




'''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

 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 				SCREAMING_SNAKE_CASE_				(							__A       :				Any						)				->				Dict:
   # Check we're not importing a "pkg_name" directory somewhere but the actual library by trying to grab the version
   _SCREAMING_SNAKE_CASE =   importlib.util.find_spec(__A						) is not None
   if package_exists:
      try:
         _SCREAMING_SNAKE_CASE =   importlib.metadata.metadata(__A						)
         return True
      except importlib.metadata.PackageNotFoundError:
         return False
def 				SCREAMING_SNAKE_CASE_				(							)				->				bool:
   return _torch_distributed_available
def 				SCREAMING_SNAKE_CASE_				(							)				->				Optional[int]:
   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_				(							)				->				int:
   return importlib.metadata.version("oneccl_bind_pt"						)
def 				SCREAMING_SNAKE_CASE_				(							)				->				Tuple:
   return _is_package_available("transformer_engine"						)
@lru_cache
def 				SCREAMING_SNAKE_CASE_				(							__A       :				List[Any]=True						)				->				Dict:
   # 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
         _SCREAMING_SNAKE_CASE =   xm.xla_device()
         return True
      except RuntimeError:
         return False
   return _tpu_available
def 				SCREAMING_SNAKE_CASE_				(							)				->				int:
   return _is_package_available("deepspeed"						)
def 				SCREAMING_SNAKE_CASE_				(							__A       :				Optional[int]=False						)				->				Dict:
   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_				(							)				->				List[Any]:
   _SCREAMING_SNAKE_CASE =   _is_package_available("bitsandbytes"						)
   if package_exists:
      _SCREAMING_SNAKE_CASE =   version.parse(importlib.metadata.version("bitsandbytes"						)						)
      return compare_versions(__A ,  ">=" ,  "0.39.0"						)
   return False
def 				SCREAMING_SNAKE_CASE_				(							)				->				str:
   _SCREAMING_SNAKE_CASE =   _is_package_available("bitsandbytes"						)
   if package_exists:
      _SCREAMING_SNAKE_CASE =   version.parse(importlib.metadata.version("bitsandbytes"						)						)
      return compare_versions(__A ,  ">=" ,  "0.37.2"						)
   return False
def 				SCREAMING_SNAKE_CASE_				(							)				->				Any:
   return _is_package_available("bitsandbytes"						)
def 				SCREAMING_SNAKE_CASE_				(							)				->				Tuple:
   if strtobool(os.environ.get("ACCELERATE_USE_MEGATRON_LM" ,  "False"						)						) == 1:
      _SCREAMING_SNAKE_CASE =   importlib.util.find_spec("megatron"						) is not None
      if package_exists:
         try:
            _SCREAMING_SNAKE_CASE =   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 				SCREAMING_SNAKE_CASE_				(							)				->				Any:
   return _is_package_available("safetensors"						)
def 				SCREAMING_SNAKE_CASE_				(							)				->				Tuple:
   return _is_package_available("transformers"						)
def 				SCREAMING_SNAKE_CASE_				(							)				->				str:
   return _is_package_available("datasets"						)
def 				SCREAMING_SNAKE_CASE_				(							)				->				str:
   return _is_package_available("aim"						)
def 				SCREAMING_SNAKE_CASE_				(							)				->				int:
   return _is_package_available("tensorboard"						) or _is_package_available("tensorboardX"						)
def 				SCREAMING_SNAKE_CASE_				(							)				->				Any:
   return _is_package_available("wandb"						)
def 				SCREAMING_SNAKE_CASE_				(							)				->				Tuple:
   return _is_package_available("comet_ml"						)
def 				SCREAMING_SNAKE_CASE_				(							)				->				Optional[Any]:
   return _is_package_available("boto3"						)
def 				SCREAMING_SNAKE_CASE_				(							)				->				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 				SCREAMING_SNAKE_CASE_				(							)				->				Tuple:
   return _is_package_available("sagemaker"						)
def 				SCREAMING_SNAKE_CASE_				(							)				->				Union[str, Any]:
   return _is_package_available("tqdm"						)
def 				SCREAMING_SNAKE_CASE_				(							)				->				Optional[Any]:
   return _is_package_available("mlflow"						)
def 				SCREAMING_SNAKE_CASE_				(							)				->				str:
   return is_torch_version(">=" ,  "1.12"						) and torch.backends.mps.is_available() and torch.backends.mps.is_built()
def 				SCREAMING_SNAKE_CASE_				(							)				->				List[Any]:
   def get_major_and_minor_from_version(__A       :				Union[str, Any]						):
      return str(version.parse(__A						).major						) + "." + str(version.parse(__A						).minor						)

   _SCREAMING_SNAKE_CASE =   importlib.metadata.version("torch"						)
   if importlib.util.find_spec("intel_extension_for_pytorch"						) is None:
      return False
   _SCREAMING_SNAKE_CASE =   "N/A"
   try:
      _SCREAMING_SNAKE_CASE =   importlib.metadata.version("intel_extension_for_pytorch"						)
   except importlib.metadata.PackageNotFoundError:
      return False
   _SCREAMING_SNAKE_CASE =   get_major_and_minor_from_version(_torch_version						)
   _SCREAMING_SNAKE_CASE =   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_				(							__A       :				str=False						)				->				str:
   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
         _SCREAMING_SNAKE_CASE =   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 				SCREAMING_SNAKE_CASE_				(							__A       :				Union[str, Any]=False						)				->				Union[str, Any]:
   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
         _SCREAMING_SNAKE_CASE =   torch.xpu.device_count()
         return torch.xpu.is_available()
      except RuntimeError:
         return False
   return hasattr(__A ,  "xpu"						) and torch.xpu.is_available()







'''simple docstring'''



lowerCamelCase_	 =		(1, 1_53, 3_70, 3_71, 16_34, 24_67_80_51, 1_15_13_22_19_01_87_63_99_25_65_09_55_97_97_39_71_52_24_01)
lowerCamelCase_	 =		(-1_53, -1, 0, 1.2, 2_00, "A", [], {}, None)
def 				SCREAMING_SNAKE_CASE_				(							__A       :				int						)				->				bool:
   if not isinstance(__A ,  __A						) or n < 1:
      return False

   # Initialization of sum and number of digits.
   _SCREAMING_SNAKE_CASE =   0
   _SCREAMING_SNAKE_CASE =   0
   _SCREAMING_SNAKE_CASE =   n
   # Calculation of digits of the number
   while temp > 0:
      number_of_digits += 1
      temp //= 10
   # Dividing number into separate digits and find Armstrong number
   _SCREAMING_SNAKE_CASE =   n
   while temp > 0:
      _SCREAMING_SNAKE_CASE =   temp % 10
      total += rem**number_of_digits
      temp //= 10
   return n == total
def 				SCREAMING_SNAKE_CASE_				(							__A       :				int						)				->				bool:
   if not isinstance(__A ,  __A						) or n < 1:
      return False

   # Init a "histogram" of the digits
   _SCREAMING_SNAKE_CASE =   [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
   _SCREAMING_SNAKE_CASE =   0
   _SCREAMING_SNAKE_CASE =   0
   _SCREAMING_SNAKE_CASE =   n
   while temp > 0:
      _SCREAMING_SNAKE_CASE, _SCREAMING_SNAKE_CASE =   divmod(__A ,  10						)
      digit_histogram[rem] += 1
      digit_total += 1

   for cnt, i in zip(__A ,  range(len(__A						)						)						):
      total += cnt * i**digit_total

   return n == total
def 				SCREAMING_SNAKE_CASE_				(							__A       :				int						)				->				bool:
   if not isinstance(__A ,  __A						) or n < 1:
      return False
   _SCREAMING_SNAKE_CASE =   len(str(__A						)						)  # the power that all digits will be raised to
   # check if sum of each digit multiplied expo times is equal to number
   return n == sum(int(__A						) ** expo for i in str(__A						)						)
def 				SCREAMING_SNAKE_CASE_				(							)				->				Optional[Any]:
   _SCREAMING_SNAKE_CASE =   int(input("Enter an integer to see if it is an Armstrong number: "						).strip()						)
   print(f"""{num} is {"" if armstrong_number(__A						) else "not "}an Armstrong number."""						)
   print(f"""{num} is {"" if narcissistic_number(__A						) else "not "}an Armstrong number."""						)
   print(f"""{num} is {"" if pluperfect_number(__A						) else "not "}an Armstrong number."""						)


if __name__ == "__main__":
 import doctest

 doctest.testmod()
 main()

