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 						snake_case__	(      UpperCamelCase			,UpperCamelCase = False	) ->      List[str]:
  		if device_specific:
  				seed += AcceleratorState().process_index
  		random.seed(snake_case_	)
  		np.random.seed(snake_case_	)
  		torch.manual_seed(snake_case_	)
  		if is_xpu_available():
  				torch.xpu.manual_seed_all(snake_case_	)
  		elif is_npu_available():
  				torch.npu.manual_seed_all(snake_case_	)
  		else:
  				torch.cuda.manual_seed_all(snake_case_	)
  		# ^^ safe to call this function even if cuda is not available
  		if is_tpu_available():
  				xm.set_rng_state(snake_case_	)
  
  
  
  
  
  
  
  def 						snake_case__	(      UpperCamelCase = None			,UpperCamelCase = None	) ->      Dict:
  		# Get the proper rng state
  		if rng_type == RNGType.TORCH:
  				_UpperCamelCase       : int    =      torch.get_rng_state()
  		elif rng_type == RNGType.CUDA:
  				_UpperCamelCase       : Optional[Any]    =      torch.cuda.get_rng_state()
  		elif rng_type == RNGType.XLA:
  				assert is_tpu_available(), "Can't synchronize XLA seeds on an environment without TPUs."
  				_UpperCamelCase       : Dict    =      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."
  				_UpperCamelCase       : str    =      torch.npu.get_rng_state()
  		elif rng_type == RNGType.XPU:
  				assert is_xpu_available(), "Can't synchronize XPU seeds on an environment without XPUs."
  				_UpperCamelCase       : Any    =      torch.xpu.get_rng_state()
  		elif rng_type == RNGType.GENERATOR:
  				assert generator is not None, "Need a generator to synchronize its seed."
  				_UpperCamelCase       : Tuple    =      generator.get_state()
  
  		# Broadcast the rng state from device 0 to other devices
  		_UpperCamelCase       : int    =      AcceleratorState()
  		if state.distributed_type == DistributedType.TPU:
  				_UpperCamelCase       : Optional[int]    =      rng_state.to(xm.xla_device()	)
  				xm.collective_broadcast([rng_state]	)
  				xm.mark_step()
  				_UpperCamelCase       : int    =      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
  		):
  				_UpperCamelCase       : List[str]    =      rng_state.to(state.device	)
  				torch.distributed.broadcast(snake_case_			,0	)
  				_UpperCamelCase       : Dict    =      rng_state.cpu()
  		elif state.distributed_type == DistributedType.MULTI_CPU:
  				torch.distributed.broadcast(snake_case_			,0	)
  
  		# Set the broadcast rng state
  		if rng_type == RNGType.TORCH:
  				torch.set_rng_state(snake_case_	)
  		elif rng_type == RNGType.CUDA:
  				torch.cuda.set_rng_state(snake_case_	)
  		elif rng_type == RNGType.NPU:
  				torch.npu.set_rng_state(snake_case_	)
  		elif rng_type == RNGType.XPU:
  				torch.xpu.set_rng_state(snake_case_	)
  		elif rng_type == RNGType.XLA:
  				xm.set_rng_state(rng_state.item()	)
  		elif rng_type == RNGType.GENERATOR:
  				generator.set_state(snake_case_	)
  
  
  
  
  
  
  
  def 						snake_case__	(      UpperCamelCase			,UpperCamelCase = None	) ->      Tuple:
  		for rng_type in rng_types:
  				synchronize_rng_state(RNGType(snake_case_	)			,generator=snake_case_	)
  
  
  
  
  
  
style_context: 
  '''simple docstring'''
  
  from __future__ import annotations
  
  from collections.abc import Iterable
  from dataclasses import dataclass
  from typing import Any
  
  class 			UpperCAmelCase				:
  
  
  
  
  
  		"""simple docstring"""
  
  
  
  
  
  		A__  :   List[str]          =    ('_front', '_back', '_len')
  
  		@dataclass
  		class 			UpperCAmelCase				:
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  				A__  :   Any          =    None
  				A__  :   Deque._Node | None          =    None
  				A__  :   Deque._Node | None          =    None
  
  
  
  
  
  
  
  		class 			UpperCAmelCase				:
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  				A__  :   Any          =    ('_cur',)
  				def __init__(    self  ,					_snake_case     )				->    None:
  						_UpperCamelCase       : Any    =      cur
  				def __iter__(    self     )				->    Deque._Iterator:
  						return self
  
  
  
  
  				def _lowercase  (    self     )				->    Any:
  						if self._cur is None:
  								# finished iterating
  								raise StopIteration
  						_UpperCamelCase       : List[Any]    =      self._cur.val
  						_UpperCamelCase       : List[Any]    =      self._cur.next_node
  
  						return val
  
  
  
  
  
  
  
  		def __init__(    self  ,					_snake_case = None     )				->    None:
  				_UpperCamelCase       : Any    =      None
  				_UpperCamelCase       : Any    =      None
  				_UpperCamelCase       : int    =      0
  
  				if iterable is not None:
  						# append every value to the deque
  						for val in iterable:
  								self.append(_snake_case     )
  		def _lowercase  (    self  ,					_snake_case     )				->    None:
  				_UpperCamelCase       : List[str]    =      self._Node(_snake_case  ,					_snake_case  ,					_snake_case     )
  				if self.is_empty():
  						# front = back
  						_UpperCamelCase       : Union[str, Any]    =      node
  						_UpperCamelCase       : Tuple    =      1
  				else:
  						# connect nodes
  						_UpperCamelCase       : Union[str, Any]    =      node
  						_UpperCamelCase       : int    =      self._back
  						_UpperCamelCase       : List[Any]    =      node  # assign new back to the new node
  
  						self._len += 1
  
  						# make sure there were no errors
  						assert not self.is_empty(), "Error on appending value."
  		def _lowercase  (    self  ,					_snake_case     )				->    None:
  				_UpperCamelCase       : Optional[Any]    =      self._Node(_snake_case  ,					_snake_case  ,					_snake_case     )
  				if self.is_empty():
  						# front = back
  						_UpperCamelCase       : Union[str, Any]    =      node
  						_UpperCamelCase       : Optional[int]    =      1
  				else:
  						# connect nodes
  						_UpperCamelCase       : Tuple    =      self._front
  						_UpperCamelCase       : Optional[int]    =      node
  						_UpperCamelCase       : List[str]    =      node  # assign new front to the new node
  
  						self._len += 1
  
  						# make sure there were no errors
  						assert not self.is_empty(), "Error on appending value."
  		def _lowercase  (    self  ,					_snake_case     )				->    None:
  				for val in iterable:
  						self.append(_snake_case     )
  		def _lowercase  (    self  ,					_snake_case     )				->    None:
  				for val in iterable:
  						self.appendleft(_snake_case     )
  		def _lowercase  (    self     )				->    Any:
  				assert not self.is_empty(), "Deque is empty."
  
  				_UpperCamelCase       : int    =      self._back
  				_UpperCamelCase       : List[Any]    =      self._back.prev_node  # set new back
  				# drop the last node - python will deallocate memory automatically
  				_UpperCamelCase       : Union[str, Any]    =      None
  
  				self._len -= 1
  
  				return topop.val
  		def _lowercase  (    self     )				->    Any:
  				assert not self.is_empty(), "Deque is empty."
  
  				_UpperCamelCase       : List[Any]    =      self._front
  				_UpperCamelCase       : str    =      self._front.next_node  # set new front and drop the first node
  				_UpperCamelCase       : Union[str, Any]    =      None
  
  				self._len -= 1
  
  				return topop.val
  		def _lowercase  (    self     )				->    bool:
  				return self._front is None
  		def __len__(    self     )				->    int:
  				return self._len
  		def __eq__(    self  ,					_snake_case     )				->    bool:
  
  				if not isinstance(_snake_case  ,					_snake_case     ):
  						return NotImplemented
  
  				_UpperCamelCase       : Optional[int]    =      self._front
  				_UpperCamelCase       : List[Any]    =      other._front
  
  				# if the length of the dequeues are not the same, they are not equal
  				if len(self     ) != len(_snake_case     ):
  						return False
  
  				while me is not None and oth is not None:
  						# compare every value
  						if me.val != oth.val:
  								return False
  						_UpperCamelCase       : str    =      me.next_node
  						_UpperCamelCase       : List[str]    =      oth.next_node
  
  				return True
  		def __iter__(    self     )				->    Deque._Iterator:
  				return Deque._Iterator(self._front     )
  
  
  
  
  		def __repr__(    self     )				->    str:
  				_UpperCamelCase       : str    =      []
  				_UpperCamelCase       : Tuple    =      self._front
  				while aux is not None:
  						# append the values in a list to display
  						values_list.append(aux.val     )
  						_UpperCamelCase       : List[str]    =      aux.next_node
  
  				return F'''[{', '.join(repr(_snake_case     ) for val in values_list     )}]'''
  
  
  if __name__ == "__main__":
  	import doctest
  
  	doctest.testmod()
  
  
  
  
  
  