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
  
  	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  _lowerCamelCase(			lowercase__			)		->			str:
  		# Check we're not importing a "pkg_name" directory somewhere but the actual library by trying to grab the version
  		__lowercase=    importlib.util.find_spec(lowercase__			) is not None
  		if package_exists:
  				try:
  						__lowercase=    importlib.metadata.metadata(lowercase__			)
  						return True
  				except importlib.metadata.PackageNotFoundError:
  						return False
  
  
  
  
  
  
  
  def  _lowerCamelCase(			)		->			bool:
  		return _torch_distributed_available
  
  
  
  
  
  
  
  def  _lowerCamelCase(			)		->			Tuple:
  		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  _lowerCamelCase(			)		->			Tuple:
  		return importlib.metadata.version('oneccl_bind_pt'			)
  
  
  
  
  
  
  
  def  _lowerCamelCase(			)		->			Any:
  		return _is_package_available('transformer_engine'			)
  
  
  
  
  
  
  
  @lru_cache
  def  _lowerCamelCase(			lowercase__=True			)		->			Any:
  		# 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
  						__lowercase=    xm.xla_device()
  						return True
  				except RuntimeError:
  						return False
  		return _tpu_available
  
  
  
  
  
  
  
  def  _lowerCamelCase(			)		->			List[Any]:
  		return _is_package_available('deepspeed'			)
  
  
  
  
  
  
  
  def  _lowerCamelCase(			lowercase__=False			)		->			List[Any]:
  		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  _lowerCamelCase(			)		->			str:
  		__lowercase=    _is_package_available('bitsandbytes'			)
  		if package_exists:
  				__lowercase=    version.parse(importlib.metadata.version('bitsandbytes'			)			)
  				return compare_versions(lowercase__     ,				'>='     ,				'0.39.0'			)
  		return False
  
  
  
  
  
  
  
  def  _lowerCamelCase(			)		->			Optional[Any]:
  		__lowercase=    _is_package_available('bitsandbytes'			)
  		if package_exists:
  				__lowercase=    version.parse(importlib.metadata.version('bitsandbytes'			)			)
  				return compare_versions(lowercase__     ,				'>='     ,				'0.37.2'			)
  		return False
  
  
  
  
  
  
  
  def  _lowerCamelCase(			)		->			str:
  		return _is_package_available('bitsandbytes'			)
  
  
  
  
  
  
  
  def  _lowerCamelCase(			)		->			List[str]:
  		if strtobool(os.environ.get('ACCELERATE_USE_MEGATRON_LM'     ,				'False'			)			) == 1:
  				__lowercase=    importlib.util.find_spec('megatron'			) is not None
  				if package_exists:
  						try:
  								__lowercase=    parse(importlib.metadata.version('megatron-lm'			)			)
  								return compare_versions(lowercase__     ,				'>='     ,				'2.2.0'			)
  						except Exception as e:
  								warnings.warn(F'Parse Megatron version failed. Exception:{e}'			)
  								return False
  
  
  
  
  
  
  
  def  _lowerCamelCase(			)		->			Tuple:
  		return _is_package_available('safetensors'			)
  
  
  
  
  
  
  
  def  _lowerCamelCase(			)		->			List[str]:
  		return _is_package_available('transformers'			)
  
  
  
  
  
  
  
  def  _lowerCamelCase(			)		->			Optional[Any]:
  		return _is_package_available('datasets'			)
  
  
  
  
  
  
  
  def  _lowerCamelCase(			)		->			Optional[Any]:
  		return _is_package_available('aim'			)
  
  
  
  
  
  
  
  def  _lowerCamelCase(			)		->			Dict:
  		return _is_package_available('tensorboard'			) or _is_package_available('tensorboardX'			)
  
  
  
  
  
  
  
  def  _lowerCamelCase(			)		->			int:
  		return _is_package_available('wandb'			)
  
  
  
  
  
  
  
  def  _lowerCamelCase(			)		->			Dict:
  		return _is_package_available('comet_ml'			)
  
  
  
  
  
  
  
  def  _lowerCamelCase(			)		->			str:
  		return _is_package_available('boto3'			)
  
  
  
  
  
  
  
  def  _lowerCamelCase(			)		->			List[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'     ,				lowercase__			)
  				return parse_flag_from_env('ACCELERATE_ENABLE_RICH'     ,				lowercase__			)
  		return False
  
  
  
  
  
  
  
  def  _lowerCamelCase(			)		->			List[Any]:
  		return _is_package_available('sagemaker'			)
  
  
  
  
  
  
  
  def  _lowerCamelCase(			)		->			Any:
  		return _is_package_available('tqdm'			)
  
  
  
  
  
  
  
  def  _lowerCamelCase(			)		->			List[Any]:
  		return _is_package_available('mlflow'			)
  
  
  
  
  
  
  
  def  _lowerCamelCase(			)		->			Dict:
  		return is_torch_version('>='     ,				'1.12'			) and torch.backends.mps.is_available() and torch.backends.mps.is_built()
  
  
  
  
  
  
  
  def  _lowerCamelCase(			)		->			List[Any]:
  		def get_major_and_minor_from_version(lowercase__			):
  				return str(version.parse(lowercase__			).major			) + "." + str(version.parse(lowercase__			).minor			)
  
  		__lowercase=    importlib.metadata.version('torch'			)
  		if importlib.util.find_spec('intel_extension_for_pytorch'			) is None:
  				return False
  		__lowercase=    """N/A"""
  		try:
  				__lowercase=    importlib.metadata.version('intel_extension_for_pytorch'			)
  		except importlib.metadata.PackageNotFoundError:
  				return False
  		__lowercase=    get_major_and_minor_from_version(_torch_version			)
  		__lowercase=    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  _lowerCamelCase(			lowercase__=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
  						__lowercase=    torch.npu.device_count()
  						return torch.npu.is_available()
  				except RuntimeError:
  						return False
  		return hasattr(lowercase__     ,				'npu'			) and torch.npu.is_available()
  
  
  
  
  
  
  
  @lru_cache
  def  _lowerCamelCase(			lowercase__=False			)		->			Optional[int]:
  		if not parse_flag_from_env('ACCELERATE_USE_XPU'     ,				default=lowercase__			):
  				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
  						__lowercase=    torch.xpu.device_count()
  						return torch.xpu.is_available()
  				except RuntimeError:
  						return False
  		return hasattr(lowercase__     ,				'xpu'			) and torch.xpu.is_available()
  
  
  
  
  
  
  
style_context: 
  
  
  from dataclasses import dataclass
  from enum import Enum
  from typing import List, Optional, Union
  
  import numpy as np
  import PIL
  from PIL import Image
  
  from ...utils import BaseOutput, is_torch_available, is_transformers_available
  
  
  
  
  
  @dataclass
  class    A					(     A_				):
  							UpperCamelCase_     :							Optional[int] ={
  							    '''sld_warmup_steps''': 15,
  							    '''sld_guidance_scale''': 20,
  							    '''sld_threshold''': 0.0,
  							    '''sld_momentum_scale''': 0.0,
  							    '''sld_mom_beta''': 0.0,
  							}
  							UpperCamelCase_     :							Tuple ={
  							    '''sld_warmup_steps''': 10,
  							    '''sld_guidance_scale''': 1_000,
  							    '''sld_threshold''': 0.01,
  							    '''sld_momentum_scale''': 0.3,
  							    '''sld_mom_beta''': 0.4,
  							}
  							UpperCamelCase_     :							Union[str, Any] ={
  							    '''sld_warmup_steps''': 7,
  							    '''sld_guidance_scale''': 2_000,
  							    '''sld_threshold''': 0.025,
  							    '''sld_momentum_scale''': 0.5,
  							    '''sld_mom_beta''': 0.7,
  							}
  							UpperCamelCase_     :							List[Any] ={
  							    '''sld_warmup_steps''': 0,
  							    '''sld_guidance_scale''': 5_000,
  							    '''sld_threshold''': 1.0,
  							    '''sld_momentum_scale''': 0.5,
  							    '''sld_mom_beta''': 0.7,
  							}
  
  
  
  
  
  @dataclass
  class    A					(     A_				):
  							UpperCamelCase_     :							Union[List[PIL.Image.Image], np.ndarray]
  							UpperCamelCase_     :							Optional[List[bool]]
  							UpperCamelCase_     :							Optional[Union[List[PIL.Image.Image], np.ndarray]]
  							UpperCamelCase_     :							Optional[str]
  
  
  if is_transformers_available() and is_torch_available():
  	from .pipeline_stable_diffusion_safe import StableDiffusionPipelineSafe
  	from .safety_checker import SafeStableDiffusionSafetyChecker
  
  
  
  
  
  
  