






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		(  __A      :							int						,						__A      :							bool = False	) ->   Union[str, Any]:
    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        _lowerCamelCase		(  __A      :							Optional[RNGType] = None						,						__A      :							Optional[torch.Generator] = None	) ->   str:
    # Get the proper rng state
    if rng_type == RNGType.TORCH:
        _UpperCAmelCase			: Dict     					=			torch.get_rng_state()
    elif rng_type == RNGType.CUDA:
        _UpperCAmelCase			: Tuple     					=			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			: str     					=			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			: 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."
        _UpperCAmelCase			: str     					=			torch.xpu.get_rng_state()
    elif rng_type == RNGType.GENERATOR:
        assert generator is not None, "Need a generator to synchronize its seed."
        _UpperCAmelCase			: Optional[Any]     					=			generator.get_state()

    # Broadcast the rng state from device 0 to other devices
    _UpperCAmelCase			: Optional[int]     					=			AcceleratorState()
    if state.distributed_type == DistributedType.TPU:
        _UpperCAmelCase			: Union[str, Any]     					=			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			: Dict     					=			rng_state.to(state.device	)
        torch.distributed.broadcast(__A						,						0	)
        _UpperCAmelCase			: int     					=			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        _lowerCamelCase		(  __A      :							List[Union[str, RNGType]]						,						__A      :							Optional[torch.Generator] = None	) ->   List[str]:
    for rng_type in rng_types:
        synchronize_rng_state(RNGType(__A	)						,						generator=__A	)












def        _lowerCamelCase		(  __A      :							list	) ->   list:
    _UpperCAmelCase			: Dict     					=			1.3
    _UpperCAmelCase			: Any     					=			len(__A	)
    _UpperCAmelCase			: List[Any]     					=			False

    while not completed:
        # Update the gap value for a next comb
        _UpperCAmelCase			: Optional[Any]     					=			int(gap / shrink_factor	)
        if gap <= 1:
            _UpperCAmelCase			: Any     					=			True

        _UpperCAmelCase			: Optional[Any]     					=			0
        while index + gap < len(__A	):
            if data[index] > data[index + gap]:
                # Swap values
                _UpperCAmelCase					,	_UpperCAmelCase			: Union[str, Any]     					=			data[index + gap], data[index]
                _UpperCAmelCase			: Any     					=			False
            index += 1

    return data


if __name__ == "__main__":
  import doctest

  doctest.testmod()

  SCREAMING_SNAKE_CASE								=  input('Enter numbers separated by a comma:\n').strip()
  SCREAMING_SNAKE_CASE								=  [int(item) for item in user_input.split(',')]
  print(comb_sort(unsorted))




