code: 
  
  
  """simple docstring"""
  
  
  
  
  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 			a_   (  lowercase__   :int, lowercase__   :bool = False    ):
  			if device_specific:
  						seed += AcceleratorState().process_index
  			random.seed(lowercase__    )
  			np.random.seed(lowercase__    )
  			torch.manual_seed(lowercase__    )
  			if is_xpu_available():
  						torch.xpu.manual_seed_all(lowercase__    )
  			elif is_npu_available():
  						torch.npu.manual_seed_all(lowercase__    )
  			else:
  						torch.cuda.manual_seed_all(lowercase__    )
  			# ^^ safe to call this function even if cuda is not available
  			if is_tpu_available():
  						xm.set_rng_state(lowercase__    )
  
  
  
  
  
  def 			a_   (  lowercase__   :Optional[RNGType] = None, lowercase__   :Optional[torch.Generator] = None    ):
  			# Get the proper rng state
  			if rng_type == RNGType.TORCH:
  						__lowerCamelCase        =			torch.get_rng_state()
  			elif rng_type == RNGType.CUDA:
  						__lowerCamelCase        =			torch.cuda.get_rng_state()
  			elif rng_type == RNGType.XLA:
  						assert is_tpu_available(), "Can't synchronize XLA seeds on an environment without TPUs."
  						__lowerCamelCase        =			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."
  						__lowerCamelCase        =			torch.npu.get_rng_state()
  			elif rng_type == RNGType.XPU:
  						assert is_xpu_available(), "Can't synchronize XPU seeds on an environment without XPUs."
  						__lowerCamelCase        =			torch.xpu.get_rng_state()
  			elif rng_type == RNGType.GENERATOR:
  						assert generator is not None, "Need a generator to synchronize its seed."
  						__lowerCamelCase        =			generator.get_state()
  
  			# Broadcast the rng state from device 0 to other devices
  			__lowerCamelCase        =			AcceleratorState()
  			if state.distributed_type == DistributedType.TPU:
  						__lowerCamelCase        =			rng_state.to(xm.xla_device()    )
  						xm.collective_broadcast([rng_state]    )
  						xm.mark_step()
  						__lowerCamelCase        =			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
  			):
  						__lowerCamelCase        =			rng_state.to(state.device    )
  						torch.distributed.broadcast(lowercase__, 0    )
  						__lowerCamelCase        =			rng_state.cpu()
  			elif state.distributed_type == DistributedType.MULTI_CPU:
  						torch.distributed.broadcast(lowercase__, 0    )
  
  			# Set the broadcast rng state
  			if rng_type == RNGType.TORCH:
  						torch.set_rng_state(lowercase__    )
  			elif rng_type == RNGType.CUDA:
  						torch.cuda.set_rng_state(lowercase__    )
  			elif rng_type == RNGType.NPU:
  						torch.npu.set_rng_state(lowercase__    )
  			elif rng_type == RNGType.XPU:
  						torch.xpu.set_rng_state(lowercase__    )
  			elif rng_type == RNGType.XLA:
  						xm.set_rng_state(rng_state.item()    )
  			elif rng_type == RNGType.GENERATOR:
  						generator.set_state(lowercase__    )
  
  
  
  
  
  def 			a_   (  lowercase__   :List[Union[str, RNGType]], lowercase__   :Optional[torch.Generator] = None    ):
  			for rng_type in rng_types:
  						synchronize_rng_state(RNGType(lowercase__    ), generator=lowercase__    )
  
  
  
  
  
style_context: 
  
  
  """simple docstring"""
  
  
  
  
  import numpy as np
  
  
  
  
  
  def 			a_   (  lowercase__   :List[str]    ):
  			__lowerCamelCase        =			np.exp(lowercase__    )
  
  			# Add up the all the exponentials
  			__lowerCamelCase        =			np.sum(lowercase__    )
  
  			# Divide every exponent by the sum of all exponents
  			__lowerCamelCase        =			exponent_vector / sum_of_exponents
  
  			return softmax_vector
  
  
  if __name__ == "__main__":
  					print(softmax((0,)))
  
  
  
  
  