





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




def  A_(      A = None					,   A = None ):
	# Get the proper rng state
	if rng_type == RNGType.TORCH:
		UpperCAmelCase_		    =  torch.get_rng_state()
	elif rng_type == RNGType.CUDA:
		UpperCAmelCase_		    =  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_		    =  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_		    =  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_		    =  torch.xpu.get_rng_state()
	elif rng_type == RNGType.GENERATOR:
		assert generator is not None, "Need a generator to synchronize its seed."
		UpperCAmelCase_		    =  generator.get_state()

	# Broadcast the rng state from device 0 to other devices
	UpperCAmelCase_		    =  AcceleratorState()
	if state.distributed_type == DistributedType.TPU:
		UpperCAmelCase_		    =  rng_state.to(xm.xla_device() )
		xm.collective_broadcast([rng_state] )
		xm.mark_step()
		UpperCAmelCase_		    =  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_		    =  rng_state.to(state.device )
		torch.distributed.broadcast(A					,   0 )
		UpperCAmelCase_		    =  rng_state.cpu()
	elif state.distributed_type == DistributedType.MULTI_CPU:
		torch.distributed.broadcast(A					,   0 )

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




def  A_(      A					,   A = None ):
	for rng_type in rng_types:
		synchronize_rng_state(RNGType(A )					,   generator=A )









import doctest

import projectq
from projectq.ops import H, Measure




def  A_(      A ):
	UpperCAmelCase_		    =  quantum_engine.allocate_qubit()
	H | qubit
	Measure | qubit
	return int(A )


if __name__ == "__main__":
				doctest.testmod()

				# initialises a new quantum backend
				UpperCamelCase__ :			Dict       	=			projectq.MainEngine()

				# Generate a list of 10 random numbers
				UpperCamelCase__ :			Dict       	=			[get_random_number(quantum_engine) for _ in range(10)]

				# Flushes the quantum engine from memory
				quantum_engine.flush()

				print("""Random numbers""", random_numbers_list)


