code: 
  '''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  _A		(							lowercase__	):
  						# Check we're not importing a "pkg_name" directory somewhere but the actual library by trying to grab the version
  						lowercase__							 =       importlib.util.find_spec(lowercase__	) is not None
  						if package_exists:
  												try:
  																		lowercase__							 =       importlib.metadata.metadata(lowercase__	)
  																		return True
  												except importlib.metadata.PackageNotFoundError:
  																		return False
  
  
  
  
  
  def  _A		(							):
  						return _torch_distributed_available
  
  
  
  
  
  def  _A		(							):
  						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		(							):
  						return importlib.metadata.version("""oneccl_bind_pt"""	)
  
  
  
  
  
  def  _A		(							):
  						return _is_package_available("""transformer_engine"""	)
  
  
  
  
  
  @lru_cache
  def  _A		(							lowercase__=True	):
  						# 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
  																		lowercase__							 =       xm.xla_device()
  																		return True
  												except RuntimeError:
  																		return False
  						return _tpu_available
  
  
  
  
  
  def  _A		(							):
  						return _is_package_available("""deepspeed"""	)
  
  
  
  
  
  def  _A		(							lowercase__=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  _A		(							):
  						lowercase__							 =       _is_package_available("""bitsandbytes"""	)
  						if package_exists:
  												lowercase__							 =       version.parse(importlib.metadata.version("""bitsandbytes"""	)	)
  												return compare_versions(lowercase__		,		""">="""		,		"""0.39.0"""	)
  						return False
  
  
  
  
  
  def  _A		(							):
  						lowercase__							 =       _is_package_available("""bitsandbytes"""	)
  						if package_exists:
  												lowercase__							 =       version.parse(importlib.metadata.version("""bitsandbytes"""	)	)
  												return compare_versions(lowercase__		,		""">="""		,		"""0.37.2"""	)
  						return False
  
  
  
  
  
  def  _A		(							):
  						return _is_package_available("""bitsandbytes"""	)
  
  
  
  
  
  def  _A		(							):
  						if strtobool(os.environ.get("""ACCELERATE_USE_MEGATRON_LM"""		,		"""False"""	)	) == 1:
  												lowercase__							 =       importlib.util.find_spec("""megatron"""	) is not None
  												if package_exists:
  																		try:
  																								lowercase__							 =       parse(importlib.metadata.version("""megatron-lm"""	)	)
  																								return compare_versions(lowercase__		,		""">="""		,		"""2.2.0"""	)
  																		except Exception as e:
  																								warnings.warn(f'''Parse Megatron version failed. Exception:{e}'''	)
  																								return False
  
  
  
  
  
  def  _A		(							):
  						return _is_package_available("""safetensors"""	)
  
  
  
  
  
  def  _A		(							):
  						return _is_package_available("""transformers"""	)
  
  
  
  
  
  def  _A		(							):
  						return _is_package_available("""datasets"""	)
  
  
  
  
  
  def  _A		(							):
  						return _is_package_available("""aim"""	)
  
  
  
  
  
  def  _A		(							):
  						return _is_package_available("""tensorboard"""	) or _is_package_available("""tensorboardX"""	)
  
  
  
  
  
  def  _A		(							):
  						return _is_package_available("""wandb"""	)
  
  
  
  
  
  def  _A		(							):
  						return _is_package_available("""comet_ml"""	)
  
  
  
  
  
  def  _A		(							):
  						return _is_package_available("""boto3"""	)
  
  
  
  
  
  def  _A		(							):
  						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"""		,		lowercase__	)
  												return parse_flag_from_env("""ACCELERATE_ENABLE_RICH"""		,		lowercase__	)
  						return False
  
  
  
  
  
  def  _A		(							):
  						return _is_package_available("""sagemaker"""	)
  
  
  
  
  
  def  _A		(							):
  						return _is_package_available("""tqdm"""	)
  
  
  
  
  
  def  _A		(							):
  						return _is_package_available("""mlflow"""	)
  
  
  
  
  
  def  _A		(							):
  						return is_torch_version(""">="""		,		"""1.12"""	) and torch.backends.mps.is_available() and torch.backends.mps.is_built()
  
  
  
  
  
  def  _A		(							):
  						def get_major_and_minor_from_version(lowercase__	):
  												return str(version.parse(lowercase__	).major	) + "." + str(version.parse(lowercase__	).minor	)
  
  						lowercase__							 =       importlib.metadata.version("""torch"""	)
  						if importlib.util.find_spec("""intel_extension_for_pytorch"""	) is None:
  												return False
  						lowercase__							 =       """N/A"""
  						try:
  												lowercase__							 =       importlib.metadata.version("""intel_extension_for_pytorch"""	)
  						except importlib.metadata.PackageNotFoundError:
  												return False
  						lowercase__							 =       get_major_and_minor_from_version(_torch_version	)
  						lowercase__							 =       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		(							lowercase__=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
  																		lowercase__							 =       torch.npu.device_count()
  																		return torch.npu.is_available()
  												except RuntimeError:
  																		return False
  						return hasattr(lowercase__		,		"""npu"""	) and torch.npu.is_available()
  
  
  
  
  
  
  
  @lru_cache
  def  _A		(							lowercase__=False	):
  						if not parse_flag_from_env("""ACCELERATE_USE_XPU"""		,		default=lowercase__	):
  												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
  																		lowercase__							 =       torch.xpu.device_count()
  																		return torch.xpu.is_available()
  												except RuntimeError:
  																		return False
  						return hasattr(lowercase__		,		"""xpu"""	) and torch.xpu.is_available()
  
  
  
  
  
  
  
style_context: 
  '''simple docstring'''
  
  
  
  def  _A		(							lowercase__	):
  
  						# validate:
  						if not isinstance(lowercase__		,		lowercase__	) or not all(
  						    isinstance(lowercase__		,		lowercase__	) and item >= 0 for item in citations	):
  												raise ValueError("""The citations should be a list of non negative integers."""	)
  
  						citations.sort()
  						lowercase__							 =       len(lowercase__	)
  
  						for i in range(lowercase__	):
  												if citations[len_citations - 1 - i] <= i:
  																		return i
  
  						return len_citations
  
  
  if __name__ == "__main__":
  				import doctest
  
  				doctest.testmod()
  
  
  
  
  
  
  