






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_	:	int				=				True
except ImportError:
   A_	:	int				=				False


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

def 						snake_case      (UpperCAmelCase__						) ->       Dict:
  # Check we're not importing a "pkg_name" directory somewhere but the actual library by trying to grab the version
  UpperCamelCase_:      List[str]					   =       importlib.util.find_spec(UpperCAmelCase__						) is not None
  if package_exists:
    try:
      UpperCamelCase_:      Dict					   =       importlib.metadata.metadata(UpperCAmelCase__						)
      return True
    except importlib.metadata.PackageNotFoundError:
      return False

def 						snake_case      () ->       bool:
  return _torch_distributed_available

def 						snake_case      () ->       List[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 						snake_case      () ->       str:
  return importlib.metadata.version('oneccl_bind_pt'						)

def 						snake_case      () ->       str:
  return _is_package_available('transformer_engine'						)

@lru_cache
def 						snake_case      (UpperCAmelCase__=True						) ->       List[str]:
  # 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
      UpperCamelCase_:      Tuple					   =       xm.xla_device()
      return True
    except RuntimeError:
      return False
  return _tpu_available

def 						snake_case      () ->       int:
  return _is_package_available('deepspeed'						)

def 						snake_case      (UpperCAmelCase__=False						) ->       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 						snake_case      () ->       List[Any]:
  UpperCamelCase_:      Optional[int]					   =       _is_package_available('bitsandbytes'						)
  if package_exists:
    UpperCamelCase_:      int					   =       version.parse(importlib.metadata.version('bitsandbytes'						)						)
    return compare_versions(UpperCAmelCase__			,					'>='			,					'0.39.0'						)
  return False

def 						snake_case      () ->       str:
  UpperCamelCase_:      Optional[Any]					   =       _is_package_available('bitsandbytes'						)
  if package_exists:
    UpperCamelCase_:      Tuple					   =       version.parse(importlib.metadata.version('bitsandbytes'						)						)
    return compare_versions(UpperCAmelCase__			,					'>='			,					'0.37.2'						)
  return False

def 						snake_case      () ->       Dict:
  return _is_package_available('bitsandbytes'						)

def 						snake_case      () ->       Dict:
  if strtobool(os.environ.get('ACCELERATE_USE_MEGATRON_LM'			,					'False'						)						) == 1:
    UpperCamelCase_:      Union[str, Any]					   =       importlib.util.find_spec('megatron'						) is not None
    if package_exists:
      try:
        UpperCamelCase_:      Optional[Any]					   =       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 						snake_case      () ->       str:
  return _is_package_available('safetensors'						)

def 						snake_case      () ->       Union[str, Any]:
  return _is_package_available('transformers'						)

def 						snake_case      () ->       str:
  return _is_package_available('datasets'						)

def 						snake_case      () ->       str:
  return _is_package_available('aim'						)

def 						snake_case      () ->       Union[str, Any]:
  return _is_package_available('tensorboard'						) or _is_package_available('tensorboardX'						)

def 						snake_case      () ->       Optional[Any]:
  return _is_package_available('wandb'						)

def 						snake_case      () ->       str:
  return _is_package_available('comet_ml'						)

def 						snake_case      () ->       int:
  return _is_package_available('boto3'						)

def 						snake_case      () ->       Union[str, 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 						snake_case      () ->       str:
  return _is_package_available('sagemaker'						)

def 						snake_case      () ->       str:
  return _is_package_available('tqdm'						)

def 						snake_case      () ->       List[str]:
  return _is_package_available('mlflow'						)

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

def 						snake_case      () ->       int:
  def get_major_and_minor_from_version(UpperCAmelCase__						):
    return str(version.parse(UpperCAmelCase__						).major						) + "." + str(version.parse(UpperCAmelCase__						).minor						)

  UpperCamelCase_:      List[str]					   =       importlib.metadata.version('torch'						)
  if importlib.util.find_spec('intel_extension_for_pytorch'						) is None:
    return False
  UpperCamelCase_:      Optional[int]					   =       'N/A'
  try:
    UpperCamelCase_:      Any					   =       importlib.metadata.version('intel_extension_for_pytorch'						)
  except importlib.metadata.PackageNotFoundError:
    return False
  UpperCamelCase_:      Dict					   =       get_major_and_minor_from_version(_torch_version						)
  UpperCamelCase_:      str					   =       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 						snake_case      (UpperCAmelCase__=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
      UpperCamelCase_:      int					   =       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 						snake_case      (UpperCAmelCase__=False						) ->       Dict:
  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
      UpperCamelCase_:      str					   =       torch.xpu.device_count()
      return torch.xpu.is_available()
    except RuntimeError:
      return False
  return hasattr(UpperCAmelCase__			,					'xpu'						) and torch.xpu.is_available()







def 						snake_case      (UpperCAmelCase__ = 4_0_0_0_0_0_0						) ->       int:

  if n <= 1:
    return 0
  UpperCamelCase_:      Dict					   =       0
  UpperCamelCase_:      Union[str, Any]					   =       2
  UpperCamelCase_:      int					   =       0
  while 4 * b + a <= n:
    UpperCamelCase_   ,UpperCamelCase_:      List[str]					   =       b, 4 * b + a
    count += a
  return count + b


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