
import random
from typing import List, Optional, Union

import numpy as np
import torch

from ..state import AcceleratorState
from .constants import CUDA_DISTRIBUTED_TYPES
from .dataclasses import DistributedType, RNGType
from .imports import is_npu_available, is_tpu_available, is_xpu_available


if is_tpu_available(check_device=False):
						import torch_xla.core.xla_model as xm



def        lowerCamelCase_  (   __UpperCamelCase ,						__UpperCamelCase = False     ):






				if device_specific:
								seed += AcceleratorState().process_index
				random.seed(UpperCamelCase_     )
				np.random.seed(UpperCamelCase_     )
				torch.manual_seed(UpperCamelCase_     )
				if is_xpu_available():
								torch.xpu.manual_seed_all(UpperCamelCase_     )
				elif is_npu_available():
								torch.npu.manual_seed_all(UpperCamelCase_     )
				else:
								torch.cuda.manual_seed_all(UpperCamelCase_     )
				# ^^ safe to call this function even if cuda is not available
				if is_tpu_available():
								xm.set_rng_state(UpperCamelCase_     )



def        lowerCamelCase_  (   __UpperCamelCase = None ,						__UpperCamelCase = None     ):






				if rng_type == RNGType.TORCH:
								A_       	=  torch.get_rng_state()
				elif rng_type == RNGType.CUDA:
								A_       	=  torch.cuda.get_rng_state()
				elif rng_type == RNGType.XLA:
								assert is_tpu_available(), "Can't synchronize XLA seeds on an environment without TPUs."
								A_       	=  torch.tensor(xm.get_rng_state()     )
				elif rng_type == RNGType.NPU:
								assert is_npu_available(), "Can't synchronize NPU seeds on an environment without NPUs."
								A_       	=  torch.npu.get_rng_state()
				elif rng_type == RNGType.XPU:
								assert is_xpu_available(), "Can't synchronize XPU seeds on an environment without XPUs."
								A_       	=  torch.xpu.get_rng_state()
				elif rng_type == RNGType.GENERATOR:
								assert generator is not None, "Need a generator to synchronize its seed."
								A_       	=  generator.get_state()

				# Broadcast the rng state from device 0 to other devices
				A_       	=  AcceleratorState()
				if state.distributed_type == DistributedType.TPU:
								A_       	=  rng_state.to(xm.xla_device()     )
								xm.collective_broadcast([rng_state]     )
								xm.mark_step()
								A_       	=  rng_state.cpu()
				elif (
				    state.distributed_type in CUDA_DISTRIBUTED_TYPES
				    or state.distributed_type == DistributedType.MULTI_NPU
				    or state.distributed_type == DistributedType.MULTI_XPU
				):
								A_       	=  rng_state.to(state.device     )
								torch.distributed.broadcast(UpperCamelCase_ ,						0     )
								A_       	=  rng_state.cpu()
				elif state.distributed_type == DistributedType.MULTI_CPU:
								torch.distributed.broadcast(UpperCamelCase_ ,						0     )

				# Set the broadcast rng state
				if rng_type == RNGType.TORCH:
								torch.set_rng_state(UpperCamelCase_     )
				elif rng_type == RNGType.CUDA:
								torch.cuda.set_rng_state(UpperCamelCase_     )
				elif rng_type == RNGType.NPU:
								torch.npu.set_rng_state(UpperCamelCase_     )
				elif rng_type == RNGType.XPU:
								torch.xpu.set_rng_state(UpperCamelCase_     )
				elif rng_type == RNGType.XLA:
								xm.set_rng_state(rng_state.item()     )
				elif rng_type == RNGType.GENERATOR:
								generator.set_state(UpperCamelCase_     )



def        lowerCamelCase_  (   __UpperCamelCase ,						__UpperCamelCase = None     ):






				for rng_type in rng_types:
								synchronize_rng_state(RNGType(UpperCamelCase_     ) ,						generator=UpperCamelCase_     )


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 					__lowercase		(					A       ):
					__magic_name__						:							int     	=	{
					    '''sld_warmup_steps''': 15,
					    '''sld_guidance_scale''': 20,
					    '''sld_threshold''': 0.0,
					    '''sld_momentum_scale''': 0.0,
					    '''sld_mom_beta''': 0.0,
					}
					__magic_name__						:							Optional[int]     	=	{
					    '''sld_warmup_steps''': 10,
					    '''sld_guidance_scale''': 10_00,
					    '''sld_threshold''': 0.01,
					    '''sld_momentum_scale''': 0.3,
					    '''sld_mom_beta''': 0.4,
					}
					__magic_name__						:							str     	=	{
					    '''sld_warmup_steps''': 7,
					    '''sld_guidance_scale''': 20_00,
					    '''sld_threshold''': 0.0_25,
					    '''sld_momentum_scale''': 0.5,
					    '''sld_mom_beta''': 0.7,
					}
					__magic_name__						:							List[Any]     	=	{
					    '''sld_warmup_steps''': 0,
					    '''sld_guidance_scale''': 50_00,
					    '''sld_threshold''': 1.0,
					    '''sld_momentum_scale''': 0.5,
					    '''sld_mom_beta''': 0.7,
					}






@dataclass
class 					__lowercase		(					A       ):
					__magic_name__						:							Union[List[PIL.Image.Image], np.ndarray]
					__magic_name__						:							Optional[List[bool]]
					__magic_name__						:							Optional[Union[List[PIL.Image.Image], np.ndarray]]
					__magic_name__						:							Optional[str]


if is_transformers_available() and is_torch_available():
						from .pipeline_stable_diffusion_safe import StableDiffusionPipelineSafe
						from .safety_checker import SafeStableDiffusionSafetyChecker