code: 
  
  
  
  
  
  
  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  							=  True
  except ImportError:
     __A  							=  False
  
  
  # Cache this result has it's a C FFI call which can be pretty time-consuming
  __A  							=  torch.distributed.is_available()
  
  
  
  
  
  
  
  def 			lowercase__  (				A_:	List[str]		)  ->							Union[str, Any]:
  
  
  
  
  
  
  
         """simple docstring"""
  
  
  
  
  
  
         __UpperCAmelCase    							=importlib.util.find_spec(A_		) is not None
         if package_exists:
                try:
                       __UpperCAmelCase    							=importlib.metadata.metadata(A_		)
                       return True
                except importlib.metadata.PackageNotFoundError:
                       return False
  
  
  
  
  
  
  
  def 			lowercase__  (				)  ->							bool:
  
  
  
  
  
  
  
         """simple docstring"""
  
  
  
  
  
  
         return _torch_distributed_available
  
  
  
  
  
  
  
  def 			lowercase__  (				)  ->							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 			lowercase__  (				)  ->							Any:
  
  
  
  
  
  
  
         """simple docstring"""
  
  
  
  
  
  
         return importlib.metadata.version("""oneccl_bind_pt"""		)
  
  
  
  
  
  
  
  def 			lowercase__  (				)  ->							List[Any]:
  
  
  
  
  
  
  
         """simple docstring"""
  
  
  
  
  
  
         return _is_package_available("""transformer_engine"""		)
  
  
  
  
  
  
  
  @lru_cache
  def 			lowercase__  (				A_:	Union[str, Any]=True		)  ->							Dict:
  
  
  
  
  
  
  
         """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
                       __UpperCAmelCase    							=xm.xla_device()
                       return True
                except RuntimeError:
                       return False
         return _tpu_available
  
  
  
  
  
  
  
  def 			lowercase__  (				)  ->							Any:
  
  
  
  
  
  
  
         """simple docstring"""
  
  
  
  
  
  
         return _is_package_available("""deepspeed"""		)
  
  
  
  
  
  
  
  def 			lowercase__  (				A_:	List[Any]=False		)  ->							Any:
  
  
  
  
  
  
  
         """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 			lowercase__  (				)  ->							List[Any]:
  
  
  
  
  
  
  
         """simple docstring"""
  
  
  
  
  
  
         __UpperCAmelCase    							=_is_package_available("""bitsandbytes"""		)
         if package_exists:
                __UpperCAmelCase    							=version.parse(importlib.metadata.version("""bitsandbytes"""		)		)
                return compare_versions(A_     ,			""">="""     ,			"""0.39.0"""		)
         return False
  
  
  
  
  
  
  
  def 			lowercase__  (				)  ->							Any:
  
  
  
  
  
  
  
         """simple docstring"""
  
  
  
  
  
  
         __UpperCAmelCase    							=_is_package_available("""bitsandbytes"""		)
         if package_exists:
                __UpperCAmelCase    							=version.parse(importlib.metadata.version("""bitsandbytes"""		)		)
                return compare_versions(A_     ,			""">="""     ,			"""0.37.2"""		)
         return False
  
  
  
  
  
  
  
  def 			lowercase__  (				)  ->							List[str]:
  
  
  
  
  
  
  
         """simple docstring"""
  
  
  
  
  
  
         return _is_package_available("""bitsandbytes"""		)
  
  
  
  
  
  
  
  def 			lowercase__  (				)  ->							Tuple:
  
  
  
  
  
  
  
         """simple docstring"""
  
  
  
  
  
  
         if strtobool(os.environ.get("""ACCELERATE_USE_MEGATRON_LM"""     ,			"""False"""		)		) == 1:
                __UpperCAmelCase    							=importlib.util.find_spec("""megatron"""		) is not None
                if package_exists:
                       try:
                              __UpperCAmelCase    							=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 			lowercase__  (				)  ->							Any:
  
  
  
  
  
  
  
         """simple docstring"""
  
  
  
  
  
  
         return _is_package_available("""safetensors"""		)
  
  
  
  
  
  
  
  def 			lowercase__  (				)  ->							Optional[Any]:
  
  
  
  
  
  
  
         """simple docstring"""
  
  
  
  
  
  
         return _is_package_available("""transformers"""		)
  
  
  
  
  
  
  
  def 			lowercase__  (				)  ->							int:
  
  
  
  
  
  
  
         """simple docstring"""
  
  
  
  
  
  
         return _is_package_available("""datasets"""		)
  
  
  
  
  
  
  
  def 			lowercase__  (				)  ->							Dict:
  
  
  
  
  
  
  
         """simple docstring"""
  
  
  
  
  
  
         return _is_package_available("""aim"""		)
  
  
  
  
  
  
  
  def 			lowercase__  (				)  ->							Any:
  
  
  
  
  
  
  
         """simple docstring"""
  
  
  
  
  
  
         return _is_package_available("""tensorboard"""		) or _is_package_available("""tensorboardX"""		)
  
  
  
  
  
  
  
  def 			lowercase__  (				)  ->							Optional[Any]:
  
  
  
  
  
  
  
         """simple docstring"""
  
  
  
  
  
  
         return _is_package_available("""wandb"""		)
  
  
  
  
  
  
  
  def 			lowercase__  (				)  ->							str:
  
  
  
  
  
  
  
         """simple docstring"""
  
  
  
  
  
  
         return _is_package_available("""comet_ml"""		)
  
  
  
  
  
  
  
  def 			lowercase__  (				)  ->							Tuple:
  
  
  
  
  
  
  
         """simple docstring"""
  
  
  
  
  
  
         return _is_package_available("""boto3"""		)
  
  
  
  
  
  
  
  def 			lowercase__  (				)  ->							List[str]:
  
  
  
  
  
  
  
         """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"""     ,			A_		)
                return parse_flag_from_env("""ACCELERATE_ENABLE_RICH"""     ,			A_		)
         return False
  
  
  
  
  
  
  
  def 			lowercase__  (				)  ->							Optional[Any]:
  
  
  
  
  
  
  
         """simple docstring"""
  
  
  
  
  
  
         return _is_package_available("""sagemaker"""		)
  
  
  
  
  
  
  
  def 			lowercase__  (				)  ->							Optional[Any]:
  
  
  
  
  
  
  
         """simple docstring"""
  
  
  
  
  
  
         return _is_package_available("""tqdm"""		)
  
  
  
  
  
  
  
  def 			lowercase__  (				)  ->							Union[str, Any]:
  
  
  
  
  
  
  
         """simple docstring"""
  
  
  
  
  
  
         return _is_package_available("""mlflow"""		)
  
  
  
  
  
  
  
  def 			lowercase__  (				)  ->							Optional[int]:
  
  
  
  
  
  
  
         """simple docstring"""
  
  
  
  
  
  
         return is_torch_version(""">="""     ,			"""1.12"""		) and torch.backends.mps.is_available() and torch.backends.mps.is_built()
  
  
  
  
  
  
  
  def 			lowercase__  (				)  ->							List[Any]:
  
  
  
  
  
  
  
         """simple docstring"""
  
  
  
  
  
  
         def get_major_and_minor_from_version(A_:	Any		):
                return str(version.parse(A_		).major		) + "." + str(version.parse(A_		).minor		)
  
         __UpperCAmelCase    							=importlib.metadata.version("""torch"""		)
         if importlib.util.find_spec("""intel_extension_for_pytorch"""		) is None:
                return False
         __UpperCAmelCase    							="""N/A"""
         try:
                __UpperCAmelCase    							=importlib.metadata.version("""intel_extension_for_pytorch"""		)
         except importlib.metadata.PackageNotFoundError:
                return False
         __UpperCAmelCase    							=get_major_and_minor_from_version(_torch_version		)
         __UpperCAmelCase    							=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 			lowercase__  (				A_:	Dict=False		)  ->							List[Any]:
  
  
  
  
  
  
  
         """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
                       __UpperCAmelCase    							=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 			lowercase__  (				A_:	Any=False		)  ->							List[str]:
  
  
  
  
  
  
  
         """simple docstring"""
  
  
  
  
  
  
         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
                       __UpperCAmelCase    							=torch.xpu.device_count()
                       return torch.xpu.is_available()
                except RuntimeError:
                       return False
         return hasattr(A_     ,			"""xpu"""		) and torch.xpu.is_available()
  
  
  
  
  
style_context: 
  
  
  
  
  
  
  import math as m
  
  
  
  
  
  
  
  def 			lowercase__  (				A_:	Tuple     ,			A_:	str     ,			A_:	Union[str, Any]=0.0_0_1		)  ->							List[str]:
  
  
  
  
  
  
  
         """simple docstring"""
  
  
  
  
  
  
         return (f(a + h		) - f(a - h		)) / (2 * h)
  
  
  
  
  
  
  
  def 			lowercase__  (				A_:	str     ,			A_:	Union[str, Any]=0     ,			A_:	List[Any]=100     ,			A_:	Optional[Any]=0.0_0_0_1     ,			A_:	Tuple=1e-6     ,			A_:	Union[str, Any]=False		)  ->							Union[str, Any]:
  
  
  
  
  
  
  
         """simple docstring"""
  
  
  
  
  
  
         __UpperCAmelCase    							=xa  # set the initial guess
         __UpperCAmelCase    							=[a]
         __UpperCAmelCase    							=abs(f(A_		)		)
         __UpperCAmelCase    							=lambda A_		: calc_derivative(A_     ,			A_     ,			h=A_		)  # noqa: E731  Derivative of f(x)
         for _ in range(A_		):
                if fa(A_		) == 0:
                       raise ValueError("""No converging solution found"""		)
                __UpperCAmelCase    							=a - f(A_		) / fa(A_		)  # Calculate the next estimate
                if logsteps:
                       steps.append(A_		)
                if error < maxerror:
                       break
         else:
                raise ValueError("""Iteration limit reached, no converging solution found"""		)
         if logsteps:
                # If logstep is true, then log intermediate steps
                return a, error, steps
         return a, error
  
  
  if __name__ == "__main__":
     from matplotlib import pyplot as plt
  
     __A  							=  lambda x: m.tanh(x) ** 2 - m.exp(3 * x)  # noqa: E731
     __A							,    __A							,    __A  							=  newton_raphson(
         f, xa=10, maxiter=10_00, step=1E-6, logsteps=True
     )
     plt.plot([abs(f(x)) for x in steps])
     plt.xlabel("step")
     plt.ylabel("error")
     plt.show()
     print(F"""solution = {{{solution:f}}}, error = {{{error:f}}}""")
  
  
  
  
  