
"""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 					_lowerCamelCase(__UpperCamelCase					,				__UpperCamelCase = False )       ->				Union[str, Any]:
	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 )       ->				Optional[Any]:
	# 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(__UpperCamelCase					,				0 )
		_lowerCAmelCase			=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 )       ->				int:
	for rng_type in rng_types:
		synchronize_rng_state(RNGType(__UpperCamelCase )					,				generator=__UpperCamelCase )





"""simple docstring"""


class 	lowerCamelCase__      :





			'''simple docstring'''



			def __init__(							self  ,  __UpperCAmelCase=None						)				->  List[Any]:
				_lowerCAmelCase			=data
				_lowerCAmelCase			=None



			def __repr__(							self						)				->  int:
				_lowerCAmelCase			=""""""
				_lowerCAmelCase			=self
				while temp:
					string_rep += f'''<{temp.data}> ---> '''
					_lowerCAmelCase			=temp.next
				string_rep += "<END>"
				return string_rep







def 					_lowerCamelCase(__UpperCamelCase )       ->				str:

	# if elements_list is empty
	if not elements_list:
		raise Exception("""The Elements List is empty""" )

	# Set first element as Head
	_lowerCAmelCase			=Node(elements_list[0] )
	_lowerCAmelCase			=head
	# Loop through elements from position 1
	for data in elements_list[1:]:
		_lowerCAmelCase			=Node(__UpperCamelCase )
		_lowerCAmelCase			=current.next
	return head


__A 		= [1, 3, 5, 32, 44, 12, 43]
print(F"""List: {list_data}""")
print('Creating Linked List from List.')
__A 		= make_linked_list(list_data)
print('Linked List:')

print(linked_list)



