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
  
  					__magic_name__	:   Any          =							True
  except ImportError:
  					__magic_name__	:   Optional[Any]          =							False
  
  
  # Cache this result has it's a C FFI call which can be pretty time-consuming
  __magic_name__	:   Optional[int]          =							torch.distributed.is_available()
  
  
  
  
  
  def 			a_   (  lowercase__   :int    ):
  			# Check we're not importing a "pkg_name" directory somewhere but the actual library by trying to grab the version
  			__lowerCamelCase        =			importlib.util.find_spec(lowercase__    ) is not None
  			if package_exists:
  						try:
  									__lowerCamelCase        =			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__   :Dict=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
  									__lowerCamelCase        =			xm.xla_device()
  									return True
  						except RuntimeError:
  									return False
  			return _tpu_available
  
  
  
  
  
  def 			a_   (  ):
  			return _is_package_available("""deepspeed"""    )
  
  
  
  
  
  def 			a_   (  lowercase__   :List[str]=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_   (  ):
  			__lowerCamelCase        =			_is_package_available("""bitsandbytes"""    )
  			if package_exists:
  						__lowerCamelCase        =			version.parse(importlib.metadata.version("""bitsandbytes"""    )    )
  						return compare_versions(lowercase__, """>=""", """0.39.0"""    )
  			return False
  
  
  
  
  
  def 			a_   (  ):
  			__lowerCamelCase        =			_is_package_available("""bitsandbytes"""    )
  			if package_exists:
  						__lowerCamelCase        =			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:
  						__lowerCamelCase        =			importlib.util.find_spec("""megatron"""    ) is not None
  						if package_exists:
  									try:
  												__lowerCamelCase        =			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__   :Any    ):
  						return str(version.parse(lowercase__    ).major    ) + "." + str(version.parse(lowercase__    ).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 			a_   (  lowercase__   :List[Any]=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        =			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__   :Optional[Any]=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
  									__lowerCamelCase        =			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"""
  
  
  
  
  import os
  
  
  
  
  
  def 			a_   (  ):
  			__lowerCamelCase        =			os.path.dirname(os.path.realpath(lowercase__    )    )
  			__lowerCamelCase        =			os.path.join(lowercase__, """triangle.txt"""    )
  
  			with open(lowercase__    ) as in_file:
  						__lowerCamelCase        =			[[int(lowercase__    ) for i in line.split()] for line in in_file]
  
  			while len(lowercase__    ) != 1:
  						__lowerCamelCase        =			triangle.pop()
  						__lowerCamelCase        =			triangle[-1]
  						for j in range(len(lowercase__    ) - 1    ):
  									curr_row[j] += max(last_row[j], last_row[j + 1]    )
  			return triangle[0][0]
  
  
  if __name__ == "__main__":
  					print(solution())
  
  
  
  
  