






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()












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}}}""")




