code: 
  
  """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  :					Optional[Any]												=	True
  except ImportError:
  	__A  :					List[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      __SCREAMING_SNAKE_CASE		(		lowercase__   ):
  					"""simple docstring"""
  					A    =      importlib.util.find_spec(_A   ) is not None
  					if package_exists:
  										try:
  															A    =      importlib.metadata.metadata(_A   )
  															return True
  										except importlib.metadata.PackageNotFoundError:
  															return False
  def      __SCREAMING_SNAKE_CASE		(		):
  					"""simple docstring"""
  					return _torch_distributed_available
  def      __SCREAMING_SNAKE_CASE		(		):
  					"""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      __SCREAMING_SNAKE_CASE		(		):
  					"""simple docstring"""
  					return importlib.metadata.version("oneccl_bind_pt"   )
  def      __SCREAMING_SNAKE_CASE		(		):
  					"""simple docstring"""
  					return _is_package_available("transformer_engine"   )
  @lru_cache
  def      __SCREAMING_SNAKE_CASE		(		lowercase__=True   ):
  					"""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    =      xm.xla_device()
  															return True
  										except RuntimeError:
  															return False
  					return _tpu_available
  def      __SCREAMING_SNAKE_CASE		(		):
  					"""simple docstring"""
  					return _is_package_available("deepspeed"   )
  def      __SCREAMING_SNAKE_CASE		(		lowercase__=False   ):
  					"""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      __SCREAMING_SNAKE_CASE		(		):
  					"""simple docstring"""
  					A    =      _is_package_available("bitsandbytes"   )
  					if package_exists:
  										A    =      version.parse(importlib.metadata.version("bitsandbytes"   )   )
  										return compare_versions(_A    ,							">="    ,							"0.39.0"   )
  					return False
  def      __SCREAMING_SNAKE_CASE		(		):
  					"""simple docstring"""
  					A    =      _is_package_available("bitsandbytes"   )
  					if package_exists:
  										A    =      version.parse(importlib.metadata.version("bitsandbytes"   )   )
  										return compare_versions(_A    ,							">="    ,							"0.37.2"   )
  					return False
  def      __SCREAMING_SNAKE_CASE		(		):
  					"""simple docstring"""
  					return _is_package_available("bitsandbytes"   )
  def      __SCREAMING_SNAKE_CASE		(		):
  					"""simple docstring"""
  
  
  
  
  
  
  
  					if strtobool(os.environ.get("ACCELERATE_USE_MEGATRON_LM"    ,							"False"   )   ) == 1:
  										A    =      importlib.util.find_spec("megatron"   ) is not None
  										if package_exists:
  															try:
  																				A    =      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		(		):
  					"""simple docstring"""
  					return _is_package_available("safetensors"   )
  def      __SCREAMING_SNAKE_CASE		(		):
  					"""simple docstring"""
  					return _is_package_available("transformers"   )
  def      __SCREAMING_SNAKE_CASE		(		):
  					"""simple docstring"""
  					return _is_package_available("datasets"   )
  def      __SCREAMING_SNAKE_CASE		(		):
  					"""simple docstring"""
  					return _is_package_available("aim"   )
  def      __SCREAMING_SNAKE_CASE		(		):
  					"""simple docstring"""
  					return _is_package_available("tensorboard"   ) or _is_package_available("tensorboardX"   )
  def      __SCREAMING_SNAKE_CASE		(		):
  					"""simple docstring"""
  					return _is_package_available("wandb"   )
  def      __SCREAMING_SNAKE_CASE		(		):
  					"""simple docstring"""
  					return _is_package_available("comet_ml"   )
  def      __SCREAMING_SNAKE_CASE		(		):
  					"""simple docstring"""
  					return _is_package_available("boto3"   )
  def      __SCREAMING_SNAKE_CASE		(		):
  					"""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      __SCREAMING_SNAKE_CASE		(		):
  					"""simple docstring"""
  					return _is_package_available("sagemaker"   )
  def      __SCREAMING_SNAKE_CASE		(		):
  					"""simple docstring"""
  					return _is_package_available("tqdm"   )
  def      __SCREAMING_SNAKE_CASE		(		):
  					"""simple docstring"""
  					return _is_package_available("mlflow"   )
  def      __SCREAMING_SNAKE_CASE		(		):
  					"""simple docstring"""
  					return is_torch_version(">="    ,							"1.12"   ) and torch.backends.mps.is_available() and torch.backends.mps.is_built()
  def      __SCREAMING_SNAKE_CASE		(		):
  					"""simple docstring"""
  
  
  
  
  
  
  
  					def get_major_and_minor_from_version(lowercase__   ):
  										return str(version.parse(_A   ).major   ) + "." + str(version.parse(_A   ).minor   )
  
  					A    =      importlib.metadata.version("torch"   )
  					if importlib.util.find_spec("intel_extension_for_pytorch"   ) is None:
  										return False
  					A    =      "N/A"
  					try:
  										A    =      importlib.metadata.version("intel_extension_for_pytorch"   )
  					except importlib.metadata.PackageNotFoundError:
  										return False
  					A    =      get_major_and_minor_from_version(_torch_version   )
  					A    =      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		(		lowercase__=False   ):
  					"""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    =      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		(		lowercase__=False   ):
  					"""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
  															A    =      torch.xpu.device_count()
  															return torch.xpu.is_available()
  										except RuntimeError:
  															return False
  					return hasattr(_A    ,							"xpu"   ) and torch.xpu.is_available()
  
  
  
  
  
  
style_context: 
  
  """simple docstring"""
  
  
  
  
  import functools
  from math import sqrt
  from time import time
  def      __SCREAMING_SNAKE_CASE		(		lowercase__    ,							*lowercase__    ,							**lowercase__   ):
  					"""simple docstring"""
  					A    =      time()
  					A    =      func(*lowercase__    ,							**lowercase__   )
  					A    =      time()
  					if int(end - start   ) > 0:
  										print(F"""{func.__name__} runtime: {(end - start):0.4f} s"""   )
  					else:
  										print(F"""{func.__name__} runtime: {(end - start) * 1_000:0.4f} ms"""   )
  					return output
  def      __SCREAMING_SNAKE_CASE		(		lowercase__   ):
  					"""simple docstring"""
  					if n < 0:
  										raise Exception("n is negative"   )
  					if n == 0:
  										return [0]
  					A    =      [0, 1]
  					for _ in range(n - 1   ):
  										fib.append(fib[-1] + fib[-2]   )
  					return fib
  def      __SCREAMING_SNAKE_CASE		(		lowercase__   ):
  					"""simple docstring"""
  
  					def fib_recursive_term(lowercase__   ) -> int:
  										if i < 0:
  															raise Exception("n is negative"   )
  										if i < 2:
  															return i
  										return fib_recursive_term(i - 1   ) + fib_recursive_term(i - 2   )
  
  					if n < 0:
  										raise Exception("n is negative"   )
  					return [fib_recursive_term(lowercase__   ) for i in range(n + 1   )]
  def      __SCREAMING_SNAKE_CASE		(		lowercase__   ):
  					"""simple docstring"""
  
  					@functools.cache
  					def fib_recursive_term(lowercase__   ) -> int:
  										if i < 0:
  															raise Exception("n is negative"   )
  										if i < 2:
  															return i
  										return fib_recursive_term(i - 1   ) + fib_recursive_term(i - 2   )
  
  					if n < 0:
  										raise Exception("n is negative"   )
  					return [fib_recursive_term(lowercase__   ) for i in range(n + 1   )]
  def      __SCREAMING_SNAKE_CASE		(		lowercase__   ):
  					"""simple docstring"""
  					if n < 0:
  										raise Exception("n is negative"   )
  					# Cache must be outside recursuive function
  					# other it will reset every time it calls itself.
  					A    =      {0: 0, 1: 1, 2: 1}  # Prefilled cache
  
  					def rec_fn_memoized(lowercase__   ) -> int:
  										if num in cache:
  															return cache[num]
  
  										A    =      rec_fn_memoized(num - 1   ) + rec_fn_memoized(num - 2   )
  										A    =      value
  										return value
  
  					return [rec_fn_memoized(lowercase__   ) for i in range(n + 1   )]
  def      __SCREAMING_SNAKE_CASE		(		lowercase__   ):
  					"""simple docstring"""
  					if n < 0:
  										raise Exception("n is negative"   )
  					if n >= 1_475:
  										raise Exception("n is too large"   )
  					A    =      sqrt(5   )
  					A    =      (1 + sqrt_a) / 2
  					return [round(phi**i / sqrt_a   ) for i in range(n + 1   )]
  
  
  if __name__ == "__main__":
  	__A  :					Optional[Any]												=	30
  	time_func(fib_iterative, num)
  	time_func(fib_recursive, num)  # Around 3s runtime
  	time_func(fib_recursive_cached, num)  # Around 0ms runtime
  	time_func(fib_memoization, num)
  	time_func(fib_binet, num)
  
  
  
  
  
  