code: 
  
  
  
  
  
  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
  
  		_lowercase		:     str 					=True
  except ImportError:
  		_lowercase		:     str 					=False
  
  
  # Cache this result has it's a C FFI call which can be pretty time-consuming
  _lowercase		:     Tuple 					=torch.distributed.is_available()
  
  
  def 		_SCREAMING_SNAKE_CASE    (   lowerCAmelCase__	):
  		lowerCamelCase_  :    Tuple   	=			importlib.util.find_spec(lowerCAmelCase__	) is not None
  		if package_exists:
  				try:
  						lowerCamelCase_  :    Tuple   	=			importlib.metadata.metadata(lowerCAmelCase__	)
  						return True
  				except importlib.metadata.PackageNotFoundError:
  						return False
  
  
  def 		_SCREAMING_SNAKE_CASE    (   ):
  		return _torch_distributed_available
  
  
  def 		_SCREAMING_SNAKE_CASE    (   ):
  
  
  
  
  
  
  
  		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 		_SCREAMING_SNAKE_CASE    (   ):
  		return importlib.metadata.version('oneccl_bind_pt'	)
  
  
  def 		_SCREAMING_SNAKE_CASE    (   ):
  		return _is_package_available('transformer_engine'	)
  
  
  @lru_cache
  def 		_SCREAMING_SNAKE_CASE    (   lowerCAmelCase__=True	):
  
  
  
  
  
  
  
  		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_  :    Optional[Any]   	=			xm.xla_device()
  						return True
  				except RuntimeError:
  						return False
  		return _tpu_available
  
  
  def 		_SCREAMING_SNAKE_CASE    (   ):
  		return _is_package_available('deepspeed'	)
  
  
  def 		_SCREAMING_SNAKE_CASE    (   lowerCAmelCase__=False	):
  
  
  
  
  
  
  
  		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 		_SCREAMING_SNAKE_CASE    (   ):
  		lowerCamelCase_  :    int   	=			_is_package_available('bitsandbytes'	)
  		if package_exists:
  				lowerCamelCase_  :    Optional[int]   	=			version.parse(importlib.metadata.version('bitsandbytes'	)	)
  				return compare_versions(lowerCAmelCase__       ,'>='       ,'0.39.0'	)
  		return False
  
  
  def 		_SCREAMING_SNAKE_CASE    (   ):
  		lowerCamelCase_  :    Dict   	=			_is_package_available('bitsandbytes'	)
  		if package_exists:
  				lowerCamelCase_  :    Any   	=			version.parse(importlib.metadata.version('bitsandbytes'	)	)
  				return compare_versions(lowerCAmelCase__       ,'>='       ,'0.37.2'	)
  		return False
  
  
  def 		_SCREAMING_SNAKE_CASE    (   ):
  		return _is_package_available('bitsandbytes'	)
  
  
  def 		_SCREAMING_SNAKE_CASE    (   ):
  
  
  
  
  
  
  
  		if strtobool(os.environ.get('ACCELERATE_USE_MEGATRON_LM'       ,'False'	)	) == 1:
  				lowerCamelCase_  :    Optional[int]   	=			importlib.util.find_spec('megatron'	) is not None
  				if package_exists:
  						try:
  								lowerCamelCase_  :    Tuple   	=			parse(importlib.metadata.version('megatron-lm'	)	)
  								return compare_versions(lowerCAmelCase__       ,'>='       ,'2.2.0'	)
  						except Exception as e:
  								warnings.warn(F"Parse Megatron version failed. Exception:{e}"	)
  								return False
  
  
  def 		_SCREAMING_SNAKE_CASE    (   ):
  		return _is_package_available('safetensors'	)
  
  
  def 		_SCREAMING_SNAKE_CASE    (   ):
  		return _is_package_available('transformers'	)
  
  
  def 		_SCREAMING_SNAKE_CASE    (   ):
  		return _is_package_available('datasets'	)
  
  
  def 		_SCREAMING_SNAKE_CASE    (   ):
  		return _is_package_available('aim'	)
  
  
  def 		_SCREAMING_SNAKE_CASE    (   ):
  		return _is_package_available('tensorboard'	) or _is_package_available('tensorboardX'	)
  
  
  def 		_SCREAMING_SNAKE_CASE    (   ):
  		return _is_package_available('wandb'	)
  
  
  def 		_SCREAMING_SNAKE_CASE    (   ):
  		return _is_package_available('comet_ml'	)
  
  
  def 		_SCREAMING_SNAKE_CASE    (   ):
  		return _is_package_available('boto3'	)
  
  
  def 		_SCREAMING_SNAKE_CASE    (   ):
  
  
  
  
  
  
  
  		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'       ,lowerCAmelCase__	)
  				return parse_flag_from_env('ACCELERATE_ENABLE_RICH'       ,lowerCAmelCase__	)
  		return False
  
  
  def 		_SCREAMING_SNAKE_CASE    (   ):
  		return _is_package_available('sagemaker'	)
  
  
  def 		_SCREAMING_SNAKE_CASE    (   ):
  		return _is_package_available('tqdm'	)
  
  
  def 		_SCREAMING_SNAKE_CASE    (   ):
  		return _is_package_available('mlflow'	)
  
  
  def 		_SCREAMING_SNAKE_CASE    (   ):
  		return is_torch_version('>='       ,'1.12'	) and torch.backends.mps.is_available() and torch.backends.mps.is_built()
  
  
  def 		_SCREAMING_SNAKE_CASE    (   ):
  
  
  
  
  
  
  
  		def get_major_and_minor_from_version(lowerCAmelCase__	):
  				return str(version.parse(lowerCAmelCase__	).major	) + "." + str(version.parse(lowerCAmelCase__	).minor	)
  
  		lowerCamelCase_  :    Any   	=			importlib.metadata.version('torch'	)
  		if importlib.util.find_spec('intel_extension_for_pytorch'	) is None:
  				return False
  		lowerCamelCase_  :    Optional[Any]   	=			'N/A'
  		try:
  				lowerCamelCase_  :    Optional[Any]   	=			importlib.metadata.version('intel_extension_for_pytorch'	)
  		except importlib.metadata.PackageNotFoundError:
  				return False
  		lowerCamelCase_  :    List[Any]   	=			get_major_and_minor_from_version(_torch_version	)
  		lowerCamelCase_  :    Tuple   	=			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 		_SCREAMING_SNAKE_CASE    (   lowerCAmelCase__=False	):
  
  
  
  
  
  
  
  		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_  :    Tuple   	=			torch.npu.device_count()
  						return torch.npu.is_available()
  				except RuntimeError:
  						return False
  		return hasattr(lowerCAmelCase__       ,'npu'	) and torch.npu.is_available()
  
  
  
  
  
  @lru_cache
  def 		_SCREAMING_SNAKE_CASE    (   lowerCAmelCase__=False	):
  
  
  
  
  
  
  
  		if not parse_flag_from_env('ACCELERATE_USE_XPU'       ,default=lowerCAmelCase__	):
  				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_  :    int   	=			torch.xpu.device_count()
  						return torch.xpu.is_available()
  				except RuntimeError:
  						return False
  		return hasattr(lowerCAmelCase__       ,'xpu'	) and torch.xpu.is_available()
  
style_context: 
  
  
  
  
  
  from __future__ import annotations
  
  
  def 		_SCREAMING_SNAKE_CASE    (   lowerCAmelCase__	):
  		lowerCamelCase_  :    int
  		lowerCamelCase_  :    list[str]   	=			list(lowerCAmelCase__	)
  		lowerCamelCase_	,			lowerCamelCase_  :    List[str]   	=			_vector[:-1], int(_vector[-1]	)
  		lowerCamelCase_  :    list[int]   	=			[int(lowerCAmelCase__	) for digit in __vector]
  
  		vector.reverse()
  		for i, digit in enumerate(lowerCAmelCase__	):
  				if i & 1 == 0:
  						lowerCamelCase_  :    int   	=			digit * 2
  						if doubled > 9:
  								doubled -= 9
  						check_digit += doubled
  				else:
  						check_digit += digit
  
  		return check_digit % 10 == 0
  
  
  if __name__ == "__main__":
  		import doctest
  
  		doctest.testmod()
  		assert is_luhn("""79927398713""")
  		assert not is_luhn("""79927398714""")
  