
'''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__ :		List[Any]					=True
except ImportError:
     A__ :		Union[str, Any]					=False


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







def 							A_  (    __SCREAMING_SNAKE_CASE    :	Union[str, Any]			)					->							Any:





  """simple docstring"""

  __A		:  str       				=		importlib.util.find_spec(__SCREAMING_SNAKE_CASE			) is not None
  if package_exists:
    try:
      __A		:  Optional[int]       				=		importlib.metadata.metadata(__SCREAMING_SNAKE_CASE			)
      return True
    except importlib.metadata.PackageNotFoundError:
      return False







def 							A_  (    )					->							bool:





  """simple docstring"""

  return _torch_distributed_available







def 							A_  (    )					->							List[str]:





  """simple docstring"""

  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_  (    )					->							List[str]:





  """simple docstring"""

  return importlib.metadata.version("""oneccl_bind_pt"""			)







def 							A_  (    )					->							Any:





  """simple docstring"""

  return _is_package_available("""transformer_engine"""			)







@lru_cache
def 							A_  (    __SCREAMING_SNAKE_CASE    :	Tuple=True			)					->							int:





  """simple docstring"""

  if torch.cuda.is_available():
    return False
  if _tpu_available and check_device:
    try:
      # Will raise a RuntimeError if no XLA configuration is found
      __A		:  Dict       				=		xm.xla_device()
      return True
    except RuntimeError:
      return False
  return _tpu_available







def 							A_  (    )					->							int:





  """simple docstring"""

  return _is_package_available("""deepspeed"""			)







def 							A_  (    __SCREAMING_SNAKE_CASE    :	Any=False			)					->							Tuple:





  """simple docstring"""

  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_  (    )					->							Optional[Any]:





  """simple docstring"""

  __A		:  Dict       				=		_is_package_available("""bitsandbytes"""			)
  if package_exists:
    __A		:  List[str]       				=		version.parse(importlib.metadata.version("""bitsandbytes"""			)			)
    return compare_versions(__SCREAMING_SNAKE_CASE      ,  """>="""      ,  """0.39.0"""			)
  return False







def 							A_  (    )					->							Any:





  """simple docstring"""

  __A		:  List[str]       				=		_is_package_available("""bitsandbytes"""			)
  if package_exists:
    __A		:  Optional[Any]       				=		version.parse(importlib.metadata.version("""bitsandbytes"""			)			)
    return compare_versions(__SCREAMING_SNAKE_CASE      ,  """>="""      ,  """0.37.2"""			)
  return False







def 							A_  (    )					->							Union[str, Any]:





  """simple docstring"""

  return _is_package_available("""bitsandbytes"""			)







def 							A_  (    )					->							List[Any]:





  """simple docstring"""

  if strtobool(os.environ.get("""ACCELERATE_USE_MEGATRON_LM"""      ,  """False"""			)			) == 1:
    __A		:  List[Any]       				=		importlib.util.find_spec("""megatron"""			) is not None
    if package_exists:
      try:
        __A		:  List[Any]       				=		parse(importlib.metadata.version("""megatron-lm"""			)			)
        return compare_versions(__SCREAMING_SNAKE_CASE      ,  """>="""      ,  """2.2.0"""			)
      except Exception as e:
        warnings.warn(F"Parse Megatron version failed. Exception:{e}"			)
        return False







def 							A_  (    )					->							Any:





  """simple docstring"""

  return _is_package_available("""safetensors"""			)







def 							A_  (    )					->							Optional[Any]:





  """simple docstring"""

  return _is_package_available("""transformers"""			)







def 							A_  (    )					->							Optional[int]:





  """simple docstring"""

  return _is_package_available("""datasets"""			)







def 							A_  (    )					->							Tuple:





  """simple docstring"""

  return _is_package_available("""aim"""			)







def 							A_  (    )					->							Union[str, Any]:





  """simple docstring"""

  return _is_package_available("""tensorboard"""			) or _is_package_available("""tensorboardX"""			)







def 							A_  (    )					->							Optional[int]:





  """simple docstring"""

  return _is_package_available("""wandb"""			)







def 							A_  (    )					->							int:





  """simple docstring"""

  return _is_package_available("""comet_ml"""			)







def 							A_  (    )					->							int:





  """simple docstring"""

  return _is_package_available("""boto3"""			)







def 							A_  (    )					->							Dict:





  """simple docstring"""

  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"""      ,  __SCREAMING_SNAKE_CASE			)
    return parse_flag_from_env("""ACCELERATE_ENABLE_RICH"""      ,  __SCREAMING_SNAKE_CASE			)
  return False







def 							A_  (    )					->							List[str]:





  """simple docstring"""

  return _is_package_available("""sagemaker"""			)







def 							A_  (    )					->							str:





  """simple docstring"""

  return _is_package_available("""tqdm"""			)







def 							A_  (    )					->							List[Any]:





  """simple docstring"""

  return _is_package_available("""mlflow"""			)







def 							A_  (    )					->							str:





  """simple docstring"""

  return is_torch_version(""">="""      ,  """1.12"""			) and torch.backends.mps.is_available() and torch.backends.mps.is_built()







def 							A_  (    )					->							Union[str, Any]:





  """simple docstring"""

  def get_major_and_minor_from_version(__SCREAMING_SNAKE_CASE    :	List[Any]			):
    return str(version.parse(__SCREAMING_SNAKE_CASE			).major			) + "." + str(version.parse(__SCREAMING_SNAKE_CASE			).minor			)

  __A		:  int       				=		importlib.metadata.version("""torch"""			)
  if importlib.util.find_spec("""intel_extension_for_pytorch"""			) is None:
    return False
  __A		:  List[str]       				=		"""N/A"""
  try:
    __A		:  List[Any]       				=		importlib.metadata.version("""intel_extension_for_pytorch"""			)
  except importlib.metadata.PackageNotFoundError:
    return False
  __A		:  List[Any]       				=		get_major_and_minor_from_version(_torch_version			)
  __A		:  List[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 							A_  (    __SCREAMING_SNAKE_CASE    :	Any=False			)					->							List[str]:





  """simple docstring"""

  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
      __A		:  Union[str, Any]       				=		torch.npu.device_count()
      return torch.npu.is_available()
    except RuntimeError:
      return False
  return hasattr(__SCREAMING_SNAKE_CASE      ,  """npu"""			) and torch.npu.is_available()







@lru_cache
def 							A_  (    __SCREAMING_SNAKE_CASE    :	Optional[Any]=False			)					->							List[str]:





  """simple docstring"""

  if not parse_flag_from_env("""ACCELERATE_USE_XPU"""      ,  default=__SCREAMING_SNAKE_CASE			):
    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
      __A		:  str       				=		torch.xpu.device_count()
      return torch.xpu.is_available()
    except RuntimeError:
      return False
  return hasattr(__SCREAMING_SNAKE_CASE      ,  """xpu"""			) and torch.xpu.is_available()





'''simple docstring'''
from __future__ import annotations







def 							A_  (    __SCREAMING_SNAKE_CASE    :	float | int      ,  __SCREAMING_SNAKE_CASE    :	float | int      ,  __SCREAMING_SNAKE_CASE    :	float | int      ,  )					->							list[float | int]:





  """simple docstring"""

  if not all((nth_term, start_term_a, common_ratio_r)			):
    return []
  __A		:  list[float | int]       				=		[]
  __A		:  Union[str, Any]       				=		1
  __A		:  List[str]       				=		common_ratio_r
  for _ in range(int(__SCREAMING_SNAKE_CASE			)			):
    if not series:
      series.append(__SCREAMING_SNAKE_CASE			)
    else:
      power += 1
      series.append(float(start_term_a * multiple			)			)
      __A		:  List[str]       				=		pow(float(__SCREAMING_SNAKE_CASE			)      ,  __SCREAMING_SNAKE_CASE			)
  return series


if __name__ == "__main__":
     import doctest

     doctest.testmod()

     A__ :		Optional[int]					=float(input('Enter the last number (n term) of the Geometric Series'))
     A__ :		List[Any]					=float(input('Enter the starting term (a) of the Geometric Series'))
     A__ :		List[Any]					=float(
         input('Enter the common ratio between two terms (r) of the Geometric Series')
     )
     print('Formula of Geometric Series => a + ar + ar^2 ... +ar^n')
     print(geometric_series(nth_term, start_term_a, common_ratio_r))



