



'''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_   (	lowerCAmelCase_			,  lowerCAmelCase_ = False						)->						Tuple:





       '''simple docstring'''


       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        snake_case_   (	lowerCAmelCase_ = None			,  lowerCAmelCase_ = None						)->						Optional[Any]:





       '''simple docstring'''


       if rng_type == RNGType.TORCH:
              _UpperCAmelCase    :		int     	=					torch.get_rng_state()
       elif rng_type == RNGType.CUDA:
              _UpperCAmelCase    :		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."
              _UpperCAmelCase    :		int     	=					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    :		Optional[int]     	=					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    :		int     	=					torch.xpu.get_rng_state()
       elif rng_type == RNGType.GENERATOR:
              assert generator is not None, "Need a generator to synchronize its seed."
              _UpperCAmelCase    :		List[str]     	=					generator.get_state()

       # Broadcast the rng state from device 0 to other devices
       _UpperCAmelCase    :		Union[str, Any]     	=					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    :		Any     	=					rng_state.to(state.device						)
              torch.distributed.broadcast(lowerCAmelCase_			,  0						)
              _UpperCAmelCase    :		Tuple     	=					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        snake_case_   (	lowerCAmelCase_			,  lowerCAmelCase_ = None						)->						Dict:





       '''simple docstring'''


       for rng_type in rng_types:
              synchronize_rng_state(RNGType(lowerCAmelCase_						)			,  generator=lowerCAmelCase_						)










"""simple docstring"""






from collections import defaultdict

def    __lowerCamelCase     (							SCREAMING_SNAKE_CASE = 5				)		->							int:






       """simple docstring"""




       _UpperCAmelCase			     =  defaultdict(SCREAMING_SNAKE_CASE				)
       _UpperCAmelCase			     =  0

       while True:
              _UpperCAmelCase			     =  get_digits(SCREAMING_SNAKE_CASE				)
              freqs[digits].append(SCREAMING_SNAKE_CASE				)

              if len(freqs[digits]				) == max_base:
                     _UpperCAmelCase			     =  freqs[digits][0] ** 3
                     return base

              num += 1

def    __lowerCamelCase     (							SCREAMING_SNAKE_CASE				)		->							str:






       """simple docstring"""




       return "".join(sorted(str(num**3				)				)				)


if __name__ == "__main__":
    print(F'''{solution() = }''')






