


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()










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






