any]:
						if not parse_flag_from_env("""ACCELERATE_USE_XPU"""						,     default=UpperCAmelCase__     ):
												return False
						if is_ipex_available():
												import torch

												if is_torch_version("""<="""						,     """1.12"""     ):
																		return False
						else:
												return False

						import intel_extension_for_pytorch  # noqa: F401

						if check_device:
												try:
																		# Will raise a RuntimeError if no XPU  is found
																		UpperCAmelCase__   		=						torch.xpu.device_count()
																		return torch.xpu.is_available()
												except RuntimeError:
																		return False
						return hasattr(UpperCAmelCase__						,     """xpu"""     ) and torch.xpu.is_available()


"""simple docstring"""







class      _lowercase						:

				def __init__(       self    :      Tuple    ,      _lowercase    :      int       ):


										'''simple docstring'''
										UpperCAmelCase__   		=						data
										UpperCAmelCase__   		=						None
										UpperCAmelCase__   		=						None


def 					a__ (						UpperCAmelCase__    :     BinaryTreeNode | None						,     UpperCAmelCase__    :     int     )->		BinaryTreeNode | None:
						if node is None:
												UpperCAmelCase__   		=						BinaryTreeNode(UpperCAmelCase__     )
												return node

						# binary search tree is not empty,
						# so we will insert it into the tree
						# if new_value is less than value of data in node,
						#  add it to left subtree and proceed recursively
						if new_value < node.data:
												UpperCAmelCase__   		=						insert(node.left_child						,     UpperCAmelCase__     )
						else:
												# if new_value is greater than value of data in node,
												#  add it to right subtree and proceed recursively
												UpperCAmelCase__   		=						insert(node.right_child						,     UpperCAmelCase__     )
						return node


def 					a__ (						UpperCAmelCase__    :     None | BinaryTreeNode     )->		list[int]:  # if node is None,return
						if node:
												UpperCAmelCase__   		=						inorder(node.left_child     )
												UpperCAmelCase__   		=						[*inorder_array, node.data]
												UpperCAmelCase__   		=						inorder_array + inorder(node.right_child     )
						else:
												UpperCAmelCase__   		=						[]
						return inorder_array


def 					a__ (						)->		BinaryTreeNode | None:
						UpperCAmelCase__   		=						insert(UpperCAmelCase__						,     1_5     )
						insert(UpperCAmelCase__						,     1_0     )
						insert(UpperCAmelCase__						,     2_5     )
						insert(UpperCAmelCase__						,     6     )
						insert(UpperCAmelCase__						,     1_4     )
						insert(UpperCAmelCase__						,     2_0     )
						insert(UpperCAmelCase__						,     6_0     )
						return root


def 					a__ (						)->		None:
						# main function
						UpperCAmelCase__   		=						make_tree()
						print("""Printing values of binary search tree in Inorder Traversal."""     )
						inorder(UpperCAmelCase__     )


if __name__ == "__main__":
						import doctest

						doctest.testmod()
						main()

