






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

		lowerCamelCase_		  =						True
except ImportError:
		lowerCamelCase_		  =						False


# Cache this result has it's a C FFI call which can be pretty time-consuming
lowerCamelCase_		  =						torch.distributed.is_available()
def   lowerCamelCase      (		a_			)			->    Dict:
							# 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(a_			) is not None
							if package_exists:
														try:
																					lowerCAmelCase_       		=  importlib.metadata.metadata(a_			)
																					return True
														except importlib.metadata.PackageNotFoundError:
																					return False
def   lowerCamelCase      (		)			->    bool:
							return _torch_distributed_available
def   lowerCamelCase      (		)			->    List[str]:
							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      (		)			->    Optional[Any]:
							return importlib.metadata.version('oneccl_bind_pt'			)
def   lowerCamelCase      (		)			->    List[str]:
							return _is_package_available('transformer_engine'			)
@lru_cache
def   lowerCamelCase      (		a_=True			)			->    int:
							# 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   lowerCamelCase      (		)			->    List[Any]:
							return _is_package_available('deepspeed'			)
def   lowerCamelCase      (		a_=False			)			->    Optional[int]:
							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      (		)			->    List[str]:
							lowerCAmelCase_       		=  _is_package_available('bitsandbytes'			)
							if package_exists:
														lowerCAmelCase_       		=  version.parse(importlib.metadata.version('bitsandbytes'			)			)
														return compare_versions(a_				,		'>='				,		'0.39.0'			)
							return False
def   lowerCamelCase      (		)			->    Any:
							lowerCAmelCase_       		=  _is_package_available('bitsandbytes'			)
							if package_exists:
														lowerCAmelCase_       		=  version.parse(importlib.metadata.version('bitsandbytes'			)			)
														return compare_versions(a_				,		'>='				,		'0.37.2'			)
							return False
def   lowerCamelCase      (		)			->    Optional[Any]:
							return _is_package_available('bitsandbytes'			)
def   lowerCamelCase      (		)			->    int:
							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(a_				,		'>='				,		'2.2.0'			)
																					except Exception as e:
																												warnings.warn(F'''Parse Megatron version failed. Exception:{e}'''			)
																												return False
def   lowerCamelCase      (		)			->    Dict:
							return _is_package_available('safetensors'			)
def   lowerCamelCase      (		)			->    Dict:
							return _is_package_available('transformers'			)
def   lowerCamelCase      (		)			->    int:
							return _is_package_available('datasets'			)
def   lowerCamelCase      (		)			->    List[str]:
							return _is_package_available('aim'			)
def   lowerCamelCase      (		)			->    str:
							return _is_package_available('tensorboard'			) or _is_package_available('tensorboardX'			)
def   lowerCamelCase      (		)			->    Dict:
							return _is_package_available('wandb'			)
def   lowerCamelCase      (		)			->    Any:
							return _is_package_available('comet_ml'			)
def   lowerCamelCase      (		)			->    List[Any]:
							return _is_package_available('boto3'			)
def   lowerCamelCase      (		)			->    List[str]:
							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'				,		a_			)
														return parse_flag_from_env('ACCELERATE_ENABLE_RICH'				,		a_			)
							return False
def   lowerCamelCase      (		)			->    Optional[int]:
							return _is_package_available('sagemaker'			)
def   lowerCamelCase      (		)			->    int:
							return _is_package_available('tqdm'			)
def   lowerCamelCase      (		)			->    str:
							return _is_package_available('mlflow'			)
def   lowerCamelCase      (		)			->    List[str]:
							return is_torch_version('>='				,		'1.12'			) and torch.backends.mps.is_available() and torch.backends.mps.is_built()
def   lowerCamelCase      (		)			->    Union[str, Any]:
							def get_major_and_minor_from_version(a_			):
														return str(version.parse(a_			).major			) + "." + str(version.parse(a_			).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      (		a_=False			)			->    List[Any]:
							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(a_				,		'npu'			) and torch.npu.is_available()



@lru_cache
def   lowerCamelCase      (		a_=False			)			->    List[str]:
							if not parse_flag_from_env('ACCELERATE_USE_XPU'				,		default=a_			):
														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(a_				,		'xpu'			) and torch.xpu.is_available()











def   lowerCamelCase      (		a_ = 4_000_000			)			->    int:

							if n <= 1:
														return 0
							lowerCAmelCase_       		=  0
							lowerCAmelCase_       		=  2
							lowerCAmelCase_       		=  0
							while 4 * b + a <= n:
														lowerCAmelCase_		,     lowerCAmelCase_       		=  b, 4 * b + a
														count += a
							return count + b


if __name__ == "__main__":
		print(f'''{solution() = }''')



