




"""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

			__A 		= True
except ImportError:
			__A 		= False


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



def 					_lowerCamelCase(__UpperCamelCase )       ->				Any:
	_lowerCAmelCase			=importlib.util.find_spec(UpperCamelCase_ ) is not None
	if package_exists:
		try:
			_lowerCAmelCase			=importlib.metadata.metadata(UpperCamelCase_ )
			return True
		except importlib.metadata.PackageNotFoundError:
			return False



def 					_lowerCamelCase()       ->				Union[str, Any]:
	return _torch_distributed_available



def 					_lowerCamelCase()       ->				Optional[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 					_lowerCamelCase()       ->				Dict:
	return importlib.metadata.version("""oneccl_bind_pt""" )



def 					_lowerCamelCase()       ->				str:
	return _is_package_available("""transformer_engine""" )



@lru_cache
def 					_lowerCamelCase(__UpperCamelCase=True )       ->				Tuple:






	if torch.cuda.is_available():
		return False
	if _tpu_available and check_device:
		try:
			# Will raise a RuntimeError if no XLA configuration is found
			_lowerCAmelCase			=xm.xla_device()
			return True
		except RuntimeError:
			return False
	return _tpu_available



def 					_lowerCamelCase()       ->				List[Any]:
	return _is_package_available("""deepspeed""" )



def 					_lowerCamelCase(__UpperCamelCase=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 					_lowerCamelCase()       ->				Any:
	_lowerCAmelCase			=_is_package_available("""bitsandbytes""" )
	if package_exists:
		_lowerCAmelCase			=version.parse(importlib.metadata.version("""bitsandbytes""" ) )
		return compare_versions(UpperCamelCase_					,				""">="""					,				"""0.39.0""" )
	return False



def 					_lowerCamelCase()       ->				List[Any]:
	_lowerCAmelCase			=_is_package_available("""bitsandbytes""" )
	if package_exists:
		_lowerCAmelCase			=version.parse(importlib.metadata.version("""bitsandbytes""" ) )
		return compare_versions(UpperCamelCase_					,				""">="""					,				"""0.37.2""" )
	return False



def 					_lowerCamelCase()       ->				Tuple:
	return _is_package_available("""bitsandbytes""" )



def 					_lowerCamelCase()       ->				Tuple:






	if strtobool(os.environ.get("""ACCELERATE_USE_MEGATRON_LM"""					,				"""False""" ) ) == 1:
		_lowerCAmelCase			=importlib.util.find_spec("""megatron""" ) is not None
		if package_exists:
			try:
				_lowerCAmelCase			=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 					_lowerCamelCase()       ->				List[str]:
	return _is_package_available("""safetensors""" )



def 					_lowerCamelCase()       ->				str:
	return _is_package_available("""transformers""" )



def 					_lowerCamelCase()       ->				str:
	return _is_package_available("""datasets""" )



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



def 					_lowerCamelCase()       ->				List[Any]:
	return _is_package_available("""tensorboard""" ) or _is_package_available("""tensorboardX""" )



def 					_lowerCamelCase()       ->				Any:
	return _is_package_available("""wandb""" )



def 					_lowerCamelCase()       ->				Tuple:
	return _is_package_available("""comet_ml""" )



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



def 					_lowerCamelCase()       ->				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 					_lowerCamelCase()       ->				Union[str, Any]:
	return _is_package_available("""sagemaker""" )



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



def 					_lowerCamelCase()       ->				Optional[int]:
	return _is_package_available("""mlflow""" )



def 					_lowerCamelCase()       ->				Optional[Any]:
	return is_torch_version(""">="""					,				"""1.12""" ) and torch.backends.mps.is_available() and torch.backends.mps.is_built()



def 					_lowerCamelCase()       ->				str:






	def get_major_and_minor_from_version(__UpperCamelCase ):
		return str(version.parse(UpperCamelCase_ ).major ) + "." + str(version.parse(UpperCamelCase_ ).minor )

	_lowerCAmelCase			=importlib.metadata.version("""torch""" )
	if importlib.util.find_spec("""intel_extension_for_pytorch""" ) is None:
		return False
	_lowerCAmelCase			="""N/A"""
	try:
		_lowerCAmelCase			=importlib.metadata.version("""intel_extension_for_pytorch""" )
	except importlib.metadata.PackageNotFoundError:
		return False
	_lowerCAmelCase			=get_major_and_minor_from_version(_torch_version )
	_lowerCAmelCase			=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 					_lowerCamelCase(__UpperCamelCase=False )       ->				List[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
			_lowerCAmelCase			=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 					_lowerCamelCase(__UpperCamelCase=False )       ->				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
			_lowerCAmelCase			=torch.xpu.device_count()
			return torch.xpu.is_available()
		except RuntimeError:
			return False
	return hasattr(UpperCamelCase_					,				"""xpu""" ) and torch.xpu.is_available()





"""simple docstring"""


def 					_lowerCamelCase(__UpperCamelCase					,				__UpperCamelCase					,				__UpperCamelCase )       ->				float:
	if mass < 0:
		raise ValueError("""The mass of the body cannot be negative""" )
	if radius <= 0:
		raise ValueError("""The radius is always a positive non zero integer""" )
	return (mass * (velocity) ** 2) / radius


if __name__ == "__main__":
			import doctest

			doctest.testmod(verbose=True)



