import doctest

import projectq
from projectq.ops import H, Measure






def 					SCREAMING_SNAKE_CASE_			(		lowerCAmelCase_: projectq.cengines._main.MainEngine     ):
      snake_case_     :			int       =       quantum_engine.allocate_qubit()
      H | qubit
      Measure | qubit
      return int(lowerCAmelCase_     )


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

     # initialises a new quantum backend
     UpperCAmelCase							=       projectq.MainEngine()

     # Generate a list of 10 random numbers
     UpperCAmelCase							=       [get_random_number(quantum_engine) for _ in range(1_0)]

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

     print("Random numbers", random_numbers_list)




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






def 					SCREAMING_SNAKE_CASE_			(		lowerCAmelCase_: Optional[RNGType] = None ,	lowerCAmelCase_: Optional[torch.Generator] = None     ):
      # Get the proper rng state
      if rng_type == RNGType.TORCH:
            snake_case_     :			List[Any]       =       torch.get_rng_state()
      elif rng_type == RNGType.CUDA:
            snake_case_     :			Dict       =       torch.cuda.get_rng_state()
      elif rng_type == RNGType.XLA:
            assert is_tpu_available(), "Can't synchronize XLA seeds on an environment without TPUs."
            snake_case_     :			Tuple       =       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."
            snake_case_     :			List[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."
            snake_case_     :			List[str]       =       torch.xpu.get_rng_state()
      elif rng_type == RNGType.GENERATOR:
            assert generator is not None, "Need a generator to synchronize its seed."
            snake_case_     :			Optional[Any]       =       generator.get_state()

      # Broadcast the rng state from device 0 to other devices
      snake_case_     :			List[Any]       =       AcceleratorState()
      if state.distributed_type == DistributedType.TPU:
            snake_case_     :			int       =       rng_state.to(xm.xla_device()     )
            xm.collective_broadcast([rng_state]     )
            xm.mark_step()
            snake_case_     :			List[str]       =       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
      ):
            snake_case_     :			Dict       =       rng_state.to(state.device     )
            torch.distributed.broadcast(lowerCAmelCase_ ,	0     )
            snake_case_     :			Dict       =       rng_state.cpu()
      elif state.distributed_type == DistributedType.MULTI_CPU:
            torch.distributed.broadcast(lowerCAmelCase_ ,	0     )

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






def 					SCREAMING_SNAKE_CASE_			(		lowerCAmelCase_: List[Union[str, RNGType]] ,	lowerCAmelCase_: Optional[torch.Generator] = None     ):
      for rng_type in rng_types:
            synchronize_rng_state(RNGType(lowerCAmelCase_     ) ,	generator=lowerCAmelCase_     )



