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

def _UpperCamelCase (							SCREAMING_SNAKE_CASE_ = None	,							SCREAMING_SNAKE_CASE_ = None				):
     # Get the proper rng state
     if rng_type == RNGType.TORCH:
          lowercase_       :							Optional[Any]  					=       torch.get_rng_state()
     elif rng_type == RNGType.CUDA:
          lowercase_       :							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."
          lowercase_       :							List[Any]  					=       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."
          lowercase_       :							List[Any]  					=       torch.npu.get_rng_state()
     elif rng_type == RNGType.XPU:
          assert is_xpu_available(), "Can't synchronize XPU seeds on an environment without XPUs."
          lowercase_       :							Optional[Any]  					=       torch.xpu.get_rng_state()
     elif rng_type == RNGType.GENERATOR:
          assert generator is not None, "Need a generator to synchronize its seed."
          lowercase_       :							int  					=       generator.get_state()

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

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






def _UpperCamelCase (							SCREAMING_SNAKE_CASE_	,							SCREAMING_SNAKE_CASE_ = None				):
     for rng_type in rng_types:
          synchronize_rng_state(RNGType(SCREAMING_SNAKE_CASE_				)	,							generator=SCREAMING_SNAKE_CASE_				)





'''simple docstring'''




def _UpperCamelCase (							SCREAMING_SNAKE_CASE_				):
     return " ".join([word[::-1] for word in input_str.split()]				)


if __name__ == "__main__":
 import doctest

 doctest.testmod()




